Developing Neuroimaging Methods to Disentangle

Mild Traumatic Brain Injury

A Dissertation Submitted to the Faculty of

The Graduate School

Baylor College of Medicine

In Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

by

Cyrus Eierud

Houston, Texas

June, 2014

  ii  

APPROVED BY THE DISSERTATION COMMITTEE

____________________________________________ Stephen M. LaConte, Ph.D (Chairman)

____________________________________________ Steven J. Ludtke, Ph.D

____________________________________________ Wei Ji Ma, Ph.D

____________________________________________ Read P. Montague, Ph.D

____________________________________________ Timothy M. Ellmore, Ph.D

____________________________________________ Ramiro Salas, Ph.D

APPROVED BY THE DEPARTMENT OF STRUCTURAL AND COMPUTATIONAL BIOLOGY AND MOLECULAR BIOPHYSICS (SCBMB)

____________________________________________ Wah Chiu, Ph.D (Director of Graduate Studies)

APPROVED BY THE DEAN OF GRADUATE SCIENCES

____________________________________________

Deborah L. Johnson, Ph.D.

_______________________________________ Date

  iii  

Acknowledgements

Thank you to Dr. Anders Eklund and Dr. Catherine Ngo-Wilde for enlightening

discussions about computers, statistics, fMRI, and traumatic brain injuries. When I

reminisce about my best research discussions, they invariably involve you.

Upon becoming a remote student at Virginia Tech Carilion Research Institute (VTCRI), I

accumulated extra challenges with commuting and handling forms. Thank you, Amy

Jackson, Lourdes Fernandez, Amber Eakin, Meagan Simpson, LaRaun Lindsey, and Ben

Bachman, for all your help solving my problems when I could not physically be in

Houston. Thanks for great guidance and friendship within the SCBMB goes to Ben

Bachman, Caleb Davis, Merry-Lynn McDonald, Rossi Irobalieva, Chris Myers, Kit

Menlove, and many more. At VTCRI it has been a great support to know Olivia Fitch,

Dr. Quentin Fischer, Dr. Susanna Kiss, and Dr. George Wilson.

Thanks also go to my lab members for being supportive and professional about our

research. Apart from Dr. Eklund and Dr. Charles Muller, who directly helped me with

this thesis, I got help from Dr. Cameron Craddock, Sean Fletcher, Manek Aulakh,

Jonathan Lisinski, Brittany Hamilton, Allison McKinnon, Kate McRoberts, and others in

the lab.

  iv  

Thanks to Wah Chiu for being the most generous and inspiring program chair, as well as

to my strong and persistent committee! Throughout my PhD and especially as I moved to

Virginia, all of the committee members have been rock solid in their support of my

research.

During my first years in the LaConte lab, Dr. Weiji Ma’s lab was next door and his door

was always open. His openness was further proven when he became my Baylor advisor

upon my move to Virginia. Very open, too, was Dr. Tim Ellmore, whom I always could

consult about neuroimaging matters, which was especially important as his research may

have been the closest to mine among the committee members. Even though Dr. Steven

Ludtke’s research may have been furthest away from mine, his expertise in imaging was

rock solid. I believe Steven is the one who, among all of us, best knows the nitty-gritty

details about Fourier Transforms, filters, and other image processing. Most importantly,

Dr. Ludtke has been immensely supportive and stepped up to become my Baylor advisor

after Dr. Ma got a position at New York University. Also, I had the honor of having Dr.

Read Montague on my committee; he was a major reason why I decided to apply to

Baylor and a big asset at VTCRI. Finally, big thanks go to Dr. Ramiro Salas for his

sharpness and friendliness. I was truly surprised how quickly Dr. Salas grasped my work

when he stepped into my committee at last minute to enhance the Baylor College of

Medicine’s presence therein.

  v  

The final thank you goes to my ingenious advisor, Stephen LaConte, for letting me work

on interesting and foundational low-level fMRI topics, which is why I stepped into this

PhD in the first place. Also, I appreciate your ever-growing support for me throughout

my PhD process.

Thanks, as well, to everybody else I forgot to thank! I believe my PhD experience has

been the strongest learning curve in my entire life, as a result of all of your help.

  vi  

Abstract

In the nineteenth century, posthumous histology acknowledged pathologic effects caused

by mild traumatic brain injury (mTBI). Despite contemporary impressive neuroimaging

technology, which images brains non-invasively, mTBI injuries remain obscure and are

often untreatable. Some progress after 1990 included studies that detected mTBI in cross-

sectional studies in accordance with meta-analyses (Eierud et al., 2014). Leading

neuroimaging methods in the mTBI field include diffusion tensor imaging (DTI) and

functional MRI (fMRI) (Belanger et al., 2007; Eierud et al., 2014).

In this thesis, verification of consistency across neuroimaging studies using meta-

analyses found significant DTI abnormalities in frontal regions and fMRI abnormalities

in analogous frontal regions (independently). Frontal lobe vulnerabilities were associated

with mTBI before neuroimaging was used (Gurdjian, 1975). In addition, our meta-

analysis found that the DTI results reversed, temporally with time post injury, to become

significant in the opposite direction for chronic mTBI (decreased anisotropy) compared

to acute mTBI (increased anisotropy). Many leading mTBI experts (Belanger et al., 2007;

Hunter et al., 2012; Niogi & Mukherjee, 2010; Prabhu, 2011; Pulsipher et al., 2011),

believe that neuroimaging may compliment or exceed the sensitivity of

  vii  

neuropsychiatric testing alone. The next natural step would be to use other neuroimaging

than CT clinically to support clinical mTBI diagnosis.

According to previous literature, support vector machines (SVMs) have been proven to

predict multidimensional data accurately and are well applicable for fMRI (Cox & Savoy,

2003; Fan et al., 2007; LaConte et al., 2005; LaConte et al., 2007; LaConte et al., 2011;

Mitchell et al., 2004). In this thesis we characterized SVMs so that they may become

more competitive and useful. Our SVM characterization results found that SVM weight

maps match general linear model (GLM) contrast images spatially and that support vector

regression (SVR) can decode motor task rates from fMRI data (p < 2*10-16). Our final

step would be to specialize SVMs to track mTBI, but before that, the task of imaging

mTBI and creating a model system for mTBI were explored.

There are several problems specifically with mTBI that may be separate from

neuroimaging in general. Two problems are that the mTBI population is heterogeneous

(Rosenbaum & Lipton, 2012; Saatman et al., 2008) and that mTBI patients develop

unique outcomes as various mental disorders (Rosenbaum & Lipton, 2012; American

Psychiatric Association, 2000). That conventional neuroimaging of many mTBI patients

does not show any visible abnormalities is not helpful for mTBI detection. Also, the

Glasgow coma scale (GCS) indicator (practical to clinically classify patients) is too brief

to be of much help to neuroimaging since mTBI develops so heterogeneously (Saatman

et al., 2008). These problems were pro tempore avoided using a cognitive insults model

  viii  

system for our mTBI methods, yielding reversible cognitive insults that were

significantly detected both behaviorally by use of the executive function and by neural

response. Decrease of executive function has been significantly associated with mTBI

across many studies (McCrea et al., 2003; Rohling et al., 2011). In addition, the model

system helped us to significantly detect practice effects in our executive function task (p

< 0.017, 1-sided, by behavior and p < 0.05, 2-sided and with multiple comparisons

correction, by neural response). Practice effects were not supposed to exist in the multi-

source interference task, which we used according to previous literature (Bush et al.,

2003; Bush & Shin, 2006; Sheu et al., 2012). Unfortunately, this practice effect altered

our final mTBI analysis model. However, some behavioral results from our longitudinal

mTBI subjects showed that they significantly improved their reaction times with time

post injury. Future analysis will have to disentangle how much that improvement was

from mTBI recovery or from practice effects.

  ix  

Notations and Abbreviations

X matrix containing feature vectors

x feature vector

w weight vector

y vector of labels

x scalar

t time point (often for a sample)

T total number of t and number of samples

  x  

Table of Contents

Approvals ........................................................................................................................ ii

Acknowledgements ........................................................................................................ iii

Abstract .......................................................................................................................... vi

Notations and Abbreviations .......................................................................................... ix

Chapter 1: Introduction and Background ........................................................................ 1

Chapter 2: Neuroimaging Meta-Analysis of Mild Traumatic Brain Injury .................. 20

Introduction ............................................................................................................... 21

Materials and Methods .............................................................................................. 23

Results ....................................................................................................................... 27

Discussion ................................................................................................................. 43

Chapter 3: Detecting Motor Rate using Support Vector Machines .............................. 51

Introduction ............................................................................................................... 51

Materials and Methods .............................................................................................. 65

Results ....................................................................................................................... 69

Discussion ................................................................................................................. 87

Chapter 4: mTBI Model System Using Executive Function ........................................ 89

Introduction ............................................................................................................... 89

Materials and Methods .............................................................................................. 92

Results ..................................................................................................................... 100

Discussion ............................................................................................................... 109

Chapter 5 Longitudinal mTBI ..................................................................................... 113

  xi  

Introduction ............................................................................................................. 113

Materials and Methods ............................................................................................ 114

Results ..................................................................................................................... 115

Discussion ............................................................................................................... 117

Chapter 6: Summary, Significance, and Future Goals ................................................ 118

References ................................................................................................................... 122

  xii  

List of Figures

Figure 1: Hydrogen net magnetization ............................................................................. 12

Figure 2: Tissue-specific T1 and T2 relaxations ................................................................ 13

Figure 3. Hemodynamic response. .................................................................................... 17

Figure 4: Overview of mTBI and neuroimaging .............................................................. 28

Figure 5: Time course of structural neuroimaging studies ............................................... 30

Figure 6: Time course of functional neuroimaging studies .............................................. 31

Figure 7: Themes studied with different imaging modalities ........................................... 33

Figure 8. Activation likelihood estimate (ALE) analysis of fMRI mTBI publications .... 36

Figure 9. Anisotropy abnormalities mapped spatially in mTBI ....................................... 38

Figure 10. Anisotropy compared temporally in relation with mTBI or performance ...... 42

Figure 11. Supervised learning ......................................................................................... 56

Figure 12. Hypothetical classification and feature space scatterplots .............................. 57

Figure 13: Hyperplanes layout for DAG multi-classification ........................................... 61

Figure 14: Direct acyclic graph (DAG) decision tree ....................................................... 62

Figure 15: Hypothetical support vector regression scatter plot ........................................ 64

Figure 16. Stimuli paradigm for real-time motor rate effect ............................................ 68

Figure 17. Group behavior motor rate mean with standard deviation .............................. 71

Figure 18. Support vector regression prediction variance ................................................ 72

Figure 19: Similarities between GLM, SVC, and SVR statistical brain patterns ............. 73

Figure 20: Support vector regression of single subject ..................................................... 79

Figure 21. Support vector regression errors per subject graphed ..................................... 80

  xiii  

Figure 22: Comparing support vector classification to regression prediction errors ........ 84

Figure 23: Simulated SVM signal and results .................................................................. 85

Figure 24: Theoretical and empirical response time series for signal variations within

rates ........................................................................................................................... 86

Figure 25: Behavioral improvement after real-time feedback .......................................... 88

Figure 26: Multi-source interference task (MSIT) ............................................................ 95

Figure 27. Sleepiness according to the Stanford sleepiness scale (SSS) .......................... 96

Figure 28: Behavioral response from MSIT cognitive insult treatments ........................ 101

Figure 29: MSIT behavioral practice effects .................................................................. 103

Figure 30: MSIT neural contrast image of control case ................................................. 105

Figure 31: Neural contrast image of MSIT practice effects ........................................... 106

Figure 32: Neural contrast image of MSIT sleep-deprivation treatment ........................ 108

Figure 33: Real-time MSIT feedback ............................................................................. 111

Figure 34: mTBI reaction time improvements with time post-concussion ..................... 116

  xiv  

List of Tables

Table 1. mTBI vs. control cluster centroids with FDR (p < 0.05) using ALE ................. 37

Table 2. Center of mass of white matter structures in the ICBM-81 atlas ........................ 39

Table 3. Cluster volumes in μ-liters using support vector machine ................................. 76

Table 4. Support vector classification accuracies from 1 to 5 Hz per subject .................. 78

Table 5. Support vector regression correlation and root mean square error (RMSE)

between predicted and true rate ................................................................................ 81

Table 6: Behavioral data from the MSIT sleep-deprived group ....................................... 97

Table 7: Behavioral data from the MSIT pure control group ........................................... 98

Table 8. Multi-source interference task behavior statistics ............................................ 102

  1  

Chapter 1: Introduction and Background

In the fourth century BC the Hippocratic Corpus (an ancient Greek medical work)

described and named a disorder similar to mild traumatic brain injury (mTBI). “mTBI”

and “concussion” may be used interchangeably and signify a mental state after a blow to

the head. Most mTBI patients recover, but citing the Centers for Disease Control and

Prevention (CDC) (Alexander, 1995; Kushner, 1998), “15% of patients diagnosed with

mTBI may have experienced persistent disabling problems,” which is the same as post-

concussion syndrome (PCS). Although criteria have been established by the Diagnostic

and Statistical Manual of Mental Disorders IV (APA, 2000) and International Statistical

Classification of Diseases and Related Health Problems (ICD-10), PCS is difficult to

diagnose, and its symptoms are nonspecific. Although mTBI has long been considered a

noncritical injury, serious short-term (McCrea et al., 2003; Rohling et al., 2011) and long-

term effects have been documented (Kraus et al., 2007; Geary et al., 2010). In addition,

there is broad acceptance that multiple mTBIs can have serious long-term consequences

(Guskiewicz et al., 2003). It is hypothesized that mTBI includes neural contusions

(Adams et al., 1980; Beaumont and Gennarelli, 2006; Brandstack et al., 2006; Levin et

al., 1992) or axonal injury (Buki and Povlishock, 2006; Gennarelli et al., 1982; Meythaler

et al., 2001; Povlishock et al., 1992).

  2  

In 2014 the Brain Injury Association of America (BIAA; the largest TBI organization)

wrote on its home page, “Brain injury is not an event or an outcome. It is the start of a

misdiagnosed, misunderstood, under-funded neurological disease” (BIAA, 2014). Even

though the BIAA includes moderate and severe TBI, mTBI is taken very seriously for

several reasons including that mTBI is a more frequent TBI disability compared to

moderate and severe TBI (Cassidy et al., 2004; Langlois et al., 2004).

About a million people are affected by mTBI every year (Cassidy et al., 2004; Langlois et

al., 2004). Until recently, work to track or alleviate the effects of mTBI has been

unfruitful. Many other important injuries are successfully treated using modern

technology, while mTBI injuries are obscure and often untreatable despite impressive

improvements in neuroimaging and other mTBI-related techniques. Neuroimaging,

except for computed tomography (CT), is seldom used to support diagnoses of mTBI or

PCS. Note that the CT used to image mTBI subjects is primarily used to detect

hematomas, but not to diagnose mTBI per se. It would also be interesting if neuroimaging

could help us understand how to cure chronic mTBI disabilities. Still, tools to peek into

the brain have been explored, and studies have found relevant patterns that detect mTBI

at group levels. Currently DTI (Niogi & Mukherjee, 2010) and fMRI (McAllister et al.,

2006; Ptito et al., 2007) are promising in the mTBI research field among the different

imaging modalities. Animal studies suggest that white-matter neural tissue is affected by

mTBI and that DTI may detect this damage (MacDonald et al., 2007). In addition, DTI

studies have found significant differences between mTBI and control groups in 18 of 21

  3  

studies (Eierud et al., 2014). In addition, seven fMRI-mTBI studies have found

significant differences between mTBI and control as well (Eierud et al., 2014). The PCS

diagnosis has been debated for hundreds of years (Evans, 1992). Today, with increasingly

impressive neuroimaging tools, we still cannot pinpoint a major PCS marker using

neuroimaging and many mTBI disorders are still only confirmed posthumously using

histology or other techniques (Evans, 1992).

A few important challenges of mTBI and PCS, may include following. First, mTBI can

affect any spatial structure in the brain (Guskiewicz et al., 2007; Rosenbaum & Lipton,

2012) and may cause different mental disorders depending on what structure the injury

affects. Secondly, it is hard to pair the subjective information reported by the people

suffering from PCS with the objective image; e.g., it is believed that athletes at times do

not want to miss any games and play even if their physicians recommend them not to.

Conversely, people without medical insurance may exaggerate their symptoms to receive

economic benefits (Paniak et al., 2002).

Based on mTBI and neuroimaging, this thesis chapter focuses on four areas.

(1) Our first focus is that we used the PubMed database for a meta-analysis, finding

significant DTI and fMRI markers that have been hypothesized in literature.

(2) The brain images used to detect mTBI are either 3D or 4D (when time is included),

often with more than 100,000 voxels (3D pixels) per image. It has been shown in many

cases that SVM predicts multi-dimensional data very well, including for fMRI (Cox &

  4  

Savoy, 2003; Fan et al., 2007; LaConte et al., 2005; LaConte et al., 2007; LaConte et al.,

2011; Mitchell et al., 2004). Using multi-dimensional data for classification is rather new

and may have started in 1936 when Ronald A. Fisher wrote a pioneering article

suggesting pattern recognition, which is closely related with early literature leading to

SVM (Vapnik & Lerner, 1963). To explore whether SVM is suitable to track mTBI is the

second major focus of this thesis; e.g., to explore whether neuroimaging of a person with

mTBI could predict a future of recovery or chronic disability, which may benefit PCS

diagnosis.

(3) The third focus was to create an mTBI model system to improve our mTBI methods.

First, the neuroimaging sensitivity to detect mTBI or its repercussions is almost strong

enough to be supportive for clinical practice. However, in order to validate a novel

method, there is an advantage to using data from an unquestioned source yielding a clear

signal. Although the end goal is to detect mTBI, mTBI is hard to control for, and an

alternate reversible neural condition that affects the executive function was used to model

mTBI at least from a naïve perspective. The naïve perspective signifies that there is no

requirement that the brain pattern match that of mTBI. We found that sleep deprivation

fulfilled our requirements as a naïve mTBI model system. This model system enabled us

to characterize the efficacy and strengths of our methods without having to induce mTBI

in humans.

(4) The fourth part of this thesis was to tie the knowledge from the meta-analysis, SVM,

and model system to analyze our own mTBI subjects that we have collected longitudinal

data of within a year following their concussions.

  5  

The intermediate goal of this thesis is to characterize SVM and then to evaluate how well

the SVM methods apply to the cognitive insults model. The final goal is to use the

sensitive SVM methods developed to track mTBI.

Mild Traumatic Brain Injury

Pioneering mTBI research has found significant symptomatic and executive function

differences between the mTBI group and a control group within a couple of days after

concussion (McCrea et al., 2003; Rohling et al., 2011). Since the last two decades, many

experts have categorically imaged mTBI subjects in contrast with subjects in a control

group to find significant differences as well. Imaging is not used to diagnose mTBI itself,

but to test for hematomas as well as to rule out head injury complications from more

severe trauma. Various guidelines for diagnosing mTBI exist, most of which rely on the

Glasgow Coma Scale (GCS; Teasdale & Jennett, 1974; Holm et al., 2005; Rosenbaum &

Lipton, 2012) and details of the injury (such as self- and witness-reported descriptions of

the accident, loss of consciousness, and evaluation of sustained trauma; Ruff et al., 2009).

The GCS assesses motor, verbal, and eye responses; while there is some variability in the

categories, a GCS 8 or below is often considered severe, from 9 to 12 is often considered

a moderate TBI, and from 13 to 15 is considered a mild TBI (Jennett, 1998; Parikh et al.,

2007). Ultimately, the diagnosis of TBI and its severity is made by a clinician. Although

mTBI has long been considered a noncritical injury, serious short- and long-term effects

have been documented (Alexander, 1995; Kraus et al., 2007; Kushner, 1998; Lipton et

  6  

al., 2009). Additionally, there is broad acceptance that multiple mTBIs can have serious

long-term consequences (Guskiewicz et al., 2003). After initial injury, secondary

mechanisms elicit biochemical, metabolic, and cellular changes in the time frame of

minutes, days, and months (Giza and Hovda, 2001; Loane and Faden, 2010; Xiong et al.,

1997). Within the first 15 minutes post-injury, there is an extreme dip in

neuropsychological performance (McCrea et al., 2002), and deficits can often linger for a

week or longer (McCrea et al., 2003; Rohling et al., 2011). The definition of the acute

time frame varies across publications, and some studies report acute periods of up to 1

month post-injury (Landre et al., 2006). This thesis uses the term “acute mTBI” for up to

2 weeks post-injury. Using the term “acute” or “semi-acute” for periods up to 2 weeks

post-injury is common in the literature (Gasparovic et al., 2009; Mac Donald et al., 2011;

Mayer et al., 2010; Messe et al., 2011).

Most mTBI patients recover, but a substantial minority (perhaps 15%) have

persistent disabling problems (Alexander, 1995; Kushner, 1998), known as PCS.

Although criteria have been established by the Diagnostic and Statistical Manual of

Mental Disorders IV (APA, 2000) and ICD-10, PCS is difficult to diagnose, and its

symptoms are nonspecific. PCS also manifests symptoms similar to those of other

disorders such as major depression (Iverson, 2006; Iverson and Lange, 2003), chronic

pain (Smith-Seemiller et al., 2003), and other diseases such as somatization disorder.

Indeed, neuropsychological testing in chronic stages of mTBI (even on the time scale of

months) has been criticized as nonspecific and insensitive (Iverson, 2005; McCrea and

American Academy of Clinical Neuropsychology, 2008), and several studies have

  7  

questioned the ecological validity of these assessments (Satz et al., 1999; Silver, 2000)

and proposed improved approaches for detecting persisting cognitive deficits and linking

these to neuroimaging results (Geary et al., 2010). Heterogeneity of injury and current

limitations in the sensitivity of imaging are challenges to developing diagnostic tools as

well as predictors of recovery. Thus, the physical size and heterogeneous distribution of

injury in the brain make detection in an individual challenging and further make reliance

on group averages problematic. In addition, since the time course of the injury leads to

lingering post-concussive symptoms in a small number of injuries (Alexander, 1995;

Kushner, 1998), it is a statistically challenging goal to try to predict which individuals

will not recover fully. Finally, longitudinally, the presence or absence of CT findings

does not correlate with long-term outcomes such as PCS (Hanlon et al., 1999; Huynh et

al., 2006; Kurca et al., 2006; Lee et al., 2008; McCullagh et al., 2001; Tellier et al.,

2009). To our knowledge there are no existing publications that claim to significantly

detect mTBI in a single subject using neuroimaging. To summarize, imaging is

challenging at both acute and chronic stages of mTBI, and attempting to characterize the

full time course compounds the level of complexity. Despite the challenges, there has

been a growing research effort to characterize structural and functional effects of mTBI.

