BIDIRECTIONAL EEG NEUROFEEDBACK TRAINING OF ......Gruzelier, 2001), beta (Egner & Gruzelier, 2004)...

BIDIRECTIONAL EEG

NEUROFEEDBACK TRAINING OF

THETA COHERENCE IMPROVES

VISUAL ATTENTION

Ksenia Folomeeva &

Ove Mathias Langerud Nesheim

Master of Philosophy in Psychology

Cognitive Neuroscience discipline at the Department of

Psychology

UNIVERSITY OF OSLO

May 2015

III

Bidirectional EEG neurofeedback training of theta coherence improves

visual attention

By Ksenia Folomeeva & Ove Mathias Langerud Nesheim

Submitted as a master thesis in Cognitive Neuroscience

Department of Psychology

University of Oslo

May 2015

IV

Copyright Ksenia Folomeeva and Ove Mathias Langerud Nesheim

2015

Bidirectional EEG neurofeedback training of theta coherence improves visual attention

Authors: Ksenia Folomeeva and Ove Mathias Langerud Nesheim

Supervisors: Bruno Laeng, Markus Handal Sneve, Svetla Velikova

http://www.duo.uio.no

VI

Abstract

Authors: Ksenia Folomeeva and Ove Mathias Langerud Nesheim

Title: Bidirectional EEG Neurofeedback Training of Theta Coherence Improves Visual

Attention

Supervisors: Bruno Laeng, Markus Handal Sneve (co-supervisor) and Svetla Velikova

(external supervisor)

Neurofeedback (NF) has the potential to enhance cognitive functioning through

learned regulation of brainwave activity. However, NF for optimizing performance in healthy

people is still in its infancy and currently not fully explored. Here, we present an experiment

where 12 subjects undergo 10 sessions of a novel NF protocol with eyes-closed bidirectional

theta coherence training. This protocol was selected based on several ideas: contemporary

neuroscience suggests that neural coherence support neuronal communication, and high task-

related coherence is often observed with higher performance. At the same time, brain’s theta

waves have been shown to be particularly involved in attentional processes. In addition, it

can be argued that neural flexibility should encompass the ability to regulate up and down in

accordance with the cognitive demands of the environment. In order to evaluate the success of

the NF training in the experimental group, a multiple object tracking (MOT) task was

administered both pre- and post-training while both electroencephalogram (EEG) and

pupillometry were recorded simultaneously. A passive control group performed the test twice

for comparisons, with the same time lag. The results indicate that NF training was successful

in enhancing attentional processes, since behavioural improvements were found in both

accuracy and response time (RT) during MOT, and only in the NF group. In addition, lower

task-related pupil dilations suggested that less mental effort was deployed during post-training

MOT by the experimental group compared to the control group. The baselines of resting EEG

recorded before each NF session were compared to the initial baseline and revealed

widespread increases in coherence in all frequency bands. Analysis of task-related EEG

indicated higher levels of longitudinal coherence in the experimental group during the post-

training MOT. However, we cannot exclude that confounding variables related to changes in

motivational factors could make comparisons between the control group and experimental

group problematic. We can only tentatively conclude that the novel NF protocol employed in

the current experiment shows promising support for beneficial effects of bidirectional theta

NF on cognition. The current experiment should be regarded as an exploratory study. The NF

protocol was developed in collaboration with Smartbrain AS (Oslo, Norway) and their

experts. All the collection and analysis of data was done by Ksenia Folomeeva and Ove

Mathias Langerud Nesheim (authors of the thesis).

VIII

Acknowledgements

We would like to thank Prof. Bruno Laeng (supervisor) for his advice, feedback and

guidance on theoretical issues and pupillometry and, most of all, for helping to organize the

collaboration which made this project possible.

We would like to thank Dr. Markus Handal Sneve (co-supervisor) for guidance on the

design of the experiment, helping with the generation of MOT videos as well as for valuable

comments during the writing of the thesis.

We would like to thank Svetla Velikova (MD, PhD) for her advice, helping to develop

the Neurofeedback (NF) protocol and guiding NF sessions, EEG analyses and interpretations.

Also, we are grateful for the hospitality at SmartBrain AS and Haldor Sjåheim’s support

along the way.

A special thank goes to Jonas Meier Strømme for helping us with writing the python

script during long winter nights. We also thank Fredrik Svartdal Færevaag, Bendik Holm and

Pelle Bamle for participating in the pilot testing of the MOT task, EEG and pupillometry

recordings.

Contents

Introduction ............................................................................................................................................ 1

Attentional systems of the brain and multiple object tracking (MOT) ............................................ 1

Pupillometry and attention ................................................................................................................ 2

EEG and attention ............................................................................................................................... 4

Theta coherence .............................................................................................................................. 5

EEG Neurofeedback and attention .................................................................................................... 6

Hypothesis and predictions ................................................................................................................ 8

Methods ................................................................................................................................................ 10

Participants ....................................................................................................................................... 10

Procedure and design ....................................................................................................................... 10

Tasks and Equipment ........................................................................................................................ 11

MOT task ....................................................................................................................................... 11

Pupillometry .................................................................................................................................. 12

EEG recordings .............................................................................................................................. 12

Neurofeedback protocol\training ................................................................................................. 13

Preprocessing and analysis of data .................................................................................................. 14

Pupillometry .................................................................................................................................. 14

Behavioral data.............................................................................................................................. 15

EEG analysis ................................................................................................................................... 15

Results ................................................................................................................................................... 18

MOT results ....................................................................................................................................... 18

