Sex Differences in the Connectivity of the Subgenual ... · Sex Differences in the Connectivity of...

Sex Differences in the Connectivity of the Subgenual Anterior Cingulate Cortex: Implications for Pain

Habituation

by

Gang Wang

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science

University of Toronto

© Copyright by Gang Wang 2013

ii

Sex Differences in the Connectivity of the Subgenual

Anterior Cingulate Cortex: Implications for Pain Habituation

Gang Wang

Master of Science

Institute of Medical Science

University of Toronto

2013

Abstract

Women exhibit greater habituation to painful stimuli than men. The neural mechanism

underlying this sex difference is unknown. However, pain habituation has been

associated with pain-evoked activity of the subgenual anterior cingulate cortex (sgACC),

implicating a connection between the sgACC and the descending pain antinociceptive

system. Therefore, the thesis hypothesis was that women have stronger connectivity than

men between the sgACC and the descending antinociceptive system. Healthy subjects

provided informed consent. 3T MRI images included anatomical diffusion-weighted

imaging for structural connectivity analyses (SC) with probabilistic tractography and

resting-state functional images for functional connectivity (FC) analyses. Women had

stronger sgACC FC with nodes of the descending pain modulation system (raphe, PAG)

and the medial thalamus. In contrast, men had stronger sgACC FC with nodes of the

salience/attention network (anterior insula, TPJ) and stronger sgACC SC with the

hypothalamus. These findings implicate a mechanism for pain habituation and its

associated sex differences.

iii

Acknowledgments

I would like to first thank Dr. Karen D Davis for her guidance. She is an exceptional

supervisor for her patience with her students. Not only did she teach me how to conduct

research, but, more importantly, she taught me how to educate myself in conducting

research. She helped shape me into an independent and autodidactic researcher. By

asking me the right questions without providing direct answers, Karen has enabled me to

explore the field of Neuroscience - Neuroimaging and Pain. Her suggestions for me to

participate in journal clubs and seminars made it possible for me to interact with and

learn from honourable and modest researchers, e.g., Dr. Barry Sessle, Dr. Limor Avivi-

Arber, and Dr. Jonathan Dostrovsky.

I also want to express my gratitude for Dr. Adrian Crawley, who has always made time in

his office, in the hallway, or by phone for answering my questions related to statistical

analyses and my project. As one of the most humble supervisor, Dr. Adrian Crawley

often provided suggestions that are critical for the success of my project and future

publications.

I would like to thank Dr. Judith Hunter, who has always shown interest and optimism in

my project and ability to carry it out successfully. Her expertise in Neuroscience has

urged and helped me explore the discipline and look for more implications of my

research results. Her humour and cheerfulness during my PAC meetings has motivated

me to remain in academia in the future.

From Drs. Karen D Davis, Adrian Crawley, and Judith Hunter, I have learned to read,

analyze, and think more critically and from a broader perspective. They have changed the

way I understand academic publications and research results.

I would also like to acknowledge a number of students in the Davis lab: Dr. Nathalie

Erpelding who generally shared with me her collected MRI data – the start of my thesis

project; Dr. Massieh Moayedi who has shown me exemplary critical thinking skills;

Danielle Desouza who have always been cheerful and energetic; Aaron Kucyi who

showed me early approach for fMRI analysis and who provided me the learning

iv

opportunity in engineering the TENS Driver as well as programming the online pain

rating software in E-Prime; and Dr. Ruma Goswami who I had the pleasure to collaborate

with in her early stages of probabilistic tractography analyses. She has been one of the

most humble post-doctorate I have ever met.

When I had trouble with understanding fMRI analysis approaches, Mr. Geoff Pope had

always been available, helpful, and patient in answering my questions. His understanding

of neuroimaging techniques as well honesty in his extend of knowledge has guided me in

the right direction.

In a number of labs where I conducted research, supervisors are usually the leaders for

the research group, providing directions. The Davis Lab is unique in the sense that every

member in the lab is a leader in her/his own field, and that each person is able to work

independently and successfully to challenge and to broaden the boundary of the

knowledge that is currently known. What I learned at the Davis Lab will not only guide

me in research but also in any aspect of my future when I aspire for excellence.

I am grateful for my funding sources: OGS, IMS Entrance Scholarship, TWRI poster

design award, CIHR grant (Dr. Karen D Davis), and IMSSA Award for Community

Leadership.

A special acknowledgement to my grandmother, who recently passed away. may she

always live in my heart. May her kindness, selflessness, unconditional love, humour,

optimism, and altruism be constantly reflected in my actions to lighten people, so that

these qualities—her legacy—will perpetually be relived by and be passed onto others for

the many days to come ...

At last, I want to thank my parents, who gave me life and made significant sacrifices to

bring me to Canada, making it possible for me to learn from the very best and the very

brightest minds in the world.

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Table of Contents

Acknowledgments.............................................................................................................. iii

Table of Contents ................................................................................................................ v

List of Tables .................................................................................................................... vii

List of Figures .................................................................................................................. viii

List of Appendices ............................................................................................................ xii

List of Abbreviations ....................................................................................................... xiii

1 INTRODUCTION, AIM, AND HYPOTHESIS ........................................................ 1

2 LITERATURE REVIEW ........................................................................................... 3

2.1 Pain ......................................................................................................................... 3

2.1.2 Ascending Pain Pathways ........................................................................... 4

2.1.3 Descending pain modulation....................................................................... 5

2.1.4 Adaptation and habituation ......................................................................... 9

2.1.5 Diffuse noxious inhibitory controls .......................................................... 10

2.1.6 Sex differences .......................................................................................... 11

2.2 Subgenual anterior cingulate cortex ...................................................................... 16

2.2.2 Anatomy and connectivity ........................................................................ 16

2.2.3 sgACC function ........................................................................................ 18

2.3 Resting state BOLD-fMRI .................................................................................... 19

2.3.2 MRI ........................................................................................................... 20

2.3.3 Decay of MR Signal.................................................................................. 21

2.3.4 BOLD signal ............................................................................................. 22

2.3.5 Aliasing ..................................................................................................... 23

2.3.6 Smoothing ................................................................................................. 23

2.4 Diffusion tensor imaging ...................................................................................... 27

3 METHODS ............................................................................................................... 30

3.1 Participants ............................................................................................................ 30

3.2 Brain Imaging Acquisition .................................................................................... 30

3.2.2 Pre-processing and correlation analysis .................................................... 31

3.2.3 Subject level statistical analyses ............................................................... 32

3.2.4 Group level statistical analyses ................................................................. 33

3.3 Probabilistic tractography ..................................................................................... 36

3.3.2 Pre-processing ........................................................................................... 36

3.3.3 Subject level statistical analyses ............................................................... 37

3.3.4 Group level statistical analyses ................................................................. 38

4 RESULTS ................................................................................................................. 43

4.1 Resting-state fMRI ................................................................................................ 43

4.1.2 Overview of findings ................................................................................ 43

vi

4.1.3 sgACC functional connectivity: group findings ....................................... 43

4.1.4 Sex differences in sgACC functional connectivity ................................... 44

4.2 Probabilistic tractography ..................................................................................... 62

4.2.2 Overview of findings ................................................................................ 62

4.2.3 sgACC anatomical connectivity in males and females ............................. 62

4.2.4 Sex differences in sgACC anatomical connectivity.................................. 63

5 DISCUSSION ........................................................................................................... 71

5.1 SUMMARY OF MAIN FINDINGS .................................................................... 71

5.2 DELINEATION OF PAIN PATHWAYS WITH MRI-BASED CONNECTIVITY

TECHNIQUES: ADVANTAGES AND LIMITATIONS ................................................ 72

5.2.2 PAG and descending modulation pathway ............................................... 75

5.2.3 Raphe and descending modulation pathway ............................................. 76

5.2.4 MD thalamus and medial system .............................................................. 77

5.2.5 Salience and attention network ................................................................. 79

5.2.6 Hypothalamus and descending modulation pathway ................................ 82

5.3 FUTURE DIRECTIONS ...................................................................................... 86

5.4 CONCLUSION ..................................................................................................... 87

References ......................................................................................................................... 91

Appendices ...................................................................................................................... 108

Appendix I: Tractograms in both Women and Men ............................................... 109

Appendix II: Tractograms in Women....................................................................... 133

Appendix III: Tractograms in Men ............................................................................ 157

vii

List of Tables

Table 3-1. Combined thresholding ................................................................................... 35

Table 3-2. Probabilistic tractography seed and target definition ...................................... 39

Table 4-1. Resting-state group FC to sgACC: summary of main sex differences findings

........................................................................................................................................... 55

Table 4-2. Resting-state group FC to sgACC: main findings of interest .......................... 56

Table 4-3. Resting-state group FC to sgACC seeds: additional findings ......................... 58

Table 4-4. Group SC to sgACC: summary of main findings ............................................ 69

Table 4-5. sgACC anatomical connectivity in females and males ................................... 70

viii

List of Figures

Figure 2-1. A sine wave with 1 second period (Olshausen, 2000) ................................... 26

Figure 2-2. Gaussian kernel distribution and full width at half max (FWHM) ................ 26

Figure 2-3. Measuring diffusion with MRI....................................................................... 29

Figure 3-1. sgACC seeds for rs-fMRI............................................................................... 34

Figure 3-2. Classification probabilistic tractography method ........................................... 40

Figure 3-3. Probabilistic tractography – analysis 1 .......................................................... 41

Figure 3-4. Probabilistic tractography – analysis 2 .......................................................... 42

Figure 4-1. Resting-state group FC to sgACC: summary of main findings in sex

differences. ........................................................................................................................ 46

Figure 4-2. Representative individual subject examples of the time series of resting state

activity within seed-target pairs of regions that show sex differences. ............................ 47

Figure 4-3. Resting-state female & male group FC with sgACC seed A, C, E, H ........... 48

Figure 4-4. Resting-state female group FC with sgACC seed H ...................................... 49

Figure 4-5. Resting-state male group FC with sgACC seed B, C, D, E, F, J ................... 50

Figure 4-6. Stronger resting-state functional connectivity with sgACC seeds A, L, and H

in female group than male group ...................................................................................... 51

Figure 4-7. Stronger resting-state FC with sgACC seeds A, B, C, D, E, F, and J in male

group than female group ................................................................................................... 52

Figure 4-8. Main regions of stronger sgACC FC in females compared to males ............. 53

Figure 4-9. Main regions of stronger sgACC FC in males compared to females ............. 54

Figure 4-10. Summary – sgACC structural connectivity in the group of all subjects ...... 64

Figure 4-11. Stronger anatomical connectivity between left sgACC and left Hy in male

group than female group ................................................................................................... 65

Figure 4-12. Female group anatomical connectivity between sgACC seeds and targets . 66

Figure 4-13. Male group anatomical connectivity between sgACC seeds and targets ..... 67

Figure 4-14. Female and male group anatomical connectivity between sgACC seeds and

targets ................................................................................................................................ 68

Figure 5-1.Structural connections from sgACC (green lines) or from sgACC-associated

regions (purple lines) reviewed from structural studies .................................................... 88

Figure 5-2. Descending modulation axonal projections ................................................... 89

Figure 5-3. Descending pain modulation pathways .......................................................... 90

Figure A.I-1. Structural connectivity between the left subgenual anterior cingulate and

periaqueductal gray in all subjects .................................................................................. 109


left hypothalamus in all subjects ..................................................................................... 110


right hypothalamus in all subjects................................................................................... 111


left amygdala in all subjects ............................................................................................ 112


right amygdala in all subjects ......................................................................................... 113


left anterior insula in all subjects .................................................................................... 114

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right anterior insula in all subjects .................................................................................. 115


left lateral thalamus in all subjects .................................................................................. 116


left medial thalamus in all subjects ................................................................................. 117


right medial thalamus in all subjects ............................................................................... 118


right lateral thalamus in all subjects................................................................................ 119

Figure A.I-12. Structural connectivity between the right subgenual anterior cingulate and

periaqueductal grey in all subjects .................................................................................. 120


left hypothalamus in all subjects ..................................................................................... 121


right hypothalamus in all subjects................................................................................... 122


left amygdala in all subjects ............................................................................................ 123


right amygdala in all subjects ......................................................................................... 124


right anterior insula in all subjects .................................................................................. 125


left anterior insula in all subjects .................................................................................... 126


left lateral thalamus in all subjects .................................................................................. 127


left medial thalamus in all subjects ................................................................................. 128


right medial thalamus in all subjects ............................................................................... 129


right lateral thalamus in all subjects................................................................................ 130

Figure A.I-23. Structural connectivity between the right subgenual anterior cingulate

(seed H) and left anterior midcingulate in all subjects ................................................... 131

Figure A.I-24. Structural connectivity between the right subgenual anterior cingulate

(seed H) and right anterior midcingulate in all subjects ................................................. 132

Figure A.II-1. Structural connectivity between the left subgenual anterior cingulate and

periaqueductal grey in women. ....................................................................................... 133


left hypothalamus in women ........................................................................................... 134


right hypothalamus in women ......................................................................................... 135


left amygdala in women .................................................................................................. 136


right amygdala in women................................................................................................ 137

x


left anterior insula in women .......................................................................................... 138


right anterior insula in women ........................................................................................ 139


left lateral thalamus in women ........................................................................................ 140


left medial thalamus in women ....................................................................................... 141


right medial thalamus in women ..................................................................................... 142


right lateral thalamus in women ...................................................................................... 143

Figure A.II-12. Structural connectivity between the right subgenual anterior cingulate and

periaqueductal grey in women ........................................................................................ 144


left hypothalamus in women ........................................................................................... 145


right hypothalamus in women ......................................................................................... 146


left amygdala in women .................................................................................................. 147


right amygdala in women................................................................................................ 148


right anterior insula in women ........................................................................................ 149


left anterior insula in women .......................................................................................... 150


left lateral thalamus in women ........................................................................................ 151


lateral medial thalamus in women .................................................................................. 152


right medial thalamus in women ..................................................................................... 153


right lateral thalamus in women ...................................................................................... 154

Figure A.II-23. Structural connectivity between the right subgenual anterior cingulate

(seed H) and left anterior midcingulate in women .......................................................... 155

Figure A.II-24. Structural connectivity between the right subgenual anterior cingulate

(seed H) and right anterior midcingulate in women ....................................................... 156

Figure A.III-1. Structural connectivity between the left subgenual anterior cingulate in

men. ................................................................................................................................. 157

Figure A.III-2. Structural connectivity between the left subgenual anterior cingulate and

left hypothalamus in men ................................................................................................ 158


right hypothalamus in men.............................................................................................. 159


left amygdala in men ....................................................................................................... 160

xi


right amygdala in men .................................................................................................... 161


left anterior insula in men ............................................................................................... 162


right anterior insula in men ............................................................................................. 163


left lateral thalamus in men ............................................................................................. 164


left medial thalamus in men ............................................................................................ 165


right medial thalamus in men .......................................................................................... 166


right lateral thalamus in men........................................................................................... 167

Figure A.III-12. Structural connectivity between the right subgenual anterior cingulate

and periaqueductal grey in men ...................................................................................... 168


and left hypothalamus in men ......................................................................................... 169


and right hypothalamus in men ....................................................................................... 170


and left amygdala in men ................................................................................................ 171


and right amygdala in men .............................................................................................. 172


and right anterior insula in men ...................................................................................... 173


and left anterior insula in men ........................................................................................ 174


and left lateral thalamus in men ...................................................................................... 175


and left medial thalamus in men ..................................................................................... 176


and right medial thalamus in men ................................................................................... 177


and right lateral thalamus in men .................................................................................... 178

Figure A.III-23. Structural connectivity between the subgenual anterior cingulate (seed

H) and left anterior midcingulate in men ........................................................................ 179

Figure A.III-24. Structural connectivity between the subgenual anterior cingulate (seed

H) and right anterior midcingulate in men ...................................................................... 180

xii

List of Appendices

Appendices ...................................................................................................................... 108

Appendix I: Tractograms in both Women and Men ............................................... 109

Appendix II: Tractograms in Women....................................................................... 133

Appendix III: Tractograms in Men ............................................................................ 157

xiii

List of Abbreviations

ADC Apparent diffusion coefficient

aINS Anterior insula

aMCC Anterior midcingulate

Amy Amygdala

BA Brodmann area

BOLD Blood oxygenation level dependent

CL Centrolateral nucleus

CNS Central nervous system

CPM Conditioned pain modulation

DBS Deep brain stimulation

DLF Dorsolateral funiculus

DLPFC Dorsolateral prefrontal cortex

DNIC Diffuse noxious inhibitory control

DTI Diffusion tensor imaging

F Female

FA Fractional anisotropy

FC Functional connectivity

fMRI Functional magnetic resonance imaging

FSL Functional magnetic resonance imaging of brain software library

FWHM full width at half max

Glu Glutamate

Hi Hippocampus

Hy Hypothalamus

ICA Independent component analysis

INS Insula

L Left

LC Locus ceruleus nucleus

M Male

MD Medial dorsal thalamic nucleus

MD Mean diffusivity

MDvc Ventrocaudal part of medial dorsal nucleus

NACs Nucleus accumbens

NCF Nucleus cuneiformis

NMDAR N-Methyl-D-aspartic acid receptor

NRM Nucleus raphe magnus

OFC Orbitofrontal cortex

PAG Periaqueductal gray

xiv

PB Parabrachial nucleus

PCA Principal component analysis

PCC Posterior cingulate cortex

Pf Parafascicular nucleus

PFC Prefrontal cortex

pgACC Pregenual ACC

pINS Posterior insula

PVG Paraventricular gray

R Right

RF Reticular formation

ROI Region of interest

rs Resting state

RVM Rostroventral medulla

S1 Primary somatosensory cortex

S2 Secondary somatosensory cortex

s24 Subgenual BA24

s32 Subgenual BA32

SC Structural connectivity

SC DH Spinal cord dorsal horn

sgACC Subgenual anterior cingulate

SHT Spinohypothalamic tract

SPM Statistical parametric mapping

STT Spinothalamic tract

TE Echo time

Th Thalamus

TI Inversion time

TPJ Temporoparietal junction

TR Repetition time

VAN Ventral attention network

VAS Visual analog scale

VL Ventrolateral nucleus

VMpo Posterior part of ventromedial nucleus

VP Ventroposterior nucleus

1

1 INTRODUCTION, AIM, AND HYPOTHESIS

Pain perception is a product of both incoming signals relayed from the body to the brain,

and modulatory pathways that include descending pathways that arise from cortical and

brainstem regions. Two types of modulation are known as pain adaptation (to sustained

stimuli) and pain habituation (to repeated stimuli). Dysfunctional pain habituation has

been associated with chronic pain conditions (Flor, et al., 2004; Peters, et al., 1989;

Proietti Cecchini, et al., 2003; Valeriani, et al., 2003). Thus, uncovering the mechanism

of pain habituation might lead to more targeted treatments for chronic pain. An fMRI

study has shown that pain habituation over the course of eight days is associated with an

increase in pain-evoked activity of the subgenual anterior cingulate cortex (sgACC, area

25) that then resolves after one year (Bingel, et al., 2008; Bingel, et al., 2007). Given the

role of sgACC and of descending modulation network in pain habituation, these findings

indicate a possible connection between the sgACC and the descending pain

antinociceptive system mediating pain habituation. Further, psychophysical studies have

shown that women exhibit greater heat pain adaptation to a prolonged painful stimulus

and greater habituation to repeated painful stimuli than men (Hashmi and Davis, 2009).