A full range of neuroimaging technologies have been brought to bear on this issue,

including CT, positron emission tomography (PET), single-photon emission computed

tomography (SPECT), magnetoencephalography (MEG), electroencencephalography

(EEG), and 12 subtypes of Magnetic resonance imaging (MRI), such as diffusion tensor

imaging (DTI), magnetic resonance spectroscopy (MRS), arterial spin labeling (ASL),

  8  

and fMRI. This thesis has information about most imaging modalities related to mTBI,

but mostly it will focus on fMRI and DTI.

Magnetic Resonance Imaging

MRI evolved from nuclear magnetic resonance (NMR) spectroscopy, which was first

developed to image chemical compositions. The MRI differs mainly in two senses. First,

the MRI is expanded to be able to independently receive measurements at several spatial

locations as opposed to from a single point. Secondly, it is designed to image a single

atom type, often protium (hydrogen), since it is very abundant in living tissues. Protium

is the most abundant hydrogen (99.98%). Sixty-three percent of all atoms in the human

body are protium (Freitas, 1998), which is also MRI active. Body fluids and fatty tissues

are extremely protium abundant. Further on, this thesis focuses on protium because

signals from any other atoms are negligible in this case. MRI can detect many different

tissue properties, but most importantly it can create an image. Since the first human MRI

study (Damadian, 1974) was published, MRI has evolved into the preferred clinical

imaging method. Quantum physics, cryogenics, superconduction, strong and precise

magnetic fields, Fourier transforms, radio-wave emitters and receivers, and vast

complicated calculations are used to generate MRI. Today MRIs are acquired similarly to

the method used in the first animal MRI study (Damadian, 1971), with the exception of

using magnetic field gradients (Lauterburg, 1973) and Fourier transforms formalism

(Likes, 1981), which have radically improved the speed with which an image could be

  9  

acquired. The following short description of how MRI is acquired may be read in more

detail (Westbrook et al., 2013). The process includes four steps:

(1) The subject needs to be positioned in a strong and homogenous magnetic field

called the B0 field. Many images presented in this thesis were acquired by a 3-

tesla magnet, which is about 60,000 times stronger than the earth’s magnetic field.

Each hydrogen atom has two magnetization poles, like a bar magnet. A strong

external magnetic field (B0) aligns a majority of the hydrogen atoms parallel to

the magnetic direction of B0, and the rest are antiparallel to B0 (Figure 1B). In

addition, the B0 field sets the hydrogen precession frequency (!) to ! = !×!,

where ! is the gyromagnetic ratio (42.57 MHz/T for hydrogen) and ! is the

external magnetic field (often B0 = 3 T). However, at this point all hydrogen

atoms are precessing at random phase, so there is no net magnetization vector in

the rotating (precessing) transversal (xy) plane. Exposed to B0, this hydrogen

configuration yields the lowest energy level within any measured macroscopic

region, which has a constant net magnetization vector pointing in the same

direction as the B0 field (Figure 1B).

(2) Applying a second magnetic gradient (B1) across the subject so that one part of

the tissue has less than 3T and the other part has more than 3T results in a slice

with exactly 3T between the two tissue parts. After the desired slice position in

space is chosen (arbitrarily controlled by varying B1), a specific radio pulse (RF)

excites the hydrogen atoms only in the 3T-slice (Figure 1C,D). The energy

excitation, which can be described as the net magnetization vector (NMV)

  10  

shifting from pointing in the same direction as the B0 field (the lowest energy

level) to rotating in the transversal plane (orthogonally to the B0 field). After the

excitation and slice is selected the B1 field is removed leaving just the B0 field.

The NMV rotation speed is the hydrogen’s precession frequency. Hence,

hydrogen atoms are now precessing in phase, as opposed to before the excitation

(when phases were completely random). This excited slice will gradually relax to

its lowest energy level (as it was before the excitation) depicted in Figure 1B. The

relaxation depends on the tissue-specific time constant T1, which is the time it

exponentially will recover 63% of its original NMV (Figure 1B). For example, for

cerebrospinal fluids, T1 may be around 4 s. Before any significant T1-related

relaxation occurs, the NMV rotates in the transversal plane, yielding a measurable

signal. However, another relaxation occurs that is much faster than the T1-related

relaxation, which is that the atoms’ precessions, within the excited slice, will

dephase from each other. This dephasing decays the signal even faster than the

T1-related relaxation, and has a tissue-specific time constant named T2, which

decays to 37% of its maximum at time T2. Tissues affect the T1 and T2 constants

differently. Therefore it is possible to change the image contrast (to enhance

different tissues) by weighting reception from either the T1 or the T2 signal. A few

tissues’ T1 and T2 exponential relaxations are depicted in Figure 2.

(3) The B1 field that was turned off after excitation can again be used, but now to

encode the precession frequency spatially across the excited slice. In addition, the

signal from the spatially encoded slice can then be received and decoded into

  11  

meaningful images. The process of how to encode and decode the signals into

images is called sequencing and may be done in many different ways to enhance

different tissue properties. Sequencing, that may use other techniques not

mentioned here, is out of scope for this thesis, and may be found in other

literature (e.g. Bernstein et al., 2004). One note is that to use the Fourier transform

to encode and decode MRI-sequences is extremely practical (Likes, 1981).

(4) For most MRIs, the magnitude of the inverted Fourier transform of the signal

yields the raw image that is a projection of the brain that may be displayed. Still,

many corrections may be needed for statistical imaging analysis. We will discuss

a few of those corrections in the section on preprocessing below.

The MRI repetition time (TR) has different meanings depending on different sequences.

However, in this thesis the TR will always refer to the echo planar imaging (EPI) version

of TR that is used for fMRI acquisition. In this case the entire brain volume is imaged

within the time represented by the TR (often 2 s).

  12  

Figure 1: Hydrogen net magnetization (A) If no significant external magnetic field exists, hydrogen atoms align their magnetic poles randomly. The magnetic vectors point in the direction of the atoms’ magnetic field. (B) In a strong homogenous external magnetic field, B0 (e.g., 3T) atoms’ magnetic vectors are only parallel or antiparallel to B0. However, most atoms, in the macroscopic volume, align in the same direction as B0, generating a static net magnetization vector (NMV). (C) Using a specific radio frequency (RF) pulse, the NMV is rotated 90° into the transversal (xy) plane that also is an excited state (energy-wise). (D) In the transversal plane the NMV rotates in sync with hydrogen’s precession frequency. (E) The NMV is really composed of many hydrogen atoms precessing in phase. However, the synced phases among the atoms deteriorate (F) with time according to the T2 constant, which is also tissue specific.

  13  

Figure 2: Tissue-specific T1 and T2 relaxations Different T1 and T2 time constants representing the relaxation curves for different tissues. Graph shows the salient intensity differences between tissues at time the MRI receives the signal (the red line). This salient contrast between tissues is a core MRI property leading to the clear images characteristic for MRI. The magnetization represents the signal emitted from the tissues. Acronyms are: white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The red line depicts the time point to receive the MRI echo from the tissue. (A) depicts the tissue-specific net magnetization in the z-direction that yields the T1 images. Analogously, (B) depicts the net magnetization rotating in the transversal (xy) plane that yields the T2 images. M0 is the net magnetization right after slice excitation.

  14  

Functional MRI

The T2-related MRI sequence, called T2*, receives stronger signals from tissues that are

highly water abundant. However, this sequence is also very susceptible to magnetic

distortion. Using the T2* sequence to image neural tissues, one receives blood-oxygen-

level dependent (BOLD) images, which are core to fMRI. Since oxygenated blood is

diamagnetic and deoxygenated blood is paramagnetic, it affects the BOLD signal

conveniently to increase the contrast of the BOLD signal. A well-perceived hypothesis of

how the BOLD relates to neural activation has been formulated by Buxton et al. (1998).

Basically, the BOLD is a function (Equation 1) of the blood volume and concentration of

deoxyhemoglobin content. Independently, deoxyhemoglobin (Equation 2) and blood

volume content (Equation 3) are differential functions of the metabolic rate of the neural

tissue. The link between metabolic rate and neural activity hypothesized by Buxton et al.

has been shown to be accurate empirically (Glover, 1999).

Equation 1: !!!" =  !![!! ! − ! + !!(! − (!/!)) + !!(! − !)]

, where

!! is the resting blood volume,

!!,!!,!! are constants based on an oxygen-extraction fraction,

! = !/!! (normalized total deoxyhemoglobin), and

! = !/!! (normalized total blood volume)

Additional variables are used: t is for time, fin is for the stimulus paradigm (often tightly

correlated with the metabolic rate of oxygen), E(t) is for approximate transport conditions

(this variable is actually time dependent and may be written as ! !(!) using Equation 4),

E0 is the resting net extraction of oxygen by the capillary bed, fout(v) depends on venous

  15  

pressure, and τ0 is a scale constant. Equation 4 is an approximation of a wide range of

oxygen-transport conditions.

Equation 2: !"!"= !

!!!!" !

! !!!

− !!"# !! !! !

Equation 3: !"!"= !

!!!!" ! − !!"# !

Equation 4: ! ! = ! − (! − !!)!!

Neurons are thought to induce the hemodynamic response by controlling vasodilation and

vasoconstriction of the blood vessels in the neural tissue in accordance with Figure 3.

Neuroimage Preprocessing

The raw images have many artifacts that are corrected for by preprocessing, which

includes routines such as slice time correction, motion correction, smoothing, and

normalization.

• Slice time correction: a single functional MRI is often acquired during 2 s and

contains several slices that have been acquired during this time. However, the first

and last slices in the raw image are almost 2 s apart in time, which is often

corrected for.

• Motion correction corrects the image for subject movements during imaging.

  16  

• Normalization is done since everybody’s uniquely shaped brain needs to be in

the same space for group analysis. Often this is begun by choosing a brain

template, which could be the Talairach-Tournoux system. The Talairach template

was originally a 3D grid system for neurosurgery (Talairach and Szikla, 1967).

Then each subject’s neural image is geometrically transformed to match the

template (Cox, 1996). Today there are many algorithms that transform the subject

space into the Talairach template. This normalizes every subject’s neural tissue

into the same space and makes it possible to run statistics across subjects.

• Smoothing is also used to increase agreement across subjects. Even after perfect

normalization, a specific action that activates a specific neural region may be

located slightly differently across subjects. For instance, if two subjects wiggle

their thumbs, their neural responses may differ by a few millimeters even after

motion correction and normalization. If a couple of millimeters of smoothing are

applied, the regions of responses will overlap from both subjects to satisfy

statistical tests. Smoothing is a 3D filter operation, often using a Gaussian

blurring across the entire image, where the Gaussian kernel has a specific full

width at half the maximum in millimeters (Cox, 1996).

The above steps are basic preprocessing steps that are often used in today’s fMRI

software packages, such as AFNI (Cox, 1996) or FSL (Smith et al., 2004).

  17  

Figure 3. Hemodynamic response. Since neurons lack intrinsic energy storage, an immediate supply of oxygenated blood is essential after neural activation. This is accomplished by neural signaling to dilate blood capillaries in the close vicinity (B), which generates a supply of freshly oxygenated blood to its location. However, the fresh blood with an increased concentration of oxygenated hemoglobin over deoxyhemoglobin is delayed in accordance to the hemodynamic response function (HRF) depicted in (C). When neurons are inactive, capillaries are constricted (A), and usually most oxygen has been consumed, leaving high deoxyhemoglobin concentration. Both blood volume and oxygenated hemoglobin concentration increase the intensity of the blood-oxygen-level dependence (BOLD) independently. Situation (A) includes two factors (less blood volume and lower oxygenated hemoglobin concentration) to decrease the image intensity that often occurs in brain regions that have low neuronal activity. Situation (B) depicts an increase of both independent factors that often occur in regions with high neuronal activity. This is hypothesized to explain the strong relationship between BOLD signal and neuronal activity.

  18  

Statistical Analysis of fMRI

The general linear model (GLM) has been very popular in assessing the correlation

between regional neural responses and a behavior paradigm. Briefly, GLM is used to

depict the correlation between the blood-oxygen level dependence (BOLD) responses in

each voxel time series, with a time series of a set of regressors. Each time-dependent

regressor is often the time series of something behavioral the subject performs; e.g., if the

regressor is the subject’s eye state, the regressor could be set to 1 at time points when the

subject has his or her eyes open and set to 0 when subject has his or her eyes closed.

Since the neural response is delayed, the regressors are also convolved with the

hemodynamic response function (HRF; Glover, 1999), or similar approximation, before

the correlation is made (Figure 3C).

For each voxel, Equation 5 optimizes bi for each index i, to produce the smallest sum of

errors (ei) using the least squares fit.

Equation 5:

!! = !! + !!!!,! + ⋯!! = !! + !!!!,! + ⋯

+!!!!,! + !!+!!!!,! + !!

⋮ ⋮!! = !! + !!!!,! + ⋯

⋮+!!!!,! + !!

, where !! is the BOLD intensity at each image-acquisition time point t; !! are the beta

values, i = [1,p]; !!,! is the i:th predictor; and et is the residual error at time point t.

Still, the hypothetical test has not been calculated using GLM. The hypothesis of the

study is implemented using Equation 6, using a contrast vector representing the model

  19  

hypothesis; e.g., if one hypothesizes that regressors 1 and 2 (!!,! and !!,!) affect the brain

analogously, the null hypothesis would be that the first and second beta values are the

same (!!: !! = !!), which is the same as !!: !! − !! = 0. After the beta values are

rewritten so the null hypothesis sum to zero, the coefficients of the beta values represent

the contrast vector, which in this case would be [1, -1]. Then we may calculate a t-value

for each voxel by using the contrast vector in accordance with any arbitrary hypothesis

(e.g. [1, -1]), according to Equation 6. After the t-value is calculated for each voxel of the

neural image, the contrast image is the result.

Equation 6: ! = !!!!"# ! !!(!!!)!!!

, where c is the contrast vector

  20  

Chapter 2: Neuroimaging Meta-Analysis of Mild Traumatic Brain Injury

Preface

This chapter was published in Neuroimage Clinical in January 2014. A few alterations

were made to fit the thesis. The title was “Neuroimaging after mild traumatic brain

injury: Review and meta-analysis,” and the research article was authored by Cyrus

Eierud, Cameron Craddock, Sean Fletcher, Manek Aulakh, Brooks King-Casas, Damon

Kuehl, and Stephen LaConte.

Dr. Stephen LaConte (advisor) started an mTBI journal club with an aim to write a meta-

analysis, the membership of which club included the author, Dr. Cameron Craddock (a

post-doc in our lab), Sean Fletcher (a medical student in our lab), and Manek Aulakh

(another medical student in our lab). Dr. LaConte added well-acquainted mTBI experts

Dr. Damon Kuehl, M.D., and Dr. Brooks King-Casas to help us write the publication.

Early on, Dr. Craddock informed us about tools we could use to produce a meta-analysis,

including Ginger ALE and LimeSurvey. We made a PubMed search encompassing the

majority of neuroimaging-mTBI literature, which resulted in 298 publications that we

split among ourselves to extract core information to the LimeSurvey database. Dr. Kuehl

and Dr. King-Casas occasionally checked our work and contributed their expertise to the

publication. All figures in this chapter were crafted by the author and Stephen LaConte,

with various input from all other team members.

  21  

This chapter reviews the literature on neuroimaging of mTBI.

Introduction

Presented in this chapter are the results from a broad review and meta-analysis of mTBI

across the spectrum of neuroimaging modalities. Studies have examined mTBI at both

acute and chronic stages of injury. In reviewing the literature, it is important to note that

the mean time between neural imaging and the mTBI incidents of a study can affect its

participant-exclusion criterion, leading to prospective and symptomatic mTBI groups

(Dikmen et al., 1992). In prospective mTBI studies, the exclusion criteria are independent

of mTBI (e.g., specific age ranges or drug dependences). Symptomatic mTBI studies

recruit chronic participants. Based on estimated recovery rates, this corresponds to

effectively excluding the majority of those who sustain mTBIs. In other words, studies of

symptomatic groups enroll participants because they have lingering complaints caused

(presumably) by their head injuries, whereas prospective studies recruit based on mTBI

records at the time of concussion (before any chronic mTBI is known).

This paper broadly reviews mTBI neuroimaging studies of structure and function to

highlight the tremendous effort that has been made to investigate the spectrum of acute to

chronic time scales. We additionally provide meta-analyses to examine the current utility

of MRI for studying both structure and function. In terms of structure, some reports have

claimed that MRI is more sensitive to detect complicated mTBIs than CT (Mittl et al.,

1994). Similar to other authors, we use complicated mTBIs to include the broad range of

abnormalities that lead to non-negative imaging results (Arciniegas et al., 2005; Iverson,

  22  

2005; Williams et al., 1990). It should be pointed out that definitions of “mild” have

varied widely among both clinicians and researchers. Thus while many studies have

excluded participants with imaging findings, this is not universally the case. Among the

other neuroimaging methods, MRI is also unique in that it can be used to study both

structure and function. Many physical parameters provide MRI with a wide range of

contrast mechanisms, enabling “traditional” T1- and T2-weighted structural scans, neural

correlates of brain function using fMRI, white-matter microstructure by diffusion MRI,

and biochemistry through MRS.

The meta-analyses presented here are focused on both structure and function and are MRI

specific. The first meta-analysis is motivated by the heterogeneity of fMRI findings and

focuses on the question of anatomical consistency for fMRI. Similarly, the second

analysis examines the issue of white matter vulnerability to mTBI. Looking at

anatomically localized findings, previous neuroimaging data have suggested that anterior

regions of the brain are more vulnerable to abnormalities (Hashimoto and Abo, 2009;

Lipton et al., 2009; McAllister et al., 1999; Niogi et al., 2008a). This agrees with previous

literature on moderate and severe TBI that has found increased vulnerability in ventral,

frontal, and temporal regions (Bigler, 2007). In addition, it is thought that these regions

have both fragile brain structures and hard skull bones close by. For mTBI, however,

published reports have been highly heterogeneous in their findings of regional white

matter changes. Therefore, we examined whether anatomical consistency in mTBI lesions

exists in the literature. Our third meta-analysis examines the apparent inconsistency in

diffusion-based anisotropy findings across studies, which has led to debates about

  23  

whether or not anisotropy values increase, decrease, or even change at all after mTBI

(Lange et al., 2012) as well as whether anisotropy levels positively or negatively correlate

with performance levels in neuropsychological assessments (FitzGerald & Crosson,

2011). Recent reports have suggested that it is important to consider the time post-injury

in diffusion-weighted imaging (Mayer et al., 2011; Niogi & Mukherjee, 2010). For

example, Niogi & Mukherjee (2010) suggest that anisotropy is increased in the acute

phase and decreased in the chronic phase in symptomatic TBI patients. Similarly, Mayer

et al. (2011) note that anisotropy values can be either reduced or increased in semi-acute

time points, but they tend to be decreased in later, chronic stages of symptomatic mTBI.

Based on these considerations, we tested the hypotheses that anisotropy is increased in

the acute phase and decreased in the chronic phase. Specifically, we performed a meta-

analysis that considered the time post-injury of each study’s mTBI cohort. Our results

support the hypothesis that acute mTBI is associated with elevated anisotropy values and

that chronic mTBI complaints are correlated with depressed anisotropy.

Materials and Methods

We queried PubMed (http://www.ncbi.nlm.nih.gov/pubmed) for articles on imaging-

based mTBI studies. We extracted key information from these articles and entered items

into a LimeSurvey database (http://www.limesurvey.org) that we developed for this

study. LimeSurvey is an open-source web-based survey application that features an

unlimited number of participants in a survey (in our case, each paper constituted a survey

participant) and flexible export functions to multiple text and software formats. The

  24  

LimeSurvey web interface enabled the authors to collaborate from different locations.

Data from the articles were manually entered into LimeSurvey, and these data were

examined and summarized using custom-built Python parsing scripts.

We examined publications spanning 21 years, from 1990 to 2011, using a query focused

on mTBI and neuroimaging:

(mTBI or “mild traumatic brain injury” or “post concussive syndrome” or “post

concussion syndrome” or “postconcussion syndrome” or “postconcussive syndrome”)

and (neuroimaging or “magnetic resonance imaging” or MRI or “positron emission

tomography” or PET or magnetoencephalography or MEG or electroencephalography or

EEG or “functional magnetic resonance imaging” or fMRI or “diffusion tensor imaging”

or DTI or T2 or “diffusion spectrum imaging” or DSI or “diffusion weighted imaging” or

DWI or SWI or “susceptibility weighted imaging” or “T2*” or “CT” or “computed

tomography” or FLAIR or “diffusion kurtosis imaging” or “diffusional kurtosis imaging”

or DKI or “single photon emission computed tomography” or SPECT or NIRS or “near-

infrared spectroscopy” or fNIRS or “functional near-infrared spectroscopy” or “resting

state” or “functional connectivity” or “default mode network”) and (“1990” [Publication

Date]: “2011” [Publication Date]).

Our search date was November 11, 2011. This query resulted in 298 publications, from

which we excluded 176, using seven exclusion criteria. Specifically, we excluded 85

review articles, 47 nonimaging articles (most of which mentioned CT in relation to

subjects’ TBI status, but without any association with a statistical variable), 13 case

studies, 12 animal studies, 9 articles that mixed mTBI with moderate/severe TBI or

  25  

posttraumatic stress disorder (PTSD), 7 articles that were not in English, and 3 others (a

video article, a fatigue article, and a mold exposure article).

We extracted the following into the LimeSurvey database: 1) Time after concussion (the

median/mean time between injury and neuroimaging). If the studies were longitudinal or

had multiple groups of subjects, multiple time points were reported. 2) Range of time

after concussion (the minimum and maximum time between injury and neuroimaging). In

publications where only standard deviations were provided, we approximated the range

as the median ± 1.6 standard deviations. Note that this is analogous to the 90%

confidence interval. 3) Number of subjects (sum of mTBI and control groups). Note that

some publications (such as longitudinal studies) included multiple separate time points.