Analysis of accuracy....................................................................................................................... 18

Analysis of RT ................................................................................................................................. 20

Pupillometry results ......................................................................................................................... 22

EEG results ........................................................................................................................................ 23

Regression analysis of resting baseline EEG. ................................................................................. 23

Full-spectrum analysis ................................................................................................................... 25

Coherence during MOT1 and MOT2. ............................................................................................ 27

Discussion ............................................................................................................................................. 31

Limitations and future directions ..................................................................................................... 34

Conclusion ............................................................................................................................................. 35

References ........................................................................................................................................ 36

Appendix ............................................................................................................................................... 44

Bidirectional EEG Neurofeedback Training of Theta Coherence Improves Visual Attention

1

Introduction

The main goal of cognitive neuroscience is to understand how the mind/brain works

but an important target is also to find practical applications of this knowledge. Perhaps

reflecting the challenges of the Information Age, there has recently been an increasing interest

in techniques of cognitive enhancement. Attention, the ability to focus on some information

while ignoring the rest seems fundamental to cognitive processes like memory and learning,

as well as our interaction with other human beings. People practice meditation or use brain-

boosting pills and even play brain-training games as attempts to train attention. One approach

endorsed by neuroscientists is based on the “operant conditioning” of brainwaves, generally

known as neurofeedback (NF). Recent advances in technology have made NF more accessible

to researchers and practitioners seeking to improve attention. Typically, investigators have

focused on up-regulating EEG power values, like the sensorimotor rhythm (Egner &

Gruzelier, 2001), beta (Egner & Gruzelier, 2004) and frontal midline theta (Fm-theta)

(Enriquez-Geppert, Huster, Figge, & Herrmann, 2014). Alternative NF protocols involve

training of EEG coherence, but these have been less explored. Coherence can be interpreted

as a measure of functional connectivity of distant brain regions (Fries, 2005; Mitchell,

McNaughton, Flanagan, & Kirk, 2008), which makes it a relevant target for NF. In the current

experiment, we set out to test the efficacy of a novel NF protocol, involving both up- and

down-regulation of theta coherence, in order to enhance sustained visual attention in healthy

participants. The outcome measure was a behavioral task for divided visual attention while

simultaneously monitoring usage of cognitive load or mental effort by recording pupil

dilations (Kahneman, 1973).

Attentional systems of the brain and multiple object tracking (MOT)

Visual attention selects information relevant to our internal and external goals, while

ignoring distractions. When playing a game of football, humans rely on visual attention to

attend to the ball, team mates and opponents while ignoring the crowd, the referee and other

distracting aspects. This process is likely to be ensured by top-down control, attributed to a

dorsal frontoparietal attention network (Corbetta, Patel, & Shulman, 2008). If the football

field suddenly gets invaded by hooligans, bottom-up processes kicks in to redirect behavior

from the game to the unexpected situation. This process is supported by a ventral attentional

network, working as an alarm system. The ability to operate among relevant and irrelevant


2

sensory stimuli is therefore achieved through interaction of the top-down and bottom-up

networks (Corbetta, Kincade, & Shulman, 2002).

The Multiple Object Tracking (MOT) task was developed by Pylyshyn and Storm

(1988) in order to study early visual processes of spatial indexing. During MOT, the subject is

required to visually track several moving targets among distractors while fixating on a central

cross on a computer screen. This task may pose a continual demand on the visuo-attentional

system. The MOT-paradigm has been used to test different models of how the attentional

system is capable of tracking several objects at once through serial and/or parallel processes

(Howe, Cohen, Pinto, & Horowitz, 2010; Pylyshyn & Storm, 1988) . On average, the

participants are able to track 4-5 targets in a single trial (Pylyshyn & Storm, 1988), however,

the performance depend on the targets\distractors ratio, speed of the moving objects and

tracking time (Alvarez & Franconeri, 2007). By varying these parameters, the cognitive load

can be operationalized and studied.

Moreover, as shown by neuroimaging studies, a tracking network including regions in

the frontal, parietal and occipital cortices is engaged during MOT (Alnæs et al., 2014; Culham

et al., 1998; Howe, Horowitz, Morocz, Wolfe, & Livingstone, 2009), covering areas of the

dorsal frontoparietal attention network (Corbetta et al., 2008). Subcortical activations during

tracking (compared to passive viewing) has been found in the thalamus with the pulvinar

nucleus, the basal ganglia and the locus coeruleus (LC) among others (Alnæs et al., 2014).

Moreover, activity in the dorsal attention network and the LC has been found to be closely

linked to task-related pupil dilations during MOT (Alnæs et al., 2014; Murphy, O'Connell,

O'Sullivan, Robertson, & Balsters, 2014), paralleling the finding that LC activity correspond

with the demands of attentional tasks (Raizada & Poldrack, 2007). Based on the stability of

the pupil dilation towards the MOT task, which was showed in 9 individuals in a follow-up

study after a few years (Alnæs et al., 2014), pupillometry can be considered a reliable

estimate of attentional effort .

Pupillometry and attention

The allocation of limited attentional resources relates to the psychological construct of

‘mental effort’, as a special kind of arousal according to Kahneman (1973). Through a series

of experiments on different mental tasks, a subject’s pupil dilation has proved to be a sensitive

measure of mental effort (Laeng, Sirois, & Gredebäck, 2012). For example, Beatty (1982)

claimed that fluctuations of the mental activity could be detected through changes in pupil


3

size recorded simultaneously with the task performance. A task-related pupillary response can

be compared to an event-related brain potential recorded by EEG: task-related pupil-size

changes appear within a short time gap (100 or 200 ms) following task onset (Beatty, 1982b).