The neural mechanism underlying this sex difference in habituation is unknown but given

the findings from the Bingel group, it could involve sgACC connectivity with

antinociceptive pathways.

Thus, the AIM of this thesis was to delineate the connectivity of the sgACC to other

brain regions implicated in pain and its modulation and to determine whether there are

sex differences in these connectivities.

Towards this aim, the HYPOTHESIS tested was:

Women have stronger functional connectivity (FC) and structural connectivity (SC) than

men between the sgACC and 1) brain areas implicated in pain processing including the

thalamus; as well as 2) key nodes of the descending antinociceptive system including the

periaqueductal gray (PAG), the raphe nucleus, insula, amygdala, and hypothalamus.

2

This thesis tested the two hypotheses from the perspective of both functional and

structural brain connectivity by using resting state functional magnetic resonance imaging

(fMRI) and MRI-diffusion tensor imaging based probabilistic tractography methods.

3

2 LITERATURE REVIEW

2.1 Pain

The International Association for the Study of Pain has defined pain as ―an unpleasant

sensory and emotional experience associated with actual or potential tissue damage, or

described in terms of such damage‖ (Merskey and Bogduk, 1994). Pain exists to motivate

an organism to avoid noxious stimuli, thereby protecting it against injury (Woolf, 2004).

Pain can be classified by cause and by duration. The former divides pain into nociceptive,

inflammatory, and neuropathic groups. The latter divides pain into transient, acute, and

chronic types. Transient pain occurs as a result of small or no tissue damage whereby the

pain stops when the stimulus is removed or shortly afterwards (Melzack and Wall, 1996).

However, acute pain typically endures somewhat longer until the healing process takes

place (Fields, et al., 1999). Examples of acute pain include traumatic and post-surgical

pain. Chronic pain is a persistent and debilitating type of pain, which lingers on even

after healing and after the cause of pain appears to be gone (typically greater than 3-6

months).

The pain experience is often described in terms of three dimensions: sensory-

discriminative, cognitive-evaluative, and affective-emotional (Melzack and Casey, 1968).

The sensory dimension describes attributes of pain such as its location, duration, temporal

characteristics, quality, and intensity. While the cognitive-evaluative dimension takes into

consideration of attention, past experience, and cognitive factors that modulates sensory

intensity and quality, the affective-motivational dimension describes the unpleasant

feeling of pain (Melzack and Casey, 1968).

4

2.1.2 Ascending Pain Pathways

There are many types of primary afferent nociceptive neurons that fall into the general

category of either small diameter, myelinated A-delta nociceptors that conduct signals at

approximately 3-30m/s (Melzack and Wall, 1996), and smaller diameter, unmyelinated

C-fibre nociceptors with conduction velocities of approximately 0.5-2.5m/s (Melzack and

Wall, 1996). Both A-delta and C-fibre nociceptors can respond to either a single modality

or to multiple modalities (i.e., polymodal) of stimulus energies (e.g., heat, cold,

mechanical, and chemical). Nociceptors can also be classified according to their

thresholds (e.g., A-mechanoheat type 1 and type 2 nociceptors), encoding of stimulus

intensity, and adaptation properties to sustained stimuli (e.g., slowly adapting vs rapidly

adapting) (Meyer, et al., 2006).

The primary afferents nociceptors are first order neurons and their cell bodies reside in

the dorsal root ganglia. The sensory information received at the periphery is conducted

along the axon into the CNS to terminate in the spinal dorsal horn – laminae I, IV, and V

(Fields, et al., 1999). Axons of the second order spinal cord neurons cross over to the

contralateral side of the spinal cord and ascend to the thalamus where they synapse onto

third order neurons. Some spinal cord neurons also send projects to the brainstem. Thus,

peripheral nociceptive signals can reach the brain via many ascending pathways,

including spinomedullary projections, spinobulbar projections, the spinohypothalamic

tract (SHT) and the spinothalamic tract (STT) (Dostrovsky and Craig, 2006). The third

order neurons then project to various sensory processing regions in the brainstem and

cortex.

The STT relays sensory information related to pain, temperature, and crude touch from

the body to the cortex. The STT consists of both an anterior and lateral system. The

anterior STT is located within the ventral funiculus, and the lateral STT resides within the

lateral funiculus. These two pathways synapse onto thalamic neurons in many medial and

lateral thalamic nuclei including posterior part of ventromedial nucleus (VMpo),

ventroposterior nucleus (VP), ventrolateral nucleus (VL), centrolateral nucleus (CL),

parafascicular nucleus (Pf) , and ventrocaudal part of medial dorsal nucleus (MDvc).

These thalamic neurons then relay the sensory information to various cortical regions for

5

further processing. Neurons in the VP receive input from laminae IV and V and project to

the somatosensory cortex (Rausell and Jones, 1991). There is a dense projection of STT

axons from lamina I to VMpo neurons (Craig, 2003), from where sensory information is

then relayed to the dorsal posterior insula. Moreover, MDvc receives moderate amount of

STT projections from lamina I (Albe-Fessard, et al., 1975) and relays the sensory signals

to neurons in BA24 of cingulate cortex. Other than MDvc, the remaining MD thalamic

nuclei relay the sensory information to prefrontal cortex (PFC) and orbitofrontal cortex

(OFC) (Craig, 2003; Ray and Price, 1993). Furthermore, VMpo sends projections to

dorsal posterior insula, while VPI projects to second somatosensory cortex (S2) and

retroinsula (Dostrovsky and Craig, 2006).

Pain perception is thought to be the final product of activity among multiple cortical

regions including the primary somatosensory cortex (S1), insula, anterior and mid

cingulate cortex (ACC/MCC), and PFC (Treede, et al., 1999). There are several other

cortical regions that have been implicated in pain processes. For instance, the dorsolateral

PFC (DLPFC) can modulate pain processing, which was shown by the successful use of

DLPFC as a target in repetitive transcranial magnetic stimulation (rTMS) for treating

chronic migraine patients (Brighina, et al., 2004). Based on human electrophysiology

studies, single neurons in the MCC can be specifically activated by noxious stimuli

(Hutchison, et al., 1999). Another electrophysiology study on rabbits revealed that

noxious colon distention increased activity in ACC/MCC, suggesting the role of

ACC/MCC in nociception processing (Sikes, et al., 2008).

2.1.3 Descending pain modulation

Pain is thought to be attenuated through endogenous mechanisms in many situations, e.g.,

accidents or battlefield (Beecher, 1946). One such modulation system is through the

descending pain modulatory pathway, with main hubs in the periaqueductal grey (PAG)

and nucleus raphe magnus (NRM) of the rostroventral medulla (RVM) that impact the

activity of neurons in the spinal cord dorsal horn. In this pathway, PAG sends neuronal

projections to RVM (Beitz, 1982b), which sends serotonergic projections to the spinal

6

cord dorsal horn via dorsal part of lateral fasciculus; these serotonergic projections inhibit

the dorsal horn nociceptors (Basbaum and Fields, 1984).

This endogenous descending system was first discovered in 1969, when electrical

stimulation in the dorsolateral central grey (an area now referred to as the PAG) in rats

was shown to produce analgesia without adverse effects on motor function (Reynolds,

1969). In 1977, deep brain stimulation of the PAG/periventricular gray (PVG) was shown

to be effective in producing analgesia in six patients with chronic pain (Hosobuchi, et al.,

1977). The following year, electrophysiology studies revealed that electrical stimulation

and opiate microinjection at PAG could excite cells in the NRM, whose activity was

reduced when opioid antagonist, naloxone, was injected in PAG (Fields and Anderson,

1978), implicating raphe’s role in modulating the nociceptive afferent signals from spinal

dorsal horn. Finally, in the same year, an endogenous pain control circuitry was

proposed. Activated by pain, the circuit consisted of cells in the midbrain (PAG) that

excite serotonergic neurons in the rostral medulla, which then inhibit the nociceptive

afferent input to spinal cord dorsal horn cells (Basbaum and Fields, 1978).

Opioids play an important role in the descending pain modulation, and the main opioids

receptors involved are mu and kappa receptors (Stamford, 1995). How are the

nociceptive afferent signals modulated? They are thought to be regulated in a top-down

fashion by higher brain areas including cingulo-frontal regions, amygdala, and

hypothalamus (Hadjipavlou, et al., 2006). Early probabilistic tractography study has

shown significant SC between PAG and cortical regions (PFC, amygdala, thalamus,

hypothalamus, RVM) (Kong, et al., 2010b), providing the anatomical basis for

modulation of pain.

In another study, the activity in the anterior cingulate cortex (ACC) reduced pain

perception from noxious stimuli in rats with nerve injuries; this pain attenuation required

an intact PAG, suggesting a pain modulation pathway involving ACC and PAG (LaBuda

and Fuchs, 2005). In rats, antinociception, which was facilitated by microinjection of a

mu receptor agonist into the amygdala, was modulated by opioid microinjections into

either PAG or RVM, suggesting that antinociception may involve the neurocircuitry

7

involving these three regions (Helmstetter, et al., 1998). In rhesus monkeys, bilateral

lesions in the amygdala led to lack of antinociception and fear response, implicating the

role of amygdala in antinociception (Manning, et al., 2001). In rat studies, the

hypothalamus was postulated to release arginine vasopressin to NRM, leading to pain

modulation and inhibition (Yang, et al., 2008; Yang, et al., 2009a; Yang, et al., 2009b).

2.1.3.1 Periaqueductal grey

The PAG is a region located medially around the cerebral aqueduct in the tegmentum of

the midbrain, in a region that extends from the opening of the third ventricle to the

pericerulear area (regions surrounding locus ceruleus) in the pons (Basbaum and

Bushnell, 2009; Heinricher and Ingram, 2009). The hypothalamus stretches into the PAG,

which terminates at the anterior border of the fourth ventricle (Nieuwenhuys, et al.,

2008). Animal studies have reported that opiate injection at PAG induces analgesia

(Jacquet and Lajtha, 1976; Lewis and Gebhart, 1977), which can then be blocked by

opiate antagonist injection (Jacquet and Lajtha, 1976; Tsou and Jang, 1964; Vigouret, et

al., 1973).

The PAG receives input from the spinal cord ascending fibres – lamina I (Azkue, et al.,

1998; Hylden, et al., 1986; Menetrey, et al., 1982), nucleus cuneiformis (NCF), nucleus

raphe magnus (Beitz, 1982a), PFC, insula (INS), amygdala, and the hypothalamus (An, et

al., 1998; Bandler and Shipley, 1994; Floyd, et al., 2000). The output targets of PAG

neurons include the RVM, locus coeruleus/subcoeruleus (LC/SC), A5 noradrenergic cell

group, pontine parabrachial nuclei, nucleus tractus solitarius (NTS), hypothalamus, and

amygdala (Basbaum and Bushnell, 2009; Heinricher and Ingram, 2009). In addition,

PAG projects to the medial thalamus and to OFC in rats (Cameron, et al., 1995a;

Coffield, et al., 1992).

The output of the PAG varies across its subregions. The ventrolateral PAG projects to the

ventrolateral RVM and neighbouring reticular formation (Fields, et al., 1999). The

8

dorsolateral PAG projects to the pontine tegmentum and ventrolateral medulla - a region

of autonomic control (Bajic and Proudfit, 1999; Van Bockstaele, et al., 1991).

Deep brain stimulation (DBS), targeting the PAG/PVG region, has produced effective in

pain treatment but its effectiveness varies across studies and patient populations (Bittar,

et al., 2005) (Adams, et al., 1974; Dieckmann and Witzmann, 1982; Gybels and Kupers,

1987; Hosobuchi, 1986; Hosobuchi, et al., 1975; Kumar, et al., 1997; Levy, et al., 1987;

Mazars, 1975; Meyerson, et al., 1993; Nguyen, et al., 1997; Parrent, et al., 1992; Plotkin,

1982; Richardson and Akil, 1977; Siegfried, et al., 1980; Tasker and Vilela Filho, 1995;

Tsubokawa, et al., 1991a; Tsubokawa, et al., 1991b; Turnbull, et al., 1980; Young, et al.,

1985).

2.1.3.2 Rostral ventromedial medulla

The RVM is an important intermediary region between PAG and the spinal cord dorsal

horn. It contains the reticular formation and the nucleus raphe magnus (Fields, et al.,

2006). Animal studies have identified a prominent sensory projection ascending from the

spinal cord to nucleus reticularis gigantocellularis, which projects to the raphe within

RVM (Basbaum, et al., 1978; Gallager and Pert, 1978). The RVM also receives input

from the PAG via projections from dorsal raphe via serotonergic neurons (Beitz, 1982b)

and from the insula (Hermann, et al., 1997). Several studies have demonstrated that the

RVM transmits information from the PAG to the spinal cord. For instance, PAG

stimulation was less effective in facilitating analgesia when RVM was inhibited by

lidocaine (Gebhart, et al., 1983) or amino acid antagonist injection (Aimone and Gebhart,

1986; Fields, et al., 1991).

Via the dorsolateral funiculus (Abols and Basbaum, 1981), the RVM projects to neurons

in the dorsal horn of the spinal cord including laminae I, II, and V (Basbaum and Fields,

1978), which receive nociceptive afferent signals. Signals along this pathway can be

attenuated by interneurons in laminae I and II, which contain inhibitory neurotransmitters

including enkephalin and GABA (Todd, et al., 1992).

9

The RVM consists of neurons that can both inhibit and facilitate nociception. These

actions are due to on cells and off cells in the RVM. While the former fire when RVM is

facilitating pain, the latter fire when RVM is inhibiting pain, leading to analgesia

(Heinricher, et al., 1989; Heinricher and Ingram, 2009).

2.1.4 Adaptation and habituation

Pain adaptation has been used to describe the decrease of pain during a single

administration of painful stimulus (Greene and Hardy, 1962). A related phenomenon is

called pain habituation, which occurs when there is a decrease of pain when a stimulus is

repeated (Bingel, et al., 2007; Glaser and Whittow, 1953; Hashmi and Davis, 2009). A

term related to the perceptual phenomena of habituation is ―fatigue‖, which has been used

to describe a neurophysiological phenomena whereby A-delta or C nociceptive afferent

activity attenuates over the administration of multiple, repeated noxious stimuli (Peng, et

al., 2003). Nociceptors can recover from fatigue within minutes. For instance, clinical

studies have shown that nociceptive-C fibres require about 4-10 min to recover after 49 C

thermal stimulation (LaMotte and Campbell, 1978). On the contrary, pain habituation

may last for days.

Habituation to thermal stimuli can occur in response to stimuli delivered in a relatively

short time frame or long time frames. For instance, habituation to repeated heat pain

stimuli over many minutes was observed in a psychophysics study of 32 subjects

(Hashmi and Davis, 2009). On the other hand, habituation to heat pain was also observed

when delivered in test sessions repeated over a period of 22 days in an fMRI study that

found increased sgACC activity in parallel to the habituation (Bingel, et al., 2007). This

group also reported that subjects that showed habituation had cortical thickening of the

MCC and S1 cortex (Teutsch, et al., 2008).

Pain habituation is associated with the antinociceptive system in the nervous system. The

activation of sgACC during habituation suggests its importance in mediating habituation.

In fact, sgACC is associated with pain modulation by hypnosis- and placebo-induced

analgesia (Kupers, et al., 2005; Petrovic, et al., 2002). Such endogenous antinociception

10

is associated with endogenous opioid systems (Petrovic, et al., 2002). Understanding the

mechanism of pain habituation is important, because dysfunctional habituation to pain

has been proposed as a potential mechanism that may lead to chronic pain (Bingel, et al.,

2007).

2.1.5 Diffuse noxious inhibitory controls

The term ―diffuse noxious inhibitory controls‖ (DNIC) refers to a phenomenon whereby

a noxious-evoked response is inhibited by another noxious stimulus applied to remote,

widespread areas of the body. This effect was initially described in a 1979

electrophysiology study in which the response of dorsal horn neurons to noxious stimuli

was inhibited by heterotopic noxious stimuli such as noxious pinch, transcutaneous

electrical nerve stimulation (TENS), and bradykinin (Le Bars, et al., 1979a). Thereafter,

Le Bars’ group coined the term, ―diffuse noxious inhibitory controls‖, because the stimuli

that produced this effect a) could be applied to sites anywhere in the body outside of the

test stimulus receptive field, and b) needed to be in the noxious range. They also found

that the duration of the conditioning stimulus determined the length of the DNIC effect

(Le Bars, et al., 1979a), and that DNIC effects were only found for neurons that received

inputs from both A-fibres and C-fibres (Le Bars and Willer, 1988). In human studies,

the DNIC effect can last about 5 minutes (Campbell, et al., 2008; Kakigi, 1994; Kosek

and Ordeberg, 2000) although other studies found slightly longer DNIC effects lasting 5-

8 minutes (France and Suchowiecki, 1999; Serrao, et al., 2004). Recently, a newer term –

―conditioned pain modulation (CPM)‖ - was introduced for studies of DNIC-like effects

in humans, replacing ―DNIC‖, which is reserved for usage in animal studies (Yarnitsky,

2010; Yarnitsky, et al., 2010).

The DNIC effect related to an earlier clinical phenomenon, in which noxious stimuli,

administered via TENS, led to pain relief in chronic pain patients (Melzack, 1975). The

cause of this pain relief, as reflected by results of McGill Pain Questionnaire, was at the

time referred to as ―counter-irritation‖.

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Over the last 50 years, studies have shown that DNIC requires supraspinal brain regions.