4) Imaging modality (CT, 12 subtypes of MRI including DTI and fMRI, PET, SPECT,

MEG, EEG, and near-infrared spectrometry). 5) Hypothesized effect of mTBI motivating

the imaging study, which we termed the “theme.” For example, several studies

hypothesized that mTBI would lead to white matter abnormalities, and thus to

characterize this they measured anisotropy. In contrast, studies that hypothesized that

connectivity would be affected could address this research “theme” by using not only

diverse measures including fMRI but also diffusion imaging-based tractography. 6) Other

imaging results (anatomical coordinates, ROIs with significant anisotropy, and other

parameters).

  26  

Statistical Analysis

The fMRI meta-analysis used Ginger ALE (Eickhoff et al., 2009, Eickhoff et al., 2012;

Turkeltaub et al., 2012). Ginger ALE is an activation likelihood estimator based on

results from multiple publications. All fMRI coordinates were converted into Talairach

space. We used the default Ginger ALE parameters (Eickhoff et al., 2009) and added the

number of subjects per experiment. We chose a false discovery rate threshold level of

0.05. We used all available fMRI publications of mTBI, most of which used working

memory tasks, but the tasks also included resting state fMRI, an auditory oddball task,

and a spatial navigation task (Chen et al., 2007; Krivitzky et al., 2011; Mayer et al., 2009,

Mayer et al., 2011; McAllister et al., 2001; McAllister et al., 2011; Slobounov et al.,

2010; Witt et al., 2010). Only coordinates from contrast images using a two-sample test

(mTBI and control) were included. AFNI (Cox, 1996) was used to present fMRI meta-

analysis images in Talairach space.

For the DTI meta-analyses, each reported region of interest (ROI) was coded using the

ICBM-81 atlas (Mori et al., 2008). ROIs that were outside of this atlas were only used in

the analyses involving time post-injury and omitted for the spatial DTI analysis. Most

publications reported fractional anisotropy (FA) values; however, we also included

studies that reported relative anisotropy (RA). Publications found anisotropy differences

in two predominant ways: either categorically, by examining anisotropy means between

mTBI and control groups, or parametrically, through regression analyses correlating

anisotropy values with neuropsychological performance. AFNI (Cox, 1996) was used to

present DTI meta-analysis results in Montreal Neurological Institute (MNI-152) space.

  27  

As in other publications that account for diverse sets of assessment scores (Bazarian et

al., 2007), we normalized neuropsychological results using algebraic negation

(multiplying the original score with a negative one) such that better performance would

correspond to positive scores. For example, we did not transform results from the

California verbal learning test, in which scores should be higher for normal performance

than for impaired performance, but we did transform (negate) completion time for Trail

Making A test (in this case, better raw scores are lower, corresponding to faster

completion times). Our motivation for doing this was that it would enable us to

unambiguously discuss negative and positive correlations between neuropsychological

results with changes in imaging measures.

Results

The number of mTBI publications has dramatically increased over the past two decades.

A shows the number of publications with respect to time for the neuroimaging articles we

analyzed in the context of mTBI research as a whole. The more general mTBI PubMed

search in Figure 4A excluded our neuroimaging keywords (to include both imaging and

non-imaging studies). Specifically, we used (mTBI or “mild traumatic brain injury” or

“post concussive syndrome” or “post concussion syndrome” or “postconcussion

syndrome” or “postconcussive syndrome”) and (“1990”[Publication Date]:

“2011”[Publication Date]). The figure shows that the rate of mTBI publications has

increased over time, with a notable upsurge occurring after 2007. Figure 4B classifies

  28  

Figure 4: Overview of mTBI and neuroimaging mTBI publications between 1990 and 2011: The levels of mTBI studies have been increasing over the past two decades, and the focus on neuroimaging has increased proportionately with the field as a whole. (A) The histogram shows 1,314 mTBI articles published from 1990 to 2011. (B) focuses on the 122 imaging-based studies and provides a graphical breakdown of what imaging modalities were used. To date, the predominant imaging modalities have been CT and MRI.

  29  

imaging modalities of the publications we analyzed over time. Most of the imaging

modalities are MRI based. The number of studies relying (at least in part) on CT is

relatively large, primarily because it is used ubiquitously (and almost exclusively) in

emergency room settings. Thus, CT was used as part of the clinical characterization of

the subjects in many studies. The functional modalities (EEG/MEG, fMRI, SPECT/PET,

and ASL) have been steadily used during the time period shown. Figure 5 and Figure 6

show that structural and functional neuroimaging studies have examined mTBI across

acute to chronic time scales with a wide range of imaging modalities. It is important to

note that the number of subjects in these publications represents the total number of

mTBI and control participants for each experiment, which could include different

participant cohorts. Also note that many experiments have large variability for subjects’

post-injury data collection times. In addition, while the number of subjects includes

control participants, for obvious reasons these controls did not contribute to the post-

injury collection times in Figure 5 or Figure 6. Focusing on some observations from each

figure, Figure 5 shows that CT studies have been performed on both small and large

cohorts, but the majority of these studies occur soon after injury, usually using the

subjects’ clinical CTs. DTI and the other structural modalities have a much better

coverage of the time post-injury. For Figure 6, one observation is that many EEG studies

also occur soon after injury but in contrast to CT, EEG has also been broadly applied to

the entire time course post-injury and to a large number of subjects. Figure 6 also shows

that task-based fMRI has been similarly broadly applied, while resting state fMRI studies

have predominantly

  30  

Figure 5: Time course of structural neuroimaging studies Structural neuroimaging studies of mTBI vary widely in terms of both the number of subjects studied and the time range after injury. Shown is a depiction of the number of subjects (mTBIs as well as any controls in each experiment) and the time post-injury (for the mTBIs) of data collection for structural imaging studies. The colored backgrounds indicate the time axis scales (days, weeks, and years). The imaging modality is indicated by color, and each line indicates the study’s post-injury scan range (earliest and latest reported times post-injury). The line’s ellipse represents the median time after injury. To keep all data “visible,” overlapping lines have been shifted up by two subjects.

  31  

Figure 6: Time course of functional neuroimaging studies Functional neuroimaging studies of mTBI also vary widely in terms of both the number of subjects studied and the time range after injury. Conventions are the same as in Figure 4.

  32  

focused on acute and semi-acute time ranges. PET, ASL, CE-MRI, and MEG seem to be

rarely used. In addition, there is a lack of PET studies of acute mTBI, and both PET and

MRS publications used groups that were smaller than 40 subjects.

The research areas of neuroimaging studies tend to follow distinct structural and

functional categories, as shown in Figure 7. As mentioned previously, CT was used as

part of the clinical characterization of the subjects in many studies. In papers where CT

was a research focus, it was used to examine structural abnormalities arising from mTBI.

Although the category of “mild” TBI sometimes includes the stipulation of negative

findings, CTs can nonetheless be used to detect abnormalities from a wide range of

trauma severities that appear as hyper- or hypo-intensities observed in gray matter or

white matter regions as well as hematomas, cerebral swelling, and hemorrhages. Gray

matter is composed largely of neuronal cell bodies, glial cells, and the capillaries. White

matter abnormalities are thought to reflect stretched or sheared axon bundles or abnormal

cellular microstructures. Abnormalities not clearly falling into the categories of

hematomas or white/gray matter findings have been designated as “complicated mTBI”

in Figure 7. Complicated mTBI was examined 56 times in our publication sample, mainly

using CT and MRI. Gray matter (GM) abnormalities were only reported in one T1 and

one CT study. Abnormal blood–brain barrier (BBB) permeabilities were reported by

three publications using SPECT and CT (CT studies also detected tau/S100b proteins in

blood). White matter (WM) abnormalities were examined 38 times (primarily with DTI).

Hematomas were only reported in two CT-based studies. Metabolites were analyzed by a

PET, a SPECT, and five MRS publications. Perfusion was examined by one PET, one

  33  

Figure 7: Themes studied with different imaging modalities Two major goals of neuroimaging studies are to find structural and functional markers of mTBI and to establish links between neuropsychological assessments and neuroimaging. Shown is a breakdown of imaging methods and study focus “themes.” Themes include gray matter (GM) abnormalities; white matter (WM) abnormalities; intracranial hematomas; complicated mTBIs (non-negative imaging results, excluding hematomas and abnormalities localized to GM and WM such as increased blood–brain barrier permeability, contusions, intracranial lesions, and micro bleeds); metabolites (changes in magnetic resonance spectroscopic results); blood–brain barrier (BBB) permeability; perfusion deficits; task-based imaging; connectivity analysis; and neuropsychological assessments (used in conjunction with a neuroimaging modality).

  34  

ASL, and 12 SPECT publications. Task-based imaging was used in 17 fMRI and 15 EEG

studies, often linking tasks to brain activity. Connectivity analyses were examined 27

times, detecting low-frequency activity, connectivity differences between neural regions,

EEG pathologies, altered resting state networks (using fMRI), and connectivity maps

(using white matter tractography). Neuropsychological assessments were used in 72

publications and were the one theme of investigation (despite not relying on any

particular imaging modality itself) that spanned all structural and functional modalities.

Figures 8–10 display results from our MRI meta-analyses. Figure 8 shows results from a

Ginger ALE analysis of the fMRI publications summarized in Table 1. Table 1 also

examines how many independent publications support each activation likelihood

estimated (ALE) peak. The analysis produced six regions that were consistently more

active in mTBI compared to control groups and seven regions with lower activity for

mTBI. Spatially, these regions suggest an anterior-to-posterior pattern in which activity is

reduced in anterior regions and increased in posterior regions. Of the seven regions with

decreased mTBI activity, six were in the frontal lobe or anterior cingulate, with the

remaining one being relatively posterior (temporal lobe/BA 39). The regions with

increased mTBI activity consisted of two coordinates in the cerebellum, two insula

regions, and two foci in the parietal lobe (BA 40). The mean Talairach anterior-to-

posterior coordinate for regions with reduced activity was Y = 15 mm, compared to Y =

−23 for increased mTBI activity, and a two-sample t-test of Y between the decreased and

increased regions was significant (p = 0.05, two-tailed).

  35  

Close examination of the number and anatomical locations of publications reporting

white matter abnormalities revealed significant anatomical heterogeneity, as shown in

Figure 9. See Table 2 for white matter regions in the ICBM-81 atlas as well as their

abbreviations and center-of-mass coordinates. It is important to note that several ICBM-

81 regions have not been reported by the publications we examined. These include the

pontine crossing tract, medial lemniscus, inferior cerebellar peduncle, cerebral peduncle,

posterior thalamic radiation, cingulum to hippocampus, superior fronto-occipital

fasciculus, tapetum, and inferior longitudinal fasciculi. These unreported regions were

coded in two ways; both leading to statistically significant correlations between

anatomical location along the anterior-to-posterior gradient and the number of

abnormalities. Specifically, we calculated a Spearman correlation, 1) omitting these

regions from the statistical test (using only regions that had reported abnormal findings in

the literature), resulting in the correlation of ρ2 = 0.26 (p < 0.026); and 2) including the

unreported regions (coding their frequency as zero), resulting in ρ2 = 0.32 (p < 0.0025).

We did not find significant correlations in the inferior-to-superior or left-to-right

directions. Laterality of the regions was also not significant.

  36  

Figure 8. Activation likelihood estimate (ALE) analysis of fMRI mTBI publications Activation likelihood estimate (ALE) analysis of fMRI mTBI publications shows both increased and decreased BOLD responses for mTBI. As shown, with mTBI have increased response in the cerebellum, insula, and inferior parietal regions (BA 40) compared to controls. Relative to mTBI, control subjects have increased response in several regions in the frontal lobe and in BA 39. Maps have a threshold of p < 0.05 using a false-discovery rate (FDR) correction and a minimal cluster size of 64 μL. Results are displayed on a Talairach brain template.

  37  

ROI abbrev.

Vol. (μL)

ALE cluster coordinate (X, Y, Z) a

ROI name Supporting publications (ALE coord. within 20 mm)b

mTBI > Control

CBT 304 33 -53 -39 r. cerebellar tonsil Wi2010, Sl2010, Kr2011 BA40 152 58 -32 39 r. inf. par. lobule / BA 40 Wi2010 CBC 144 -33 -34 -26 l. culmen Wi2010, Mc2011 IFG 144 29 9 -12 r. inf. front. gyrus Wi2010, Ma2011 Insula 64 33 17 7 r. insula Wi2010, Ma2011 SMG 64 57 -47 33 r. supramarg. gyrus Wi2010, Ma2011 Control > mTBI

MdFG 1296 32 28 24 r. mid. frontal gyrus Wi2010, Ma2011, Mc2011, Ch2007

ACC 856 3 30 10 r. ant. cingulate Wi2010, Ma2011 MTG 616 51 -59 23 r. mid. temp. gyrus / BA

39 Wi2010, Ma2011, Mc2011

BA9 496 45 17 34 r. precentral gyrus / BA 9 Wi2010, Mc2011, Ch2007 MdFG 304 -50 14 35 l. mid. frontal gyrus Mc2011 BA46 184 46 40 10 r. DLPFC / BA 46 Ma2011, Mc2001, Mc2011 BA46 184 -39 36 19 l. DLPFC / BA 46 Ma2011, Mc2011, Ch2007 Table 1. mTBI vs. control cluster centroids with FDR (p < 0.05) using ALE a The ALE cluster coordinate is the center of mass of the cluster calculated by the ALE algorithm. b Publications codes Wi2010, Sl2010, Kr2011, Mc2001, Mc2011, Ma2011, and Ch2007 represent Witt et al., 2010; Slobounov et al., 2010; Krivitzki et al., 2011; McAllister et al., 2001; McAllister et al., 2011; Mayer et al., 2011; and Chen et al., 2007, respectively. Supporting publications have coordinates at least within 20 mm of the ALE coordinate.

  38  

Figure 9. Anisotropy abnormalities mapped spatially in mTBI Shown are the ICBM-81 white matter regions, colored to indicate the number of publications reporting white matter abnormalities (regions with no abnormal findings in the literature are not shown). The Montreal Neurological Institute (MNI-152) template is added for anatomical reference. Using the center of mass for each ICBM-81 structure, we determined that a significant anterior-to-posterior relationship exists between frequency in the literature and anatomical location. See Table 2 for full names of the anatomical labels. Note that since more lateral structures are only partially visible, the anatomical labels point to a convenient, visible location and do not necessarily reflect a structure’s center of mass. For example, SLF is mostly covered by more medial structures and is only visible at its most posterior-inferior part. Coordinates are displayed in MNI-152 space.

  39  

ROI C o o r d i n a t e s Abnorm- ROI Acronym X(L)a Y(P)b Z(I)c alities Name ACR 22 28 10 6 anterior corona radiata alIC 18 8 8 2 anterior limb of internal capsule bCC 1 -5 27 1 body of corpus callosum bFX 1 -6 13 1 body of fornix cFX 28 -24 -6 1 fornix crus CgC 8 -11 30 4 cingulate cortex CgH 21 -31 -13 0 hippocampal part of cingulum CP 12 -18 -12 0 cerebral peduncle CST 6 -25 -33 1 corticospinal tract EC 30 0 1 2 external capsule gCC 0 26 7 6 genu of corpus callosum ICP 8 -44 -37 0 inferior cerebellar peduncle MCP 1 -40 -35 1 middle cerebellar peduncle ML 6 -37 -33 0 medial lemniscus PCR 24 -38 28 2 posterior corona radiata PCT 1 -29 -31 0 pontine crossing tract plIC 20 -13 8 5 posterior limb of internal capsule PTR 34 -55 7 0 posterior thalamic radiation rIC 31 -29 6 1 retrolenticular part of internal capsule sCC 1 -42 18 5 splenium of corpus callosum SCP 6 -42 -25 1 superior cerebellar peduncle SCR 24 -8 30 3 superior corona radiata SFO 20 2 21 0 superior fronto-occipital fasciculus SLF 36 -26 26 2 superior longitudinal fasciculi SS, IFO, ILF

40 -32 -9 2 sagittal stratum, inf. fronto-occipital fasc., inf. longitudinal fasc.

TAP 28 -46 14 0 tapetum UNC 34 0 -16 4 uncinate fasciculi Table 2. Center of mass of white matter structures in the ICBM-81 atlas a X-coordinate assumes symmetry between left and right hemispheres (mean absolute value of centroid reported here). b Y-coordinate increases with anterior direction. c Z-coordinate increases with superiority. All coordinates are in MNI-152 space. Reported anisotropy-abnormalities indicate the number of publications that reported an anisotropy abnormality in ROI.

  40  

Based on observations by Niogi & Mukherjee (2010) as well as Mayer et al. (2011), we

wanted to examine the hypothesis that anisotropy would be elevated at acute time points

and depressed afterwards. Figure 10 demonstrates the critical importance of accounting

for the time post-injury in diffusion-weighted imaging reports. Shown are the time-

resolved anisotropy results for each study. In Figure 10A, studies compared mTBI groups

to control groups. In Figure 10B, studies correlated mTBI anisotropy values with

neuropsychological performance. Note that some publications report experimental results

at two different chronological points (Grossman et al., 2012; Inglese et al., 2005; Mac

Donald et al., 2011; Mayer et al., 2011; Messe et al., 2011) and thus are represented by

two bars. Another complication with the literature is that some research groups seem to

have used overlapping mTBI cohorts across publications (see potential overlap in boxed

bars in Figure 10). Thus counting each bar in Figure 10 as an independent measure likely

leads to inflated estimates of significance. To account for this, we report the most

conservative statistical estimate by using each boxed group as a single experiment. As

shown, elevated anisotropy values are more frequently reported for studies of acute mTBI

(before 2 weeks), while depressed anisotropy findings are reported more frequently for

post-acute mTBI (after 2 weeks). In Figure 10A, a two-sample t-test confirms that

anisotropy is greater in the acute phase (one-tailed, 14 DOF, p = 0.02). In Figure 10B,

acute-phase anisotropy was also significantly and negatively correlated with

neuropsychological performance compared to a predominant positive correlation in the

post-acute phase (one-tailed, 7 DOF, p < 0.006). Finally, to put these results into the

context of the literature, it is important to note that the participant populations were

  41  

recruited using different criteria in the acute and chronic periods. Specifically, all of the

acute studies recruited prospective mTBI subjects, the majority of whom would be

expected to recover from their injuries. Conversely, many of the post-acute mTBI studies

of anisotropy selected for symptomatic subjects.

  42  

Figure 10. Anisotropy compared temporally in relation with mTBI or performance (A) Elevated anisotropy following mTBI is more frequently reported for studies of acute mTBI, while depressed anisotropy is reported more frequently for studies after the acute phase. Each bar represents the ratio of increased (red) to decreased (blue) regions in mTBI vs. a control group for each publication. The gray boxes represent experiments with insignificant FA difference between mTBI and control groups. The lines connecting the bars to the time axis mark the study’s median time post-injury. Also indicated is the number of subjects per experiment and whether the mTBI subjects were prospective (P) or selected (S). Boxed bars indicate potentially overlapping subject cohorts. The colored backgrounds indicate the time axis scales (days, weeks, and years). For statistical tests, we have defined post-injury times less than 14 days as an acute mTBI and post-acute times as 2 weeks and greater. Based on a one-sided, two-sample t-test, the acute anisotropy was significantly greater than the anisotropy after the acute phase (p = 0.02). (B) Shows the analogous information for studies that reported significant relationships between anisotropy measures and neuropsychological performance. Using a t-test analogous to that in 7A, we show that the acute phase anisotropy was significantly anti-correlated with neuropsychological performance compared to a predominant positive correlation after the acute phase (p < 0.006). Statistical significance was designated as * for p < 0.05. Publications are I) Bazarian et al. (2007); II) Chu et al. (2010); III) Wilde et al. (2008); IV) Wu et al. (2010); V) Yallampalli et al. (2010); VI) Inglese et al. (2005); VII) Miles et al. (2008); VIII) Lipton et al. (2009); IX) Mayer et al. (2011); X) Mayer et al. (2010); XI) Messe et al. (2011); XII) Mac Donald et al. (2011); XIII) Holli et al. (2010); XIV) Smits et al. (2011); XV) Grossman et al. (2012); XVI) Lipton et al. (2008); XVII) Maruta et al. (2010); XVIII) Niogi et al. (2008a); XIX) Niogi et al. (2008b); XX) Geary et al. (2010); XXI) Lo et al. (2009).

  43  

Discussion

An obvious key limitation of this analysis (and of any review that attempts to report a

snapshot of a vibrant field) is that components of it are immediately outdated. Although

we believe that the conclusions we draw here will be qualitatively accurate for the next

few years, the literature we sampled just barely supported any meaningful fMRI meta-

analyses, and the issue of the anisotropy values’ dependence on time post-injury is far

from conclusive. Part of what we have hoped to accomplish here is an assessment of the

modalities, questions, and times post-injury that recent publications have examined. We

hope that research teams can use this assessment to strategically bolster the areas of this

field that would most benefit from additional effort.

Our review of the mTBI literature highlights the fact that currently about one-tenth of

published studies have a human neuroimaging component and that several small and

large studies spanning acute to chronic time points have been conducted. These studies

have examined both structural and functional changes with mTBI, using virtually every

available medical imaging modality. Two key commonalities have been used across the

majority of imaging studies. The first is the comparison between mTBI and control

populations. The second is the attempt to link imaging results with neuropsychological

assessments. Indeed, in addition to highlighting the fact that research topics have largely

(but not exclusively) fallen into either structural or functional categories and providing a

sense of the proportion of work that has been devoted to the two, Figure 7 strikingly

shows that neuropsychology appears to be the predominant commonality across the

imaging literature. This underscores the fact that mTBI neuroimaging efforts have

  44  

prioritized attempting to link brain measurements with neuropsychological assessments.

Despite the significant efforts to date, neuroimaging methods still lack the individual

patient-level sensitivity and specificity to serve as a diagnostic tool for mTBI. Similar to

other reviews in this area (Belanger et al., 2007; Hunter et al., 2012; Niogi & Mukherjee,

2010; Prabhu, 2011; Pulsipher et al., 2011), we believe that ultimate motivations for

further neuroimaging work are to provide data to predict an individual’s recovery, to

measure her or his transient and persistent cognitive deficits at a level that compliments

or exceeds the sensitivity of neuropsychiatric testing, and to quantify the success of

cognitive and drug-based interventions (McAllister et al., 2011). In the meantime, the

field needs further effort to provide stronger correlative understanding of the

relationships between neuroimaging and neurology, and both types of measures also need

improvement.