Classical experiments have shown that the dilation of the pupil follows second by second

alterations in short-term memory load (Kahneman & Beatty, 1966), is sensitive to the level of

abstraction in a language processing task (Wright & Kahneman, 1971), is sensitive to the

difficulty of mental arithmetic problems (Hess & Polt, 1964), can be used to signal perceptual

thresholds for visual detection (Hakerem & Sutton, 1966; Kahneman, Beatty, & Pollack,

1967) and indicates the level of performance during tasks requiring sustained attention

(Beatty, 1982a).

Ahern and Beatty (1979) have investigated the association between a subject’s

pupillary response to arithmetic problems and his or her Scholastic Aptitude Test (SAT)

score. The participants who had higher SAT scores showed less pupil dilation (suggesting use

of less mental effort in order to complete the task) compared to the participants with lower

scores. More recent studies have confirmed that pupillometry can be a reliable measurement

of attentional effort during the performance of a task (Gilzenrat, Nieuwenhuis, Jepma, &

Cohen, 2010; Laeng, Ørbo, Holmlund, & Miozzo, 2011; Wierda, van Rijn, Taatgen, &

Martens, 2012).

The pupil dilation response from cognitive processing is thought to stem from the

release of norepinephrine (NE) in the LC through inhibitory connections to the Edinger-

Westphal nucleus (EWN; (Wilhelm, Ludtke, & Wilhelm, 1999). The EWN in turn innervates

ciliary ganglion supporting the sphincter pupillae muscle, controlling the constriction of the

pupil. While the pupil size can vary considerably (2 mm - 8 mm) with the amount of light that

impinges on the retina, the diameter variations stemming from mental effort are much

smaller. Cognitively evoked pupil dilations are rarely larger than 0.5 mm (Beatty & Lucero-

Wagoner, 2000). In a model of LC activity (Aston-Jones & Cohen, 2005), two modes are

described. The tonic mode is an exploration mode where behavior is adaptively adjusted to

the environmental changes. In the phasic mode, attention is filtered to optimize performance

of task-specific behavior. LC phasic activity therefore signals task-related activity. In order to

capture the cognitively evoked pupil dilations, investigators usually subtract the tonic pupil

dilation (baseline) from the phasic response (task-related pupil dilation).

One current model suggests that the LC-NE system is also partly responsible for the

deactivation of the ventral attention network during focused attention (Corbetta et al., 2008;

Thatcher, 1992, 1998). In the study described above, the pupil dilation response was shown to


4

predict activity in the dorsal attention network and LC better than a simple “load” variable,

operationalized as the number of targets to-be-tracked in the MOT task (Alnæs et al., 2014).

Thus, for the present experiment, pupil dilations were recorded to give an index of mental

effort during MOT, in turn reflecting subcortical activations related to mental effort and

sustained attention.

EEG and attention

Electroencephalography (EEG) enables the user to study electrical activity stemming

from neural circuits in the brain. The application of quantitative EEG (qEEG) allows

transformation of the EEG signal from the time domain to the frequency domain by the

application of Fourier analysis (Cooley & Tukey, 1965). The transformed EEG signal is

characterized by amplitude (measured in μV), power (representing the squared amplitude) and

frequency (measured in Hz). On the basis of their frequencies, brain rhythms are subdivided

into the following main bands: delta (1-3Hz), theta (4-7Hz), alpha (8-12Hz), beta (13-30 Hz)

and gamma (30-50Hz). These bands may be functionally distinct, and can reveal oscillatory

brain activity related to cognitive processes.

Regarding attention, theta has received particular interest from investigators (Ishii et

al., 1999; S. Makeig et al., 2004; Missonnier et al., 2006). It covers the frequencies in the

range 4-7 Hz and has been named after the thalamus to which the origin of cortical theta has

been attributed (Walter & Dovey, 1944). The thalamus sends rhythmic activity to the cortex

by means of pacemaker cells, which participate in producing rhythmic EEG activity (Steriade,

2005). Another source of EEG recorded theta is the anterior cingulate cortex (ACC) (Asada,

Fukuda, Tsunoda, Yamaguchi, & Tonoike, 1999), producing the frontal midline theta (Fm-

theta) activity widely implicated in attentional processes and cognitive demand (Mitchell et

al., 2008). Increased Fm-theta is observed during mental calculation (Harmony et al., 1999),

visuo-spatial N-back tasks (Smith, McEvoy, & Gevins, 1999), the Sternberg memory task

(Fernandez et al., 2000) episodic memory tasks (Klimesch, Schimke, & Schwaiger, 1994) and

video game playing (Pellouchoud, Smith, McEvoy, & Gevins, 1999). Several researchers

have tried to determine whether Fm-theta reflects attentional processes or working memory

(WM) processes (Gomarus, Althaus, Wijers, & Minderaa, 2006; Sauseng, Hoppe, Klimesch,

Gerloff, & Hummel, 2007), and suggest that Fm-theta reflect attention. Furthermore,

frontoparietal theta coherence was found to indicate integration of sensory information into

executive functions (Sauseng et al., 2007).


5

Theta coherence. In addition to the theta power, EEG theta coherence has been

demonstrated to correlate with tasks of attention (Makeig et al., 2002) and working memory

(Klimesch, 1999). Coherence reflects the synchronization of activity between two EEG

electrodes and can have values between 0 (no coherence) and 1 (maximum coherence).