For instance, in rat studies, the DNIC effect was eliminated by spinal cord transection at

the cervical level (Cadden, et al., 1983; Le Bars, et al., 1979b). Additional studies in cats

showed that noxious electrical stimulation inhibited neuronal activity in the dorsal horn

due to a noxious heat stimulus, illustrating the DNIC effect. However, this effect was

gone after lidocaine injection in the raphe nucleus, implying the necessity of raphe

nucleus activity in producing the DNIC effect (Morton, et al., 1987). In the 1990s, lesion

studies in rats revealed that the PAG, cuneiformis nucleus, parabrachial nucleus

(Bouhassira, et al., 1990), rostral ventromedial medullar (Bouhassira, et al., 1993), and

the subnucleus reticularis dorsalis (SDR) (Bouhassira, et al., 1992) are required for

DNIC. Involvement of a supraspinal mechanism was further supported by clinical

findings in which tetraplegic patients with severed spinal cords lacked DNIC-like effects

(Roby-Brami, et al., 1987).

2.1.6 Sex differences

Many chronic pain conditions show sex differences in prevalence, often being

predominant in women (Berkley, 1997). In some cases, this has been presumed to be due

to differences in pain sensitivity (Mogil, 2012; Racine, et al., 2012a; Racine, et al.,

2012b). For example, many studies have found that women have lower pain thresholds,

especially heat-evoked pain (Fillingim, et al., 2009). Also, among 23 studies that

involved experimental heat pain, 81% (12/17) reported lower pain threshold in women,

and 94% (15/16) of the studies showed less pain tolerance in women (Fillingim, et al.,

2009). However, using sustained, repeated suprathreshold stimuli, a different picture of

sex differences emerged in that women showed greater within-stimulus adaptation and

habituation to repeated painful stimuli (Hashmi and Davis, 2009; Hashmi and Davis,

2010). Thus, this issue of sex differences in pain sensitivity is complex and depends upon

the type of measure used to assess pain sensitivity.

In a 2010 review, among 13 studies that examined sex differences in DNIC, 50% showed

significant sex differences, with the majority demonstrating greater DNIC effects in men

12

than women (Serrao, et al., 2004). For instance, in 2003, a study used contact heat pain as

the test stimulus and muscle pain as the conditioning stimulus to induce DNIC in healthy

controls and female fibromyalgia patients. While healthy male controls showed

significant DNIC effect, this effect was absent in both female controls and patients

(Weissman-Fogel, et al., 2008). Another study in 2004 utilized the nociceptive flexion

reflex – a nociception correlate - as the test stimulus and cold pressor test as the

conditioning stimulus to elicit DNIC in healthy men and women. Specifically, they found

stronger DNIC in men than women (van Wijk and Veldhuijzen, 2010). Further, in 2008, a

study employed contact heat pain as the test stimulus and muscle pain as the conditioning

stimulus on healthy men and women to induce DNIC. They observed greater DNIC in

men than women (Staud, et al., 2003).

2.1.6.1 Sex difference in pain processing

Sex differences may play a role in the variation of cortical response to pain (Apkarian, et

al., 2005; Fillingim, et al., 2009), which has been supported by electrophysiology, PET,

and fMRI studies. For example, mechanical C-fibre nociceptive thresholds in rats were

found to be higher in females than males. However in humans, many studies have

reported that women have lower pain threshold (Racine, et al., 2012a).A number of PET

studies have shown sex differences in cortical activation in subjects receiving noxious

stimuli. For instance, in a PET study found that 50°C contact heat stimuli evoked greater

pain , and contralateral thalamus and anterior insula activation to a greater extent in

women than men (Paulson, et al., 1998). In another PET study, laser thermal stimuli were

administered with equal subjective pain intensity levels to the back of their right hands.

Compared to men, women required less laser energy and had greater activation of the

ACC while men had greater activation of the contralateral prefrontal cortex, S1, S2, and

insula (Derbyshire, et al., 2002). In another PET study involving 10 women and 10 men

responding to laser heat stimuli, men showed more activations in areas of the parietal

cortex, S2, prefrontal cortex and insula, whereas women had more activation in the

perigenual cortex – BA24 (Derbyshire, et al., 2002).

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A number of fMRI studies have also shown sex differences in cortical activation in

subjects receiving noxious stimuli. For example, in an fMRI study involving 34 women

and 26 men, images were acquired during low (5/20 sensory scale) and high pain (15/20)

invoked by thermal stimulation to the right arm; results. The results revealed that men

had greater PAG-FC with left amygdala and left thalamus, left precentral gyrus, right

cuneus, right putamen, right inferior supplemental motor area (SMA), and right caudate

compared to women. On the contrary, women had greater PAG functional connectivity

(FC) with right superior SMA than men (Linnman, et al., 2012). In another fMRI study

examining intrinsic FC , women were found to have greater PAG-FC with MCC, and

men demonstrated greater PAG-FC with left medial orbital prefrontal cortex and uncus;

right insula and operculum; and PFC (Kong, et al., 2010b). In another fMRI study,

temperature levels corresponding to low (5/20 sensory scale) and high (15/20 sensory

scale) heat pain intensities were determined by individual subjects. During low pain

trials, men had stronger BOLD signals in left insula/operculum than women. During high

pain trials, men had stronger BOLD signals in the insula/operculum, S2, thalamus, ACC,

left brainstem, MPFC, and right DLPFC. Women did not show stronger BOLD signals in

any trials than men, and both sexes demonstrated similar cortical deactivations (Kong, et

al., 2010a). In another fMRI study involving 18 women and 18 men, the subjects received

electrical stimulation in their left index finger tips at four different intensities: below

perceivable threshold; perceivable and innocuous; mildly painful; and moderately painful

(Straube, et al., 2009). In the last intensity level, women demonstrated greater activation

in the pregenual MPFC than men. Compared to men, women showed significantly lower

threshold for noxious stimulations in terms of electrical current level (Straube, et al.,

2009). Furthermore an fMRI study involving 17 women and 11 men who received

noxious heat stimuli in the dorsum of their left foot, found that men had greater BOLD

signal amplitude than women in the S1 and DLPFC (Moulton, et al., 2006). These studies

suggest a sex difference in the cortical circuitry for processing incoming nociceptive

information.

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2.1.6.2 Sex difference in somatosensation and pain

2.1.6.2.1 Sensory discriminations

There have been many studies that reported women’s superior ability than men to

discriminate thermal sensory information. In a study involving 42 women and 29 men,

two thermostimulators were set against the dorsum of the left feet of the subjects, who

were asked to determine whether one thermostimulator is warmer or colder than the

other. The study found that women had lower thermal discrimination thresholds in terms

of temperature difference than men (Doeland, et al., 1989), indicating women’s greater

ability to discriminate temperature differences. Another study involving 20 women and

20 men who rated their pain level using a visual analog scale (VAS), the subjects

received two sets of noxious heat stimuli: 45-49°C and 46-50°C. The results revealed that

women were significantly better at discriminating between the sets of noxious stimuli

between sessions than men (Feine, et al., 1991).

There have also been several studies demonstrating women’ superior ability to

discriminate mechanical sensory information than men. For instance, in a study involving

20 women and 28 men, brush stimulation was applied to subjects’ skin covering their

inferior mandible. Compared to men, women had greater peak sensitivity, ability to

discern the direction of motion, and motion velocity of the brush stimulation (Essick, et

al., 1988).

2.1.6.2.2 Hormonal effects

Sex differences also exist in the central nervous system and may be influenced by sex

hormones such as testosterone and estrogen. An immunocytochemistry study in rats

showed that estrogen could significantly reduce the endorphine – an endogenous opioid

peptide - levels in the hypothalamus, anterior pituitary, and pituitary neurointermediate

lobe (Forman and Estilow, 1986). Another immunocytochemistry study in rats showed

that the intermediate region of the locus ceruleus nucleus (LC) – a region that plays a role

15

in pain modulation and arousal- is larger in females than males (Luque, et al., 1992).

Such volume difference in LC disappears when female rats were treated with testosterone

(Guillamon, et al., 1988). In rat studies using opioid stressor (intermittent cold-water

swims) and nonopioid stressor (continuous cold-water swims), the induced analgesia

were decreased by gonadectomy, but the analgesia returns when testosterone was injected

into the animals (Bodnar, et al., 1988).

A number of studies have also been done to show the effect of female hormones in pain

modulation. In rats, administration of progesterone increased the luteinizing hormone

level, which hindered morphine antinociception (Berglund, et al., 1988). Another rat

study showed that the luteinizing hormone-release hormone could avert morphine

antinociception in females (Ratka and Simpkins, 1990). It has been hypothesized that

sensory thresholds decrease in menses, which is supported by the finding that pain

threshold is lowest in the luteal phase and highest in the follicular phase (Aloisi, 2000).

These findings may provide insight into the finding that migraine is associated with high

progesterone and estrogen levels (Epstein, et al., 1975) and lessens with menopause

(Aloisi, 2000). In a rat study, the animals were systematically-injected with estrogen for

ten days, resulting the enlargement of the receptive field of the rats’ mechanoreceptive

trigeminal neurons (Bereiter, et al., 1980). This implies that females may have larger

mechanosensory receptive field than males due to the presence of estrogen.

2.1.6.2.3 Opiate action differences

Sex differences also exist in opiate responses (Apkarian, et al., 2005). Animal studies

have shown that, in male rats, morphine lead to greater analgesic effects than female rats

(Baamonde, et al., 1989; Badillo-Martinez, et al., 1984; Kavaliers and Innes, 1987).

Furthermore, female rats have higher opiate–receptor binding in the hypothalamic

preoptic area than male rats (Hammer, 1984).

2.1.6.2.4 Key Neurotransmitter effects

16

Compared to men, women have greater density of mu opioid receptors in the brain. For

instance, in a PET study involving 18 women and 12 men, women demonstrated greater

mu opioid binding than men in ACC, PFC, parietal cortex, temporal cortex, amygdala,

thalamus, caudate, pons, and cerebellum (Zubieta, et al., 1999). In the same study, post-

menopausal women demonstrated lower mu receptor densities in regions including

amygdala and thalamus than men. In a clinical study of 27 women and 57 men, the

former sex group was found to be more sensitive to morphine than the latter sex group

(Zacny, 2001).

In the pain pathway, primary afferent neurons can potentiate and increase the excitability

of second order neurons when the latter are depolarized and their N-Methyl-D-aspartic

acid receptor (NMDAR) receives glutamate (Glu) - a neurotransmitter from the primary

neuron. Depolarization and Glu binding open and activate the NMDAR, leading to a

cellular cascade that increases the secondary neuron excitability, sensitizing it. Rat

studies have shown that NMDAR activation in females generate 2.8 times larger currents

than in males, making central sensitization easier in females (McRoberts, et al., 2007).

Serotonin plays an important role in descending pain inhibition in the central nervous

system (CNS). It is pronociceptive and mediates inflammation. Rat studies indicate that

reduction of serotonin deprivation in the forebrain due to midbrain lesions could decrease

morphine-induced analgesia (Samanin, et al., 1970). Serotonin levels are inversely related

to estrogen levels (Marcus, 1995), which may suggest that females may have reduced

morphine-induced analgesia because of their higher estrogen and lower serotonin levels

than males .

2.2 Subgenual anterior cingulate cortex

2.2.2 Anatomy and connectivity

The anterior portion of the corpus callosum curves inferiorly and posteriorly, forming a

knee (genu) shape. The part of the ACC that surrounds the genu is known as the

17

perigenual ACC, and is divided into two subregions: pregenual ACC (pgACC) and

subgenual ACC (sgACC). While pgACC is the portion of ACC anterior to the genu,

sgACC is located inferior to the genu. The sgACC consists of BA25 as well as the

subgenual parts of BA32 and of BA24 (Johansen-Berg, et al., 2008; Vogt, 2009).

A number of anatomical tracer studies in monkeys have shown sgACC SC with various

cortical (e.g., BA9) and subcortical (PAG, hypothalamus, raphe nucleus, amygdala,

striatum, and putamen) regions. For instance, retrograde and anterograde tracing

determined that the sgACC was reciprocally connected to the orbitofrontal cortex

(Carmichael and Price, 1996). Another study revealed inputs to area BA25 from regions

in the frontal lobe (BA46, 9, OFC (BA11, 14)) and other regions of the cingulate cortex

(BA24b, 24c, 23b), as well as from the amygdala lateral (LB) and accessory basal (AB)

nuclei (Vogt and Pandya, 1987). Also, BA24 received input from other cingulate regions

including B23 and BA25), frontal lobe (BA9-13, 46), LB, AB, insula, and posterior

hippocampal cortex (Vogt and Pandya, 1987). A tracer study also found BA25 efferents

terminating in the hypothalamus, whereas another set of projections terminated in the

brainstem, including the parabrachial nucleus, raphe nuclei, and PAG (Barbas, et al.,

2003). Moreover, BA25 were found to project to the PAG, lateral parabrachial nucleus,

hypothalamus, and stria terminalis (Freedman, et al., 2000). In another study on macaque

monkeys, BA25 and 32 were reported to project densely to hypothalamus (Ongur, et al.,

1998). A retrograde tracer study on rhesus monkeys, BA24a, 24b, 25 were found to

project to striatum, nucleus accumbens (Kunishio and Haber, 1994). In further study on

macaque monkeys, BA25 was found to project to caudate, accumbens nucleus, and

ventromedial putamen; BA24 was found to project to striatum (Ferry, et al., 2000).

Another tracer study on macaque monkeys showed that BA25 projects to the medial

ventral striatum (Haber, et al., 1995). There is also evidence that the MPFC network

including BA25 and 32 projected to and terminated in the dorsal column of PAG whereas

axons from BA24b and 9 terminated in the lateral column of PAG (An, et al., 1998).

In humans, MRI studies have also revealed sgACC connectivity to various brain regions.

In a DTI study involving 11 male and 6 female healthy subjects, probabilistic

tractography showed a significant number of streamline samples reaching from the

18

sgACC to amygdala, nucleus, accumbens, hypothalamus, and OFC (Johansen-Berg, et

al., 2008). Another DTI study involving 13 healthy subjects revealed ipsilateral sgACC-

connection to medial frontal cortex, ACC, PCC, medial temporal lobe, MD thalamus,

hypothalamus, nucleus accumbens, and dorsal brain stem (Gutman, et al., 2009). In a

resting-state fMRI study involving 15 male and 9 female healthy subjects, the sgACC

was found to be functionally connected to the striatum, OBF, PCC (Margulies, et al.,

2007).

2.2.3 sgACC function

The sgACC has been associated with pain processing and modulation. For example, in a

PET study involving healthy male subjects, sgACC was shown to increase mu opioid

binding during pain evoked by hypertonic saline infusion into jaw muscles and during

placebo analgesia (Zubieta, et al., 2005). Further, in an fMRI study of four women and 15

men, who received laser stimuli on the dorsum of their hands, sgACC was activated

during analgesia induced by placebo pain-relieving cream (Bingel, et al., 2006). In

another fMRI study sgACC was activated when the subjects reported less noxious heat-

evoked pain as a result of distracting task (Bantick, et al., 2002). Thus, the sgACC may

be a cortical node regulating pain habituation. In support of this, is an fMRI study in 20

healthy men that found sgACC increased activity when there was pain habituation

(Bingel, et al., 2007) that persisted for up to 1 year (Bingel, et al., 2008).

The sgACC has also been implicated in sad emotions. For instance, tasks of emotional or

affective content activate sgACC (Allman, et al., 2001) and especially the BA32, which

plays an important role in emotional events (Lane, 1997). sgACC activity is associated

with sadness. Recently, neuronal recordings from the human sgACC have identified cells

responsive to emotional stimuli in emotional categories (e.g., disturbing, sad, happy,

exhilarating) (Laxton, et al., 2013). Furthermore, PET data also indicated that the sgACC

responds to various emotions expressed by sad and happy faces and by recalling

memories associated with these states (George, et al., 1995). In another PET study,

sadness, invoked by having healthy subjects recall sad experiences, increased blood flow

19

to sgACC and blood flow decreased with improvement of clinical depression (Mayberg,

et al., 1999). In a similar PET study involving eight healthy women, whose sadness state

were induced by rehearsing their sad autobiographic scripts, contrasted with neutral

emotional state in subjects, the sad emotional state was associated with activation of the

sgACC and deactivation of right PFC (BA9) (Liotti, et al., 2000). In another clinical PET

study involving 13 unipolar depressed patients, decrease of blood in sgACC was

associated with depression improvement due to paroxetine treatment (Goldapple, et al.,

2004).

It has been proposed that negative memories are stored in sgACC (Vogt, 2009), assigning

it an important role in mediating depression symptoms. For instance, mood disorder

patients and sad healthy subjects have hyperperfusion in sgACC and anterior insula

(Liotti, et al., 2002). Deep brain stimulation (DBS) in the sgACC effectively treats

treatment-resistant depression by reducing sgACC activity (Johansen-Berg, et al., 2008).

In 2008, it was shown that DBS of the sgACC was beneficial in six treatment-resistant

depression patients. As a result, the clinical trial has added 14 more depression patient,

who will receive similar DBS therapy for 12 months (Lozano, et al., 2008). DBS

targeting sgACC has been evaluated, and the results show no adverse effect on cognitive

function in the treated patients, supporting its cognitive safety (McNeely, et al., 2008).

sgACC deactivation has been implicated with anticipation of noxious stimuli. For

instance, in an fMRI study involving 26 subjects who received painful subcutaneous

ascorbic acid injection, sgACC was deactivated when the subjects were anticipating the

noxious stimuli (Porro, et al., 2002). In a PET study involving 16 subjects who received

noxious electrical stimuli in their index and middle fingers, pain anticipation lead to the

reduction of blood flow to the sgACC (Simpson, et al., 2001).

2.3 Resting state BOLD-fMRI

Functional connectivity (FC) refers to the correlation of neuronal activity with respect to

time in structurally distinct brain areas (Aertsen, et al., 1989; Friston, et al., 1993). The

20

neuronal activity is measured in terms of BOLD signal and oscillates slowly at 0.01-0.1

Hz. FC measures the synchrony of blood flow fluctuations, which indirectly reflect

neuronal activity, in different cortical regions. A high synchrony among cortical regions

presumably suggests that the regions work together. Resting state fMRI (rs-fMRI) is a

tool that can be used to examine spontaneous FC in subjects during rest, during which the

subjects are told to think of nothing specifically and to just relax. One way to use rs-fMRI

is the seed method, which depends on a pre-determined model. This method exploits the

FC between a seed, a brain region of interest (ROI), and the rest of the brain. Such

analysis usually concludes with a FC map - a statistical map, which shows the strength of

FC at different brain regions (van den Heuvel and Hulshoff Pol, 2010). FC maps for

individual subjects can be aggregated to form group level or second level FC maps,

which can be used to compare for group differences. Another way to use rs-fMRI is the

model-free method, in which a pre-defined seed is unnecessary. This method explores the

FC patterns across the brain in a data-driven fashion. Under the umbrella of the model-

free methods, a number of techniques can be used, including the independent component

analysis (ICA), principal component analysis (PCA), normalized cut clustering,

Laplacian method, and hierarchical method.