Our fMRI and DTI meta-analyses show, however, that there is hope for consistent

neuroimaging markers of structure and function. The fMRI meta-analysis results in

Figure 8 and Table 1 show a frontal vulnerability in mTBI, demonstrated by decreased

signals compared to controls. Studies on fMRI consistently report decreased activity in

mTBI in frontal regions such as the right middle frontal gyrus (MFG), the anterior

cingulate cortex (ACC), and the right precentral gyrus. Our finding of bilateral decreases

in the dorsolateral prefrontal cortex (DLPFC) is consistent with three published studies

(Chen et al., 2007; Mayer et al., 2011; McAllister et al., 2011). The DLPFC may be

involved in working memory. Specific localization of working memory intense regions

was found in nonhuman research (Funahashi et al., 1989) and in human research using

  45  

fMRI (D’Esposito et al., 1995). Even though DLPFC responds to working memory, it is a

very complex region that serves a multitude of cognitive functions (Kane and Engle,

2002). Interestingly, among the most significant mTBI increases, right cerebellar tonsil

and left culmen were noted in four independent publications (Chen et al., 2007; Mayer et

al., 2011; McAllister et al., 2011; Witt et al., 2010). Krivitzky et al. (2011) noted that the

cerebellum has been implicated for regulating behavior, working memory, and other

aspects of executive control. In addition, it has long been recognized that cerebellar

lesions can lead to postural deficits (Horak and Diener, 1994), which is a prominent issue

in mTBI (Guskiewicz et al., 1996; McCrea et al., 2003). Finally, it is important to note

that the well-known fMRI issue of multiple-comparisons correction may be a limitation

of our Ginger ALE analysis. Most of the publications that we used corrected their

findings for multiple comparisons. However, notable exceptions include McAllister et al.

(2001) and Witt et al. (2010). Another limitation of the fMRI meta-analysis was that,

unlike the diffusion-weighted literature, the number of fMRI studies published did not

have power enough for a statistical analysis using time post-injury. We feel that “time-

resolving” fMRI studies could be an important area for future investigation.

Structurally, diffusion-weighted imaging is currently one of the most promising

techniques for characterizing the subtle and heterogeneous changes that occur with

mTBI. Figure 9 demonstrates that differences in the frequency of reported mTBI white

matter anisotropy values have an anterior-to-posterior gradient. Taken independently,

each DTI study shows white matter differences that seem to be sporadic. When

integrating across all reported findings, these frequencies of abnormalities have a

  46  

systematic spatial distribution, corroborating previous suggestions that anterior regions of

the brain are more vulnerable to abnormalities (Hashimoto and Abo, 2009; Lipton et al.,

2009; McAllister et al., 1999; Niogi et al., 2008a). That TBI is particularly vulnerable in

frontal and ventral regions is well known (Bigler, 2007). Anterior regions may also be

related to executive/cognitive function disabilities in mTBI (McCrea et al., 2003).

Using the existing literature to time-resolve results, Figure 10 suggests that white matter

anisotropy can serve as an important potential marker of both chronic symptoms as well

as of an acute response to secondary injuries. Taken together, the literature demonstrates

that elevated anisotropy values (and negative correlations with neuropsychological

performance) are more frequently reported for studies of acute mTBI, while depressed

anisotropy findings (and positive correlations with neuropsychological performance) are

reported more frequently for chronic studies. Our analyses show that elevated anisotropy

values are more frequently reported in studies of acute mTBI, while depressed anisotropy

findings are reported more frequently in post-acute studies, corroborating conjectures of

Mayer et al. (2011) and Niogi & Mukherjee (2010). Thus, Figure 10 helps to clarify an

apparent inconsistency in the literature about anisotropy changes after mTBI by “time-

resolving” the results. In addition, Figure 10B raises important new questions about the

interactions of anisotropy, neuropsychological performance, and time post-injury. In

particular, the fact that cognitive performance is negatively correlated with anisotropy

during acute stages but positively correlated with anisotropy chronically is puzzling.

At least two issues remain outstanding with the results in Figure 10. The first is the

inconsistency in Figure 10A of the results for paper XXI (Lo et al., 2009). In discussing

  47  

their results, Lo et al. (2009) were careful to point out that the small sample size of their

study called for more future work. However, they also suggested potential explanations

for their findings, including the possibility that the observed increases in FA could reflect

recovery from injury; that specific degradation mechanisms could target subsets of fibers,

leading to enhanced FA; or, finally, that the increases in FA are compensatory for other

observed decreases in other brain regions. The second issue relates to the heterogeneity of

the neuropsychological testing in the papers used to generate Figure 10B. Thus a

limitation in the literature (and in our results) is the lack of consistency of

neuropsychological assessment. As previously mentioned, we have “normalized” the

scores such that better performance corresponds to a positive score, regardless of which

test was used. We note, however, in light of the literature’s “uncontrolled assessment

variance,” that our results are conservative as well as robust across cognitive domains and

testing variability. Undoubtedly, more sensitive, standardized assessments that are

specifically adapted to mTBI are needed to further reduce the limitations of these types of

studies.

The results of Figure 10 and the outstanding issues that we have raised suggest that more

studies focused on mTBI and anisotropy are warranted and necessary. At this point we

can only conjecture on possible explanations for our meta-analysis results. One

possibility for the increase in FA, in people with acute mTBI, is that MR diffusion-based

measurements are not purely sensitive to white matter microstructure but are also inflated

by acute-stage secondary injury or compensatory mechanisms (Mayer et al., 2011; Niogi

& Mukherjee, 2010). The idea behind this is that inflammation and secondary injury

  48  

factors (including ischemia, cerebral hypoxia, and cerebral edema) may increase

anisotropy in the acute phase, but that these factors do not contribute in the chronic

phase. Chronically, though, residual damage of white matter could lead to a decreased

anisotropy signal. A second possible explanation of Figure 10B, however, is that there is

not a simple causal relationship between anisotropy and cognitive performance.

Whatever the mechanism, Figure 10B indicates that poor neuropsychological

performance is associated with high anisotropy scores immediately after injury and with

low anisotropy in the chronic phase. From a statistical point of view, Figure 10A

demonstrates a main effect between anisotropy and time post-injury, but Figure 10B

additionally indicates an interaction with cognitive performance. We have previously

mentioned that there is controversy surrounding anisotropy changes after TBI.

Nonetheless, FitzGerald & Crosson (2011) noted in their review of the literature a general

acceptance that decreases in FA values are expected after TBI-induced axonal injury.

However, they also pointed out that mechanisms for increases in FA are also possible

(e.g., unequal injury in one fiber in voxels where two or more fibers cross). Furthermore,

there have been examples of other disorders (such as autism) for which widespread

differences have been noted in the relationship of FA with cognitive function (Ellmore et

al., 2013). Better understanding of the underlying mechanisms of anisotropy changes

following TBI will likely need to rely on methods and validation studies in animal

models such as those of Budde et al. (2011) and Mac Donald et al. (2011) to relate

pathology to diffusion tensor imaging findings. In addition, a study comparing age and

mTBI effects with DTI (Ilvesmäki et al., 2014) has shown that age effects may be

  49  

stronger than effects from mTBI. Important future work will be to evaluate if we may

find any age bias in our collected dataset. Finally, the majority of the literature has

focused on FA, but some publications have reported radial diffusion (RD), axial diffusion

(AD), and mean diffusion (MD) diffusion as well. The four measures are related to each

other; usually FA and AD are significant in the same direction, while MD and RD tend to

be significant in the opposite direction.

To summarize, this review and meta-analysis have demonstrated several important points

about the ongoing use of neuroimaging to understand the functional and structural

changes that occur throughout the time course of mTBI recovery. A broad range of

neuroimaging modalities are being applied to this problem across the range of post-injury

time scales, and an important component of many studies is the effort to link imaging

measurements to neuropsychological assessments of cognitive deficits. Upon closer

scrutiny of two MRI-based modalities, first, the importance of interpreting white-matter

measures of anisotropy in the context of time post-injury was demonstrated. Secondly,

consistent findings were available for evaluating anatomical vulnerability to mTBI for

both structure and function. The fact that we have been able to produce statistically

significant results in this area is encouraging. Based on the complexity of the injury,

however, much more work in this area is required. Future publications focused in depth

for each image modality would greatly complement this generic analysis and the field of

mTBI. Here we have been able to preliminarily examine important factors related to

anatomical vulnerability and time post-injury, but these issues need to be refined with

future data, and many other factors remain to be studied (e.g., injury mechanism

  50  

differences, number and timing of previous concussions, and psychological comorbidities

including major depression and post-traumatic stress; Iverson and Lange, 2003;

Rosenbaum & Lipton, 2012). Ultimately a major driving motivation for continued study

is the further efficacy of neuroimaging for clinical diagnosis at both acute and chronic

stages and the synergistic improvement with neuropsychological assessments and

rehabilitation strategies.

  51  

Chapter 3: Detecting Motor Rate using Support Vector Machines

Introduction

According to the mTBI-fMRI ALE meta-analysis by Eierud et al. (2014), fMRI studies

may detect mTBI (Figure 8). Even though the results are significant using false discovery

rate (FDR) correction, the mTBI effects are significant only at the group level, and

detection needs to be sensitive at a single-subject level in order to be of use clinically.

Almost all studies included in the meta-analysis were hypothesis driven using the general

linear model (GLM; Friston et al., 1995). In contrast to the hypothesis driven GLM,

SVM, using supervised learning (Cortes & Vapnik, 1995) to make predictions may

complement GLM. In fMRI, the ability of SVM to find neural patterns that can be used

for powerful predictions has been successfully proven (Cox & Savoy, 2003; Fan et al.,

2007; LaConte et al., 2005; LaConte et al., 2007; LaConte et al., 2011; Mitchell et al.,

2004). Furthermore, SVM’s prediction accuracy may increase its sensitivity to track

mTBI at subject level, which may be needed for clinical usage. Bringing neuroimaging

closer to clinical practice by using SVM is the end goal for this chapter. It would be most

beneficial if neuroimaging right after a concussion could predict any chronic mTBI

effects. This could reduce the lengthy and problematic diagnosis of PCS, which is a very

  52  

costly disability that could benefit from image objectivity and less chance of

misdiagnosis (Evans, 1992).

Even if it does not fully achieve that final goal, this chapter will take a few steps in that

direction. The first step taken in this thesis is to characterize support vector classification

and the newer support vector regression algorithm.

For fMRI and mTBI, a behavioral paradigm is needed that may be either a default mode

network (DMN) or task fMRI paradigm. DMN is the neural signal from a person who is

not performing any behavioral paradigm at all and seems to be at rest (Raichle & Snyder,

2007). The mTBI-fMRI literature is more abundant for fMRI tasks than for DMN in

accordance to the previous literature (Eierud et al., 2014) that was important in our case.

Behavioral Paradigm

In order to characterize SVM properly, the following were required from the behavioral

paradigm:

(1) The maximal neural response signal in order to receive predictions as robust as

possible.

(2) Practicality and accuracy to handle large amounts of labels accurately, preferably by

automation, to save working hours.

(3) A task with abundant previous literature to allow referencing results.

(4) A behavioral paradigm with a wide stimulus spectrum that generates a span of low

response to strong neural response, to maximize the signal from the paradigm.

  53  

These requirements were fulfilled by a unimanual motor rate task. Motor tasks are known

to yield a strong response. For example, it was seen in Glover (1999) that a motor

response can yield a stronger signal than an auditory stimulus. The motor rate design we

used contained more than 14,000 labels that were automatically and accurately logged.

The abundance of data samples benefitted the SVM train/test. Automation of the labeling

process saved many work hours and accuracy. Many publications in the previous

literature have used similar stimuli for reference. A representative sample of motor rate

publications was listed in the comprehensive meta-analysis of Witt et al. (2008). It was

possible to widen the motor stimuli spectrum, spanning from a very slow to extremely

fast rates that induced neural responses accordingly.

Unimanual Motor Rate Literature

Many unimanual research articles exploring rate effects have found a positive rate effect

between motor rate and neural response in the following regions: contralateral (primary)

sensorimotor cortex (M1), ipsilateral sensorimotor cortex (SMC), supplementary motor

area (SMA), contralateral thalamus, contralateral lentiform nucleus, and cerebellum. The

meta-analysis of Witt et al. (2008) listed 11 publications that studied tapping rate tasks.

However, this chapter is MRI specific, so we eliminated two PET studies (Blinkenberg et

al., 1996; Kawashima et al., 1999) and one TMS study (Rounis et al., 2005). In addition,

two studies were not rate specific in any way comparable to our study (Aramaki et al.,

2006; Lehericy et al., 2006). The remaining six publications matched our study (Jäncke et

al., 1998; *Jäncke et al., 1999; Jäncke et al., 2000; Riecker et al., 2006; *Deiber et al.,

  54  

1999; Lutz et al., 2004), with two studies that were ROI specific and did not overlap with

all regions our study explored (noted with an asterisk).

Support Vector Machines

An SVM is a supervised learning model. This means that subject data need to be divided

into a training part and a test part. First the training part is fed to the SVM together with

labels describing each data sample, and this generates the SVM brain model. This model

has two features. First, the brain model’s weights may be used to present what regions are

most important for predictions. Secondly, the model enables a second phase of prediction

of the remaining dataset (Figure 11). The model is constructed differently for support

vector classification (SVC) and support vector regression (SVR), which are described in

greater detail below (and see Figure 11). In general, SVC may be used to separate

between different mental states, whereas SVR may be used to decode the degree of a

single mind state. For example, SVC could classify mTBI subjects into two classes, such

as “likely to recover” and “likely transition into PCS,” while mTBI SVR could predict a

probability that the subject will transition into PCS by a percentage, where 0% would

mean recovery and 100% would mean maximal probability to transition into PCS.

Support Vector Classification Background

First the SVC algorithm was trained using at least 4 minutes of images, using stimulus

labels for each image, to generate a specific normal vector (W), also called a weight

  55  

vector or brain model. Then, in the second phase, the SVC could predict unlabeled data

samples (fMRI volumes) using the brain model.

For explanatory purposes, the most rudimentary SVC algorithm (called a “hard margin”)

is described that only classifies two fully separable classes in the following six steps:

1. Each data sample (brain volume) needs to be transformed into a feature vector (x) in

the feature space. Because our data samples contained at least 30,000 voxels, the feature

space would contain at least 30,000 dimensions, which is impossible to depict on 2D

paper. Therefore, a scatterplot of a hypothetical brain with only two voxels was depicted

to demonstrate the idea (Figure 12).

  56  

Figure 11. Supervised learning Both the support vector classification (SVC) and the support vector regression (SVR) algorithms are supervised learning algorithms, which means that they have to be trained using labeled data before they can predict unlabeled data. Step 1 (training), shows the data samples (fMRI volumes) being acquired every other second. Simultaneously, the subject’s behavior and stimuli is labeled and tagged to each fMRI volume. The SVM algorithm uses several fMRI volumes to develop an optimized brain model. In Step 2 (testing) the brain model may be used to classify unlabeled fMRI volumes. Figure is adapted from LaConte et al. (2007) with permission from Dr. LaConte and John Wiley & Sons.

  57  

Figure 12. Hypothetical classification and feature space scatterplots (A) Depicts a hypothetical two-class paradigm. The class y = +1 (star labeled) could be a motor stimulus, and the y = -1 class (diamond labeled) could be a resting state. (B) A feature space scatter plot of a hypothetical two-voxel brain with voxels X1 and X2, within constraints for a hard-margin SVC. (C) is analogous to (B) with the difference that feature vectors now are allowed on the wrong side of the margin with the penalty of the variable ξt.

  58  

The first step of describing SVC is to explain the rudimentary hard-margin SVC, which

only works in cases where the data are fully separable. The SVC decision function D(x)

is the dot product of the weight vector and the feature vector in accordance with Equation

7.

Equation 7: D ! = ! ⋅ ! +w!

w is the weight vector and x is a feature vector of the data sample. w!/ ! is the

distance between the origin and the hyperplane.

In the hard-margin case, all feature vectors have to be above the margin (Equation 8) for

Task 1 labels and below the margin (Equation 9) for Task 2 labels. For example, the

decision function is usually 1 or -1 (for the two classes). However, in the case a data

sample causes the decision function to result in 0 (D ! = 0) that data sample is placed

right on the hyperplane that is not allowed in the hard margin case.

Equation 8: Case y! = +1 (task 1)  ⇒ ! ⋅ ! +w! / ! ≥ +Δ

Equation 9: Case y! = −1 (task 2)  ⇒ ! ⋅ ! +w! / ! ≤ −Δ

The Δ is the width of the margin. To normalize the margin with the weight vector such

that ! Δ = 1 both Equation 8 and Equation 9, it may be rewritten as

Equation 10: y! ! ⋅ ! +w! ≥ 1

  59  

!(!) =!!! !! +

12‖!‖!

!

!!!

 

 

The weight vector that is constrained by Equation 10 and minimizes ! ! = ! !, for

t = 1,… ,T separates the two classes with the widest possible margin (optimal weight

vector) and is called the hyperplane. Since η ! = ! ! is minimized at an identical w

to Equation 11, which is easier to calculate, Equation 11 is most often used in the

literature for the SVC minimization.

Equation 11: η ! = !!! !

There are different ways to minimize ! ! in Equation 11, but in literature it is common

to use Lagrangian multipliers.

Since empirical data clusters from different classes often are close enough to overlap, the

hard margin SVM is not very practical. For this reason, the soft-margin SVM was

introduced (Cortes & Vapnik, 1995), making SVMs more usable and popular. The soft-

margin SVM is extended with a term to accept margin errors on top of the equations for

the hard-margin SVM. Equation 10 and Equation 11 are extended into Equation 12 and

Equation 13, respectively, as follows:

Equation 12: y! ! ⋅ ! + !! ≥ 1− !!

Equation 13:

  60  

Feature vectors overstepping the margin boarders, and the degree to which they do so, are

registered by a slack variable in accordance with Equation 12 (Figure 12C). Then the

slack variables (noise) are summed up for penalty (Equation 13). In the soft-margin case,

Equation 12 accepts overlapping clusters, but instead the width of the margin may be

varied depending on C, which is a constant that modifies the hyperplane margin width. If

C is high, the margin is narrow, and if C is low, the margin becomes wider. This way the

SVM can run for any data, and the C value can be adjusted to optimize the SVM results

for the dataset. Further details on SVC can be found in textbooks such as Cherkassky and

Mulier (2007).

Since an SVC hyperplane only separates two classes, several classes may be predicted by

combining SVC with another algorithm such as the directed acyclic graph (DAG)

algorithm. Combining DAG with SVC a hyperplane (brain volume) requires estimating

each two-class combination, totaling n(n-1)/2 brain volumes for n classes. For example, if

4 classes need to be classified, 6 brain models need to be estimated. An example of a

DAG multiclass classification using several hyperplanes (brain models) is depicted in

Figure 13. A decision tree for a 4-class DAG classification is depicted in Figure 14.

  61  

Figure 13: Hyperplanes layout for DAG multi-classification (A) Depicts four clustered classes of data separated by borders crafted by six 2-class hyperplanes and direct acyclic graph (DAG). (B) The six 2-class hyperplanes were estimated for the identical clusters as in (A).

  62  

Figure 14: Direct acyclic graph (DAG) decision tree The unlabeled brain image (Test Input) is tested against six 2-class brain models before final prediction of which of the four classes it belongs to. The six brain models (SVC1 to SVC6) have previously been trained in all possible unique 2-class combinations possible.

  63  

 !(!) =!!!(ξ! + ξ!∗) +

12‖!‖!

!

!!!

 

 

Support Vector Regression Background

The linear support vector regression (SVR) may become a great complement to SVC for

detecting mTBI. There are algorithms for nonlinear SVR, which are out of scope of this

research. In the test case, SVR decodes a neural image into a continuous value

representing the level of a mind state or condition. The condition may be a mental

disability such as a pain disorder, but in our case we want to compare how well SVR

decodes fMRI. SVR predicts continuous real numbers instead of integers (representing

classes), which SVC predicts. Very differently from SVC (which uses a hyperplane), the

SVR encapsulates an epsilon tube (ε-tube), preferably outside the feature vectors. The

regression is then decoded by the length of the ε-tube at which the normal of the feature

vector falls. Feature vectors outside the ε-tube are allowed, but they are penalized using a

slack variable, and feature vectors on the edge of the ε-tube are support vectors. A

hypothetical 2D feature space and ε-tube in the SVR case is depicted in Figure 15B. The

loss function for the slack variables is depicted in Figure 15C. The SVR minimizing

function is as

Equation 14 and is constrained by Equation 15 as follows:

Equation 14:

Equation 15: y! − w ⋅ x! −w! ≤ ℇ+ ξ!y! − w ⋅ x! −w! ≤ ℇ+ ξ!∗      ξ!, ξ!∗ ≥ 0,where  t = 1,… ,T  

Further SVR details may be found in textbooks such as Cherkassky and Mulier (2007).

  64  

Figure 15: Hypothetical support vector regression scatter plot The SVC uses a hyperplane to separate two classes. Conversely, the SVR models an epsilon tube that ideally should have the feature vectors clustered along its centerline. Any feature vector outside the epsilon tube is a support vector and may even have a slack variable. Since regression models a single variable intensity (perhaps of a mind state), all feature vectors originate from the same stimulus type (B). (A) shows the SVC from Figure 12 for comparison. (C) shows the loss for the feature vector from the center of the epsilon tube. Within the tube there is no loss, but the farther away the feature vector is from the margin, the higher loss it receives.

  65  

Examples of Differences Between GLM and SVM

GLM is hypothesis driven. Often the result is a single image after using data across the

entire data collection. Conversely, SVM is a supervised learning algorithm. In the test

phase, the SVM generates a prediction from each individual data point. Because each

subject often has several test images (in SVM), there may be a large number of

predictions at every time point in the test phase. Using a group of predictions, inferences

may be drawn in a complementary way to GLM. Also, an SVM prediction, which is

represented by a single number, may be simpler to use in automated systems than GLM.

In the case of GLM, one may need to interpret the GLM contrast image before it can be

used for automation. An automated system could be a real-time feedback system.