Coherence is calculated by correlating the spectral content of the two electrodes over a certain

time window within distinct frequency bands, and provides a measure of the signals’ linear

dependence. If spectral content contained within specific frequency bands correlates

continuously over the time period, coherence is high (Saltzbertg, Burton, Burch, Fletcher, &

Michaels, 1986), even in the presence of highly uncorrelated activity in other frequencies. A

possible confounding variable when analyzing coherence is increased power of a source

localized between the two synchronized electrodes, whose signal reaches both electrodes

(Fein, Raz, Brown, & Merrin, 1988).

Thatcher and colleagues (Thatcher, Krause, & Hrybyk, 1986) have developed a model

of EEG coherence showing that it depends on cortico-cortical interactions and strength of

synaptic connections between the brain regions (Thatcher, 1992, 1998). Therefore, coherence

can be defined as “Coherence = (Nij*Sij)”, where N stands for the number of cortico-cortical

connections, and S stands for the strength of those connections. According to his model,

increased coherence could be attributed either to an increase in numbers or strength of

synaptic connections between two areas in the cortex. Findings from studies on patients with

neurogenic pain, however, suggest that EEG coherence might also reflect an active output

pathway from thalamus to the cortex as the amount of thalamocortical coherence was

comparable to the amount of cortical coherence in the theta range (Sarnthein & Jeanmonod,

2008).

Processing of complex information is likely to require functional integration across a

number of distant brain regions. Coherence analysis between EEG electrodes during

performance on a specific task could be used to measure this integration, however, relatively

few studies have pursued this possibility. The highly influential communication-through-

coherence hypothesis claims that distant brain regions are only able to communicate

efficiently when they oscillate coherently (Fries, 2005). Coherent oscillation could allow the

excitability of a region in the network to be predictive, creating “temporal windows” for

effective communications (Fries, 2005; Pajevic, Basser, & Fields, 2014). Thalamic nuclei

with wide projections to the cortex have cells with intrinsic oscillating properties that render

the nuclei ideal “broadcasting centers” of rhythmic activity (Steriade, 2005). These cells’


6

influences on cortical regions could establish functional selectivity through the specific

distributed rhythms, including theta rhythms.

In support of the above accounts, increased frontoparietal theta coherence has been

observed during the retention period of a working memory task (Von Stein & Sarnthein,

2000). Increased theta coherence across the scalp has been reported during encoding of

correctly recalled nouns (Weiss, Müller, & Rappelsberger, 2000). During the performance of

both verbal and spatial intelligence tests, people with higher IQ’s have been shown to display

higher long-distance coherence in the theta band (Anokhin, Lutzenberger, & Birbaumer,

1999), which is thought to reflect their brain’s ability to establish integration of the involved

cortical regions. Regarding EEG during resting state, coherence values seem to be less

predictive of task performance than task-related EEG (Anokhin et al., 1999). Some have

found negative correlation between resting EEG coherence and intelligence (Thatcher, North,

& Biver, 2005), while others reported a positive relationship in alpha coherence (Marosi et al.,

1995).

As theta power and coherence have been proved essential for performance in cognitive

tasks, we should note that EEG neurofeedback training often focuses on this frequency in

order to improve the attentional abilities.

EEG Neurofeedback and attention

During EEG neurofeedback, the EEG signal is analyzed real-time, and when the

subjects manage to regulate their brain activity above a certain threshold for a fixed period of

time, a type of visual or auditory reward is fed back. Over time, the subject learns to produce

more of the desired brain activity. The exact strategy used by the trainee in order to learn to

regulate the brain may vary considerably among trainees, ranging from positive thinking,

relaxing, and visual imagery and so on. In fact, conscious awareness of how one learns to

regulate the brain activity in accordance with the NF training may not be a prerequisite for

successful learning (Gruzelier, 2014b).

Today, there is no established standard for how to analyze the learned control of brain

wave activity caused by NF during training sessions. Gruzelier (2014b) describes three main

types of analysis present in the literature. Across-session learning involves analyses of

changes from session to session in the ability to regulate brain activity during the actual NF

training. Within-session learning involves analysis of NF training during certain periods

within one NF session. Baseline increments analysis is used to investigate changes in pre-


7

training baseline EEG recordings from session to session. Looking at baseline increments

might be the most direct way of analyzing lasting changes from NF training (Gruzelier,

2014b; Ros et al., 2013). Even though changes are hypothesized to occur in the trained

frequency bands and electrodes, NF training might also induce changes in electrodes and

frequency bands outside the trained ones. Gruzelier (2014b) points out that analysis of the full

frequency spectrum is rare, yet should be an essential requirement for NF studies. This

requirement is followed in the analysis of the present experiment.

NF in a clinical setting has been applied for years in order to improve dysfunctions in

brain activity related to different disorders, including ADHD (Lofthouse, Arnold, & Hurt,

2012), autism spectrum disorders (Coben, Linden, & Myers, 2010), cerebral stroke (Bearden,

Cassisi, & Pineda, 2003), and consequences of brain and spinal cord damages (Cavinato et al.,

2011) among other disorders. More recently, investigators have also turned their attention to

cognitive enhancement of healthy subjects and have applied neurofeedback for improvement

of sustained attention (Egner & Gruzelier, 2004; Egner & Gruzelier, 2001), musical

performance (Egner & Gruzelier, 2003), working memory (Vernon et al., 2003) or visuo-

motor skills (Ros et al., 2009) etc. With the possibility to modulate neural oscillations,

neurofeedback has the potential to inform cognitive neuroscience of more than just

correlations between cognitive tasks and brain oscillations.