2.3.2 MRI

The nucleus of an element can spin with the spin axis passing through the centre of the

nucleus. Such spin is called the nuclear spin and occurs in all elements in the periodic

table excluding argon and cerium (Brown, 2003). The nuclei consist of positive protons

and neutral neutrons, rendering it a net positive charge. The nuclear spin of a nucleus

generates a nuclear magnetic (dipole) moment at its two opposite ends. As long as an

element has an odd atomic number and weight, its nuclear magnetic dipole will be non-

zero. Elements with non-zero dipoles can be influenced by external magnetic field and

detected in magnetic resonance imaging (MRI) (Brown, 2003). During MRI imaging, a

strong magnetic field (B0: 1.5 ~ 4 T) is applied through a subject; it can force the

precession axes of hydrogen atoms to align parallel (low energy, predominating state) or

21

anti-parallel (high energy state) with the field. The human body mainly consists of fat and

water, which contains hydrogen ions or protons.

At certain a resonant radio frequency or the Larmor frequency, a radio pulse (B1) can

provide just the right amount of energy to flip the nuclear spin of protons from a low

energy state to high energy state, leading to a phenomenon, called the ―nuclear magnetic

resonance‖. This process is called ―excitation‖ or ―transmission‖ (Huettel, et al., 2004).

After the excitation, the proton magnetic moments in the transverse plane will start to go

out of sync (dephase) because protons in different environment spin at slightly different

rates, reducing the magnetization in the transverse plane. To rephrase these magnetic

moments, another radio frequency pulse is applied to flip the net magnetization by 180°.

Finally, as the protons returns to their original spin orientation, they release photons,

which is the MR signal detected by the MRI machine in the process of reception or

detection. As such, the machine can collect MR signals from all the protons in a subject’s

body. The MR signals can then be analyzed to generate a 3D image of the subject.

In addition to the magnetic that provides a continuous B0, there are also gradient magnets

in the x, y, and z plane. These gradient coils generate magnetic field gradients, which

results in a gradient of frequency in the photons released by the subject. This frequency

gradient can then be used to locate of the x, y, and z coordinates of the photon signals

from the subject. During an MRI acquisition, the gradient coils continuously vary the

orientation of their magnetic field gradient, allowing the image acquisition in different

directions. As electrical current flows through the coil, the coil generates a magnetic

field, which creates a magnetic forces that the coil windings and mountings. These

motions create the banging sound during MRI scans.

2.3.3 Decay of MR Signal

After the initial radio frequency excitation, an MR signal – generated by the magnetized

sample - is created and decays over time. The decay process consists of two phenomena:

longitudinal relaxation and transverse relaxation. Longitudinal relaxation (a.k.a. spin-

lattice relaxation or T1 recovery) describes the process of the hydrogen spin from anti-

parallel to parallel state. It is the period, which takes the net magnetization to go from the

22

transverse plane to the longitudinal axis. On the other hand, transverse relaxation consists

of T2 and T2*decay. T2 describes how fast the transverse magnetization decays or

dephases as a result of phase difference due to spin-spin interactions or interactions from

neighbour protons. Although similar to T2, T2* additionally considers the external

magnetic field inhomogeneities and is always faster than T2. T1-weighted images are

depicted indirectly using inversed T1 values; in the MRI scans, short T1 appear bright

while long T1 appears dark. Analogous logic applies to T2* weighted images, which are

used in BOLD-fMRI experiments (Huettel, et al., 2004).

While repetition time is the time interval between excitation pulses, echo time is the

period from the start of excitation pulse to the maximum of the signal (Hornak, 2011).

According to Equation 2-1, TE and TR can be used to control the contrast between

different tissue types, thereby manipulating tissue contrast in MRI scans. Further, the

inversion time (TI) describes the period between the excitation pulse and the rephasing

pulse.

2.3.4 BOLD signal

The BOLD signal is attributed to the magnetic properties of hemoglobin (Hb) and the

relationship between blood flow and neuronal activity. Oxygenated Hb has negligible

effect on the magnetic field of an MRI scanner. On the contrary, deoxygenated Hb

disrupts the magnetic field in proportion to the amount of oxygen dissociated from Hb.

When there is an increase in neuronal activity, the associated increase in oxygenated

blood supply surpasses the oxygen demand in the active region. Thus, there is an increase

in the ratio of oxygenated Hb to deoxygenated Hb, which increases BOLD signal.

Imaging signals are extracted from parcelations of brain volume or voxels; each in fMRI

is about 3.125mm x 3.125mm x 4mm large. Each voxel reflects the signal and activity of

approximately 105 neurons (de Courten-Myers, 1999).

23

2.3.5 Aliasing

According to Nyquist sample theorem (Equation 2-2), the sampling frequency (fs) should

be at least twice the highest frequency contained in a signal (fc) to resolve the signal.

Otherwise, there will be aliasing, which means ―different or another‖ in Latin. Aliasing

occurs when a signal is too undersampled to detect its changes (Olshausen, 2000).

Temporal aliasing occurs when a signal is sampled too slowly to resolve its changes. For

instance, if a 1 Hz signal wave (Figure 2-1) is only observed or sampled at 1 Hz from the

start, only the peaks of the wave will be detected, leading to the incorrect conclusion that

the signal is near the peak all the time. On the other hand, if the signal is observed at a

frequency of 2 Hz, it becomes plausible to see both the peak and the trough of the wave,

leading to the correct conclusion that the wave is varying periodically from peak to

trough. Indeed, to correctly observe changes of a signal, it must be sampled at minimum

twice the signal frequency. In fMRI, studies are mostly focused on neuronal activities

instead of cardiac or respiratory activity, so signals arisen from the latter activities should

be account as a regressor of non-interest. However, since a human’s heart rate is about 1

Hz, the sample rate of an MR scanner (TR: 0.5 Hz) is too slow to resolve the heart beat,

which becomes a confounding variable that remains unaccounted for and that is aliased

into the fMRI data. Respiration activity is aliased info fMRI data for similar reasons.

2.3.6 Smoothing

In order to bring MRI images from individual subjects into a common space for analysis,

these images are smoothed or blurred. This blends the signals of every voxel, especially

near its edge, with the counterpart of its neighbours. Smoothing increases the overlap and

smoothes the spatial transitions between voxels. Smoothing is done by convolving an

imaging with a Gaussian function before examined (right side of Figure 2-2). Spatial

smoothing involves convolution, in which the original signal in voxels are varied slightly

by the work of a Gaussian kernel.

Equation 2-4 shows a Gaussian kernel in the x-axis (2D), which is proportional to the

probability density function of a normal distribution. The greater the variance of the

24

function, the greater breadth or radius of the spatial smoothing. In 2D spatial smoothing,

Equation 2-3 is used as the Gaussian kernel; in 3D spatial smoothing, Equation 2-4 is

used. Equation 2-5 presents the relationship between the variance of the Gaussian kernel

and the full width at half max (FWHM).

25

Equation 2-1. Contrast of MRI as a function of TE and TR. CAB: contract between tissue

A and B; M0A: magnetization of tissue A; T1A & T2A: T1 and T2 values for tissue A.

(Huettel, et al., 2004)

Equation 2-2. Nyquist sample theorem. fs: sampling frequency; fc: highest frequency

contained in the sample.

Equation 2-3. 2D Gaussian kernel. xi: mean; x: some distance away from xi; σx2: variance

or the spread of the function.

Equation 2-4. 3D Gaussian kernel. xi: mean; x: some distance away from xi; σx2: variance

or the spread of the function. While x represents the Gaussian kernel in the y-z plane, y

and z variables represent analogous variables for kernels in x-z and x-y plane,

respectively.

Equation 2-5. Full width at half max (FWHM) as a function of Gaussian kernel variance

26

Figure 2-1. A sine wave with 1 second period (Olshausen, 2000)

Figure 2-2. Gaussian kernel distribution and full width at half max (FWHM)

27

2.4 Diffusion tensor imaging

Diffusion or Brownian motion is defined as random motion caused by temperature above

absolute zero (Callaghan, 2011). Random diffusion in all directions is known as isotropic

diffusion, but, when there is a barrier or boundary that restricts or channels diffusion,

there is restricted or anisotropic diffusion – a type of diffusion that is different in different

directions. The boundaries of interest could be created by tissue, membranes, or

microstructures. For instance, anisotropic diffusion occurs in white matter, while

isotropic diffusion occurs in grey matter and CSF (Beaulieu, 2011). The direction of

anisotropic diffusion is generally assumed to equate the direction of nerve fibres. In MRI,

the magnitude of water diffusion is represented by the apparent diffusion coefficient

(ADC), and the diffusion within a tissue follows the Gaussian distribution (Ackerman

and Neil, 2011).

In diffusion-weighted imaging, the MRI scanner captures images as it changes the

magnetic field gradient. For each voxel, the MRI signal is represented by Equation 2-6, in

which the b-factor is the combined magnetic field gradient parameter. Diffusion gradients

are applied right before and after a rephrasing radio frequency pulse. The first gradient

dephases the spins of immobile water, while the second gradient rephases them. The time

interval between the gradients is the diffusion time (Δ) (Figure 2-3). Equation 2-7

describes the relationship between b-value, gradient strength, and the diffusion time.

Different from stationary water, mobile and diffusing water is not rephrased by the

second gradient; instead, they dephase and attenuate the magnetic field, which is detected

and represented by the diffusion tensor (Equation 2-6). The diffusion tensor is a 3 by 3

matrix, with its diagonal elements being the diffusion displacement variances

(eigenvalues) or the ADCs and with its off-diagonal elements being proportional the

covariances of the displacements (Basser and Ozarslan, 2011). The variance of the

eigenvalues represents the fractional anisotropy (FA), while the mean of the eigenvalues

represents the mean diffusivity (MD).

For each gradient direction, the MRI signal is attenuated when water diffusion occurs

parallel to the gradient direction. Since the ventricles contain much water and provide an

28

isotropic environment, they are often the region of signal attenuation, making them dark

in the DTI images. DTI presumes that directional water diffusion indicates the existence

of white matter tracts or axon bundles, which restrain water movement. As such, DTI can

be used to map cortical SC in terms of white matter tracts. Each voxel in a DTI image

(about 2.4 mm wide) contains about 104 – 10

8 axons (0.1-10 μm in diameter (Filley,

2011)). Even so, white matter fibres are organized into large bundles of hundreds to

thousands of axons connecting different cortical areas, which make tractography adept in

measure macroscopic connections within the brain.

One way to utilize DTI and examine the SC of brain areas is tractography, which models

and reconstructs white matter tracts based on the diffusion parameters extracted from

DTI. There are two main types of tractography: probabilistic and streamline tractography.

In probabilistic tractography, a probability map is calculated per voxel to approximate the

likelihood of water diffusion in each direction in that voxel. Then a number (5000 by

default) of streamline samples are sent out in many directions from a seed ROI to the rest

of the brain, based on the directional probability distribution of each voxel. In streamline

tractography, only one streamline sample is projected from the seed ROI instead of 5000.

This sample will follow the direction of the primary eigenvector in the diffusion tensor,

producing in a single tract. Users can define the curvature angle threshold for the tract.

Because this thesis examined the existence of SC between certain regions in the brain,

tractography was used in spite of other analysis techniques, e.g., tract-based-spatial

statistics, which are more frequently used to characterize white matter tracts, which have

already been shown to exist.

29

Equation 2-6. Diffusion tensor model

sj: measured signal after applying gradient j; s0: measured signal without diffusion

gradient; D: diffusion tensor; x: vector with direction of gradient j; bj: gradient b-factor

sj = s0 exp( -bj xjT D xj )

Figure 2-3. Measuring diffusion with MRI

Δ: diffusion time; A: diffusion gradient strength; 90: 90° radiofrequency pulse; 180: 180°

radiofrequency pulse

Equation 2-7. b-factor

The b-factor is the combined magnetic field gradient parameter. Δ: diffusion time; A:

diffusion gradient strength

30

3 METHODS

3.1 Participants

A total of 80 healthy subjects were previously recruited for study by Nathalie Erpelding

(Erpelding, et al., 2012) who obtained informed written consent to experimental

procedures, which were approved by the University Health Network Research Ethics

Board. The subject pool consisted of 40 females and 40 males who were all right-handed

and fluent in English. They ranged in age from 19 to 36 years (mean

age ± SD = 24.5 ± 4.9 years). Subjects were screened for for the presence of neurological

and psychiatric conditions and other standard exclusion criteria for MR imaging

(potential pregnancy, claustrophobia, metal fragments, etc.). Specific exclusion criteria

based on self-report were: 1) current or regular pain (other than menstrual cramps) in the

last 6 months (e.g., headache, toothache, etc.), 2) pain lasting more than 3 months in the

last year, 3) any current or previous diagnosis of a psychiatric disorder (e.g., depression,

ADHD, etc.), 4) any chronic illness, 5) claustrophobia, 6) braces or metal in their body,

7) possibility of pregnancy, 8) medication/drug use at the dose, frequency and duration

potentially impacting pain or cognitive function.

3.2 Brain Imaging Acquisition

All imaging data were obtained on a 3T MRI scanner (GE Medical Systems, Milwaukee,

WI, USA) fitted with an 8-channel phased-array head coil). Subjects were instructed to

relax and to lie still for all scans. For each subject, the imaging session consisted of the

following scans:

1) Anatomical scan. High-resolution whole brain structural images were obtained with a

T1-weighted inversion recovery prepped, 3-dimensional fast spoiled gradient echo (IR-

FSGPR) sequence (flip angle = 15°, TE = 3 ms, TR = 7.8 ms, TI = 450 ms, 256x256

31

matrix, 1-mm slice thickness, 25.6 cm field of view – producing 1mm x 1mm x 1mm

voxels).

2) A non-task (resting state) fMRI scan. For this scan, subjects were instructed to lie still,

not think of anything in particular and to keep their eyes closed. This BOLD fMRI scan

was acquired from 40 4mm thick transverse whole-brain slices (interleaved EPI

sequence; T2*-weighted images; TR = 2000 ms; TE = 30 ms; 64x64 matrix; and 20 cm

field of view yielding 3.125mm x 3.125mm x 4mm voxels, 308 s).

3) Diffusion weighted images. Two diffusion weighted scans were obtained using the

following parameters: 96x96 matrix; 2.4mm x 2.4mm x 2.4mm voxels; 64 transverse

slices; 60 isotropic and non-collinear directions; TR = 17 s; TE = 83.3 ms; 23 cm field of

view; b = 1000 s/mm2. In addition, ten non-diffusion weighted images (b = 0 s/mm

2)

were obtained.

3.2.2 Pre-processing and correlation analysis

In the sgACC seed region, there was substantial signal dropout of the BOLD signal

(BOLD signal intensity below 65% of the mean intensity within non-zero intensity

voxels) in 10 males and 14 females and so these subjects were excluded from the rs-fMRI

analysis. Thus, the analysis of resting state fMRI data was done for 30 males and 26

females (18-37 years old, mean SD age 24.6 5.1).

Seed-to-voxel correlational analyses were carried out by the functional connectivity

(CONN) toolbox Ver 13i

(http://web.mit.edu.myaccess.library.utoronto.ca/swg/software.htm) and SPM 8. The pre-

processing pipeline of the functional images consisted of the following steps:

1) Co-registered to structural images

2) Realigned for motion in six axes: 3 translations and 3 rotational axes

3) Spatially normalized to the Montreal neurological Institute (MNI) template

http://www.sciencedirect.com.myaccess.library.utoronto.ca/science?_ob=RedirectURL&_method=externObjLink&_locator=url&_issn=10538119&_origin=article&_zone=art_page&_plusSign=%2B&_targetURL=http%253A%252F%252Fweb.mit.edu%252Fswg%252Fsoftware.htm

32

4) Smoothed with a Gaussian kernel of 6 mm

5) Band-pass filtering from 0.01 Hz - 0.1 Hz

After these pre-processing steps, the CompCor strategy (Behzadi, et al., 2007) was

implemented, which extracted signal noise from WM and CSF by the principal

component analysis (PCA). The analyses did not include global signal regression to avoid

the potential introduction of false anticorrelations to the results. For discussion of this

issue, see (Murphy, et al., 2009). Further, noise from CSF and WM as well as the six

realignment parameters were removed as confounds from the functional data via

regression.

3.2.3 Subject level statistical analyses

A first level analysis was done using the CONN toolbox, which applied SPM functions to

perform spatial statistical analyses in each subject. A general linear model (GLM) was

applied to examine significant BOLD signal correlation with respect to time between

each seed and each voxel. The resulting correlation coefficients were Fisher transformed

to standard scores (Z-scores), which were then input into t-tests.

3.2.3.1 Definition of Seeds

A total of 6 bilateral spherical seeds in the sgACC were defined based on locations

previously reported to be involved in pain habituation (Bingel, et al., 2008; Bingel, et al.,

2007). Spheres have been commonly used as a seed shape in fMRI analyses (Chang and

Glover, 2010; Grova, et al., 2006; Kong, et al., 2010b; Margulies, et al., 2007; Zotev, et

al., 2011). The seeds were drawn as sphere of radii 3-mm centred at [5, 25, -10], [-5, 25, -

10], [5, 34, -9], [-5, 34, -9], [5, 34, -4], [-5, 34, -4], [6, 27, -10], [-6, 27, -10], [6, 30, -9], [-

6, 30, -9], [6, 33, -9], and [-6, 33, -9] (Figure 3-1).

33

3.2.4 Group level statistical analyses

A second level analysis used the CONN toolbox, including t-tests to examine sex

differences. To compensate for multiple comparisons, the second level results were

thresholded at a corrected p < 0.05 based on a Monte Carlo simulation implemented in

AlphaSim (http:// afni.nimh.nih.gov/afni/doc/manual/AlphaSim) by applying a

combination of uncorrected p values and cluster size thresholds (Table 3-1). For instance,

for small subcortical areas, the threshold combination consisted of a small cluster size

and a low uncorrected p value threshold, whereas, when thresholding large cortical

regions, a larger cluster size and higher uncorrected p value were used.