In the real-time feedback, a prediction may be made after each image is acquired (such as

a 2-s delay). If the labels are “left” and “right,” the subject could then control a cursor

going left or right on a screen with only thought (LaConte et al., 2007). Real-time

feedback, also called brain computer interface (BCI), is an exciting avenue for future

research. Our lab has developed several BCI models that give real-time feedback to

subjects during imaging (e.g., LaConte et al., 2007; Papageorgiou et al., 2013).

Materials and Methods

All subjects were approved by Baylor College of Medicine’s institutional review board

(IRB).

  66  

The following protocol was used for the motor rate experiment (N = 15):

• Neural imaging protocols were acquired using a Siemens 3T Trio.

o Structural T1 used MPRAGE with 1mm isotropic voxels.

o fMRIs were acquired using a 3T Trio scanner with the following echo

planar imaging (EPI) parameters:

TE/TR = 30/2000 ms

Flip angle (FA) = 90°

Firld of view (FOV) 220×220×132 mm3

Matrix (X, Y, Z) = 64×64×30

• The subject’s task was to press a button with the right index finger in sync with a

metronome while fMRIs were acquired. Further explanation of the task is

provided below.

• All image processing used the AFNI (Cox, 1996):

o Deobliqued images. Images were oblique (10 to 45 degrees) in order to

maximize cerebellar tissue in field of view.

o Motion-corrected images

o Aligned images to scullstripped T1 image

o Detrended all voxels temporally

o Smoothed all images using Gaussian blur with 6mm full width half

maximum (FWHM)

o Processed images using 3dsvm

• For weight maps, a 3dttest was used at the second level.

  67  

Unless otherwise stated, all the analyses used a whole-brain SVM. This means that no

part of the brain was masked.

Behavioral Task

Basically, the subjects tapped a button in sync with an audible metronome. The rate of

the metronome was 1, 2, 3, 4, or 5 hertz, which enabled five classes of stimuli. The

metronome tick played at the same rate during each 30-s active block. It was always

followed by a block of rest, which was 10 s. During the active block, the rate was kept

constant, but each block was pseudorandomly set to different rates according to the five

classes. Not very important to the task, we added or subtracted 0.2 Hz for each block to

eliminate any possible habituation effects from any subject. In addition to the auditory

stimuli, the fixation cross was flashing at every metronome tick. During rest, subjects

only gazed at a stable fixation cross and were not allowed to tap the button, as there were

no metronome ticks. Each subject performed 3 runs, which included 16 active blocks.

The 3 runs were almost 33 min long. For analysis, only the last 26 s of each block

yielding 13 TRs (brain volumes) were used for training and testing, unless otherwise

stated. This would eliminate any transitional effects in the beginning of the block. Since

there are 9 to 10 blocks of each rate, 117 to 130 samples per rate exist per subject. Note

that our subjects’ responses were inaccurate at the highest motor rates before we used the

real-time feedback bar that helped subjects to tap more accurately. The feedback bar

indicated the degree of deviation from the perfect tapping rate. The task is depicted in

Figure 16.

  68  

Figure 16. Stimuli paradigm for real-time motor rate effect The stimulus task consisted of a subject pressing a button in sync with the metronome clicks from the headphones. In addition, the subject was instructed to fixate on the fixation cross that was flashing in sync with the metronome clicks. Since many subjects found it hard to tap the button at the fast pace of 5 Hz, we constructed the user interface above with a real-time feedback bar. If the real-time feedback bar was placed by the grid lines at an inaccurate rate to the left of the fixation cross, the subject was instructed to tap faster. Conversely, the subject was instructed to tap slower if the dynamic feedback bar was shown by the grid lines to be at an inaccurate rate on the right side. Increased behavior accuracy resulted in increased SVM prediction accuracy.

  69  

Simulation Model

For SVM simulations MATLAB and AFNI (Cox, 1996) with 3dsvm (LaConte et al.,

2005; Joachims, 1999) was used in following 3 steps.

(1) MATLAB generated random 2D images including a signal to be predicted and the

image label. The labels were numbers 1 to 5 to match the rates we measured from

our subject-based data. The signal that MATLAB provided for each image was

corrupted with Gaussian noise to the degree that the signal overlapped across

labels.

(2) AFNI used the built in 3dsvm plugin and half of the MATLAB generated images

with their labels to train an SVM-brain-model.

(3) AFNI used the built in 3dsvm plugin, the SVM-brain-model, and the remaining

images generated from MATLAB to predict the labels.

(4) Even though SVM was not provided the true labels (generated from MATLAB in

Step 1) for the images it predicted those “true” labels existed to validate the SVM

predictions with. Sorting the predicted labels by the true labels

Results

To increase SVM prediction accuracy, the subject had to perform the task accurately.

Since it was important to have a wide spectrum of rates to predict from, the rate extended

up to 5 Hz. With the support from the real-time feedback bar, all rates were very accurate

with the slight exception of a negligible rate discrepancy at 5 Hz (Figure 17). Statistics of

all subjects revealed that SVR prediction is correlated with the true rate (p < 2*10-16),

  70  

which bolsters that the motor rate predictions were associated with the true motor rates

(Figure 18).

As seen in Figure 19, our motor rate effects were depicted using a GLM contrast map, an

SVC weight, and an SVR weight map. The maps depicted significant extreme points at

analogous regions across the three methods. Note that it was easy to separate individual

ROIs using the standard threshold of p < 0.05 (FDR corr.) for the SVM weight maps.

However, the GLM method was so sensitive that many regions became inseparable at

that threshold. Thus the GLM threshold was changed to p < 0.002 (FDR corr.), which

kept the GLM ROIs separated. It was possible to see that all regions with positive effect

between BOLD response and motor rate in both SVR and SVC were inclusive in the

neural pattern for GLM contrast image. For GLM, we noted an additional rate effect for

SMA (not found using SVM). SMA, which was not as consistently found in the previous

literature (as M1), may be because the SMA rate effect was less significant than for M1.

M1 was consistently found in previous literature and in our data to have stronger

significance. The SVM weight maps were not as sensitive as the GLM maps, which may

explain why the SMA was missing, but the M1 was present in the SVM

  71  

Figure 17. Group behavior motor rate mean with standard deviation Block trial behavioral motor rate mean from the 15 subjects included in the motor rate experiment and their deviations. Ideally the mean rate (line with darker color) should constantly be the rate at which it is suggested to press the button (color coded according to legend), with minimal deviation (lighter color), throughout the trial. It can be seen that subjects got slightly fatigued with time at 5 Hz, but that was negligible.

  72  

Figure 18. Support vector regression prediction variance Support vector regression (SVR) predicts the motor rate with the significance of p < 2*10-16 according to linear fit. The X-axis represents the true motor rate. The Y-axis represents the predicted rate. SVR performed 624 predictions for each subject (N=15).

  73  

Figure 19: Similarities between GLM, SVC, and SVR statistical brain patterns It is noticeable that the weight maps for both SVC and SVR agree with those of GLM, which is the gold standard. As seen in the top row, the GLM contrast image is more salient than both the support vector weight maps. The red color represents regions where the response is higher at faster motor rates. Conversely, blue represents regions with higher response for slower motor rates. Apart from a few blue regions (in E, F, G, and H), it is noticeable that all the regions with higher responses at higher motor rates in both the SVR and SVC cases are inclusive in the GLM pattern. The variable q means a false discover rate (FRD) corrected p-value. The anatomical locations are mostly labeled in the middle row because that contrast map had fewer problems with overlapping ROIs than the top row and at the same time was more significant than the bottom row. All maps are group results from 15 subjects engaged in the motor rate task. The GLM and SVR used all five motor rates, while the SVC had sorted away all data for 2, 3, and 4 Hz for (D) and (F) and 1, 3, and 4 Hz for (E). Anatomical acronyms are: supplemental area (SMA); inferior temporal gyrus (ITG); superior temporal gyrus (STG); sensorimotor cortex (SMC); primary motor cortex (M1); and middle occipital gyrus (MOG).

  74  

weight maps. Figure 19 depicts our results from the three algorithms. The strongest

weighted region for both SVM and SVR was the M1 rate effect in the positive direction.

Spatially, the SVM results match the GLM results seen in Figure 19 at slice y = -20mm.

The direction and strong intensity were corroborated by all publications that analyzed M1

(Jäncke et al., 1998; Jäncke et al., 2000; Riecker et al., 2006; Lutz et al., 2004). This

suggests that M1 is rate dependent. SMA may also be rate dependent, but further analysis

is needed to clarify the SMA dependence on behavioral rate changes since only Riecker

et al., (2006) found a positive rate effect, using Witt et al.’s (2008) list. Three

publications did not find any significant rate effect in the SMA when the dominant hand

was used (Jäncke et al., 1998; Jäncke et al., 2000; Lutz et al., 2004). The listed

publications were only used as well-representative samples of all unimanual motor rate

studies. Other publications have found both positive motor rate effects with SMA (Rao et

al., 1996; Wurster et al., 2014) and alternate (nonpositive) motor rate effects with SMA

(Sadato et al., 1996) by using analysis of variance (ANOVA) to find the difference. In

addition, previous literature found a positive unimanual rate effect in the ipsilateral dorsal

SMC (Jäncke et al., 1998; Lutz et al., 2004; Rao et al., 1996; Riecker et al., 2006;

Wurster et al., 2014), ipsilateral cerebellum (Figure 19 at Z = -13; Jäncke et al., 1999;

Lutz et al., 2004; Riecker et al., 2006), and contralateral thalamus (Riecker et al., 2006;

Wurster et al., 2014). Interestingly, our positive rate effect in the lentiform nucleus

(putamen), found only in the case of GLM and SVC, was in the negative direction

opposite to the previous literature (Riecker et al., 2006; Wurster et al., 2014). This

discrepancy was not predicted, but may be an effect from our visual interface that did not

  75  

exist in the other two studies. Further, our GLM analysis found a positive rate effect in

the cingulate motor area in accordance with the previous literature (Deiber et al., 1999;

Lutz et al., 2004), which may be related with projections from the spinal cord (He et al.,

1995). We also found small negative rate effects in the ventral SMC bilaterally in the

SVC case and in the right ventral SMC in the SVR case. Ventral SMC was not

corroborated by previous literature, to our knowledge. Except from the discrepancy of

direction of rate effect in the lentiform nucleus several regions matched well with

previous literature. Further analysis about the directional discrepancy may be needed to

fully elucidate that it is caused by the visual component including the feedback feature of

our task.

Because our task also included stimulus rates from visual and auditory senses, we

received additional responses exclusive to motor-related stimuli. The auditory aspect of

our task may have contributed a positive rate effect, in the cases of SVM and GLM, in

the thalamus, and in the bilateral superior temporal gyri (STG). In the SVM case, a small

negative rate effect was found in the right anterior insula. These three regions agreed with

the previous literature about the auditory rate effect (Ackermann et al., 2001).

Rate effects from the flashing visual stimuli were found in the SVC and GLM cases,

bilaterally in middle occipital gyri (MOG; located in Brodmann area 19, which is part of

visual cortex) and in the right inferior temporal gyrus (ITG). ITG may be related to visual

processes such as face recognition (Gross, 1992). A more elaborate table of regions that

regressed with or against the motor rate stimuli were listed in Table 3.

  76  

Neural ROIs 1vs 2Hz

1vs 3Hz

1vs 4Hz

1vs 5Hz

2vs 3Hz

2vs 4Hz

2vs 5Hz

3vs 4Hz

3vs 5Hz

4vs 5Hz

SVR

1) L Sensorimotor (dors) 2154 4186 7477 10159 2479 5445 6583 2403 2) L STG 894 1747 5933 7843 1625 3982 4186 1890 3) R STG 1260 4186 4389 1544 1829 935 1593 4) R Culmen (cerebell.) 3779 3617 813 2276 1138 297 5) R Sensorimotor (vent) 2235 1829 975 894 486 6) R Sensorimotor (dors) 1544 1138 7) L Sensorimotor (vent) 1057 8) R MOG (Visual) 1057 1788 9) R Inf. Temp Gyr (ITG) 1219 10) L MOG 1097 11) L Lentiform Nucleus 1016 12) L Thalamus 1016 13) R Insula 975 243 Table 3. Cluster volumes in μ-liters using support vector machine Cluster volumes in μ-liter (μL) using SVC for different rates and SVR. All results were thresholded at p < 0.05 using FDR correction. Columns 2 to 11 are results of SVCs between different pairs of motor rates. The last column shows the regression results.

  77  

Using the direct acyclic graph algorithm, SVC prediction accuracy was 44%, which was

significantly greater than chance (20% given the five prediction classes). SVC prediction

accuracies were also tabulated by subject in Table 4. In addition, SVR, which is

continuous, could be advantageous for certain measurements such as to predict the level

of pain (DeCharms et al., 2005). Our study used SVR to predict the motor rate of the

subject at the instant the neural image was acquired. SVR prediction from neuroimaging

of motor rate for the best subject CEA013 was depicted over time in Figure 20. The

predicted rate clearly correlated with the true rate, as shown in Figure 21. In Table 5, it

can be seen that all subjects had significant correlation and prediction accuracies. Overall

subjects correlations, and root-mean-square errors (RMSEs) along with their standard

deviations were: ρ = 0.79, σρ = 0.10 and RMSE = 0.84, σRMSE = 0.17.

  78  

Table 4. Support vector classification accuracies from 1 to 5 Hz per subject Support vector classification prediction accuracy by subject. Predictions in the middle of the rate range (3 Hz) are so poor that they are often below chance level. This is because the predicted rate still has a wide distribution even after our optimizations. When the true rate is at the extremes, the SVC prediction is supported by out-of-range corrections. For example, if the true 5-Hz rate was predicted as 7 Hz, it would be corrected to the closest valid rate, which is 5 Hz and becomes accurate. Since the middle rate gets the least out-of-range correction, it gets the most errors. That the rates are well predictable is shown when looking at results from the combined rates. Future work may correct for the poor middle rate predictions, which may improve the overall SVC performance.

  79  

Figure 20: Support vector regression of single subject The red line shows the true motor rate performed by the subject. The black line shows the motor rate the SVR predicted the subject would have only after analyzing the neural images. Rest blocks between active blocks have been removed to save graph space.

  80  

Figure 21. Support vector regression errors per subject graphed Root mean square error (RMSE) of support vector regression (SVR) per subject for the motor rate task.

  81  

CEA0 13 30 31 32 34 35 37 38 39 40 41 42 43 44 04 ρ 0.91 0.73 0.76 0.78 0.86 0.67 0.65 0.84 0.60 0.90 0.78 0.85 0.83 0.85 0.78

RMSE 0.55 0.97 0.93 0.85 0.70 1.10 1.07 0.76 1.11 0.61 0.87 0.76 0.78 0.75 0.86 Table 5. Support vector regression correlation and root mean square error (RMSE) between predicted and true rate

  82  

The extremes (1 and 5 Hz) SVC rates had fewer errors than the center classes, as depicted

in Figure 22A. These results were found across all 15 subjects. SVR could greatly

complement SVC by providing a continuous prediction. Conversely to SVC, SVR

yielded larger errors at the extremes (Figure 22B). That the optimal performance for SVC

(at extreme rates) and SVR (at middle rate) were opposite was puzzling so we performed

different simulations to recreate these results. We noted that if we simulated that the

source BOLD data was widely distributed (noisy; Figure 23A) we received an analogous

situation with SVC (Figure 23B) and SVR (Figure 23C) as in our empirical case. To

induce this characteristic effect in both SVR and SVC predictions simultaneously we had

to simulate our signal with such a noise that the standard deviation was so large that it

overlapped across labels. After trying many other simulations that got alternate results we

decided to explore our subject data further. Using the region with most significant effect,

namely the primary motor cortex (M1), we found such a widely distributed noise, which

was systematic and time dependent. Since the motor rate paradigm is a block paradigm

over 30 seconds the BOLD signal should be fully stable after 10 s (Figure 24A)

according to a leading contemporary hypothesis of the hemodynamic response function

(Buxton et al., 1998; Glover, 1999). However, our empirical data from M1 included a

strong time dependent perturbation. To make the exploration as accurate as possible we

used all the user data possible by averaging 9-10 blocks for all 15 subjects per rate. This

resulted in that each of the five mean motor rates were an average of 135 to 150 blocks

depicted in Figure 24B. It is clear from Figure 24B that the signal from M1 has strongly

time dependent overlaps (e.g. at 5 Hz rate at 12 s there is an overlap with the 3 Hz rate at

  83  

30s) that should not overlap in accordance with contemporary theory (Figure 24A). This

may indicate that the Balloon model (Buxton et al., 1998) may be corrected in our case to

gain higher accuracies. Analogous time dependent perturbations for block paradigms

have been found in previous literature (Fox et al., 2005). If this time dependent

perturbation can be corrected for the predictions may improve, which may be important

to resolve in order to detect mTBI.

  84  

Figure 22: Comparing support vector classification to regression prediction errors Support vector classification (SVC) has more errors in the middle of the spectrum, while support vector regression (SVR) has more errors at the edges of the distribution. The chance level for percent errors is 80% (for SVC). The chance level of root mean square error (RMSE) is 1.37 in our model.

  85  

Figure 23: Simulated SVM signal and results

(A) Simulated source signal intensities (inside images) with wide noise profile that intensity wise overlaps across different labels. (B) shows the resulting support vector classification prediction of the simulated images with higher accuracy at extremes of true labels. (C) shows the support vector regression (SVR) had higher prediction accuracy of the simulated images in the middle range of the true labels. The diagonal purple line marks the ideal predictions (and intersects the true and predicted 1-labels as well as the true and predicted 5-labels). As seen for the SVR the true label 5 images are predicted too low, while the true label 3 images are predicted with an accurate mean. The true label 1 images are predicted too high, but may have a smaller mean error than the label 5 images.

  86  

Figure 24: Theoretical and empirical response time series for signal variations within rates

The theoretical BOLD response in accordance with contemporary literature (Buxton et al., 1998; Glover, 1999) compared to the empirical response may be divergent for block paradigms such as our motor rate task. Both diagrams show the BOLD time series for different rates in accordance to the color label in the legend. (A) shows the theoretical BOLD as a convolution between the hemodynamic response function (Glover, 1999) and our 30 s box-car time course (marked block stimulus) of when task was performed. (B) shows the empirical BOLD from the primary motor cortex (M1) by averaging all 15 subjects and trials per rate. Each motor rate time series was averaged by 135 to 150 blocks (of same motor rate).

  87  

Discussion

We found that both the wide spectrum of subject motor responses spanning from 1 to 5

Hz and correct behavior were essential for the SVM to be able to predict anything at all.

When we tested experiments with comfortable motor rates (2 to 4 Hz) for subjects, their

behavior was great, but SVM could not predict the results. The interface for our first

experiment of 1 to 5 Hz lacked real-time feedback, so the subjects’ behavioral accuracies

were poor at 4Hz and above, which led to prediction problems. Finally, we rebuilt our

interface to look like Figure 16 and included a real-time feedback bar that gave

quantitative feedback to subjects about their rate discrepancy. This real-time feedback

improved the behavior (Figure 25) and may have improved the SVM predictions as well.

The final results demonstrated that SVR and SVC clearly decodes the neuroimaging data

and that the SVM weight maps detect neural regions that are associated with the

paradigm the subject performs.

We also found that the BOLD response may deviate from the contemporary hypothesis

about the neural response. The contemporary HRF hypothesis (Buxton et al., 1998;

Glover, 1999) may corrupt our SVM predictions and may be corrected for. Our

significant findings in fMRI and SVM research may have brought the fields closer to

mTBI. However, further research is needed to complete the bridge between SVM and

mTBI.

  88  

Figure 25: Behavioral improvement after real-time feedback Each trace in the quadrants is mean and deviation, from 9 or 10 trials, of the subject’s true motor rate. Ideally the mean (trace in darker color) should match the rate color, with minimal deviation (lighter color), throughout the trial. The left column shows behavior for two subjects before the dynamic real-time feedback bar was implemented into the user interface, and the right column shows the results for the same two subjects after the implementation. Qualitatively, it can be seen that the results are remarkably more stable and accurate after the implementation of the real-time feedback bar.

  89  

Chapter 4: mTBI Model System Using Executive Function

Introduction

As mentioned before, fMRI may detect mTBI (Figure 8), but these results may be

impeded by heterogeneity (Rosenbaum & Lipton, 2012) and other factors. One such

factor is that the GCS indicator (practical to classify patients clinically) is too brief since

mTBI develops heterogeneously in many ways. A more detailed and standardized

diagnosis could provide valuable information for neuroimaging analysis of mTBI

(Saatman et al., 2008). For example, the same GCS may be used for widely different CT

abnormalities such as epidural hematomas, contusions and parenchymal hematomas,

diffuse axonal injury, subdural hematoma, subarachnoid hemorrhage and intraventricular

hemorrhage, and diffuse brain swelling. However, the different abnormalities, lost if only

looking at GCS, may affect the patient differently at a later time points (Saatman et al.,

2008). The different types of injuries listed in Saatman et al. (2008) are also proof that

TBI (including mTBI) is very heterogeneous. Two other problematic heterogeneities are

individual mTBI uniqueness and diverse outcomes leading patient into different mental

disorders (APA, 2000; Rosenbaum & Lipton, 2012; Saatman et al., 2008). If the

heterogeneous nature of the imaging findings are not taken into account for the

neuroimaging detection method it may corrupt the results. Conversely, taking the

heterogeneity into account may complicate the detection method. That conventional

  90  

neuroimaging of most mTBI patients do not have any abnormalities may also challenge

detection (Shenton et al., 2012; Rosenbaum & Lipton, 2012). An additional shortcoming

when developing methods to detect mTBI is the loss of a pre-concussion baseline. There

are also problems occurring in mTBI groups with unknown premorbidity, but because

this thesis focuses on longitudinal models (the same participant measured at several time

points) the premorbidity issue may be less problematic than the premorbidity issue in

cross-sectional studies. Although our end goal is to detect mTBI, we present an

intermediate goal, which is to characterize how our GLM, SVM, permutation tests, and

regular t-test methods work before testing them on mTBI directly. A practical solution is

a model system that may include a pre-baseline and decrease executive function similarly

to mTBI (McCrea et al., 2003; Rohling et al., 2011). Note that we are not claiming our

model system will match the actual brain pattern of mTBI, but most other aspects should

be analogous. From the perspective that the patterns are not matching one may call our

model system naïve. We found that sleep deprivation fulfilled our requirements as a naïve

mTBI model. In addition, a control group without treatment is required from the same

population as the model system is provided. According to previous literature, sleep

deprivation increased reaction time (Drummond et al., 2006; Cain et al., 2011) and

induced significant neural patterns in the posterior and anterior cingulate, bilateral insula,

bilateral temporal parietal junction, and other regions (De Havas et al., 2012; Rosales-

Lagarde et al., 2012; Chee & Chuah, 2007).