In seeking to enhance a cognitive function, NF-studies aims to modulate the EEG-

waves activity related to that function, to analyze NF induced changes in tonic or phasic EEG,

and to investigate cognitive improvement by some cognitive test. For example, training up

the amplitude of SMR (sensorimotor rhythm) and beta1 (12.5–16 Hz) have shown an effect

on sustained attention in healthy participants (Egner & Gruzelier, 2004; Egner & Gruzelier,

2001). Enriquez-Geppert and colleagues (Enriquez-Geppert et al., 2014) investigated nineteen

participants undergoing eight sessions of NF on Fm-theta to improve executive functioning

(EF). Importantly, outcome measures of NF success and EFs were compared to twenty-one

participants who had undergone pseudo-neurofeedback. During pseudo-neurofeedback,

participants typically receive random feedback or feedback from someone else’s brain. The

experimental group was able to up-regulate Fm-theta amplitude better and showed improved

EFs in two out of four tests, compared to the pseudo-neurofeedback group. Wang and Hsieh

(2013) reported similar findings of Fm-theta amplitude training (including pseudo-

neurofeedback) where the NF-group improved working memory and attention. A recent

review suggest that NF seems to have great potential as a method of improving cognitive

functioning (Gruzelier, 2014a).


8

While a number of studies have investigated neurofeedback of Fm-theta amplitude in

relation to cognitive performance, fewer studies have investigated regulation of theta

coherence. In fact, we are not aware of any study optimizing performance employing NF

training of coherence. In this article, we present an experiment where twelve human subjects

undergo ten sessions of theta coherence neurofeedback. The training protocol tested here

includes increasing of theta power and cyclic increase and decrease of theta coherence on

interhemispheric electrodes. A test of MOT is given pre- and post-training with simultaneous

task-related EEG and pupillometry recordings. Pre-training EEG baselines are also analyzed

to evaluate the effect of NF training on the tonic EEG.

The NF protocol applied here was developed in collaboration with Smartbrain AS

(Oslo, Norway) in order to explore a novel NF approach, and therefore there are not previous

published data on it. The training was done with eyes-closed, as theta rhythm were shown to

be more profound on the EEG recordings with eyes-closed (Barry, Clarke, Johnstone, Magee,

& Rushby, 2007). Therefore, an auditory reward was used as a feedback signal. The protocol

starts with an increase of theta power, since the enhancement of power facilitates the

enhancement of the coherence, which is trained in the next step of the protocol. The rationale

for training theta coherence is that this band is related to attentional processing (Makeig et al.,

2002; Mitchell et al., 2008; Sauseng et al., 2007). Also, theta coherence might reflect the

integration of information in task-relevant regions through a temporal window ensuring

coherent activity (Fries, 2005), possibly supported by the recruitment of rhythmic thalamic

activity (Steriade, 2005). Both up- and down-regulation is trained due to the finding that high

coherence correlates with high cognitive performance (Anokhin et al., 1999; Weiss et al.,

2000), but is not required during rest. This way, theta coherence might become more

“adaptive” to task demands by training the ability to turn on and off coherence. As pointed

out by Gruzelier (2014b), most NF studies have chosen unidirectional NF training due to

often reported correlations between cognitive performances and either heightened or lowered

EEG power/coherence. However, it can be argued that learned control should include the

ability to regulate activity in both up and down directions, according to task demands.

Hypothesis and predictions

Our main hypothesis is that training of neuronal flexibility, in the sense of repeated

up- and down-regulation of theta coherence, will facilitate cognitive performance.

If the theta coherence training is successful, we expect trained individuals to show

improved accuracy and response time on MOT when comparing performance before training


9

to that after training, while also deploying less mental effort (as indexed by task-related pupil

dilations) after the training. Furthermore, during task-related EEG, we expect higher theta

coherence to be associated with higher cognitive performance, similar to what has been

observed in several studies (Anokhin et al., 1999; Weiss et al., 2000). For both resting and

task-related EEG, changes in other frequency bands can be expected due to the frequently

reported non-specific effects of NF (Gruzelier, 2014b), and these bands will therefore also be

analyzed. However, as we are not aware of any bidirectional NF protocols reminiscent of the

protocol deployed in the present experiment, we are not specifically predicting the direction

of the effect of NF on the resting EEG. Since the present NF training does not specifically

target the “tracking network” (Howe et al., 2009), large effect sizes should not be expected.

For both the behavioral measures and pupillometry, the experimental group is predicted to

change significantly more than the control group. In general, for the control group, a stable

pattern of performance and neurophysiological measures is expected.


10

Methods

Participants

Twenty-nine volunteer participants were recruited by asking students at campus and

by announcing the project on Facebook. However, given the demanding schedule to be met

for this experiment, 7 participants terminated the experiment due to their busy work schedules

before completion. A total of 8 females and 15 males were able to participate in all training

sessions and tests (mean age: 26.09, range 19-36, SD=4.68). Every participant read a

document with inclusion criteria for the study, making sure that none of the participants had a

mental disorder or history of head trauma, or was currently on medications that could affect

cognition. One participant was shifted from the experimental group to the control group after

having finished the first day of MOT testing because of a hearing impairment, making him

unsuitable for the auditory neurofeedback sessions.

Procedure and design

The experiment included an experimental group (N=12) and a control group (N=11).