34

Figure 3-1. sgACC seeds for rs-fMRI

Each seed (red) is assigned a letter on the top right corner of the brain. A total of nine

different seed locations, each 3mm in radius, were used for the rs-fMRI analysis. L: left;

R: right. The X, Y, Z coordinates of the seeds are: Seed A: [-5, 25, -10]; B: [5, 25, -10];

C: [-5, 34, -9]; D: [5, 34, -9]; E: [-6, 33, -9]; F: [6, 33, -9]; G: [-5, 34, -4]; H: [5, 34, -4]; I:

[-6, 27, 10]; J: [6, 27, -10]; K [-6, 30, -9]; N: [6, 30, -9]

35

Table 3-1. Combined thresholding

Per-voxel p-value and cluster size were thresholded to achieve a corrected p<0.05 as

validated by a Monte Carlo simulation implemented in AlphaSim (http://

afni.nimh.nih.gov/afni/doc/manual/AlphaSim). The thresholding combination was chosen

to minimize the cluster size and to identify brain regions most precisely.

Cluster size threshold (MNI152 space)

Masked with gray matter No mask (whole brain)

Corrected p value <

Per voxel p value threshold voxel mm

3 voxel mm

3

0.05 0.001 32 256 54 432

0.05 0.0005 24 192 42 336

0.05 0.0001 13 104 26 208

0.05 0.00005 10 80 21 168

0.05 0.00001 5 40 14 112

36

3.3 Probabilistic tractography

3.3.2 Pre-processing

Due to signal dropout (DWI signal intensity below 32% of mean intensity within non-

zero intensity voxels) at the sgACC seed regions, data from one male and two females

were excluded from the DWI analysis. Therefore, the final structural analysis was done

for a total of 39 males and 38 females (18 to 37 years old, mean SD age 24.5 5.0).

Diffusion data were pre-processed by FMRIB’s Diffusion Toolbox (Behrens, et al., 2003;

Smith, et al., 2004) (www.fmrib.ox.ac.uk/fsl). The pre-processing steps were as follows:

1) Format conversion. MRI image files were converted in batch from DICOM to NIFTI

in Linux.

2) Eddy current correction. Changing the magnetic field in a conductor induces a current,

known as the Eddy current, which creates shears and stretches in the diffusion weighted

images. Such distortions varied depending on the different gradient directions. Thus,

Eddy current corrections were done on the NITFTI images.

3) Brain extraction. The brain extraction tool, BET, (Smith, 2002) was used to extract the

brain from the surrounding tissues for the diffusion and structural images.

4) Motion correction and linear registration tool. Following motion correction, FMRIB’s

linear image registration tool. FLIRT, was used to create transformation matrices among

the diffusion, structural, and standard spaces to allow cross-spatial image registration.

5) Identification of crossing WM fibres. FMRIB’s diffusion toolbox (FDT) was used to

identify crossing fibres. The FDT includes a utility – BEDPOSTX (Bayesian estimation

of diffusion parameters obtained using sampling techniques for modeling crossing

fibres). BEDPOSTX carries out Markov Chain Monte Carlo sampling to calculate

diffusion parameters for each voxel in a DTI image file. Then, a multi-fibre diffusion

model (Behrens, et al., 2007) was fitted to the diffusion data to approximate the

probability distributions of diffusion directions for each voxel.

37

3.3.3 Subject level statistical analyses

Probabilistic tractography was conducted by tracing streamline samples from each seed

voxel with the trace paths being constrained by the probability distribution. As such, a

total of 5000 streamline samples were sent out from every seed voxel. The number of

times, for which the streamline samples passed a brain voxel, correlates to the anatomical

connectivity between the voxel and the seed ROI. This allowed the mapping of an

anatomical connectivity distribution from the seed ROI to the remaining brain.

Termination and waypoint tractography was conducted to obtain tractograms and view

SC qualitatively; this type of tractography did not output numbers needed for statistical

analyses. Therefore, classification tractography was used complementarily to obtain the

numerical data for quantitative analyses, e.g., t-tests.

With classification probabilistic tractography, 5000 streamline samples were sent out

from each seed voxel. Data were collected on how many times the streamline samples

(Jones, et al., 2013) reached a specific target from a seed voxel (Figure 3-2). These

numbers were called the voxel-level seed-target anatomical connectivity, SCindiv-vox.

Further, after thresholding the connectivity values at 2, non-zero SCindiv-vox were averaged

for each seed region to yield SCindiv, which are called the individual level seed-target

connectivity.

3.3.3.1 Seeds & targets definition

The first analysis used three seeds - A, N, and H (Figure 3-1) - based on the resting state

fMRI results. The targets were defined in the bilateral TPJ, aMCC, and PAG (Table 3-2

& Figure 3-3).

In a second analysis, larger seed ROIs (Figure 3-4) were manually defined to include the

following regions: [-9, 30, -12] (Bingel, et al., 2008), [-6, 30, -9] (Bingel, et al., 2007), [3,

36, -12] (Bingel, et al., 2007), and brain areas, which were previously defined as the

sgACC (Johansen-Berg, et al., 2008). The following regions were chosen as targets:

38

bilateral aINS, bilateral Th, PAG, bilateral hypothalamus, bilateral amygdala, and NRM

(Table 3-2 & Figure 3-4). Minimum intensity thresholds were applied to shrink targets

that were too large.

3.3.4 Group level statistical analyses

SCindiv were pooled for all individual subjects within a group to make up the group-level

seed-target connectivity, or SCgroup-mean, with standard error, SCgroup-SE. t tests (2 tailed)

were then used to test for sex differences between the male and female groups (ACfemale-

mean and SCmale-mean).

39

Table 3-2. Probabilistic tractography seed and target definition

Targets Bilateral/left/right(B/L/R) Target definition source

Minimum

intensity

threshold

PAG B FSLView & Duvernoy's Atlas 2009 -

NRM B FSLView & Duvernoy's Atlas 2009 -

Amygdala

L

FSLView & Harvard-Oxford Subcortical

Structural Atlas 0.9

R


Structural Atlas 0.9

Hypothalamus

L FSLView & Duvernoy's Atlas 2009 -

R FSLView & Duvernoy's Atlas 2009 -

Thalamus

L (medial)


Structural Atlas & Talairach's Atlas &

Netter's Neurology 2E) 0.9

L (lateral)




R (medial)




R (lateral)




Anterior insula

L

FSLView & Freesurfer

lh.aparc.a2009s.annot -

R

FSLView & Freesurfer

rh.aparc.a2009s.annot -

TPJ

L Secondary seeds (Kucyi, et al., 2012) -

R Secondary seeds (Kucyi, et al., 2012) -

aMCC

L

Freesurfer lh.aparc.a2009s.annot &

FSLView -

R

Freesurfer rh.aparc.a2009s.annot &

FSLView -

40

Figure 3-2. Classification probabilistic tractography method

(A) An example of the probabilistic tractography method is shown for a right sgACC

seed (red) and a right hypothalamus target (blue). (B) In the seed, each voxel sends out

5000 sample projections in random directions. Each time a projection reaches a target

voxel the connectivity value in the seed voxel increases by 1. (C) With this logic, if a

seed voxel’s projections hit the target 3 times out of 5000, it will have a connectivity

value of 3. (D) Similar events occur to other seed voxels. This voxel has 2 (out of 5000)

samples hitting the target, giving it a connectivity value of 2. (E) Analogous processes

apply to the remaining seed voxels, which will each have a connectivity value.

41

Figure 3-3. Probabilistic tractography – analysis 1

sgACC seeds (3mm radii, red) and targets (blue), projected to the MNI152 standard brain

(2mm, T1) image provided in FSL. For seed letter assignment, see Figure 3-1. Slice

numbers are in voxel coordinates. L: left; R: right; sgACC: subgenual anterior cingulate

cortex; aMCC: anterior middle cingulate cortex; TPJ: temporoparietal junction; PAG:

periaqueductal grey.

42

Figure 3-4. Probabilistic tractography – analysis 2

sgACC seeds (red) and targets (blue & yellow) projected to the MNI152 standard brain

(2mm, T1) image provided in FSL. Yellow: lateral thalami. L: left; R: right; sgACC:

subgenual anterior cingulate cortex; NRM: nucleus raphe magnus; Th: thalamus; Amy:

amygdala; Hy: hypothalamus; PAG: periaqueductal gray

43

4 RESULTS

4.1 Resting-state fMRI

4.1.2 Overview of findings

The mean resting state FC of the 6 left and 6 right sgACC seeds was determined for 1) all

subjects, 2) females, 3) males, 4) the contrast of female>male, and 5) the contrast of

male>female. These analyses revealed that the sgACC show FC (as exhibited by

statistically significant correlations in resting state activity) to targets in multiple cortical

and subcortical regions. For some targets, correlations were found across the entire

combined subject pool, while for other targets, the correlations were found only in the

female or male group. Furthermore, there was stronger connectivity of the sgACC with

some targets in females (MD thalamus, raphe, PAG, aMCC) and in other targets in the

male (TPJ, anterior insula) group (see below, Figure 4-1and Table 4-1). It should also be

noted that findings varied somewhat for the different seed locations within the sgACC,

although many of the main findings were found for multiple sgACC seed locations.

Examples of the time-series resting state activity in representative individual subjects are

shown in Figure 4-2. These examples illustrate some of the key sex differences in the

synchronicity of the time course of sgACC brain activity with the activity in target

regions. Details of the findings are presented below for all statistically significant

findings (p<0.05 corrected as validated by a Monte Carlo simulation implemented in

AlphaSim [http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim]). Data from regions of

interest are shown in Table 4-2, and additional findings are listed in Table 4-3.

4.1.3 sgACC functional connectivity: group findings

A group analysis of all subjects (Figure 4-3), revealed that the sgACC had significant FC

with MCC, insula, PCC, and TPJ. There was also significant sgACC FC with several

other areas targets, e.g., regions of the fusiform gyrus, parahippocampal gyrus, middle

44

temporal gyrus (BA21), ventral tegmental area, middle frontal gyrus (BA8), precuneus,

superior medial frontal lobe, and inferior temporal pole. Table 4-2 and Table 4-3 provide

the details of the location each significant seed-target pair.

For some seed regions, there was sgACC connectivity with specific parts within a target

that were detected in only the male group or only the female group. Thus, an analysis of

sgACC FC in the female group alone (Figure 4-4) revealed significant connectivity with

regions within the aMCC, raphe nucleus, regions within the inferior temporal gyrus,

middle temporal pole, parahippocampal gyrus, cerebellum, medial frontal gyrus, and

amygdala.

In the male only group analysis, significant sgACC FC (Figure 4-5, Table 4-2, Table 4-3)

was found for the aMCC; anterior insula; posterior insula; PCC; regions in the inferior,

middle and superior temporal lobe; BA6, 8, 9, 45 and 47; parahippocampal gyrus;

precuneus; fusiform gyrus; and amygdala.

4.1.4 Sex differences in sgACC functional connectivity

Sex differences in the sgACC connectivity were investigated based on the contrasts of

Females> Males and Males>Females (p<0.05 corrected as validated by a Monte Carlo

simulation implemented in AlphaSim

[http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim]: per voxel p ≤ 0.001; cluster size

32 voxels). This analysis indicated greater connectivity in females than males (Figure

4-6) in the aMCC, raphe nucleus, MD thalamus, and PAG. Additionally, there was

greater sgACC connectivity in the females within regions of the cuneus, cerebellum,

fusiform gyrus, parahippocampal gyrus, and pontine nuclei. However, the males

exhibited greater sgACC-FC than females (Figure 4-7) in the anterior insula; TPJ; and

areas of BA45 and BA47 (Figure 4-7).

There are four scenarios in which a group contrast can result in sex differences in sgACC

connectivity. For example: 1) Both females and males could have connectivity but there

is greater connectivity in females; 2) Females may have connectivity but males do not; 3)

http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim

45

Females have no connectivity, but males have a negative connectivity; 4) Females and

males both have negative connectivity but females have less negative connectivity.

Therefore, plots of connectivity strength (Z scores) were constructed to examine how the

above sex difference findings arose. In the main findings (Figure 4-8, Figure 4-9), the sex

difference in aMCC arose from significant positive FC in females and non-significant

positive FC in males. The sex difference in the raphe nucleus arose from significant

positive FC in females and negative FC in males. The sex difference in MD and PAG

arose from non-significant positive FC in females and negative FC in males. The sex

difference in TPJ arose from non-significant positive FC in males and negative FC in

females. The sex difference in anterior insula arose from significant positive FC in males

and negative FC in females.

With respect to the seed locations within sgACC, the anterior sgACC demonstrated

greater FC with aMCC and raphe in women, and with aINS in men than the opposite sex.

On the other hand, the posterior sgACC showed stronger FC with PAG and MD thalamus

in women, and with TPJ in men than the opposite sex. With regard to laterality of the

results, the peak coordinates of aMCC – only reported in women - resided in the left side

of the brain hemisphere for both ipsilateral and contralateral sgACC seed locations.

Posterior insula demonstrated bilateral FC with a number of sgACC seeds in male,

female, and both sex groups while orbitofrontal regions (BA47, BA45, anterior insula)

showed left lateralized FC with a number of sgACC seeds and in male group.

46

Figure 4-1. Resting-state group FC to sgACC: summary of main findings in sex

differences.

In females, sgACC exhibited greater FC to targets (pink): MD, PAG, raphe nucleus, and

aMCC than males. In males, sgACC exhibited greater FC to targets (blue): TPJ and aINS

than females. Left sgACC seeds are shown in purple; right seeds are shown in green. FC:

functional connectivity; sgACC: subgenual anterior cingulate; MD: medial dorsal

thalamic nucleus; PAG: periaqueductal grey; TPJ: temporoparietal junction; aMCC:

anterior midcingulate; aINS: anterior insula. Brain outline image adapted from

(http://www2.le.ac.uk/departments/gradschool/training/events/caferesearch/cafe-brain).

47

Figure 4-2. Representative individual subject examples of the time series of resting

state activity within seed-target pairs of regions that show sex differences.

A: FC between sgACC (seed H) and aMCC is stronger in this female subject (r = 0.56)

than a male subject (r = 0.03). B: FC between sgACC (seed H) and raphe nucleus is

stronger in this female subject (r = 0.82) than the male subject (r = 0.18) shown. C: FC

between sgACC (seed E) and anterior insula is stronger in this male subject (r = 0.40)

than the female subject (r = 0.10). sgACC: subgenual anterior cingulate cortex; aINS:

anterior insula; aMCC: anterior midcingulate cortex; FC: functional connectivity.

Norm

aliz

ed f

MR

I sig

nal

-4

-2

0

2

4

6sgACC(M)

Raphe nucleus(M)

-3

-2

-1

0

1

2

3sgACC (F)

Raphe nucleus (F)

-4-3-2-10123

sgACC(M)

aMCC(M)

-4

-2

0

2

4

6

8

sgACC (F)

aMCC (F)

A

B

C

X Data

-3-2-101234

sgACC(F)

aINS(F)

Time (s)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

-3-2-101234

sgACC

aINS(M)

48

Figure 4-3. Resting-state female & male group FC with sgACC seed A, C, E, H

(p<0.05 corrected as validated by a Monte Carlo simulation implemented in AlphaSim

(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim) Statistical maps are projected to a

T1 brain image provided in SPM and xjview. MCC: midcingulate cortex; pINS: posterior

insula; TPJ: temporoparietal junction; FC: functional connectivity; sgACC: subgenual

anterior cingulate; Sup: superior; R: right; L: left.


49

Figure 4-4. Resting-state female group FC with sgACC seed H



T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate;

aMCC: anterior midcingulate cortex; FC: functional connectivity; R: right; L: left.


50

Figure 4-5. Resting-state male group FC with sgACC seed B, C, D, E, F, J



T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate; Hy:

hypothalamus; aINS: anterior insula; pINS: posterior insula; Hi: hippocampus; Sup:

superior; FC: functional connectivity; R: right; L: left.


51

Figure 4-6. Stronger resting-state functional connectivity with sgACC seeds A, N,

and H in female group than male group



T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate; PAG:

periaqueductal grey; MD: medial dorsal nucleus; aMCC: anterior cingulate cortex; R:

right; L: left.


52

Figure 4-7. Stronger resting-state FC with sgACC seeds A, B, C, D, E, F, and J in

male group than female group



T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate;

aINS: anterior insula; TPJ: temporoparietal junction; FC: functional connectivity; R:

right; L: left.


53

Figure 4-8. Main regions of stronger sgACC FC in females compared to males

A: sgACC (seed H) and aMCC; B: sgACC (seed H) and raphe nucleus; and C: sgACC

(seed A) and MD; D: sgACC (seed N) and PAG (*P < 0.05 corrected). aMCC: anterior

midcingulate cortex; MD: medial dorsal nucleus; PAG: periaqueductal grey; sgACC:

subgenual cingulate cortex; FC: functional connectivity.

54

Figure 4-9. Main regions of stronger sgACC FC in males compared to females

A: sgACC (seed A) and TPJ; B: sgACC (seed E) and aINS (*P < 0.05 corrected). aINS:

anterior insula; TPJ: temporoparietal junction; sgACC: subgenual cingulate cortex; FC:

functional connectivity.

55

Table 4-1. Resting-state group FC to sgACC: summary of main sex differences findings

Target F M F>M M>F

PAG x x √ X

Raphe nucleus √ x √ X

MD x x √ X

aMCC √ √ √ X

TPJ x x x √

aINS x √ x √

√: significant functional connectivity - corrected p<0.05 as validated by a Monte Carlo

simulation implemented in AlphaSim

(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim)

x: non-significant functional connectivity

F: female; M: male; aMCC: anterior midcingulate cortex; aINS: anterior insula; pINS:

posterior insula; PAG: periaqueductal grey; TPJ: temporoparietal junction; MCC:

midcingulate cortex; MD: medial dorsal nucleus; Amy: amygdala; sgACC: subgenual

cingulate cortex; FC: functional connectivity.