To measure the severity of mTBI, it is popular to behaviorally measure an executive

function task (Miles et al., 2008; Niogi & Mukherjee, 2010). The multi-source

  91  

interference task (MSIT) is such a paradigm and is closely related with the Stroop task

(Bush et al., 2003; Sheu et al., 2012). Even though the MSIT has not been used to detect

mTBI, the Stroop task has successfully been used to do so (Binder et al., 1997; Bohnen et

al., 1992; Echemendia et al., 2001; Voller et al., 1999). The aim of this chapter is to

detect reversible effects from reversible executive function treatments using both fMRI

and behavioral measurements and the MSIT. Note that the decreased executive function

found in mTBI is reversed when the injured person recovers (McCrea et al., 2003). The

reaction time interference effect is more than twice as strong in MSIT (312 ms) compared

to the Stroop test (142 ms; Bush et al., 2003; Carter et al., 2000). In addition, the MSIT

neuroimaging signal is so robust in the dorsal anterior cingulate cortex that measurement

time can be decreased compared to other fMRI paradigms, including the Stroop task

(Bush et al., 2003). The dorsal anterior cingulate cortex may be the most important

location known to evaluate executive function using neuroimaging (Bush et al., 2003).

Decreased scanning time may be valuable in clinical settings. The MSIT has easy task

instructions, and both behavior and fMRI patterns are temporally stable (Bush et al.,

2003). Most importantly, Bush et al. (2003) stated that “MSIT yields temporally stable

performance and imaging data, making it useful in treatment and other types of

longitudinal studies” (p. 67), which means MSIT is not supposed to have any training

effects. Subjects in the study of Bush et al. (2003) performed the MSIT during 4 scans,

but in a single visit. Training effects for a group that makes several visits (on different

days) were not tested. The MSIT targets the executive function and induces cognitive

interference by combining three other tasks, which are the Stroop task, the Eriksen

  92  

flanker task, and the Simon effect, according to Bush et al. There are many variations on

the Stroop task using words, colors, sounds, and other stimuli. In this case, we use the

numbers 1, 2, and 3 and their positions. When 1 is in the first position, it is congruent and

in any other position incongruent. Similarly, the number 2 is congruent in the second

position, but incongruent in position 1 or 3 (Figure 26). The Eriksen flanker paradigm

delays subjects when the flanking positions contain symbols that could be targets in other

trials, but it does not in the current trial. Since the number 0 is never a target in our

version of MSIT, it has no Eriksen effect, which is used in the congruent trials. Finally,

the Simon effect has proven to decrease reaction time if an option is presented on the

screen ipsilateral to the button he or she presses compared with the button being placed

on the contralateral side. If the target information is presented on one side of the screen

and the button the subject is supposed to press is on the opposite side, the reaction time

will be slower. All three separate tasks combined into MSIT widened the reaction time

difference between the incongruent and congruent cases. Figure 26A depicts the MSIT.

Materials and Methods

Both the sleep-deprived (N = 8) and the control (N = 7) longitudinal groups used the

analogous imaging protocol at three time points (TPs) per subject. Subjects were medical

students aged 18 to 55 of both sexes. Both experiments were approved by Virginia

Tech’s institutional review board. The following protocol was identical at each TP:

• Neural imaging protocols were acquired using a Siemens 3T Trio.

o Structural T1 used MPRAGE with 1 mm isotropic voxels.

  93  

o An fMRI was acquired using a 3T Trio scanner with the following EPI

parameters:

TE/TR = 30/2000 ms

FA = 90°

FOV 220×220×145 mm3

Matrix (X, Y, Z) = 64×64×33

o Diffusion tensor imaging (DTI) was used.

o Susceptibility weighted imaging (SWI) was conducted.

• Two task types were performed while fMRIs were acquired:

o MSIT

o Default mode network (DMN)

• All image processing used the AFNI (Cox, 1996) software. The first level of

image processing used the afni_proc.py command with the following parameters:

o Smoothing with 6 mm kernel

o Contrast was: (+1){incongruent trials} (-1){congruent trials}

• Second-level analysis used either the 3dttest++ included in the AFNI package.

• Specific protocol for the sleep-deprivation group (N = 8):

o (TP1) Subjects had had 3 nights with more than 6h sleep per night.

o (TP2) Acquisition was performed 24 h after TP1, and subjects had not had any

sleep between scans. Sleepiness was measured using the Stanford Sleepiness

Scale (Figure 27).

  94  

o (TP3) Acquisition was performed between 48 h and 144 h after TP2, and

subjects had had two nights with more than 6 h sleep per night before the

scan.

o Participants’ sleep was assessed using a tablet actigraphy application.

• Control group (N = 7):

o (TP1) Acquisition was performed without treatment.

o (TP2) Acquisition was performed about 30 min after TP1, still without

treatment.

o (TP3) Acquisition was performed 24 h to 120 h after TP1 and TP2.

Statistical Analyses

The MSIT contained 288 congruent and 288 incongruent trials per visit (at TP1, TP2 or

TP3) and subject. From this the averages for congruent and incongruent reaction times

per subject and time point were calculated (tabulated in Table 6, and Table 7). For both

behavior and image data, TP1 and TP3 were treated as baselines. The null hypothesis was

set such that the behavior at TP2 was equal to the mean of the baselines for any specific

subject. Therefore, subtracting the average of the baselines from TP2 would result in zero

if the null hypothesis was true. Even though Bush et al. (2003) stated that MSIT does not

have any practice effect, this baseline subtraction approach works if practice effects are

present or not, assuming a linear approximation. In this way the three time points are

collapsed into a single value, for both behavior data and image data.

  95  

Figure 26: Multi-source interference task (MSIT) Above is the multisource interference task (MSIT) behavioral paradigm that mTBI and control subjects performed. (A) It consists of a difficult (incongruent) number combination and a simple (congruent) number combination. We see the distribution of reaction time in the congruent case (B) and in the incongruent case (C). (D) is the combination of B and C distributions.

  96  

Figure 27. Sleepiness according to the Stanford sleepiness scale (SSS) Eight subjects were kept awake between first and second neuroimaging time points (TP). The mean of their Stanford sleepiness scale (SSS) score was plotted.

  97  

Time point 1 Time point 2 Time point 3

Subject # Cong. [s] Incong. [s] Cong. [s] Incong. [s] Cong. [s] Incong. [s] MAA001 0.60 1.08 0.64 1.09 0.49 0.93 MAA002 0.62 0.97 0.72 0.98 0.59 0.88 MAA003 0.61 0.85 0.63 0.88 0.62 0.84 MAA004 0.54 0.90 0.53 0.83 0.51 0.79 MAA005 0.59 0.94 0.70 0.96 0.57 0.83 MAA006 0.59 0.95 0.66 0.96 0.54 0.82 MAA007 0.54 0.91 1.06 1.32 0.54 0.88 MAA008 0.66 0.90 0.73 0.96 0.60 0.83

Mean 0.59 0.94 0.71 1.00 0.56 0.85 SD 0.04 0.07 0.15 0.15 0.04 0.04

Table 6: Behavioral data from the MSIT sleep-deprived group List of acronyms: standard deviation (SD); congruent (Cong); incongruent (Incong); seconds (s).

  98  

Time point 1 Time point 2 Time point 3

Subject # Cong. [s] Incong. [s] Cong. [s] Incong. [s] Cong. [s] Incong. [s] SFACON1 0.55 0.87 0.49 0.80 0.49 0.80 SFACON2 0.63 1.01 0.63 1.01 0.59 0.88 SFACON3 0.92 1.05 0.65 0.98 0.55 0.84 SFACON4 0.75 1.09 0.71 1.08 0.63 0.97 SFACON5 0.52 0.78 0.50 0.76 0.48 0.71 SFACON6 0.54 0.87 0.51 0.80 0.54 0.79 SFACON7 0.62 0.85 0.57 0.80 0.57 0.81

Mean 0.65 0.93 0.58 0.89 0.55 0.83 SD 0.14 0.12 0.09 0.13 0.05 0.08

Table 7: Behavioral data from the MSIT pure control group Table acronyms: standard deviation (SD); congruent (Cong); incongruent (Incong); seconds (s).

  99  

Imaging Analyses

The fMRI software package AFNI (Cox, 1996) was used for all steps. A couple of types

of imaging were used. The simplest was the default MSIT neural pattern, which

generated at the first level contrast images for each control subject using the contrast +1

for incongruent beta values and -1 for congruent beta values. Continuing in a random

effects design, a one-sample t-test was then applied across all available first-level contrast

images, resulting in a second-level contrast image that revealed significant similarities

across the sample representing the default MSIT neural pattern (Bush et al., 2003).

For treatment effects, we used all three time points analogous to the statistical analysis of

the behavioral effects. After the first-level analysis (as described above, using contrast +1

and -1 for incongruent and congruent beta values) were performed for all three time

points. Then we collapsed the three contrast images for each time point into a single

image ( = 2*TP2-TP1-TP3) for each subject, which was a baseline subtraction (analogous

to what was previously done for the behavioral data). The second level was simply a two-

sample t-test between the sleep deprived and control group using 3dttest++. A false

discovery rate (FDR) threshold was set to p < 0.05 (two-tailed).

In order to maximize our effect and to test for an image detecting any practice effects a

paired t-test approach subtracting each subjects contrast image at TP3 from TP1 was

used. Still, the FDR was too conservative so we used the family wise approach and the

command 3dClustSim with an initial uncorrected threshold of p < 0.01. These settings

yielded a minimal cluster size of 70 voxels for a multiple comparisons corrected p < 0.05.

  100  

Results

After baseline removal, the scatterplot of each subject’s behavioral TP2 reaction time

shows that the sleep-deprived group had considerably longer reaction times than the

control group (Figure 28). All reaction times are also tabulated (Table 6 and Table 7).

A two-sample t-test between the sleep deprived group and the control group confirmed

that the sleep deprived group was significantly slower than control group. Significance

was p < 4.79*10-4 (1 tailed) in the congruent case and in the incongruent case p < 0.028

(1 tailed). Results are also tabulated in Table 8.

We hypothesized not to find any practice effects using the MSIT in accordance with

previous literature (Bush et al., 2003; Bush & Shin, 2006; Sheu et al., 2012). However,

the opposite results were significant using a two-sample t-test between TP1 and TP3 for

both congruent reaction times, p < 0.017, and even more significant practice effects for

incongruent reaction times, p < 0.001 (Figure 29; Table 8).

  101  

Figure 28: Behavioral response from MSIT cognitive insult treatments The scatter plot shows sleep deprivation (red squares) and control subjects (black crosses). One sleep-deprived outlier could not fit the graph (x=0.51 and y=0.43). The reaction times are baseline corrected by subtracting the mean of the pre- and post-baseline from time point (TP) 2. Pre-baseline is TP1 and post baseline is TP3. In addition a linear fit was applied for both sleep deprived and control subjects (green line).

  102  

Group RT type Mean (SD.) P-value N Control Congruent -0.018 (0.036) 1 7 Control Incongruent 0.011 (0.041) 1 7 Sleep-deprived Congruent 0.077 (0.045) 4.79*10-4 7 Sleep-deprived Incongruent 0.055 (0.038) 0.028 7 Sleep-deprived (including outlier)

Congruent 0.131 (0.160) 0.015 8

Sleep-deprived (including outlier)

Incongruent 0.101 (0.136) 0.056 8

Practice effects Congruent 0.017 15 Practice effects Incongruent 0.001 15 Table 8. Multi-source interference task behavior statistics Multi-source interference task (MSIT) mean reaction times (RT) and their significances compared to the control groups’ means using two-sample t-tests with exception for the practice effects groups. P-values from practice effects are all 15 subjects from TP1 compared to all subjects from TP3 using a two-sample t-test. All tests were one sided since sleep deprivation is expected to slow down RTs and practice is expected to improve RTs. SD is the standard deviation. N is number of subjects in accordance with the “Group” column.

  103  

Figure 29: MSIT behavioral practice effects Improving reaction times bolster the existence of practice effects. MSIT was first measured at time point 1 (TP1). Retest at TP3 was measured 2 to 6 days after TP1. The two-sample t-test calculated significant p-values between TP1 and TP3 using 15 subjects at both time points. Since subjects at TP2 were measured under different conditions, unrelated to practice effects, they are excluded from this figure.

  104  

The first imaging result was used for reference and is the control MSIT contrast image. It

was generated from TP1 using a one-sample t-test FDR-corrected at p < 0.05 and depicts

robust effects with pattern including the DLPFC and the dorsal anterior cingulate cortex

(dACC), which are the center of executive function (Banich, 2009; Bush et al., 2003;

Carter et al., 2000). As seen in Figure 30, the signal is abundant even at a corrected

threshold of p < 0.05 for both increased and decreased effects. These are regions that

have been implicated in various neuropsychiatric disorders (Bush et al., 2003).

Since a lot of behavioral results indicated possible practice effects we wondered if this

task might be exposed to practice effects at least for longitudinal studies despite previous

literature. We decided to test if the supposed practice effects were also visible in a

neuroimaging analysis. It is difficult to know if a decrease or increase of neural response

at the same time the behavior is improved represents practice effects and response

changes may be task dependent. However, since the Stroop task (which is closely related

to MSIT) using a similar model to our MSIT model yields a decrease of signal for

practice effects (Chen et al., 2013), a decrease in our results should represent practice

effects as well. That the Stroop task and MSIT really are similar is demonstrated by the

fact that the neural response patterns (in the control case) are extremely similar (Bush et

al., 2003; Sheu et al., 2012). Unfortunately, our test and retest MSIT contrast image

confirmed practice effects by a spatially abundant decrease for the retest that was

significant (p < 0.05, two-tailed) using a false discovery rate correction (Figure 31).

  105  

Figure 30: MSIT neural contrast image of control case Robust results are shown for the multisource interference task at time point 1 for 15 subjects. The threshold was a false discovery rate corrected at p < 0.05. Red is increased response and blue is decreased response, while z is the height in mm of the slice in accordance to the LPI convention. First-level analysis was an incongruent–congruent contrast. Second-level analysis was a one-sample t-test comparing the group to zero.

  106  

Figure 31: Neural contrast image of MSIT practice effects Practice effects induced mostly negative response (blue), but also a small positive response (red) in the ventral anterior cingulate. The response was significant (FDR corrected at p < 0.05). The signal decrease (blue) may represent a lower response at time point (TP) 3 compared to TP1. A total of 15 subjects were used in a paired t-test between T1 and T3.

  107  

A small region in the ventral anterior cingulate had a positive effect, which may be

ascribed to that the response actually is less negative in TP3 than in TP1. Note that the

ventral anterior cingulate is a region, which MSIT by default has a negative response at

(Figure 30).

Significant sleep deprivation treatment effects appeared as decreased neural responses in

several regions. Interestingly, the regions matched previous sleep deprivation literature

(Chee et al., 2008; De Havas et al., 2012) in terms of both location and direction:

posterior cingulate (increased), dorsomedial prefrontal cortex (increased), bilateral insula

(decreased), and bilateral intraparietal sulcus (decreased; Figure 32).

Since the MSIT generates a stronger signal response compared to the Stroop task (Bush

et al., 2003) it may have supported our results.

  108  

Figure 32: Neural contrast image of MSIT sleep-deprivation treatment Related response with sleep deprivation was posterior cingulate (positive; red), dorsomedial prefrontal cortex (positive), bilateral insula (negative; blue), and bilateral intraparietal sulcus (negative) in accordance with previous literature (Chee et al., 2008; Chee & Chuah, 2007; De Havas et al., 2012). The signal was significant with multiple comparisons correction (p < 0.05).

  109  

Discussion

Our longitudinal design allowed us to perform a paired statistical test, which may have a

stronger sensitivity than a regular two-sample model, having only 7 to 8 subjects per

group. The baseline before the sleep deprivation was an important advantage as well.

Using baseline correction, subjects were about 0.09 s (with a standard deviation of 0.06 s)

slower at MSIT when they were sleep deprived compared to when they had had sleep.

However, MAA007 (Table 6) was 0.65 s slower when sleep deprived. MAA007 agreed

with our hypothesis (that sleep deprivation slows down reaction time), but the enormous

slowdown for MAA007 would inflate the standard deviation of the entire group so much

that the t-test would stop working properly. MAA007 was an outlier and was eliminated

from our behavioral data.

Even though the MSIT is simple, it may be confusing (similar to how the Stroop task

may be confusing). A few subjects who reported knowing how to do the task made

incorrect behavior responses anyway. Therefore, Dr. LaConte and I developed an

interface that displays real-time subject responses (Figure 33). The interface notified the

scanner technician immediately if the subject responded in an invalid pattern, which

enabled the technician to immediately stop the data collection. Then the technician could

notify the subject of the correct behavior and restart the task. All this happened with

minimal intervention and went very smoothly. Without this real-time feature, the invalid

data would not have been discovered until the time for analysis and could have corrupted

  110  

that subject’s data for the study. In retrospect, it is clear the real-time feature saved

several subjects’ data.

Due to a slight time difference for sleep deprived and control subjects after TP1 we

decided to research if this could be an issue. Since both groups measured TP3 about 2 to

6 days after TP2 we thought deviations between groups were negligible. The MSIT is

similar to the Stroop task (Bush et al., 2003; Sheu et al., 2012), and our results are

consistent with other behavior results from the Stroop task (Chen et al., 2013). In

addition, the extremely similar reaction times mean at TP3 for the control, sleep-

deprived, and an additional unpublished group support that the time differences between

TP2 and TP3 are negligible as long they are within 10 days of TP1.

  111  

Figure 33: Real-time MSIT feedback The interface for the lab technician to monitor the subject behavior in real time is shown. If the technician noted that the subject had misunderstood an important part of the task, then the scan could be interrupted. After a quick reminder, the same subject would respond correctly. This saved valuable subject data from corruption. Both the X-axis and Y-axis represent time in seconds. The vertical lines in the bottom row show the reaction time. If the line goes below the baseline, it means that the subject made a mistake. The top row has red bars showing the mean reaction time of 3 congruent and 3 incongruent blocks. The text “Errors: 0,2,1,2,1,0” means that Block 2 had 2 errors, Block 3 had 1 error, Block 4 had 1 error, and Block 5 also had one error.

  112  

Conclusions

Sleep deprivation causes a significant behavioral reaction time increase, and neuronal

image effects decrease when using the MSIT task. The sleep deprivation paradigm was

useful for testing our methods in several respects. First of all, undocumented practice

effects for the MSIT paradigm were found. Without this knowledge, any practice effects

in the mTBI study could have led to overinterpretation of the recovering factor in an

mTBI group. Even though the sleep deprivation statistical brain pattern is probably

different from the mTBI statistical pattern, this proved that we could image a reversible

cognitive insult stage using a repeated-measures statistical model. The behavioral

response from the sleep deprivation treatment was analogous to how mTBI responds to

the Stroop task (reaction time changes). From the behavioral perspective, sleep

deprivation seems to behave similarly as mTBI that may be beneficial when used as a

model system for mTBI. Since practice effects were found, it will be much harder to

analyze the mTBI data because the improvements in the mTBI sample may be a

combination of mTBI recovery and practice effects.

  113  

Chapter 5 Longitudinal mTBI

Introduction

Previous fMRI studies have found significant differences between mTBI and control

groups (Eierud et al., 2014). Several mTBI experts have called for longitudinal mTBI

studies to increase the sensitivity to mTBI for several reasons. One reason of interest is to

image tissue changes at different stages post-injury (Loane and Faden, 2010). In short,

tissues damaged by mTBI may break down in apoptosis, heal so they will function

properly again, or progress by an alternate effect. Images at early and late stages after

injury may then be compared to pinpoint tissue modifications (Mayer et al., 2010; Niogi

et al, 2008a; Niogi et al, 2008b). Another reason is that longitudinal studies may be

required to predict between mTBI recovery and chronic mTBI disabilities (Niogi &

Mukherjee, 2010). In addition, longitudinal studies may have an advantage in using a

statistical model that includes a paired t-test compared to cross-sectional studies that may

not use paired t-tests. Further advantages of longitudinal imaging may be found in the

literature (Lo et al., 2009; Mayer et al., 2009).

Our final goal is to track the mTBI recovery temporally from acute to chronic phase

using both executive function behavior and neuroimaging. Most of our analysis on mTBI

subjects has been at the behavioral level so far, which is presented.

  114  

Materials and Methods

Twenty mTBI subjects were longitudinally measured at four time points (TPs). The mean

time after injury at which imaging was acquired was: time point (TP) 1 = 4 days (N =

20), TP2 = 3.5 months (N = 13), TP3 = 7.2 months (N = 10), and TP4 = 9.6 months (N =

7). All subjects were male or female 18 to 55 of age. All three experiments were

approved by Virginia Tech’s institutional review board. The following protocol was

identical at each TP:

• Neural imaging protocols were acquired using a Siemens 3T Trio.

o Structural T1 used MPRAGE with 1 mm isotropic voxels.

o An fMRI was acquired using a 3T Trio scanner with the following EPI

parameters:

TE/TR = 30/2000 ms

FA = 90°

FOV 220×220×145 mm3

Matrix (X, Y, Z) = 64×64×33

o Diffusion tensor imaging (DTI)

o Susceptibility weighted imaging (SWI)

• Two task types were performed while fMRIs were acquired:

o MSIT

o Default mode network (DMN)

  115  

Results

The mTBI results shared in this thesis were that MSIT reaction times significantly

improved with time and number of visits. In the congruent case, significant improvement

(p < 0.05, one sided two-sample t-test) was found between TP1 and TP3. In the

incongruent case, significant improvements (p < 0.05, two-sided two-sample t-tests) were

found between TP1 and TP3 and between TP2 and TP3. The improvements are depicted

in bar diagrams (Figure 34A and Figure 34B). Each subject usually improved reaction

times for each visit, depicted as arrow trajectories for each subject (Figure 34C).