The experimental group included 4 females (mean age: 25.25; range 21-29) and 8 males

(mean age: 25.13; range 20-31), whereas the control group included 4 females (mean age:

23.5; range 19-31) and 7 males (mean age: 28.5; range 22-36). Both groups performed pre-

training MOT (MOT1) and post-training MOT (MOT2) tasks during which EEG and

pupillometry were recorded simultaneously. The experimental group underwent 10 NF

sessions over 5 weeks, twice per week, in between MOT1 and MOT2. Before each NF

session, resting baseline EEG was recorded for later analysis. A mixed repeated measures

design including two within-subject factors (load and session) and one between-subject factor

(group) was employed and the dependent variables were MOT accuracy, response time (RT)

and task-related pupil dilations. A pre-test post-test design was employed to compare task-

related EEG.

The MOT sessions, during which task-related EEG and pupillometry were recorded,

were done in the Cognitive Laboratory of the University of Oslo (Oslo, Norway). NF sessions

were conducted in SmartBrain’s clinic (Oslo, Norway). On the first day of the experiment, all

participants signed a consent form which described the process of the experiment, main

benefits and risks of the study. Prior to data collection, the project was consulted with the

local ethical committee.


11

Figure 1. The procedure of one trial in the MOT task

Tasks and Equipment

MOT task. Videos for MOT task were generated using MATLAB (MathWorks,

Natick, MA) and the psychophysics Toolbox extension (Brainard, 1997) prior to

programming the experiment, and saved as video clips in ‘.wmv’ format. The experiment was

programmed and run in E-prime 2.0 (Psychology Software Tools, Inc) using the MOT videos

to build the MOT trials. Participants were seated approximately 60 cm from a 22 inches Dell

(Dell Inc, TX, USA) monitor with 1600*1024 resolution and asked to fixate on a central

fixation point during the task.

In the MOT task, participants were requested to track the several targets among the

distractors (Figure 1). Following the presentation of the fixation cross, 12 blue objects

appeared on the computer screen. A short target-assignment phase followed, where 2-5

objects were marked as red. After that, all objects were shown in blue and started to move for

a total duration of 8 secs. At the end of a trial, after all objects stopped moving and only one

of them (either a target or a distractor; 50% probability) was highlighted in red (probe), the

participant’s task was to judge whether the selected object was among the targets or not.


12

They indicated their choice by key presses, which also yielded a response time (RT) for the

decision. Key ‘n’ was used for a ‘no’ response, and ‘b’ was used for a ‘yes’ response. Half of

the trials included valid probes and half of them were invalid. The valid and invalid probes

were presented in a randomized order.

The task had 4 load conditions, presented in random order within each part of MOT

session (for definition of ‘part’ see below):

load2 (2 targets, 10 distractors);



load5 (5 targets; 7 distractors).

Thus the amount of objects on the screen was kept the same during every load

condition, making visual crowding constant. The objects were 0.3 degrees in diameter,

moving with a speed of 6 degrees/per second; the minimum distance allowed between the

objects was 1.6 degrees from edge to edge. The objects always moved straight and when they

reached the edge of the display or bumped into another object, their trajectories changed to a

random angle (full range allowed). All the MATLAB generated videos for MOT were

visually inspected for “bad videos” including crowding of objects or others flaws. Those

videos were excluded and replaced.

Each MOT session was divided into 4 parts separated by 5 min breaks in order to

avoid excessive tiredness of the participants. Each part consisted of 48 MOT-trials (12 MOT-

movies per load). The first part also included 8 practice trials so that the participants and the

experimenters could make sure the instructions were understood. Therefore, one complete

MOT-session included 200 movies (different for each MOT session), 50 movies per load of

the task (including practice).

Pupillometry. The pupillometry recordings were conducted using the iView X R.E.D.

eye-tracking system (Sensio-Motoric Instruments, Germany). Data was recorded with the

iView X 2.7 software at a sampling rate of 60 Hz. Before every MOT part, a personal 9 point

calibration procedure was performed on a 22 inches Dell (Dell Inc, TX, USA) monitor with

1600x1024 resolution. The illumination of the room was kept constant during both MOT

sessions.

EEG recordings. EEG was recorded during the first MOT session and the last and

also before each session of the neurofeedback training (resting baseline) for a total of 12

measurements (10 EEG recordings of a baseline and 2 EEG recordings during MOT1 and


13

MOT2). The latter recording allowed us to assess transfer effects of NF training on successive

pre-training baselines for the experimental group. The baselines were recorded with eyes-

closed since the NF training was also done with eyes-closed. For both MOT sessions and NF

sessions, the EEG preparation procedures were the same. The participants were asked to

minimize body movements, control gaze and tongue movements in order to avoid artefacts

All EEG recordings for each participant were done approximately at the same time of the day

in order to avoid differences caused by the normal circadian changes in EEG activity (Frank

et al., 1966). The distance between the nasion and inion was measured in order to determine

the suitable size of the cup for each participant and to fit the cup properly on the head. In

order to clean the ears, the NuPrep, mild abrasive gel was used. After putting on the cup, the

ECI electrogel was applied to each electrode in order to provide appropriate signal detection.

Different EEG systems were used during MOT and NF due to the availability of the

equipment for the current project.

EEG recordings during the MOT task were done with the Brainmaster Discovery 24E

acquisition system (BrainMaster, OH, USA), using 19-electrodes caps (FP1, FP2, F3, F4, Fz,

F7, F8, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1 and O2) in accordance with the 10-20

system (Jasper, 1958). BrainAvatar software was used for data storage. Impedance for each

electrode and for each ear was adjusted to < 10 kΩ, as measured by a 1089NP Checktrode

EEG Impedance meter.