56

Table 4-2. Resting-state group FC to sgACC: main findings of interest

MNI coordinate (mm)

See

d

R/

L

Contras

t

Region x y z Voxel-level

(Z)

A L

F > M MD -4 -16 14 4.35 *

*

F & M pINS 42 -12 0 4.99 ffi

pINS -40 -16 0 4.80 ffi

M > F aINS; BA47; BA45 -42 32 2 3.96 *

TPJ (BA39) -50 -48 10 4.98 †

C L

F pINS 44 -12 2 5.66 ffi

M pINS 48 -6 0 5.44 ffi

aINS -36 28 2 5.53 ffi

Amy 34 0 -22 5.20 ffi

F & M MCC 4 6 30 5.13 ffi

MCC 4 -16 38 4.78 ffi

D R

F aMCC 12 26 26 6.17 ffi

M BA47 -42 32 -4 4.50 *

aMCC -10 42 30 5.74 ffi

M > F BA47 -42 32 -4 4.41 *

E L

M aINS; BA47; BA45 -40 22 -4 5.02 *

F & M pINS 50 -8 0 5.59 ffi

pINS -40 -20 12 4.95 ffi

MCC 4 8 30 4.89 ffi

M > F aINS; BA47; BA45 -40 22 -4 4.27 *

F R

F Amy 22 -2 -18 5.67 ffi

aMCC -4 28 30 5.07 †

† M BA47 -44 32 -2 3.61 *

Amy 38 0 -26 5.53 ffi

M > F BA47 -44 -32 -2 3.88 *

G L

F pINS -42 -8 -4 7.88 ffi

M pINS 42 0 -6 5.88 ffi

pINS -46 -10 0 6.46 ffi

F & M MCC -2 -20 38 5.42 ffi

57

H R

F aMCC -2 18 24 6.88 †

†

Raphe nucleus -4 -18 -36 4.03 *

MCC 0 4 36 6.26 ffi

M pINS -40 -10 0 5.83 ffi

pINS 48 -8 2 5.86 ffi

F & M TPJ 52 -54 20 4.86 ffi

F > M aMCC -2 18 24 4.97 †

†

Raphe nucleus -4 -18 -36 4.04 *

I L

M pINS -46 -10 0 6.46 ffi

pINS 42 0 -6 5.88 ffi

J R

M BA47 -38 34 -2 3.77 *

M > F BA47 -38 34 -2 4.26 *

K L

F pINS -42 -8 -4 7.88 ffi

N R

F aMCC -12 34 22 5.88 ffi

F > M PAG -4 -28 -22 3.49 *

F: female; M: male; aMCC: anterior midcingulate cortex; aINS: anterior insula; pINS:

posterior insula; PAG: periaqueductal grey; TPJ: temporoparietal junction; MCC:

midcingulate cortex; MD: medial dorsal nucleus; Amy: amygdala; sgACC: subgenual

cingulate cortex; FC: functional connectivity.

Negative X coordinates signify left brain regions; Positive X coordinates signify right

brain regions

Corrected p<0.05 as validated by a Monte Carlo simulation implemented in AlphaSim


*per voxel p < 0.001; cluster size threshold: 256mm3

**per voxel p < 0.0005; cluster size threshold: 192mm3

† per voxel p < 0.0001; cluster size threshold: 104mm3

††per voxel p < 0.00005; cluster size threshold: 80mm3

‡ per voxel p < 0.00001; cluster size threshold: 40mm3


58

Table 4-3. Resting-state group FC to sgACC seeds: additional findings

MNI coordinate (mm)

Seed R/L Contrast Region x y z Voxel-level (Z)

A L

M Inferior temporal lobe (BA20) 34 10 -48 6.54 ffi

F & M Parahippocampal gyrus -16 -14 -28 5.67 ffi

Middle temporal gyrus (BA21) -66 -16 -8 5.04 ffi

B R

F Inferior temporal lobe -50 2 -38 5.84 ffi

Cerebellum -10 -58 -36 6.04 ffi

Inferior temporal lobe (BA20) 64 -20 -20 6.09 ffi

M Inferior temporal lobe -42 12 -40 5.92 ffi

F & M Fusiform gyrus 24 -36 -16 5.44 ffi

Middle temporal gyrus 62 -12 -14 4.89 ffi

C L

F Inferior temporal gyrus 48 0 -40 8.97 ffi

Parahippocampal gyrus 24 -22 -24 6.10 ffi

Inferior temporal lobe -58 -16 -18 7.70 ffi

M Parahippocampal gyrus 26 -10 -34 6.59 ffi

Parahippocampal gyrus -22 -24 -22 6.82 ffi

Amy 34 0 -22 5.20 ffi

Precuneous -16 -50 -2 5.79 ffi

F & M Precuneous 8 -52 14 6.81 ffi

Middle frontal lobe -40 30 48 5.60 ffi

F > M Cuneus (BA7) -8 -78 32 3.61 *

D R

F Middle temporal pole -42 16 -40 6.96 ffi



M Inferior temporal gyrus 56 -6 -34 6.01 ffi

Middle temporal pole 48 10 -30 5.36 ffi

Superior temporal gyrus 46 10 -20 5.45 ffi

BA47 -40 30 -12 7.30 ffi

BA47 50 30 -10 6.02 ffi

Middle temporal gyrus (BA21) 62 -20 -8 5.46 ffi


Superior frontal gyrus -20 50 28 5.69 ffi

Superior frontal gyrus 14 52 42 5.78 ffi

59

F & M Parahippocampal gyrus 26 -18 -24 5.50 ffi

Fusiform gyrus -24 -42 -14 5.25 ffi


Middle frontal gyrus (BA8) 28 38 46 4.81 ffi

E L

F Inferior temporal pole 48 0 -40 8.14 ffi

Inferior temporal pole -60 -10 -26 7.57 ffi


M Inferior temporal pole -34 10 -46 8.68 ffi



F & M Parahippocampal gyrus -22 -24 -24 5.99 ffi

Precuneus 8 -54 14 5.83 ffi

Precuneus -6 -56 16 4.93 ffi

F > M Occipital lobe -8 -78 30 4.27 *

F R

F Inferior temporal pole -42 18 -38 5.86 ffi

Inferior temporal pole 44 8 -40 5.70 ffi

Parahippocampus 18 -8 -32 5.96 ffi

Middle temporal pole 60 -6 -22 5.11 ffi

Amy 22 -2 -18 5.67 ffi

M Inferior temporal pole 56 -8 -36 6.28 ffi

Amy 38 0 -26 5.53 ffi

Superior temporal gyrus 46 6 -22 5.82 ffi

Middle temporal lobe 64 -18 -8 6.24 ffi

Medial frontal lobe 12 52 30 5.53 ffi


F & M Middle temporal lobe -64 -10 -12 5.48 ffi

Medial superior frontal lobe 2 34 64 5.44 ffi

F > M Medial cerebellum -4 -62 -24 4.09 ffi

G L

F Inferior temporal lobe 42 -2 -50 7.47 ffi




M Superior temporal gyrus -34 14 -44 7.40 ffi

Inferior temporal gyrus 36 -6 -44 5.62 ffi


60

Superior temporal gyrus -46 -2 -14 5.41 ffi

F & M Parahippocampal gyrus 16 -32 -10 4.87 ffi

F > M Cerebellum 16 -70 -42 4.06 *

Parahippocampal gyrus 42 -30 -16 3.93 *

Parahippocampal gyrus -40 -16 -22 4.36 *

H R

F Middle temporal pole 38 18 -42 5.75 ffi

Anterior cerebellum -14 -54 -26 6.25 ffi

Medial cerebellum -8 -64 -20 6.55 ffi

BA42 -58 -14 10 6.87 ffi

Middle temporal pole 52 6 -24 6.02 ffi

M Middle temporal pole 38 14 -38 7.73 ffi


Fusiform gyrus -22 -42 -16 5.55 ffi

Middle temporal gyrus

(BA21)

-64 -24 -6 5.92 ffi

Superior temporal gyrus -48 -18 6 5.29 ffi

Frontal superior medial

Gyrus

16 36 60 6.94 ffi

F & M Ventral tegmental area 0 -10 -12 5.35 ffi

PCC -6 -56 16 5.25 ffi

Middle temporal gyrus

(BA21)

-56 -70 20 6.15 ffi

Superior medial frontal lobe 30 38 48 4.72 ffi

F > M Medial cerebellum -10 -54 -20 5.00 ffi

I L

F Inferior temporal gyrus 50 0 -38 5.77 ffi


F & M PCC -2 -20 38 5.42 ffi

F > M Cerebellum 16 -70 -42 4.06 *

Fusiform gyrus -40 -16 -22 4.36 *

Fusiform gyrus 42 -30 -16 3.93 *

J R

F Inferior temporal pole -50 2 -38 5.97 ffi


M Inferior temporal pole 32 12 -46 6.52 ffi

Inferior temporal pole -40 12 -38 5.48 ffi

F & M Inferior temporal pole 58 -8 -34 4.98 ffi

K L

61

F Parahippocampal gyrus -26 -28 -22 6.55 ffi



M PCC 2 -14 36 4.31 ffi

Superior frontal lobe -16 24 56 3.70 ffi

F & M PCC -2 -20 38 5.42 ffi

F > M Fusiform gyrus -40 -16 -22 4.36 *

Pontine nucleus -6 -18 -34 3.66 *

Fusiform gyrus 42 -30 -16 3.93 *

M > F Middle frontal lobe -38 22 40 3.48 *

Superior frontal lobe 20 62 12 3.54 *

N R

F Inferior temporal lobe (BA20) -50 -2 -32 6.80 ffi



Medial frontal gyrus 12 54 12 6.35 ffi

Medial frontal gyrus -18 50 26 5.61 ffi

M Inferior temporal pole 34 16 -44 7.27 ffi

Fusiform gyrus 34 -4 -30 5.48 ffi

Middle temporal lobe -40 -2 -20 5.83 ffi


F & M Medial frontal lobe 4 36 62 5.30 ffi

F: female; M: male; PCC: posterior cingulate cortex; aMCC: anterior midcingulate

cortex; aINS: anterior insula; pINS: posterior insula; PAG: periaqueductal grey; TPJ:

temporoparietal junction; MCC: midcingulate cortex; MD: medial dorsal nucleus; Amy:

amygdala; sgACC: subgenual cingulate cortex; FC: functional connectivity.

Negative X coordinates signify left brain regions; Positive X coordinates signify right

brain regions

Corrected p<0.05 as validated by a Monte Carlo simulation implemented in AlphaSim


*per voxel p < 0.001; cluster size threshold: 256mm3

**per voxel p < 0.0005; cluster size threshold: 192mm3

† per voxel p < 0.0001; cluster size threshold: 104mm3

††per voxel p < 0.00005; cluster size threshold: 80mm3

‡ per voxel p < 0.00001; cluster size threshold: 40mm3


62

4.2 Probabilistic tractography

4.2.2 Overview of findings

For the purposes of this thesis, ―anatomical/structural connectivity‖ was quantified and

defined as the number of streamline samples (out of 5000) on average that reached from a

seed to a target. Two analyses were conducted, and the SC of sgACC seeds was

determined for 1) all subjects, 2) females, 3) males, 4) the contrast of female>male, and

5) the contrast of male>female. Based on the hypotheses and sex differences found for

the RS analysis, the first analysis examined sex differences in anatomical connectivity

between sgACC and targets including PAG, TPJ, and aMCC (Figure 3-3). In the second

analysis, sex differences were examined for larger sgACC seeds and targets including

anterior insula, Th, amygdala, raphe nucleus, hypothalamus, and PAG (Figure 3-4). In

addition, both analyses calculated the common connectivity, defined as the percentage of

subjects that exhibited significant SC between the sgACC and a specific target. While

low common connectivity indicates the lack of SC in some targets, high common

connectivity suggests strong SC in other targets (Table 4-4, Table 4-5). Sex difference (p

< 0.05 Bonferroni corrected) was detected among targets of strong SC with the sgACC

(see below for details).

4.2.3 sgACC anatomical connectivity in males and females

Figure 4-10 schematically shows the overall findings of sgACC anatomical connectivity.

In the two tractography analyses, the group of all subjects showed SC between sgACC

and targets including aMCC, PAG, anterior insula, thalamus, hypothalamus, and

amygdala without indication of laterality (Figure 4-14). In the first analysis, the sgACC

were anatomically connected to aMCC and PAG. From the second analysis, the sgACC

was anatomically connected to the following regions: anterior insula, PAG,

hypothalamus, amygdala, lateral and medial thalamus. Furthermore, the sgACC was also

weakly, anatomically connected to the raphe. The appendix contains slice-by-slice

tractograms of sgACC in female, male, and both sex groups.

63

4.2.4 Sex differences in sgACC anatomical connectivity

Using the number of streamlines that successfully reached from a seed ROI to a target

ROI as output by classification tractography for each subject, the data was compiled into

male group and female group. T-tests were then conducted to compare for sex differences

between the groups in each hypothesized seed-target tract. Compared to females, males

had stronger SC between left sgACC and left hypothalamus (p < 0.05 Bonferroni

corrected) (Figure 4-11). Sex differences were examined for sgACC-SC to a total of 29

targets. In other words, there were a total of 29 t-tests conducted between male and

female groups. In order to correct for multiple comparison via Bonferroni correction, the

allowable probability of type I error (p = 0.05) was divided by 29, resulting in p ≤ 0.0017.

Thus, for one of the 29 comparisons to be significant, its p value needed to be smaller

than or equal to 0.0017. Moreover, SC between the right sgACC and the right

hypothalamus, between the left hypothalamus and right amygdala, as well as between the

left sgACC and left amygdala was stronger in men than women at p< 0.05 uncorrected

but these findings did not reach statistical significance at a corrected p of 0.05.

64

Figure 4-10. Summary – sgACC structural connectivity in the group of all subjects

sgACC exhibited substantial SC (black and blue lines) to aMCC, Th, aINS, Hy, Amy,

and PAG. Males showed greater sgACC-SC than females to Hy (p < 0.05 Bonferroni

corrected) (blue lines). aMCC: anterior midcingulate; Amy: amygdala; PAG:

periaqueductal grey; aINS: anterior insula; Th: thalamus; Hy: hypothalamus; sgACC:

subgenual anterior cingulate; SC: structural connectivity. Brain outline image adapted

from (http://www2.le.ac.uk/departments/gradschool/training/events/caferesearch/cafe-

brain).

65

Figure 4-11. Stronger anatomical connectivity between left sgACC and left Hy in

male group than female group

A: Left sgACC-Hy anatomical connection in males (blue) and females (orange)

thresholded at 95% group common connectivity (35/39 subjects in male group; 34/38

subjects in female group). Tractograms are projected to the MNI152 standard brain

(2mm, T1) image provided in FSL. B: Paired t-test for anatomical connectivity (*P <

0.05 Bonferroni corrected). Hy: hypothalamus; sgACC: subgenual anterior cingulate

cortex; R: right; L: left.

66

Figure 4-12. Female group anatomical connectivity between sgACC seeds and

targets The absolute lower threshold for common connectivity was 50% or 19/38 subjects. In

some cases, this threshold was increased for display purposes. Tractograms are projected

to the MNI152 standard brain (2mm, T1) image provided in FSL. aMCC: anterior

midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior insula; Th:

thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; R: right; L: left.

67

Figure 4-13. Male group anatomical connectivity between sgACC seeds and targets

The absolute lower threshold for common connectivity was 50% or 20/39 subjects. In

some cases, this threshold was increased for display purposes. Tractograms are projected




68

Figure 4-14. Female and male group anatomical connectivity between sgACC seeds

and targets

The absolute lower threshold for common connectivity was 50% or 39/77 subjects. In

some cases, this threshold was increased for display purposes . Tractograms are projected




69

Table 4-4. Group SC to sgACC: summary of main findings

Target SC M>F

TPJ x x

aMCC √ x

PAG √ x

aINS √ x

Th √ x

Hy √ √

Raphe nucleus x x

Amy √ x

√: significant structural connectivity – common connectivity >50% of total subjects

x: non-significant structural connectivity

aMCC: anterior midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior

insula; Th: thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; SC:

structural connectivity; M: male; F: female

70

Table 4-5. sgACC anatomical connectivity in females and males Seed-target anatomical connectivity

F M F & M

Seed Target Mean SE Mean SE Mean SE % CC

Analysis 1

A R TPJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00

A left TPJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00

H L aMCC 90.39 14.53 88.08 9.22 89.22 8.50 100.00

H R aMCC 413.61 69.47 379.63 46.22 396.40 41.28 100.00

N PAG 3.55 0.47 3.28 0.44 3.41 0.32 79.22

Analysis 2

R L aINS 35.10 5.96 21.61 6.23 28.27 4.36 90.91

R L lateral Th 8.12 1.36 6.96 0.86 7.53 0.80 94.81

R PAG 4.42 0.49 4.53 0.51 4.47 0.35 92.21

R R Hy 49.83 19.24 142.82 40.95 96.93 23.28 100.00 *

R L Amy 13.76 2.42 16.90 3.40 15.35 2.09 93.51

R L medial Th 7.61 1.25 7.15 1.04 7.38 0.81 93.51

R R aINS 37.74 5.89 28.29 6.46 32.95 4.38 96.10

R R lateral Th 10.41 1.38 10.31 1.45 10.36 0.99 98.70

R L Hy 19.92 4.90 48.57 10.37 34.43 5.98 98.70 *

R Raphe nucleus 0.50 0.16 0.58 0.19 0.54 0.13 20.78

R R Amy 27.24 5.11 59.63 11.17 43.65 6.43 96.10 *

R R medial Th 12.25 2.66 12.17 2.05 12.21 1.66 98.70

L L aINS 65.58 10.19 58.34 13.86 61.91 8.59 100.00

L L lateral Th 12.45 2.43 15.78 2.70 14.14 1.82 98.70

L PAG 5.31 0.86 5.36 0.59 5.34 0.52 94.81

L R Hy 17.08 5.27 49.14 19.58 33.32 10.35 96.10

L L Amy 43.28 11.62 102.74 22.17 73.39 12.98 96.10 *

L L medial Th 12.54 3.29 15.90 2.45 14.24 2.04 98.70

L R aINS 36.02 10.81 17.55 6.42 26.67 6.30 85.71

L R lateral Th 9.49 1.32 7.36 1.15 8.41 0.88 93.51

L L Hy 49.51 17.75 250.82 53.98 151.47 30.77 100.00 **

L Raphe nucleus 0.71 0.22 0.62 0.25 0.66 0.16 22.08

L R Amy 11.41 2.30 10.03 2.09 10.71 1.54 83.12

L R medial Th 9.09 1.44 6.07 0.94 7.56 0.87 90.91

aMCC: anterior midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior

insula; Th: thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; TPJ:

temporoparietal junction; M: male; F: female; SE: standard error; CC: common

connectivity; R: right; L: left.

Anatomical connectivity: streamline samples (out of 5000) reached from a seed to target

Paired t-tests (Male > Female):

*p<0.05 uncorrected

** p<0.05 Bonferroni corrected

71

5 DISCUSSION

5.1 SUMMARY OF MAIN FINDINGS

This thesis used two different brain imaging techniques to determine how the sgACC is

connected functionally and structurally to other brain regions previously implicated in

pain modulation and assessed whether these connectivities show sex differences. The

main findings are shown schematically in (Figure 4-1, Figure 4-10). The probabilistic

tractography findings were that the sgACC is structurally connected to the PAG,

amygdala, hypothalamus, MD thalamus, aMCC, and the anterior insula, amongst other

areas. Furthermore, the sgACC SC with the hypothalamus was found to be greater in men

than in women. The main findings from resting state fMRI revealed that the sgACC was

functionally connected to the PAG, raphe, MD thalamus, aMCC as well as to the TPJ and

anterior insula, among other areas. This analysis further demonstrated that women had

stronger sgACC FC than men with nodes of the descending antinociceptive and affective

system, namely the PAG, raphe, MD thalamus, and aMCC. In contrast, men showed

greater sgACC connectivity than women with the regions of the salience network,

namely the TPJ and anterior insula. These findings conform with the a priori hypothesis.