  116  

Figure 34: mTBI reaction time improvements with time post-concussion

Depiction of mTBI subjects’ reaction time improvements for the MSIT paradigm are shown. (A) Bars depict mean reaction times, in the congruent MSIT condition for time points (TPs) 1 to 4. (B) is analogous to figure A, but shows reaction times for the incongruent condition. One asterisk means a one-sided significant decrease. Two asterisks signify a two-sided significant decrease. (C) Each arrow shows a subject’s reaction time difference between two TPs. Subjects who were measured four times are depicted with a trajectory of three arrows. The color of the arrow represents the days post-injury the measurement was taken in accordance to the legend. Including control subjects (gray dots), a significant positive correlation (ρ = 0.72, p < 2*10-16) is seen for congruent and incongruent reaction time pairs. A linear fit across all the reaction time pairs is drawn in black. Most arrow trajectories trace the subject improvements per visit for both congruent and incongruent reaction times. Further improvements follow the linear fit orderly in most cases. Subjects and time post-injury for each time point (TP) were: TP1, N = 20, 4 days; TP2, N = 13, 3.5 months; TP3, N = 10, 7.2 months; TP4, N = 7; 9.6 months.

  117  

Discussion

As seen in Figure 34, the mTBI subjects have significantly improved their MSIT reaction

times with time and visits. To some degree, this improvement may reflect mTBI

recovery. Unfortunately, this improvement may not exclusively be from mTBI recovery,

but may also be from practice effects, which was revealed in Chapter 4. To tease out

recovery from practice effects and to use SVM for neuroimaging will be important future

work.

  118  

Chapter 6: Summary, Significance, and Future Goals

Meta-Analysis Conclusions and Significance

Previous mTBI literature has been limited to studies on mTBI subjects, reviews of these

studies, and a few mTBI meta-analyses. Meta-analyses include one complete evaluation

of the mTBI neuroimaging literature (Borg et al., 2004) and a few CT related mTBI

meta-analyses often from a clinical perspective (Jagoda et al., 2008; Stein et al., 2008).

Since the neuroimaging-mTBI literature recently surpassed 122 publications, we thought

it was time to conduct a new, thorough meta-analysis. The resulting meta-analysis found

several significant results for DTI (21 publications) and fMRI (7 publications) with

significant support for the finding that anterior regions are more abundantly reported to

be abnormal in mTBI for both fMRI and DTI independently. Note that executive function

paradigms that are negatively affected in mTBI (McCrea et al., 2003; Rohling et al.,

2011) may be related with anterior regions as well (Banich, 2009; Bush et al., 2003;

Carter et al., 2000). In addition, our meta-analysis found a significant relationship

between anterior regions using DTI and scores from neuropsychological assessments.

Furthermore, the DTI abnormalities seem to have a temporal dependency. A categorical

test significantly found that acute mTBI groups tend to have higher anisotropy, and

chronic mTBI groups tend to have significantly lower anisotropy. Most remarkable

would be if the results that depend on time post-injury are true even in larger studies.

  119  

Then it may be a marker of secondary effects in mTBI, scarred tissue, or some other

process that changes over time post-injury and may become a tool that can be used

clinically to track mTBI progression. In addition, we were able to generate an mTBI ALE

map. Because only seven mTBI fMRI publications existed, we were surprised that we

could detect 13 significant clusters from our ALE analysis. This mTBI ALE map may be

of value to match future mTBI fMRI publications. If mTBI fMRI publications that image

time points after 3 months post-injury increase in number, an mTBI-fMRI meta-analysis

across time after injury (similar to Figure 10, but for fMRI) may be generated. A

temporal mTBI-fMRI analysis could give further insight of the time course of mTBI.

Support Vector Machine Conclusions and Significance

According to previous literature, SVM has been proven to predict multidimensional data

accurately and is well applicable for fMRI (Cox & Savoy, 2003; Fan et al., 2007;

LaConte et al., 2005; LaConte et al., 2007; LaConte et al., 2011; Mitchell et al., 2004). If

SVM becomes more specialized to track mTBI, it may be more sensitive than competing

fMRI methods. In this thesis we characterized support vector machines for regression and

classification, and the intermediate results were that SVR can predict rates and that the

imaging of the weights is related with the behavioral paradigm in accordance with

previous literature. We also found that the blood-oxygen-level dependence response from

our subjects deviates from the contemporary hemodynamic response function hypothesis

(Buxton et al., 1998; Glover, 1999) that may corrupt the SVM predictions. Our

significant findings in fMRI and SVM research may have brought the fields closer to

  120  

mTBI. However, further research is needed to complete the bridge between SVM and

mTBI.

From a more practical view, we found that real-time behavioral feedback improves

subject accuracy on difficult tasks, which was observed from our subjects (Figure 25).

Model System for Mild Traumatic Brain Injury

Working with mTBI may be difficult due to challenges finding subjects who are willing

to attend a study, finding subjects with the same amount of mTBI, and other reasons.

Most importantly, it is almost impossible to get a baseline before mTBI has occurred. A

sleep deprivation model system may solve these problems at the expense that the neural

pattern may not match with mTBI. The model system may still validate most parts of our

mTBI methods.

With help from our model system, we found that the MSIT paradigm was exposed to

training effect across subjects’ multiple visits, which was surprising because previous

literature has reported not having found any such effects (Bush et al., 2003; Bush and

Shin, 2006; Sheu et al., 2012).

Behaviorally, sleep deprivation slows down the executive function similarly to how

mTBI does. In the end, we will use our methods to study mTBI. Still, our model system

detected the reversible treatment of sleep deprivation. Sleep deprivation detection was

  121  

found in the posterior cingulate (increased), dorsomedial prefrontal cortex (increased),

bilateral insula (decreased), and bilateral intraparietal sulcus (decreased; Figure 32; Chee

et al., 2008; De Havas et al., 2012). Since the reversible sleep deprivation was

successfully validated, our model system has the potential for further research. The next

step could be to implement SVM into the model system before it is tested on mTBI.

Results From mTBI Subjects

In our mTBI subjects, results showed that they improved reaction times with time and

visits. However, the improvements may have been not only because of recovery from

mTBI, but also because of a training effect. Revealing that our mTBI subjects may have

been exposed to training effects complicates the interpretation of our mTBI results. It is

important to perform additional control tests to figure out how to separate the training

effect from the recovery effect within our mTBI subjects.

  122  

References

Ackermann, H., Riecker, A., Mathiak, K., Erb, M., Grodd, W., et al., 2001. Rate-

dependent activation of a prefrontal-insular-cerebellar network during passive listening to

trains of click stimuli: an fMRI study. Neuroreport 12, 4087-4092.

Adams, J.H., Graham, D.I., Scott, G., Parker, L.S., Doyle, D., 1980. Brain damage in

fatal non-missile head injury. Journal of clinical pathology 33, 1132-1145.

Alexander, M.P., 1995. Mild traumatic brain injury: pathophysiology, natural history, and

clinical management. Neurology 45, 1253-1260.

American Psychiatric Association, 2000. Diagnostic and Statistical Manual of Mental

Disorders. American Psychiatric Association, Fourth Edition, Washington, DC, Text

Revision, 760-762.

Aramaki, Y., Honda, M., Sadato, N., 2006. Suppression of the non-dominant motor

cortex during bimanual symmetric finger movement: a functional magnetic resonance

imaging study. Neuroscience 141, 2147-2153.

  123  

Arciniegas, D.B., Anderson, C.A., Topkoff, J., McAllister, T.W., 2005. Mild traumatic

brain injury: a neuropsychiatric approach to diagnosis, evaluation, and treatment.

Neuropsychiatric disease and treatment 1, 311-327.

Banich, M. T., 2009. Executive function the search for an integrated account. Current

Directions in Psychological Science, 18(2), 89-94.

Bazarian, J.J., Zhong, J., Blyth, B., Zhu, T., Kavcic, V., et al., 2007. Diffusion tensor

imaging detects clinically important axonal damage after mild traumatic brain injury: a

pilot study. Journal of neurotrauma 24, 1447-1459.

Beaumont, A., Gennarelli, T., 2006. CT prediction of contusion evolution after closed

head injury: the role of pericontusional edema. Acta neurochirurgica. Supplement 96, 30-

32.

Belanger, H.G., Vanderploeg, R.D., Curtiss, G., Warden, D.L., 2007. Recent

neuroimaging techniques in mild traumatic brain injury. The Journal of neuropsychiatry

and clinical neurosciences 19, 5-20.

Bernstein, M. A., King, K. F., & Zhou, X. J. (2004). Handbook of MRI pulse sequences.

Elsevier.

  124  

Bigler, E.D., 2007. Anterior and middle cranial fossa in traumatic brain injury: relevant

neuroanatomy and neuropathology in the study of neuropsychological outcome.

Neuropsychology 21, 515-531.

Binder, L.M., Rohling, M.L., Larrabee, G.J., 1997. A review of mild head trauma. Part I:

Meta-analytic review of neuropsychological studies. Journal of clinical and experimental

neuropsychology 19, 421-431.

Blinkenberg, M., Bonde, C., Holm, S., Svarer, C., Andersen, J., et al., 1996. Rate

dependence of regional cerebral activation during performance of a repetitive motor task:

a PET study. Journal of cerebral blood flow and metabolism : official journal of the

International Society of Cerebral Blood Flow and Metabolism 16, 794-803.

Bohnen, N., Jolles, J., 1992. Neurobehavioral aspects of postconcussive symptoms after

mild head injury. The Journal of nervous and mental disease 180, 683-692.

Borg, J., Holm, L., Cassidy, J.D., Peloso, P.M., Carroll, L.J., et al., 2004. Diagnostic

procedures in mild traumatic brain injury: results of the WHO Collaborating Centre Task

Force on Mild Traumatic Brain Injury. Journal of rehabilitation medicine : official

journal of the UEMS European Board of Physical and Rehabilitation Medicine, 61-75.

  125  

Brain Injury Association of America. (2014, August 15). Home page. Retrieved from

http://www.biausa.org/

Brandstack, N., Kurki, T., Tenovuo, O., Isoniemi, H., 2006. MR imaging of head trauma:

visibility of contusions and other intraparenchymal injuries in early and late stage. Brain

injury : [BI] 20, 409-416.

Budde, M.D., Janes, L., Gold, E., Turtzo, L.C., Frank, J.A., 2011. The contribution of

gliosis to diffusion tensor anisotropy and tractography following traumatic brain injury:

validation in the rat using Fourier analysis of stained tissue sections. Brain : a journal of

neurology 134, 2248-2260.

Buki, A., Povlishock, J.T., 2006. All roads lead to disconnection?--Traumatic axonal

injury revisited. Acta neurochirurgica 148, 181-193; discussion 193-184.

Bush, G., Shin, L.M., 2006. The Multi-Source Interference Task: an fMRI task that

reliably activates the cingulo-frontal-parietal cognitive/attention network. Nature

protocols 1, 308-313.

Bush, G., Shin, L.M., Holmes, J., Rosen, B.R., Vogt, B.A., 2003. The Multi-Source

Interference Task: validation study with fMRI in individual subjects. Molecular

psychiatry 8, 60-70.

  126  

Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation

changes during brain activation: the balloon model. Magnetic resonance in medicine :

official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic

Resonance in Medicine 39, 855-864.

Cain, S.W., Silva, E.J., Chang, A.M., Ronda, J.M., Duffy, J.F., 2011. One night of sleep

deprivation affects reaction time, but not interference or facilitation in a Stroop task.

Brain and cognition 76, 37-42.

Carter, C.S., Macdonald, A.M., Botvinick, M., Ross, L.L., Stenger, V.A., et al., 2000.

Parsing executive processes: strategic vs. evaluative functions of the anterior cingulate

cortex. Proceedings of the National Academy of Sciences of the United States of America

97, 1944-1948.

Cassidy, J.D., Carroll, L.J., Peloso, P.M., Borg, J., von Holst, H., et al., 2004. Incidence,

risk factors and prevention of mild traumatic brain injury: results of the WHO

Collaborating Centre Task Force on Mild Traumatic Brain Injury. Journal of

rehabilitation medicine : official journal of the UEMS European Board of Physical and

Rehabilitation Medicine, 28-60.

  127  

Chee, M.W., Chuah, Y.M., 2007. Functional neuroimaging and behavioral correlates of

capacity decline in visual short-term memory after sleep deprivation. Proceedings of the

National Academy of Sciences of the United States of America 104, 9487-9492.

Chee, M.W., Tan, J.C., Zheng, H., Parimal, S., Weissman, D.H., et al., 2008. Lapsing

during sleep deprivation is associated with distributed changes in brain activation. The

Journal of neuroscience : the official journal of the Society for Neuroscience 28, 5519-

5528.

Chen, J.K., Johnston, K.M., Collie, A., McCrory, P., Ptito, A., 2007. A validation of the

post concussion symptom scale in the assessment of complex concussion using cognitive

testing and functional MRI. Journal of neurology, neurosurgery, and psychiatry 78, 1231-

1238.

Chen, Z., Lei, X., Ding, C., Li, H., & Chen, A., 2013. The neural mechanisms of

semantic and response conflicts: An fMRI study of practice-related effects in the Stroop

task. NeuroImage, 66, 577-584.

Cherkassky, V., & Mulier, F. M., 2007. Learning from data: concepts, theory, and

methods. John Wiley & Sons.

  128  

Chu, Z., Wilde, E.A., Hunter, J.V., McCauley, S.R., Bigler, E.D., et al., 2010. Voxel-

based analysis of diffusion tensor imaging in mild traumatic brain injury in adolescents.

AJNR. American journal of neuroradiology 31, 340-346.

Cortes, C., & Vapnik, V., 1995. Support-vector networks. Machine learning, 20(3), 273-

297.

Cox, D.D., Savoy, R.L., 2003. Functional magnetic resonance imaging (fMRI) "brain

reading": detecting and classifying distributed patterns of fMRI activity in human visual

cortex. NeuroImage 19, 261-270.

Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magnetic

resonance neuroimages. Computers and biomedical research, an international journal 29,

162-173.

D'Esposito, M., Detre, J.A., Alsop, D.C., Shin, R.K., Atlas, S., et al., 1995. The neural

basis of the central executive system of working memory. Nature 378, 279-281.

Damadian, R., 1971. Tumor detection by nuclear magnetic resonance. Science 171, 1151-

1153.

  129  

Damadian, R., Zaner, K., Hor, D., DiMaio, T., 1974. Human tumors detected by nuclear

magnetic resonance. Proceedings of the National Academy of Sciences of the United

States of America 71, 1471-1473.

De Havas, J.A., Parimal, S., Soon, C.S., Chee, M.W., 2012. Sleep deprivation reduces

default mode network connectivity and anti-correlation during rest and task performance.

NeuroImage 59, 1745-1751.

deCharms, R.C., Maeda, F., Glover, G.H., Ludlow, D., Pauly, J.M., et al., 2005. Control

over brain activation and pain learned by using real-time functional MRI. Proceedings of

the National Academy of Sciences of the United States of America 102, 18626-18631.

Deiber, M.P., Honda, M., Ibanez, V., Sadato, N., Hallett, M., 1999. Mesial motor areas in

self-initiated versus externally triggered movements examined with fMRI: effect of

movement type and rate. Journal of neurophysiology 81, 3065-3077.

Dikmen, S., Reitan, R.M., Temkin, N.R., Machamer, J.E., 1992. Minor and severe head

injury emotional sequelae. Brain injury : [BI] 6, 477-478.

Drummond, S.P., Paulus, M.P., Tapert, S.F., 2006. Effects of two nights sleep

deprivation and two nights recovery sleep on response inhibition. Journal of sleep

research 15, 261-265.

  130  

Echemendia, R.J., Putukian, M., Mackin, R.S., Julian, L., Shoss, N., 2001.

Neuropsychological test performance prior to and following sports-related mild traumatic

brain injury. Clinical journal of sport medicine : official journal of the Canadian

Academy of Sport Medicine 11, 23-31.

Eickhoff, S.B., Bzdok, D., Laird, A.R., Kurth, F., Fox, P.T., 2012. Activation likelihood

estimation meta-analysis revisited. NeuroImage 59, 2349-2361.

Eickhoff, S.B., Laird, A.R., Grefkes, C., Wang, L.E., Zilles, K., et al., 2009. Coordinate-

based activation likelihood estimation meta-analysis of neuroimaging data: a random-

effects approach based on empirical estimates of spatial uncertainty. Human brain

mapping 30, 2907-2926.

Eierud, C., Craddock, R. C., Fletcher, S., Aulakh, M., King-Casas, B., Kuehl, D., &

LaConte, S. M., 2014. Neuroimaging after mild traumatic brain injury: Review and meta-

analysis. NeuroImage: Clinical, 4, 283-294.

Ellmore, T.M., Li, H., Xue, Z., Wong, S.T., Frye, R.E., 2013. Tract-based spatial

statistics reveal altered relationship between non-verbal reasoning abilities and white

matter integrity in autism spectrum disorder. Journal of the International

Neuropsychological Society : JINS 19, 723-728.

  131  

Evans, R.W., 1992. The postconcussion syndrome and the sequelae of mild head injury.

Neurologic clinics 10, 815-847.

Fan, Y., Rao, H., Hurt, H., Giannetta, J., Korczykowski, M., et al., 2007. Multivariate

examination of brain abnormality using both structural and functional MRI. NeuroImage

36, 1189-1199.

Fisher, R. A., 1936. The use of multiple measurements in taxonomic problems. Annals of

eugenics, 7(2), 179-188.

FitzGerald, D.B., Crosson, B.A., 2011. Diffusion weighted imaging and

neuropsychological correlates in adults with mild traumatic brain injury. International

journal of psychophysiology : official journal of the International Organization of

Psychophysiology 82, 79-85.

Freitas, R. A., 1998. Nanomedicine Foresight Institute, 749-752, 817.

Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. P., Frith, C. D., & Frackowiak, R.

S., 1994. Statistical parametric maps in functional imaging: a general linear approach.

Human brain mapping, 2(4), 189-210.

  132  

Funahashi, S., Bruce, C.J., Goldman-Rakic, P.S., 1989. Mnemonic coding of visual space

in the monkey's dorsolateral prefrontal cortex. Journal of neurophysiology 61, 331-349.

Gasparovic, C., Yeo, R., Mannell, M., Ling, J., Elgie, R., et al., 2009. Neurometabolite

concentrations in gray and white matter in mild traumatic brain injury: an 1H-magnetic

resonance spectroscopy study. Journal of neurotrauma 26, 1635-1643.

Geary, E.K., Kraus, M.F., Pliskin, N.H., Little, D.M., 2010. Verbal learning differences

in chronic mild traumatic brain injury. Journal of the International Neuropsychological

Society : JINS 16, 506-516.

Gennarelli, T.A., Thibault, L.E., Adams, J.H., Graham, D.I., Thompson, C.J., et al., 1982.

Diffuse axonal injury and traumatic coma in the primate. Annals of neurology 12, 564-

574.

Giza, C.C., Hovda, D.A., 2001. The Neurometabolic Cascade of Concussion. Journal of

athletic training 36, 228-235.

Glover, G.H., 1999. Deconvolution of impulse response in event-related BOLD fMRI.

NeuroImage 9, 416-429.

  133  

Gross, C.G., 1992. Representation of visual stimuli in inferior temporal cortex.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences

335, 3-10.

Grossman, E.J., Ge, Y., Jensen, J.H., Babb, J.S., Miles, L., et al., 2012. Thalamus and

cognitive impairment in mild traumatic brain injury: a diffusional kurtosis imaging study.

Journal of neurotrauma 29, 2318-2327.

Gurdjian, E.S., 1975. Re-evaluation of the biomechanics of blunt impact injury of the

head. Surgery, gynecology & obstetrics 140, 845-850.

Guskiewicz, K.M., McCrea, M., Marshall, S.W., Cantu, R.C., Randolph, C., et al., 2003.

Cumulative effects associated with recurrent concussion in collegiate football players: the

NCAA Concussion Study. JAMA : the journal of the American Medical Association 290,

2549-2555.

Guskiewicz, K.M., Mihalik, J.P., Shankar, V., Marshall, S.W., Crowell, D.H., et al.,

2007. Measurement of head impacts in collegiate football players: relationship between

head impact biomechanics and acute clinical outcome after concussion. Neurosurgery 61,

1244-1252; discussion 1252-1243.

  134  

Guskiewicz, K.M., Perrin, D.H., Gansneder, B.M., 1996. Effect of mild head injury on

postural stability in athletes. Journal of athletic training 31, 300-306.

Hanlon, R.E., Demery, J.A., Martinovich, Z., Kelly, J.P., 1999. Effects of acute injury

characteristics on neuropsychological status and vocational outcome following mild

traumatic brain injury. Brain injury : [BI] 13, 873-887.

Hashimoto, K., Abo, M., 2009. Abnormal regional benzodiazepine receptor uptake in the

prefrontal cortex in patients with mild traumatic brain injury. Journal of rehabilitation

medicine : official journal of the UEMS European Board of Physical and Rehabilitation

Medicine 41, 661-665.

He, S.Q., Dum, R.P., Strick, P.L., 1995. Topographic organization of corticospinal

projections from the frontal lobe: motor areas on the medial surface of the hemisphere.

The Journal of neuroscience : the official journal of the Society for Neuroscience 15,

3284-3306.

Holli, K.K., Waljas, M., Harrison, L., Liimatainen, S., Luukkaala, T., et al., 2010. Mild

traumatic brain injury: tissue texture analysis correlated to neuropsychological and DTI

findings. Academic radiology 17, 1096-1102.

  135  

Holm, L., Cassidy, J.D., Carroll, L.J., Borg, J., 2005. Summary of the WHO

Collaborating Centre for Neurotrauma Task Force on Mild Traumatic Brain Injury.

Journal of rehabilitation medicine 37, 137-141.

Horak, F.B., Diener, H.C., 1994. Cerebellar control of postural scaling and central set in

stance. Journal of neurophysiology 72, 479-493.

Hunter, J.V., Wilde, E.A., Tong, K.A., Holshouser, B.A., 2012. Emerging imaging tools

for use with traumatic brain injury research. Journal of neurotrauma 29, 654-671.