For neurofeedback sessions Deymed TruScan EEG acquisition system (32 channels;

Deymed, Czech Republic) was used together with 19-electrodes caps (FP1, FP2, F3, F4, Fz,

F7, F8, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1 and O2). Impedance for each electrode

and for each ear was adjusted to < 10 kΩ as measured by the Truscan Acquisition software.

The participants sat in a comfortable armchair with eyes closed, in a quiet room, with constant

temperature and light conditions. Each session required approximately 40 minutes to

complete.

Neurofeedback protocol\training. The NF protocol was set up on the commercially

available software Deymed TruScan (Deymed, Czech Republic). The training lasted 30 min

and included 10 rounds:

a) 3 min increasing theta power in Cz;

b) 9 min training of theta coherence on F3-F4: 3min up, 3 min down, 3min up;

c) 9 min training of theta coherence on C3-C4: 3min up, 3 min down, 3min up;

d) 9 min training of theta coherence on P3-P4: 3min up, 3 min down, 3min up.


14

In the TruScan software, the coherence values are calculated 16 times per second.

These values are averaged by the software because of the highly erratic nature of EEG.

Participants were rewarded with a short beep sound whenever their coherence values between

the respective electrodes were maintained above 60% for at least half a second. This threshold

was kept constant throughout all NF training sessions. The loudness of the reward signal was

adjusted by asking the subject to select a comfortable level.

An auditory reward was selected as it allowed participants to keep their eyes closed as

the eyes-closed condition has been shown to be characterized by more profound theta rhythm

on EEG recordings (Barry et al., 2007).

Positive relationships were maintained between the experimenters and the participants,

as this was thought to be important for the success of NF training (Gruzelier, 2014b). The

experimenters showed interest in the condition of the participants and their feelings regarding

the experiment and tried to be flexible regarding time-slots for the training to make the

process of participating more pleasant.

Preprocessing and analysis of data

Pupillometry. The SMI R.E.D. I-View system uses a patented algorithm to calculate

pupil diameter and adjusting for head movements, while a form of linear interpolating is used

to replace eye-blinks and other outliers in the raw data stream. To further preprocess the

pupillometry data, a custom made script was written in Python. Pupillometry baselines were

collected between 300 ms and 0 ms before tracking start, when all objects were present in

blue color. This interval was chosen as a baseline as no changes in color or movement

occurred, and were therefore thought to exclude task-related cognitive processing.

Furthermore, the baselines were subtracted from the average pupil size between 2.5 seconds

and 7 seconds after tracking start. This sampling interval was chosen because cognitively-

evoked pupil dilations arise slowly at the beginning of each tracking period and reach an

asymptote around 2.5 secs (see Alnæs et al., 2014). In addition, one can expect pupil dilations

related to preparatory processes for responses towards the end of the tracking (Richer &

Beatty, 1985). Baselines were subtracted from the average task-related pupil size in all trials

in order to obtain a measure of average task-related pupil dilation. The data were further

separated by the amount of targets to-be-tracked in each trial, and only trials in which a

correct answer was given were included for further analysis (in total, approximately 7750

correct trials, with around 340 trials per participant, 84 per load).


15

The pupillometry data were also checked for the presence of outliers using the outlier

detection rule described by Hoaglin and Iglewicz (1987), where the upper and lower boundary

is defined as:

Upper = Q3 + (2.2*(Q3-Q1))

Lower = Q1 – (2.2*(Q3-Q1)),

where Q3 is the 75th

percentile, Q1 is the 25th

percentile and 2,2 a constant multiplier.

Shapiro-Wilk’s test was applied in order to control data for normality. A mixed effects

analysis of variances (ANOVA) with MOT session (2 levels: MOT1 and MOT2) and load

(load2; load3; load4; load5) as within-subject factors and Group (NF and control) as between-

subjects factor was used for the task-related pupil dilation as the dependent variable for the

experimental and control group. After that, repeated measures ANOVAs were performed

separately for the control and experimental groups with load and session as within-subject

factors. A planned comparison with paired t-tests comparing the MOT1 and MOT2 for each

load was applied in order to compare the task-related pupil dilation for the experimental group

and control group, separately.

Behavioral data. MOT results were averaged across each load in MOT1 and MOT2.

Shapiro-Wilk’s test was applied in order to control data for normality and the outlier detection

rule was applied in order to exclude outliers (Hoaglin & Iglewicz, 1987).

Two separate mixed effects analysis of variances (ANOVA) with MOT session (2

levels: MOT1 and MOT2) and load (load2; load3; load4; load5) as within-subject factors and

Group (NF and control) as between-subjects factor were used for accuracy and reaction time

as the dependent variables. After that, four repeated measures ANOVAs with load and session

as within-subject factors were done separately for the experimental and control groups, for

accuracy and RT. Paired t-tests comparing the MOT1 and MOT2 for each load were applied

in order to test differences in accuracy and RT for the experimental and control group,

separately, and according to the predictions made before the experiment.

EEG analysis. The obtained EEG data were visually inspected and artifacts were

removed using NeuroGuide Deluxe (Applied Neuroscience Inc., Florida, USA) software

version 2.8.3. Both computerized and manual artifact rejection were applied. In order to

assure the quality of the selected EEG data, test-retest reliability function of the NeuroGuide

was kept higher than 0.90 for each EEG record, according to the recommendation of the

NeuroGuide (NeuroGuide Help Manual, 2002-2014). Further statistical analyses of the data

were performed using Neurostat option of the NeuroGuide software.