Given that the previous findings of greater pain habituation in women than men (Hashmi

and Davis, 2009) and association between long term habituation and sgACC activation

(Bingel, et al., 2007; Bingel and Tracey, 2008), the current findings of greater FC in

women with the descending pain modulatory pathway may reflect more effective pain

habituation, whereas stronger FC in men with regions in the salience and attention

network may reflect sustained attention to pain that hinders habituation. Given the role of

the hypothalamus in endorphin-mediated antinociception, the stronger sgACC-

hypothalamus SC in men could provide them a greater reliance on the hypothalamus-

mediated descending modulation pathway than in women. In contrast, women may more

strongly rely on the classic PAG-mediated descending pathway for modulation (Figure

5-3).

72

This thesis used both FC and SC approaches to evaluate sex differences that provide a

framework to understand how pain is experienced and modulated differently in men and

women. The findings from this thesis confirm and expand upon previous studies that

have examined the general connectivity of the sgACC that did not consider sex

differences (Beckmann, et al., 2009; Torta and Cauda, 2011; Yu, et al., 2011). For

instance, a previous tractography study in a mixed sample of men and women reported

SC between sgACC and regions including hypothalamus, orbitofrontal cortex, and

amygdala (Beckmann, et al., 2009), which were reproduced in the results of this thesis.

Other studies that had mixed samples of men and women, found FC between sgACC and

the orbitofrontal cortex (Torta and Cauda, 2011), (Margulies, et al., 2007), and Yu’s

group (Yu, et al., 2011), additionally found sgACC FC with temporal pole and medial

prefrontal cortex. A metaanalysis also revealed sgACC FC with the ventromedial PFC

and posterior insula (Torta and Cauda, 2011), which concurs with the findings of this

thesis. However, there has been a lack of experiments examining sex differences within

FC and SC, which was the motivation and novelty for this thesis project.

The sgACC is implicated in a variety of functions (see Literature Review) beyond the

scope of this thesis and so this discussion will focus on the findings in the context of pain

and antinociceptive systems. The axonal projections in the descending modulation

pathway are summarized in Figure 5-2. The anatomical connectivity from sgACC and its

associated cortical regions subregions are illustrated in Figure 5-1.

5.2 DELINEATION OF PAIN PATHWAYS WITH MRI-BASED

CONNECTIVITY TECHNIQUES: ADVANTAGES AND

LIMITATIONS

In this thesis, two approaches – rs-fMRI and probabilistic tractography - were used to

examine the SC and FC of sgACC, respectively. The SC analysis delineates the

anatomical framework of a system but FC analysis provides a different type of

framework that delineates brain regions that may be working together but not require

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monosynaptic or polysynaptic connections. In this thesis, seeding in the sgACC region,

the rs-fMRI analysis examined the temporal correlation of neuronal oscillations in

sgACC with the rest of the brain. As such, the technique identified brain nodes whose

neuronal activities oscillated almost in synchrony with neuronal activities in sgACC. The

neuronal activities between these nodes and sgACC are highly correlated and are said to

be functionally connected, which suggests that neurons within this network work and

oscillate together.

In this thesis, the general concept of probabilistic tractography is the calculation of the

probability of water diffusion in each direction per voxel, forming a number of diffusion

probability distributions. These probability distributions were then used to approximate

the likelihood that water in sgACC (seed) could diffuse to another brain region (target),

thereby mapping white matter tracts. This likelihood was estimated by counting number

of 5000 streamline samples that successfully reached the target from the seed after being

projected in random directions and after being guided by the probability distribution. The

number of successful projections reflected the strength of anatomical connectivity. Taken

together, SC reveals and outlines the general network nodes involving sgACC. In

contrast, FC characterizes how network nodes work with each other.

Technical limitations exist in both rs-fMRI and probabilistic tractography techniques.

First, in order to examine FC of sgACC, six bilateral seeds were used to cover the entire

sgACC region. This was done because the region is very large and comprises three

Brodmann areas that may have different connectivities. Although these multiple seeds

may increase the likelihood of false positive error, correcting for multiple seeds may be

extremely stringent for seed-based fMRI analyses. This is demonstrated by studies using

24 seeds (Zhang, et al., 2012), 16 seeds (Margulies, et al., 2007), 18 seeds (Adelstein, et

al., 2011), 4 seeds (Habas, 2010), and 14 seeds (Yu, et al., 2011). However, future studies

should consider multiple seed correction although, before this step, false discovery rate

(FDR) may be used for correcting for multiple voxels as opposed to the more stringent

family-wise error correction (FWE), which was used in this study.

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Second, in rs-fMRI, the MRI images were acquired every two seconds, equivalent to a

sampling rate of 0.5 Hz. This can only detect cortical activity that varies at or below 0.25

Hz. In addition, the rs- fMRI method used in this study assumes synchronous neuronal

oscillations at these low frequencies operating as a network for a common purpose.

Third, the fMRI technique is also limited by spatial resolution. Each fMRI voxel is about

3.125mm x 3.125mm x 4mm in volume, which contains a very large number of neurons

of various types; the signal of specific types of neurons can not be further delineated due

to the limited fMRI spatial resolution. However, SC offers anatomical framework to

support the functional network resulted from fMRI studies.

Fourth, estrous cycle may affect women’s pain perception (Section 2.1.5), and the data

used in this thesis was collected without controlling for estrous cycle in women.

However, the large women subject number used in this study should compensate and

account for the variations in the data due to estrous cycle.

Fifth, in order to compile individual subject results into group results, the former had to

be spatially blurred and registered to the standard space to compensate for individual

brain differences in size and shape. These processes are close to but not perfect, possibly

resulting in slight discrepancies between the result output and where the BOLD actually

occurred in the brain. For instance, the results may show grey matter activation areas,

which also spans some white matter regions. This is due to registration and spatial

blurring – a limitation of most MRI techniques.

Probabilistic tractography is limited by its spatial resolution. Tractography defines its

termination point when it reaches a region of high isotropy or when it reaches the brain

boundary. As such, the termination points do not necessarily reflect the location of axonal

terminations or synapses (Jbabdi and Johansen-Berg, 2011). In addition, it is also difficult

for tractography to differentiate branching fibres from kissing or merging fibres, which

could lead to possible false positive or false negative tracts. Moreover, tractography is

incapable of discerning 1) high curvature fibres and 2) multiple fibres that cross and form

patterns of ―T‖, ―L‖, ―+‖, and ―W‖. This creates confounds in the tractography results

(Jones, et al., 2013). Other limitations of tractography include its inability to discern fibre

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polarity, e.g., efferent vs afferent fibres, or to distinguish efferent from afferent axonal

projects; to detect precise axonal collaterals; and to track where white matter tract

terminate in a cortical layer, e.g., radial accuracy (Jbabdi and Johansen-Berg, 2011).

Finally, compared to analysis I, analysis II of the tractography used larger ROIs, which

included more voxels, to increase the sensitivity of the method. These larger ROIs reduce

the multiple comparison problem because, although composed of many voxels, each ROI

was still regarded as single entity. The ROIs’ greater volume was balanced with their

lower spatial specificity, thus not affecting the statistical stringency. Although each seed

ROI spanned a few Brodmann areas with slightly different connectivity, these areas are

all interconnected (Figure 5-1). Thus, the tractography results should be similar with the

seed ROIs either combined or separated although separating the seed ROIs unnecessarily

decreases the experimental sensitivity for detecting tracts.

5.2.2 PAG and descending modulation pathway

The rs-fMRI analysis revealed for the first time that, compared to men, women had

stronger FC between ventrolateral PAG and the sgACC, seed N which is centred in

subgenual BA32. This suggests that women may activate the descending pain modulation

pathway involving PAG more effectively than men.

A number of animal studies provide evidence for the anatomical connection between

BA32 and PAG and possible functions of this connection. For example, in monkey

studies, by using retrograde tracers in PAG and anterograde tracers in PFC, BA32 and 25

were found to project to the PAG, comprising part of the medial prefrontal network (An,

et al., 1998). The density of projections to PAG increased towards the more rostral

cingulate gyrus, specifically layer V, where pryamidal neurons and mu-opioid receptors

predominate (Vogt and Vogt, 2009). Mu-opioid receptors were believed to coordinate

emotional motor systems in the amygdala, ACC, and PAG, thereby modulating emotion-

related motor outputs (Vogt and Vogt, 2009).

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The ventrolateral PAG is responsible for facilitating opioid-mediated analgesia as

demonstrated by descending pain modulation models (Behbehani, 1995; Lovick, 1993).

For instance, in a rat study, kainic acid injection excited the ventrolateral PAG and

resulted in antinociception, which was reproduced by morphine microinjection (Morgan,

et al., 1998). In another rat study, morphine injection in the ventrolateral PAG revealed

antinociception effects in terms of pinch withdrawal latency. However, the

antinociception response was not reproduced when morphine was injected in the

dorsolateral PAG (Yaksh, et al., 1976; Yeung, et al., 1977). Further support for the

existence of an opioid system in ventral PAG, comes from the finding that the opioid

antagonist naloxone was effective in blocking antinociception only when administered in

ventral PAG (Cannon, et al., 1982).

Tracer studies have found that the PAG is heavily connected to the raphe. For example,

in cat, the ventrolateral PAG projects to the raphe (Abols and Basbaum, 1981). In rat, the

ventrolateral PAG was found to preferentially project to the caudal part of the lateral

paragigantocellular nucleus, the rostroventrolateral reticular nucleus, and raphe magnus

(Cameron, et al., 1995b). In another retrograde tracer study in rats, the ventrolateral PAG

was found to project to the RVM (Henderson, et al., 1998). Animal tracer studies have

shown that spinal cord dorsal horn neurons project contralaterally to the ventrolateral

PAG (Bandler and Keay, 1996; Keay, et al., 1997; Wiberg, et al., 1987; Yezierski, 1988)

and so the PAG is engaged both by ascending projections from the spinal cord and by

descending projections from the cortex.

5.2.3 Raphe and descending modulation pathway

The rs-fMRI analysis showed for the first time that, compared to men, women had

stronger FC between raphe and the sgACC, in seed H, which is centred in subgenual

BA24. This suggests that women may activate the descending pain modulation pathway

involving raphe more effectively than men. Anterograde and retrograde studies in

monkey have revealed the reciprocal axonal interconnection among BA24, 25, and 32

(Barbas, et al., 1999; Barbas and Pandya, 1989; Pandya, et al., 1981; Vogt and Pandya,

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1987). BA25 and 32 directly project axons to the medial and dorsal raphe as shown by

tracer studies in monkey (Chiba, et al., 2001; Freedman, et al., 2000). Thus, BA24 may

use BA25 and 32 as a relay to reach the raphe.

The raphe is an important intermediary between the spinal cord and PAG in mediating

analgesia, which has been supported by structural and functional studies in animals. First,

animal tracer studies have shown that the ventrolateral PAG sends a large number of

axons to the raphe (Beitz, et al., 1983a; Beitz, et al., 1983b; Lakos and Basbaum, 1988;

Li, et al., 1990). Second, neuropharmcological and electrophysiology studies have shown

that the resting activity and the evoked response of raphe are strongly influenced by PAG

stimulation (Behbehani, 1981; Behbehani, 1982; Behbehani and Fields, 1979; Behbehani,

et al., 1981; Pomeroy and Behbehani, 1979; Shah and Dostrovsky, 1980). Third, lesions

in the raphe were found to avert analgesia induced by electrical or morphine stimulation

in PAG (Gebhart, et al., 1983; Prieto, et al., 1983; Proudfit and Anderson, 1975;

Sandkuhler and Gebhart, 1984b). Fourth, in rat studies, electrical stimulation of the raphe

inhibited the tail flick spinal nociceptive response (Sandkuhler and Gebhart, 1984a;

Sandkuhler, et al., 1988). Fifth, retrograde tracer studies have shown that the majority of

the axons in the dorsolateral funiculus – a descending opiate tract from the brainstem -

originated from raphe and the paragigantocelluaris (Abols and Basbaum, 1981; Basbaum

and Fields, 1979; Lakos and Basbaum, 1988; Sim and Joseph, 1992).

5.2.4 MD thalamus and medial system

The rs-fMRI analysis showed for the first time that compared to men, women had

stronger FC between MD thalamus and the sgACC, in (seed A, which is centred in

subgenual BA25, the most posterior part of the sgACC. This suggests that, compared to

men, women may modulate the medial affective system involving MD thalamus more

effectively, thereby habituate to pain more efficiently. In retrograde tracer studies of

rhesus monkeys, the magnocellular part of MD thalamus as well as the entorhinal cortex

were found to project to BA25 (Bachevalier, et al., 1997). In another study, the dorsal

parvicellular part of the MD thalamus was found to project to BA25 (Vogt, et al., 1987).

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Studies in monkey also showed that BA25 projects to anterior hypothalamus, PAG,

amygdala, and MD thalamus (Freedman, et al., 2000).

Animal literature has indicated the involvement of submedial nucleus of rats, which is

developmentally related to the human MD thalamus, and its surrounding cortical regions

in pain modulation. In a study using 46 Sprague-Dawley adult rats, injection of glutamate

into the submedial nucleus decreased heat-evoked tail flick - a nocifensive response. This

response returned after injection of GABA into the ventrolateral orbital cortex and PAG.

Further, the GABA injection rendered subsequent glutamate injection in submedial

nucleus ineffective for antinociception. This suggests that submedial nucleus in rats or

MD thalamus in humans may modulate pain via a network involving the ventrolateral

orbital cortex and PAG (Zhang, et al., 1998). Adjacent to MD, the habenula is a cortical

region that contains a large amount of opiate receptors (Atweh and Kuhar, 1977). In a rat

study , morphine injection or electrical stimulation in the habenula led to analgesia after

formalin treatment, revealing habenula’s role in antinociception (Cohen and Melzack,

1993).

The MDvc is thought be involved in pain affect. In MDvc, third order neurons receive

neuronal projections from lamina I via the STT and send axons to ACC (Dostrovsky and

Craig, 2006). Both ACC and MDvc were grouped within a network associated with the

affective processing of pain. Findings from primate studies further led to the concept that

the cortical pain processing system was divided into two sub-systems: the lateral (SI, SII,

VPL, VPM, VPI) and medial system (MDvc, insula, ACC, VMpo, Pf, CL). While the

lateral system is believed to play a larger role in the sensory-discriminative pain

dimension, the medial system is thought to be more important in the affective-

motivational pain dimension (for review see (Treede, et al., 1999)).

A number of studies also found that MD thalamus partly plays a role in the sensory-

discriminative pain dimension. In an electrophysiology study using cat, thermoreceptive

and nociceptive neurons were found to project from lamina I to submedial nucleus of the

cat, or the human equivalent of MD, providing evidence for the role of MD thalamus in

sensory-discrimination pain dimension (Craig and Dostrovsky, 2001). Moreover, in male

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rhesus monkeys who trained to discern noxious heat stimuli, small increases in the

noxious heat stimuli increased the firing rate of neurons in the medial thalamus. This

suggests that MD thalamus may also contribute to the sensory-discriminative dimension

of pain (Bushnell and Duncan, 1989).

5.2.5 Salience and attention network

Human studies have shown that anterior insula, aMCC, and TPJ are involved in the

salience network, which becomes activated as one perceives an attention-grabbing

stimulus. For instance, in an fMRI study of six males and four females, the subjects

received visual, auditory, and tactile stimulations (Downar, et al., 2000). The visual

stimuli were abstract object images with different shapes and colours. The auditory

stimuli were the sound recording of running water and of frog croaking. The tactile

stimulus was the brushing of subjects’ right lower legs using a shower brush. Bilateral

TPJ, left SMA/CMA, and right anterior insula were activated in subjects in response to a

mix of three sensory cues, which suggests these brain regions as part of the attention

directing or salience network (Downar, et al., 2000). In another fMRI study involving

five males and five females, subjects were given visual and auditory stimuli and were

told to respond to one of the stimulus, which was defined as the behaviourally relevant

stimulus, by raising their index finger whenever the behaviourally relevant stimulus

changes. During both behaviourally relevant and irrelevant stimuli, bilateral TPJ, left

SMA/CMA, and bilateral anterior insula activation was significant. Specifically, the left

anterior insula was activated more strongly in response to behaviourally relevant stimulus

than behaviourally irrelevant stimulus (Downar, et al., 2001). This suggests the role of

left anterior insula in salience detection under both behaviourally relevant and irrelevant

contexts. In a follow-up fMRI study, the subjects were presented with baseline visual,

auditory, and tactile stimuli separately. In each condition, the baseline (familiar) stimulus

was intermittently interrupted by a novel stimulus. As a result, the right anterior insula

was activated more strongly during the administration of the novel stimulus than familiar

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stimulus. This implicates the role of right anterior insula in salience processing of novel

stimulus (Downar, et al., 2002).

The aMCC, TPJ, and anterior insula have also been implicated in the ventral attention

network (VAN) and so the two networks can be thought of being more less the same

network. The VAN is typically described as including the anterior insula, middle frontal

gyrus, frontal operculum, and inferior frontal gyrus, and TPJ (Corbetta, et al., 2008). In

an fMRI study of nine females and 11 males, subjects received auditory, tactile, and

visual stimuli and were asked to respond as soon as possible by pushing a button. During

control condition, subjects received the stimuli passively without the need to respond.

Results showed that TPJ, anterior insula, and aMCC were activated more strongly during

the experimental condition than control condition, revealing their role in directing

attention (Langner, et al., 2012). An fMRI study in patients who had suffered a stroke

reported the association between the VAN disruption and spatial neglect, providing

clinical evidence for the role of aMCC, TPJ, and anterior insula in salience detection and

attention (He, et al., 2007). Tractography studies revealed SC between the anterior insula

and TPJ (Umarova, et al., 2010), providing anatomical evidence for their FC in the VAN.

This thesis reports that sgACC is functionally connected to chief nodes of the salience

and attention network: anterior insula, aMCC, and TPJ. The sgACC connectivity with

these nodes will be discussed in the following sections.

5.2.5.1 Anterior insula and salience

The rs-fMRI analysis showed for the first time that, compared to women, men had

stronger FC between anterior insula and the sgACC, in seed B and E regions which are

centred at subgenual BA25 and subgenual BA24, respectively. Given that the anterior

insula is part of the attention/salience network, this result suggests that, compared to

women, men’s stronger activation of the attention network could sustain their attention to

pain, thereby hindering pain habituation.