Huynh, T., Jacobs, D.G., Dix, S., Sing, R.F., Miles, W.S., et al., 2006. Utility of

neurosurgical consultation for mild traumatic brain injury. The American surgeon 72,

1162-1165; discussion1166-1167.

Ilvesmäki, T., Luoto, T. M., Hakulinen, U., Brander, A., Ryymin, P., et al., 2014. Acute

mild traumatic brain injury is not associated with white matter change on diffusion tensor

imaging. Brain, awu095.

Inglese, M., Bomsztyk, E., Gonen, O., Mannon, L.J., Grossman, R.I., et al., 2005. Dilated

perivascular spaces: hallmarks of mild traumatic brain injury. AJNR. American journal of

neuroradiology 26, 719-724.

  136  

Iverson, G.L., 2005. Outcome from mild traumatic brain injury. Current opinion in

psychiatry 18, 301-317.

Iverson, G.L., 2006. Complicated vs uncomplicated mild traumatic brain injury: acute

neuropsychological outcome. Brain injury : [BI] 20, 1335-1344.

Iverson, G.L., Lange, R.T., 2003. Examination of "postconcussion-like" symptoms in a

healthy sample. Applied neuropsychology 10, 137-144.

Jagoda, A.S., Bazarian, J.J., Bruns, J.J., Jr., Cantrill, S.V., Gean, A.D., et al., 2008.

Clinical policy: neuroimaging and decisionmaking in adult mild traumatic brain injury in

the acute setting. Annals of emergency medicine 52, 714-748.

Jancke, L., Peters, M., Himmelbach, M., Nosselt, T., Shah, J., et al., 2000. fMRI study of

bimanual coordination. Neuropsychologia 38, 164-174.

Jancke, L., Peters, M., Schlaug, G., Posse, S., Steinmetz, H., et al., 1998. Differential

magnetic resonance signal change in human sensorimotor cortex to finger movements of

different rate of the dominant and subdominant hand. Brain research. Cognitive brain

research 6, 279-284.

  137  

Jancke, L., Specht, K., Mirzazade, S., Peters, M., 1999. The effect of finger-movement

speed of the dominant and the subdominant hand on cerebellar activation: A functional

magnetic resonance imaging study. NeuroImage 9, 497-507.

Jennett, B., 1998. Epidemiology of head injury. Archives of disease in childhood 78, 403-

406.

Joachims, T. (1999). Making large-Scale SVM Learning Practical. Advances in Kernel

Methods-Support Vector Learning. Schölkopf B. and Burges C. and Smola A. MIT

Press, 1999.

Kane, M.J., Engle, R.W., 2002. The role of prefrontal cortex in working-memory

capacity, executive attention, and general fluid intelligence: an individual-differences

perspective. Psychonomic bulletin & review 9, 637-671.

Kawashima, R., Inoue, K., Sugiura, M., Okada, K., Ogawa, A., et al., 1999. A positron

emission tomography study of self-paced finger movements at different frequencies.

Neuroscience 92, 107-112.

Kraus, M.F., Susmaras, T., Caughlin, B.P., Walker, C.J., Sweeney, J.A., et al., 2007.

White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor

imaging study. Brain : a journal of neurology 130, 2508-2519.

  138  

Krivitzky, L.S., Roebuck-Spencer, T.M., Roth, R.M., Blackstone, K., Johnson, C.P., et

al., 2011. Functional magnetic resonance imaging of working memory and response

inhibition in children with mild traumatic brain injury. Journal of the International

Neuropsychological Society : JINS 17, 1143-1152.

Kurca, E., Sivak, S., Kucera, P., 2006. Impaired cognitive functions in mild traumatic

brain injury patients with normal and pathologic magnetic resonance imaging.

Neuroradiology 48, 661-669.

Kushner, D., 1998. Mild traumatic brain injury: toward understanding manifestations and

treatment. Archives of internal medicine 158, 1617-1624.

LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X., 2005. Support vector

machines for temporal classification of block design fMRI data. NeuroImage 26, 317-

329.

LaConte, S.M., 2011. Decoding fMRI brain states in real-time. NeuroImage 56, 440-454.

LaConte, S.M., Peltier, S.J., Hu, X.P., 2007. Real-time fMRI using brain-state

classification. Human brain mapping 28, 1033-1044.

  139  

Landre, N., Poppe, C.J., Davis, N., Schmaus, B., Hobbs, S.E., 2006. Cognitive

functioning and postconcussive symptoms in trauma patients with and without mild TBI.

Archives of clinical neuropsychology : the official journal of the National Academy of

Neuropsychologists 21, 255-273.

Lange, R.T., Iverson, G.L., Brubacher, J.R., Madler, B., Heran, M.K., 2012. Diffusion

tensor imaging findings are not strongly associated with postconcussional disorder 2

months following mild traumatic brain injury. The Journal of head trauma rehabilitation

27, 188-198.

Langlois, J.A., Rutland-Brown, W., Thomas, K.E., 2004. Traumatic brain injury in the

United States: emergency department visits, hospitalizations, and deaths. Department of

Health and Human Services, Centers for Disease Control and Prevention, Division of

Acute Care, Rehabilitation Research and Disability Prevention, National Center for

Injury Prevention and Control.

Lauterburg, P.C., 1973. Image Formation by Induced LocalInteractions: Expamples

Employing Nuclear Magnetic Resonance. Nature 242, 190-191.

Lee, H., Wintermark, M., Gean, A.D., Ghajar, J., Manley, G.T., et al., 2008. Focal lesions

in acute mild traumatic brain injury and neurocognitive outcome: CT versus 3T MRI.

Journal of neurotrauma 25, 1049-1056.

  140  

Lehericy, S., Bardinet, E., Tremblay, L., Van de Moortele, P.F., Pochon, J.B., et al.,

2006. Motor control in basal ganglia circuits using fMRI and brain atlas approaches.

Cerebral cortex 16, 149-161.

Levin, H.S., Williams, D.H., Eisenberg, H.M., High, W.M., Jr., Guinto, F.C., Jr., 1992.

Serial MRI and neurobehavioural findings after mild to moderate closed head injury.

Journal of neurology, neurosurgery, and psychiatry 55, 255-262.

Likes, R. S., 1981. U.S. Patent No. 4,307,343. Washington, DC: U.S. Patent and

Trademark Office.

Lipton, M.L., Gellella, E., Lo, C., Gold, T., Ardekani, B.A., et al., 2008. Multifocal white

matter ultrastructural abnormalities in mild traumatic brain injury with cognitive

disability: a voxel-wise analysis of diffusion tensor imaging. Journal of neurotrauma 25,

1335-1342.

Lipton, M.L., Gulko, E., Zimmerman, M.E., Friedman, B.W., Kim, M., et al., 2009.

Diffusion-tensor imaging implicates prefrontal axonal injury in executive function

impairment following very mild traumatic brain injury. Radiology 252, 816-824.

  141  

Lipton, M.L., Kim, N., Park, Y.K., Hulkower, M.B., Gardin, T.M., et al., 2012. Robust

detection of traumatic axonal injury in individual mild traumatic brain injury patients:

intersubject variation, change over time and bidirectional changes in anisotropy. Brain

imaging and behavior 6, 329-342.

Lo, C., Shifteh, K., Gold, T., Bello, J.A., Lipton, M.L., 2009. Diffusion tensor imaging

abnormalities in patients with mild traumatic brain injury and neurocognitive impairment.

Journal of computer assisted tomography 33, 293-297.

Loane, D.J., Faden, A.I., 2010. Neuroprotection for traumatic brain injury: translational

challenges and emerging therapeutic strategies. Trends in pharmacological sciences 31,

596-604.

Lutz, A., Greischar, L.L., Rawlings, N.B., Ricard, M., Davidson, R.J., 2004. Long-term

meditators self-induce high-amplitude gamma synchrony during mental practice.

Proceedings of the National Academy of Sciences of the United States of America 101,

16369-16373.

Mac Donald, C.L., Dikranian, K., Bayly, P., Holtzman, D., Brody, D., 2007. Diffusion

tensor imaging reliably detects experimental traumatic axonal injury and indicates

approximate time of injury. The Journal of neuroscience : the official journal of the

Society for Neuroscience 27, 11869-11876.

  142  

Mac Donald, C.L., Johnson, A.M., Cooper, D., Nelson, E.C., Werner, N.J., et al., 2011.

Detection of blast-related traumatic brain injury in U.S. military personnel. The New

England journal of medicine 364, 2091-2100.

Maruta, J., Suh, M., Niogi, S.N., Mukherjee, P., Ghajar, J., 2010. Visual tracking

synchronization as a metric for concussion screening. The Journal of head trauma

rehabilitation 25, 293-305.

Mayer, A.R., Ling, J., Mannell, M.V., Gasparovic, C., Phillips, J.P., et al., 2010. A

prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology 74,

643-650.

Mayer, A.R., Mannell, M.V., Ling, J., Elgie, R., Gasparovic, C., et al., 2009. Auditory

orienting and inhibition of return in mild traumatic brain injury: a FMRI study. Human

brain mapping 30, 4152-4166.

Mayer, A.R., Mannell, M.V., Ling, J., Gasparovic, C., Yeo, R.A., 2011. Functional

connectivity in mild traumatic brain injury. Human brain mapping 32, 1825-1835.

McAllister, T.W., Flashman, L.A., McDonald, B.C., Ferrell, R.B., Tosteson, T.D., et al.,

2011. Dopaminergic challenge with bromocriptine one month after mild traumatic brain

  143  

injury: altered working memory and BOLD response. The Journal of neuropsychiatry and

clinical neurosciences 23, 277-286.

McAllister, T.W., Flashman, L.A., McDonald, B.C., Saykin, A.J., 2006. Mechanisms of

working memory dysfunction after mild and moderate TBI: evidence from functional

MRI and neurogenetics. Journal of neurotrauma 23, 1450-1467.

McAllister, T.W., Saykin, A.J., Flashman, L.A., Sparling, M.B., Johnson, S.C., et al.,

1999. Brain activation during working memory 1 month after mild traumatic brain injury:

a functional MRI study. Neurology 53, 1300-1308.

McAllister, T.W., Sparling, M.B., Flashman, L.A., Guerin, S.J., Mamourian, A.C., et al.,

2001. Differential working memory load effects after mild traumatic brain injury.

NeuroImage 14, 1004-1012.

McCrea, M., American Academy of Clinical Neuropsychology., 2008. Mild traumatic

brain injury and postconcussion syndrome : the new evidence base for diagnosis and

treatment. Oxford University Press, Oxford ; New York.

McCrea, M., Guskiewicz, K.M., Marshall, S.W., Barr, W., Randolph, C., et al., 2003.

Acute effects and recovery time following concussion in collegiate football players: the

  144  

NCAA Concussion Study. JAMA : the journal of the American Medical Association 290,

2556-2563.

McCrea, M., Kelly, J.P., Randolph, C., Cisler, R., Berger, L., 2002. Immediate

neurocognitive effects of concussion. Neurosurgery 50, 1032-1040; discussion 1040-

1032.

McCullagh, S., Oucherlony, D., Protzner, A., Blair, N., Feinstein, A., 2001. Prediction of

neuropsychiatric outcome following mild trauma brain injury: an examination of the

Glasgow Coma Scale. Brain injury : [BI] 15, 489-497.

Messe, A., Caplain, S., Paradot, G., Garrigue, D., Mineo, J.F., et al., 2011. Diffusion

tensor imaging and white matter lesions at the subacute stage in mild traumatic brain

injury with persistent neurobehavioral impairment. Human brain mapping 32, 999-1011.

Meythaler, J.M., Peduzzi, J.D., Eleftheriou, E., Novack, T.A., 2001. Current concepts:

diffuse axonal injury-associated traumatic brain injury. Archives of physical medicine

and rehabilitation 82, 1461-1471.

Miles, L., Grossman, R.I., Johnson, G., Babb, J.S., Diller, L., et al., 2008. Short-term DTI

predictors of cognitive dysfunction in mild traumatic brain injury. Brain injury : [BI] 22,

115-122.

  145  

Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., &

Newman, S., 2004. Learning to decode cognitive states from brain images. Machine

Learning, 57(1-2), 145-175.

Mittl, R.L., Grossman, R.I., Hiehle, J.F., Hurst, R.W., Kauder, D.R., et al., 1994.

Prevalence of MR evidence of diffuse axonal injury in patients with mild head injury and

normal head CT findings. AJNR. American journal of neuroradiology 15, 1583-1589.

Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., et al., 2008. Stereotaxic white matter atlas

based on diffusion tensor imaging in an ICBM template. NeuroImage 40, 570-582.

Niogi, S.N., Mukherjee, P., 2010. Diffusion tensor imaging of mild traumatic brain

injury. The Journal of head trauma rehabilitation 25, 241-255.

Niogi, S.N., Mukherjee, P., Ghajar, J., Johnson, C., Kolster, R.A., et al., 2008a. Extent of

microstructural white matter injury in postconcussive syndrome correlates with impaired

cognitive reaction time: a 3T diffusion tensor imaging study of mild traumatic brain

injury. AJNR. American journal of neuroradiology 29, 967-973.

  146  

Niogi, S.N., Mukherjee, P., Ghajar, J., Johnson, C.E., Kolster, R., et al., 2008b. Structural

dissociation of attentional control and memory in adults with and without mild traumatic

brain injury. Brain : a journal of neurology 131, 3209-3221.

Paniak, C., Reynolds, S., Toller-Lobe, G., Melnyk, A., Nagy, J., et al., 2002. A

longitudinal study of the relationship between financial compensation and symptoms

after treated mild traumatic brain injury. Journal of clinical and experimental

neuropsychology 24, 187-193.

Papageorgiou, T.D., Lisinski, J.M., McHenry, M.A., White, J.P., LaConte, S.M., 2013.

Brain-computer interfaces increase whole-brain signal to noise. Proceedings of the

National Academy of Sciences of the United States of America 110, 13630-13635.

Parikh, S., Koch, M., Narayan, R.K., 2007. Traumatic brain injury. International

anesthesiology clinics 45, 119-135.

Povlishock, J.T., Erb, D.E., Astruc, J., 1992. Axonal response to traumatic brain injury:

reactive axonal change, deafferentation, and neuroplasticity. Journal of neurotrauma 9

Suppl 1, S189-200.

Prabhu, S.P., 2011. The role of neuroimaging in sport-related concussion. Clinics in

sports medicine 30, 103-114, ix.

  147  

Ptito, A., Chen, J.K., Johnston, K.M., 2007. Contributions of functional magnetic

resonance imaging (fMRI) to sport concussion evaluation. NeuroRehabilitation 22, 217-

227.

Pulsipher, D.T., Campbell, R.A., Thoma, R., King, J.H., 2011. A critical review of

neuroimaging applications in sports concussion. Current sports medicine reports 10, 14-

20.

Raichle, M.E., Snyder, A.Z., 2007. A default mode of brain function: a brief history of an

evolving idea. NeuroImage 37, 1083-1090; discussion 1097-1089.

Rao, S.M., Bandettini, P.A., Binder, J.R., Bobholz, J.A., Hammeke, T.A., et al., 1996.

Relationship between finger movement rate and functional magnetic resonance signal

change in human primary motor cortex. Journal of cerebral blood flow and metabolism :

official journal of the International Society of Cerebral Blood Flow and Metabolism 16,

1250-1254.

Riecker, A., Groschel, K., Ackermann, H., Steinbrink, C., Witte, O., et al., 2006.

Functional significance of age-related differences in motor activation patterns.

NeuroImage 32, 1345-1354.

  148  

Rohling, M.L., Binder, L.M., Demakis, G.J., Larrabee, G.J., Ploetz, D.M., et al., 2011. A

meta-analysis of neuropsychological outcome after mild traumatic brain injury: re-

analyses and reconsiderations of Binder et al. (1997), Frencham et al. (2005), and Pertab

et al. (2009). The Clinical neuropsychologist 25, 608-623.

Rosales-Lagarde, A., Armony, J.L., Del Rio-Portilla, Y., Trejo-Martinez, D., Conde, R.,

et al., 2012. Enhanced emotional reactivity after selective REM sleep deprivation in

humans: an fMRI study. Frontiers in behavioral neuroscience 6, 25.

Rosenbaum, S.B., Lipton, M.L., 2012. Embracing chaos: the scope and importance of

clinical and pathological heterogeneity in mTBI. Brain imaging and behavior 6, 255-282.

Rounis, E., Lee, L., Siebner, H.R., Rowe, J.B., Friston, K.J., et al., 2005. Frequency

specific changes in regional cerebral blood flow and motor system connectivity following

rTMS to the primary motor cortex. NeuroImage 26, 164-176.

Ruff, R.M., 2011. Mild traumatic brain injury and neural recovery: rethinking the debate.

NeuroRehabilitation 28, 167-180.

Saatman, K.E., Duhaime, A.C., Bullock, R., Maas, A.I., Valadka, A., et al., 2008.

Classification of traumatic brain injury for targeted therapies. Journal of neurotrauma 25,

719-738.

  149  

Sadato, N., Ibanez, V., Deiber, M.P., Campbell, G., Leonardo, M., et al., 1996.

Frequency-dependent changes of regional cerebral blood flow during finger movements.

Journal of cerebral blood flow and metabolism : official journal of the International

Society of Cerebral Blood Flow and Metabolism 16, 23-33.

Satz, P.S., Alfano, M.S., Light, R.F., Morgenstern, H.F., Zaucha, K.F., et al., 1999.

Persistent Post-Concussive Syndrome: A proposed methodology and literature review to

determine the effects, if any, of mild head and other bodily injury. Journal of clinical and

experimental neuropsychology 21, 620-628.

Shenton, M.E., Hamoda, H.M., Schneiderman, J.S., Bouix, S., Pasternak, O., et al., 2012.

A review of magnetic resonance imaging and diffusion tensor imaging findings in mild

traumatic brain injury. Brain imaging and behavior 6, 137-192.

Sheu, L.K., Jennings, J.R., Gianaros, P.J., 2012. Test-retest reliability of an fMRI

paradigm for studies of cardiovascular reactivity. Psychophysiology 49, 873-884.

Silver, C.H., 2000. Ecological validity of neuropsychological assessment in childhood

traumatic brain injury. The Journal of head trauma rehabilitation 15, 973-988.

  150  

Slobounov, S.M., Zhang, K., Pennell, D., Ray, W., Johnson, B., et al., 2010. Functional

abnormalities in normally appearing athletes following mild traumatic brain injury: a

functional MRI study. Experimental brain research. Experimentelle Hirnforschung.

Experimentation cerebrale 202, 341-354.

Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., et al.,

2004. Advances in functional and structural MR image analysis and implementation as

FSL. NeuroImage 23 Suppl 1, S208-219.

Smith-Seemiller, L., Fow, N.R., Kant, R., Franzen, M.D., 2003. Presence of post-

concussion syndrome symptoms in patients with chronic pain vs mild traumatic brain

injury. Brain injury : [BI] 17, 199-206.

Smits, M., Houston, G.C., Dippel, D.W., Wielopolski, P.A., Vernooij, M.W., et al., 2011.

Microstructural brain injury in post-concussion syndrome after minor head injury.

Neuroradiology 53, 553-563.

Stein, S.C., Fabbri, A., Servadei, F., 2008. Routine serial computed tomographic scans in

mild traumatic brain injury: when are they cost-effective? The Journal of trauma 65, 66-

72.

  151  

Talairach, J., & Szikla, G., 1967. Atlas of stereotaxic anatomy of the telencephalon.

Masson.

Teasdale, G., Jennett, B., 1974. Assessment of coma and impaired consciousness. A

practical scale. Lancet 2, 81-84.

Tellier, A., Marshall, S.C., Wilson, K.G., Smith, A., Perugini, M., et al., 2009. The

heterogeneity of mild traumatic brain injury: Where do we stand? Brain injury : [BI] 23,

879-887.

Turkeltaub, P.E., Eickhoff, S.B., Laird, A.R., Fox, M., Wiener, M., et al., 2012.

Minimizing within-experiment and within-group effects in Activation Likelihood

Estimation meta-analyses. Human brain mapping 33, 1-13.

Vapnik, V., & Lerner, A. J., 1963. Generalized portrait method for pattern recognition.

Automation and Remote Control, 24(6), 774-780.

Voller, B., Benke, T., Benedetto, K., Schnider, P., Auff, E., et al., 1999.

Neuropsychological, MRI and EEG findings after very mild traumatic brain injury. Brain

injury : [BI] 13, 821-827.

  152  

Wilde, E.A., McCauley, S.R., Hunter, J.V., Bigler, E.D., Chu, Z., et al., 2008. Diffusion

tensor imaging of acute mild traumatic brain injury in adolescents. Neurology 70, 948-

955.

Williams, D.H., Levin, H.S., Eisenberg, H.M., 1990. Mild head injury classification.

Neurosurgery 27, 422-428.

Witt, S.T., Laird, A.R., Meyerand, M.E., 2008. Functional neuroimaging correlates of

finger-tapping task variations: an ALE meta-analysis. NeuroImage 42, 343-356.

Witt, S.T., Lovejoy, D.W., Pearlson, G.D., Stevens, M.C., 2010. Decreased prefrontal

cortex activity in mild traumatic brain injury during performance of an auditory oddball

task. Brain imaging and behavior 4, 232-247.

Wu, T.C., Wilde, E.A., Bigler, E.D., Yallampalli, R., McCauley, S.R., et al., 2010.

Evaluating the relationship between memory functioning and cingulum bundles in acute

mild traumatic brain injury using diffusion tensor imaging. Journal of neurotrauma 27,

303-307.

Xiong, Y., Gu, Q., Peterson, P.L., Muizelaar, J.P., Lee, C.P., 1997. Mitochondrial

dysfunction and calcium perturbation induced by traumatic brain injury. Journal of

neurotrauma 14, 23-34.

  153  

Yallampalli, R., Wilde, E.A., Bigler, E.D., McCauley, S.R., Hanten, G., et al., 2010.

Acute White Matter Differences in the Fornix Following Mild Traumatic Brain Injury

Using Diffusion Tensor Imaging. Journal of neuroimaging : official journal of the

American Society of Neuroimaging.

Westbrook, C., & Roth, C. K., 2013. MRI in Practice. John Wiley & Sons.

Wurster, C.D., Graf, H., Ackermann, H., Groth, K., Kassubek, J., et al., 2014. Neural

correlates of rate-dependent finger-tapping in Parkinson's disease. Brain structure &

function, 1-12.