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The FC result is supported by SC revealed by tracer studies. BA24, 25, and 32 are

strongly and reciprocally interconnected with the orbitofrontal cortex including the insula

(Morecraft and Tanji, 2009). Retrograde tracer studies in monkeys have shown that the

insula projects to BA24 and 32 (Mesulam and Mufson, 1982; Vogt, et al., 1987).

Specifically, in monkeys, retrograde tracers introduced in BA24 revealed that it received

input from dysgranula area and granular area of insula (Morecraft and Van Hoesen,

1998). Anterograde tracer studies in monkeys revealed that ACC projects mainly to the

mid-insula – dysgranular area of insula (Mufson and Mesulam, 1982; Pandya, et al.,

1981).

5.2.5.2 aMCC and salience

The rs-fMRI analysis showed that, compared to women, men had stronger FC between

MCC and seed C/E/H, which are centred at subgenual BA24. This concurs with monkey

tracer studies in that the MCC strongly interconnects with BA24, 25, and 32; and

orbitofrontal cortex including dysgranular and agranular insula, for review see (Morecraft

and Tanji, 2009). Given that the aMCC is part of the attention/salience network, this

result suggests that, compared to women, men’s stronger activation of the attention

network could sustain their attention to pain, thereby hindering pain habituation.

5.2.5.3 TPJ and salience

The rs-fMRI analysis showed that, compared to women, men had stronger FC between

TPJ and seed H, which is centred at subgenual BA24. Given that the aMCC is part of the

attention/salience network, this result suggests that, compared to women, men’s stronger

activation of the attention network could sustain their attention to pain and hinder pain

habituation. Lesion studies in monkey and tractography studies have shown SC between

TPJ and anterior insula (Pandya and Kuypers, 1969; Saur, et al., 2008; Schmahmann,

2006; Umarova, et al., 2010).

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The TPJ is located at the intersection of three cortical regions: inferior parietal lobe,

lateral occipital cortex, and superior temporal sulcus (Corbetta, et al., 2008). The role of

TPJ is pronounced not only in detecting innocuous stimuli but also in noxious stimuli.

For example, in an fMRI study in which subjects received noxious and innocuous stimuli,

the TPJ activation was activated throughout the noxious stimulation but was only

responsive to the onset and offset of the innocuous stimuli. These findings implicated the

TPJ with stimulus salience and pain (Downar, et al., 2003). Other fMRI studies in

humans have also demonstrated the role of TPJ in reorienting attention as driven by

visual stimuli (Indovina and Macaluso, 2007; Serences, et al., 2005)

5.2.6 Hypothalamus and descending modulation pathway

The probabilistic tractography analysis showed that, compared to women, men had

stronger SC between left hypothalamus and left sgACC, which spanned the entire left

sgACC. This suggests that women and men may use different descending pain

modulation systems to greater or lesser degrees (Lovick, 1993) that involve sgACC.

Specifically, women may more heavily use the sgACC-ventrolateral PAG-raphe system,

whereas men may more heavily use the hypothalamus-lateral PAG-RVM system.

Animal studies have revealed a number of evidence to support the sgACC-hypothalamus

connectivity. First, electrophysiological studies in rat reported reciprocal axonal

projections between the lateral hypothalamus and sgACC including (BA24, 25) using

such that electrical stimulation in the sgACC area could lead to either excitatory or

inhibitory effect in the lateral hypothalamus (Kita and Oomura, 1981). Second, in cat,

tracing study showed efferent projections from sgACC (BA25, 32) to dorsal and lateral

hypothalamus (Room, et al., 1985). Third, in monkeys, tracer study showed sgACC

(BA25, 32) projection to nucleus accumbens, amygdala, and ventromedial hypothalamus

(Chiba, et al., 2001). These sgACC-lateral hypothalamic projections were later found to

use glutamate as neurotransmitter in a retrograde tracer study (Csaki, et al., 2000). A

tracing study reported that the lateral hypothalamus projects to the regions including

central nucleus of amygdala and paraventricular nucleus. The latter then sends axons to

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brainstem regions including PAG, raphe, dorsolateral tegmental nucleus of pons, and

parabrachial nucleus (Berk and Finkelstein, 1982). This sgACC-hypothalamus-brainstem

axis provides evidence for the axonal connectivity, which is used in the descending

modulation.

The male-predominant connectivity of the hypothalamus in this thesis may relate to

clinical studies that have shown hypothalamic activity associated with the onset of cluster

headaches, a chronic pain disorder predominantly in men. For instance, using PET and

MR angiography, activation of hypothalamus, ACC, anterior frontal lobe, and both

insulae was found to correlate significantly with onset of cluster headache (induced by

nitroglycerin inhalation) in a study of 18 cluster headache patients (May, et al., 2000). In

addition, right hypothalamus and ACC activity correlated with the onset of acute

spontaneous cluster headache in a resting state fMRI study involving 12 male cluster

headache patients and 12 male healthy controls (Qiu, et al., 2013). Thus, the

hypothalamus was targeted in cluster headache treatment. In 2004, DBS targeting the

hypothalamus was proven effective in eliminating cluster headache attacks in a patient

with bilateral chronic intractable cluster headache (Leone, et al., 2004). Subsequently,

DBS in the posterior hypothalamus was proven to have the effect of abolishing nocturnal

cluster headache in a clinical trial of three patients having drug-resistant chronic cluster

headache (Vetrugno, et al., 2007).

Animal studies have shown that the hypothalamus contains mu opioid receptors, which

bind to endomorphins and lead to analgesia. Endomorphin-1 and 2 are tetrapeptide

opioids, which selectively bind mu receptors to facilitate analgesia (Goldberg, et al.,

1998). For an example, in an immunocytochemistry study using rats,

immunofluorescence of endomorphin-2 was observed in a number of cortical regions

containing mu-opioid receptor; these regions included spinal cord dorsal horn, septum,

amygdala, locus coeruleus, midline thalamic nuclei, nucleus accumbens, hypothalamus,

and PAG. These pain processing regions specifically bind to endomorphin-2 and no other

opioid peptides, implicating the significance of endomorphin-2 in pain processing

(Schreff, et al., 1998).

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Endomorphins released from hypothalamus at the spinal cord can cause analgesia. For

instance, in an electrophysiological study of 18 rats, endomorphin-1 decreased the

activity of C-fibre and A beta-fibre while endomorphin-2 reduced only C-fibre activity

(Chapman, et al., 1997). Moreover, in a molecular study targeting rat dorsal root ganglia

via in situ hybridization, mu receptors were expressed in 90% of neurons, which were

expressing substance P precursors (Minami, et al., 1995), suggesting a link between mu

receptors and substance P in analgesia. Another immunocytochemistry study in rats also

found co-localization of endomorphin-2 and substance P in the primary afferent terminals

at laminae I and II. A mechanism was proposed in which substance P is regulated by

endomorphin-2-induced mu autoreceptor activity (Sanderson Nydahl, et al., 2004).

In addition to endomorphin, beta endorphin can also bind and activate mu receptors. Beta

endorphin has low affinity for K opioid receptors and high affinity for mu and delta

opioid receptors (Fields, et al., 1999). Animal studies have provided anatomical evidence

that the hypothalamus uses beta endorphin in facilitating descending pain modulation.

For example, using beta endorphin as neurotransmitter, hypothalamic arcuate nucleus and

nucleus tractus solitaries send monosynpatic projections to the spinal cord dorsal horn

(Fields, et al., 1999; Millan, 2002). The arcuate nucleus also projects a significant amount

of axons to the PAG, where beta-endorphin is released as neurotransmitter, thereby

facilitating descending pain inhibition (Mansour, et al., 1995; Millan, 1986).

The analgesic effect of beta endorphin has also been elucidated in research. For instance,

in male rat studies, the injection of rabbit antiserum against beta endorphin caused an

increase of nocifensive behaviour – licking and flinching - in response to formalin

injection (Porro, et al., 1999; Wu, et al., 2001). In another rat study, induced by radio

frequency electrical stimulation, lesion in the hypothalamic arcuate nucleus attenuated

antinociception, which was measured in tail-flick latencies in response to focused light

beam. This antinociception attenuation was found to correlate to beta endorphin reduction

(Millan, et al., 1986). In another rat study involving 28 animals, a beta endorphin

precursor protein was truncated via site-directed genetic mutagenesis, which attenuated

analgesia from mild swim stress (Rubinstein, et al., 1996). These studies suggest the

importance of hypothalamic beta endorphin in facilitating analgesia.

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5.2.6.1 Spinohypothalamic tract

The spinohypothalamic tract (SHT) passes afferent signals directly from the spinal cord

to the hypothalamus. Its neuronal projections have been revealed by a number of animal

electrophysiological, tracer, and lesion studies (Burstein, et al., 1987; Burstein, et al.,

1996; Cliffer, et al., 1991). For instance, in rats, electrical stimulation of the lateral

hypothalamus antidromically activated the spinal cord dorsal horn lamina I (Burstein, et

al., 1987). SHT has been postulated to participate in autonomic as well as endocrine

regulation, and the affective pain processing (Burstein, et al., 1996; Dostrovsky and

Craig, 2006). In animal studies, electrical stimulation in the Hy elicited cardiovascular

variations (Abrahams, et al., 1960; Mancia, et al., 1972). In a electrophysiology study

done on cats, stimulation of the Hy was followed by cardiovascular changes as well as by

inhibition of the nociceptive afferent signals in the spinal cord dorsal horn (Morton and

Duggan, 1986). Thus, the Hy may be involved in both pain perception and the associated

stress responses.

Sex differences have been observed in autonomic regulation. The autonomic system

regulates blood pressure via modulation of cardiac output (Charkoudian, et al., 2005) and

peripheral resistance (Burt, et al., 1995; Charkoudian, et al., 2005; Charkoudian, et al.,

2006; Wiinberg, et al., 1995). The latter occurs from vasodilation or vasoconstriction

mediated by sympathetic nerve activity (SNA) via α-adrenergic mechanisms.

Specifically, the hypothalamus participates in the sympathomedullary (SAM) pathway, in

which, it activates the adrenal medulla, causing it to secrete adrenalin, leading to a

sympathetic response. In a 2010 study, blockage of α-adrenergic receptors led to greater

blood pressure drop in men than women, implying the stronger dependence of SNA in

men for blood pressure regulation than in women (Schmitt, et al., 2010). This conforms

with the tractography results of this thesis. Specifically, at rest, the finding that men had

stronger sgACC-SC with the hypothalamus suggests a more efficient and stronger

facilitation of the sympathetic response than in women. This mechanism also supports the

existing descending modulation model (Lovick, 1993). Compared to women, men’s

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stronger sgACC-SC with the hypothalamus also suggests that their greater preference for

hypothalamus-mediated descending controls, which can be inhibited by a number of

regions, e.g., caudal ventrolateral medulla and raphe nucleus (Lovick, 1993), making this

pathway prone to inhibition. On the other hand, women’s preferred, ventrolateral PAG-

mediated descending pathway is uninhibited (Lovick, 1993), possibly making this

pathways more efficient than men’s preferred pathway, providing women with more

effective antinociception and pain habituation. Thus, the sex difference in the preference

of descending modulation pathway provides an anatomical mechanism for the sex

difference in pain habituation.

5.3 FUTURE DIRECTIONS

This finding from thesis can be further understood in future studies. For example,

graphical analysis could be used to further characterize how the regions identified in this

thesis interact with each other. These brain areas could also be used as seeds in another

fMRI analysis while masking the brain with the exception of sgACC. By applying the

mask, the statistical threshold will be lower than a whole brain analysis, which was done

in this thesis. The lower threshold will likely yield more precise results in the sgACC

subregions, whose functions could then be further characterized. Another future study

could be to use the brain areas found to be structurally connected to the sgACC, as seeds

in another tractography analysis to further parcellate their structurally connectivity to

sgACC subregions. Another extension of this thesis would be to more directly link the

strength of the functional and structural connectivities in individual men and women to

their individual pain sensitivity and responsiveness in an in depth psychophysical study.

Finally, this thesis provides evidence for targeting sgACC in deep brain stimulation as a

potential treatment for chronic pain.

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5.4 CONCLUSION

Taken together, this thesis provides neural basis for sex difference in pain habituation.

First, women’s stronger sgACC FC with the descending pain modulation areas (raphe,

PAG) likely contributes to their greater efficacy in pain modulation than men. Second,

their greater sgACC FC with the MD thalamus may enhance their modulation of the

affective dimension of pain. Third, men’s stronger sgACC FC with the salience/attention

network (anterior insula, aMCC) may heighten and sustain their attention to pain. Fourth,

men’s stronger sgACC SC with the hypothalamus suggests their greater preference for

using the possibly slower hypothalamus-mediated descending modulation pathway than

the arcuate-mediated descending modulation pathway in women. These findings

implicate a mechanism for pain habituation and its associated sex differences.

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Figure 5-1.Structural connections from sgACC (green lines) or from sgACC-

associated regions (purple lines) reviewed from structural studies

sgACC consists of BA25, s24, and s32. Arrowed connections: from tracer studies; double

arrow: reciprocal connection. Non-arrowed connections: from tractography studies.

sgACC: subgenual anterior cingulate; 25: BA25; s24: subgenual BA24; s32: subgenual

BA32; Hy: hypothalamus; Amy: amygdala; MD: medial dorsal thalamic nucleus; PAG:

periaqueductal grey; TPJ: temporoparietal junction; aMCC: anterior midcingulate; aINS:

anterior insula. Adapted from (An, et al., 1998; Bachevalier, et al., 1997; Barbas, et al.,

1999; Barbas and Pandya, 1989; Berk and Finkelstein, 1982; Chiba, et al., 2001;

Freedman, et al., 2000; Kita and Oomura, 1981; Morecraft and Tanji, 2009; Morecraft

and Van Hoesen, 1998; Mufson and Mesulam, 1982; Pandya and Kuypers, 1969; Pandya,

et al., 1981; Room, et al., 1985; Saur, et al., 2008; Schmahmann, 2006; Umarova, et al.,

2010; Vogt and Pandya, 1987; Vogt, et al., 1987). Brain outline image adapted from


89

Figure 5-2. Descending modulation axonal projections

Black lines: descending projection. Red lines: ascending projections. Lam: lamina; DLF:

dorsolateral funiculus; NACs: nucleus accumbens; Hy: hypothalamus; Amy: amygdala;

aINS: anterior insula; PAG: periaqueductal gray: RVM: rostroventral medulla; LC: locus

coeruleus; PB: parabrachial nucleus; SC DH: spinal cord dorsal horn; RF: reticular

formation; NCF: nucleus cuneiformis; +: excitatory; -: inhibitory. Adapted from

(Basbaum and Fields, 1984; Fields, et al., 1999). Brain outline image adapted from


90

Figure 5-3. Descending pain modulation pathways

In achieving antinociception, men may prefer the hypothalamus-mediated pathway (blue)

while women may use the Arcuate-mediated pathway (pink) to a greater degree. Adapted

from (Lovick, 1993).

91

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Appendices

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Appendix I: Tractograms in both Women and Men

Figure A.I-1. Structural connectivity between the left subgenual anterior cingulate

and periaqueductal gray in all subjects

The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This

threshold was sometimes increased to display tracts more clearly. Tractograms are

projected to the single subject brain template (T1) provided in the MatLab software -

xjView. The colour flare represents the number of subjects that contributed to the map.

sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.

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and left hypothalamus in all subjects





sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.

111


and right hypothalamus in all subjects




xjView. The colour flare represents the number of subjects that contributed to the map. L:

left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.

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and left amygdala in all subjects





left; R: right. sgACC: subgenual anterior cingulate cortex; Amy: amygdala.

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and right amygdala in all subjects





left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.

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and left anterior insula in all subjects





left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.

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and right anterior insula in all subjects






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and left lateral thalamus in all subjects





left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.

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and left medial thalamus in all subjects






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and right medial thalamus in all subjects






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and right lateral thalamus in all subjects






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Figure A.I-12. Structural connectivity between the right subgenual anterior

cingulate and periaqueductal grey in all subjects






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cingulate and left hypothalamus in all subjects






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cingulate and right hypothalamus in all subjects






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cingulate and left amygdala in all subjects






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cingulate and right amygdala in all subjects






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cingulate and right anterior insula in all subjects






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cingulate and left anterior insula in all subjects






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cingulate and left lateral thalamus in all subjects






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cingulate and left medial thalamus in all subjects






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cingulate and right medial thalamus in all subjects






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cingulate and right lateral thalamus in all subjects






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cingulate (seed H) and left anterior midcingulate in all subjects





sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.

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cingulate (seed H) and right anterior midcingulate in all subjects






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Appendix II: Tractograms in Women

Figure A.II-1. Structural connectivity between the left subgenual anterior cingulate

and periaqueductal grey in women. The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and left hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and right hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and left amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and right amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and left anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and right anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and left lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and left medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and right medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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and right lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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Figure A.II-12. Structural connectivity between the right subgenual anterior

cingulate and periaqueductal grey in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and left hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and right hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and left amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and right amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and right anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and left anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and left lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and lateral medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and right medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate and right lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate (seed H) and left anterior midcingulate in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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cingulate (seed H) and right anterior midcingulate in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This





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Appendix III: Tractograms in Men

Figure A.III-1. Structural connectivity between the left subgenual anterior cingulate

in men.






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and left hypothalamus in men






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and right hypothalamus in men






160


and left amygdala in men






161


and right amygdala in men






162


and left anterior insula in men






163


and right anterior insula in men






164


and left lateral thalamus in men






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and left medial thalamus in men






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Figure A.III-10. Structural connectivity between the left subgenual anterior

cingulate and right medial thalamus in men






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Figure A.III-11. Structural connectivity between the left subgenual anterior

cingulate and right lateral thalamus in men






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Figure A.III-12. Structural connectivity between the right subgenual anterior

cingulate and periaqueductal grey in men






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cingulate and left hypothalamus in men






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cingulate and right hypothalamus in men






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cingulate and left amygdala in men






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cingulate and right amygdala in men






173


cingulate and right anterior insula in men






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cingulate and left anterior insula in men






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cingulate and left lateral thalamus in men






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cingulate and left medial thalamus in men






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cingulate and right medial thalamus in men






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cingulate and right lateral thalamus in men






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Figure A.III-23. Structural connectivity between the subgenual anterior cingulate

(seed H) and left anterior midcingulate in men






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Figure A.III-24. Structural connectivity between the subgenual anterior cingulate

(seed H) and right anterior midcingulate in men






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