16

The statistical analysis in Neuroguide involves Fast Fourier transform (FFT), a

technique used to identify the frequency components of the EEG signal. For the analysis EEG

recording is divided into 2 sec. segments or epochs, which are submitted to frequency analysis

(Kaiser & Sterman, 2000). In NeuroGuide, these epochs are sampled at a 128 samples\sec.

sampling rate which results in 256 digital time points with a frequency range from 5 to 40 Hz

with resolution of 0.5 Hz. As segmentation of the epochs in FFT is likely to produce ‘sharp

edges’ (non-zero values at the beginning and at the end of each epoch) and differences in

amplitude could result in errors of spectral information, known as ‘leakage’; mathematical

functions called ‘windows’ are applied for each of the epochs (Kaiser & Sterman, 2000). In

NeuroGuide, cosine taper windows are used for this purpose. Each 2 second FFT includes 81

rows (0 to 40 Hz frequencies) by 19 columns (electrode locations), resulting in 1539 elements

cross-spectral matrix for each individual.

Although, the mathematical windows are useful for avoiding the leakage problem,

they could smooth the frequency peaks at both edges of the epoch, ending up in analyzing

only the central frequencies of the epoch and reducing the signal power. However, the

multiple overlapping windows could be a solution (Kaiser & Sterman, 2000) and the best

quality of data was shown to be achieved by 4 windows per epoch or 75% overlapping. In

NeuroGuide, an EEG sliding average of 256 FFT cross-spectral matrixes are computed for

each individual, editing EEG by advancing in 64-point steps.

The FFT is recombined with the 64-point sliding window for 256 FFT cross-spectrum

for EEG record. All the 81 frequencies for each 19 electrode locations are log10 transformed in

order to correct the data for the normal distribution. The total amount of 2 second windows is

entered into paired t-tests and is used to compute the degrees of freedom for the statistical

analysis.

In order to evaluate the effect of NF on successive resting EEG baselines, the mean

theta coherence for each of the trained electrode pairs was subjected to linear regression

analysis with number of NF sessions as the predictor. To follow the recommendation from

Gruzelier (2014b), a full-spectrum analysis followed and included two comparisons on a

group level. Average EEG baseline recordings from sessions 1-3 were compared to average

baselines from sessions 4-6 and 8-10 using uncorrected paired t-test analysis in NeuroGuide.

Full p-value tables are attached in the Appendix.

EEG recording during MOT1 and MOT2 were preprocessed in EEGLAB by

MATLAB (Math-Works, Natick, MA) in order to select the EEG epochs corresponding with

the specific load condition (2 to 5). Only trials with the correct response given were included


17

into the analysis to maximize the likelihood that the same cognitive process is involved in

each comparison. Thereafter, EEG epochs of each load were merged together. Each EEG-

recording was manually cleaned from eye-movements, blinks, jaw tension, or other body

movement artifacts, using NeuroGuide Deluxe (Applied Neuroscience Inc., Florida, USA)

version 2.8.3.

The comparison between EEG recordings during MOT1 and MOT2 was done for the

experimental and control groups separately with paired t-tests. The EEG coherence was

compared for the recordings, averaged across all loads. By means of this analysis the overall

trend in coherence changes could be revealed for the experimental and control groups.

Additionally, paired t-tests were done separately for each load in order to explore the

differences in coherence levels from the easiest to the most difficult load of the task.


18

Results

MOT results

The outlier detection rule was applied in order to control for possible outliers (Hoaglin

& Iglewicz, 1987) and none were detected. The Shapiro-Wilk did not reach significance for

accuracy (experimentalMOT1 p=.186; experimentalMOT2 p=.09; controlMOT1 p=.415;

controlMOT2=.687) or RT (experimentalMOT1 p=.526; experimentalMOT2 p=.273;

controlMOT1 p=.921; controlMOT2 p=.359), meaning that the data did not differ

significantly from the normal distribution.

Analysis of accuracy

An independent t-test was conducted in order to compare the accuracy during MOT1

in the experimental and control groups to see if the groups differed before the experimental

group commenced training. The accuracy of each load condition was averaged for each

participant, giving each participant a total MOT accuracy score, used for the independent t-

test. The analysis indicated that the groups did not differ by accuracy level at the initial stage

of the experiment, (t(21)=-.246; p=.808; d=.10; see Figure 2).

Mixed repeated measures ANOVA with two within-subject factors (2 sessions and 4

loads) and one between-subject factor (group) revealed a significant main effect of load

(F=46.754; p=.000; ŋ2=.69), but no significant effect of session (F=.031; p=.862; ŋ

2=.001). A

significant interaction effect between Group and MOT session (F=6.622; p


19

However, no interactions between session and load (F=1.976; p=.127); load and group

(F=.633; p=.597); or session, load and group (F=1.575; p=.204) were found.

Repeated measures ANOVA for the experimental group revealed a significant main

effect of the load (F=27.070; p=.000; ŋ2=.711), but no significant main effect of session

(F=2.791; p=.123; ŋ2=.202). A significant interaction effect between the load and session was

shown (F=3.053; p=.042; ŋ2=.217). A planned comparison with 4 paired t-tests was

conducted comparing MOT1 and MOT2 for each load condition (Figure3A) and Bonferroni-

corrected to a significance level of p


20

Analysis of RT

Independent t-test was conducted in order to compare the reaction time during MOT1

in the experimental and control groups to see if the groups differed before the experimental

group commenced training. The RT of each load condition were averaged for each

participant, giving each participant a total MOT RT score, used for the independent t-test. The

analysis revealed that the groups did not differ by RT at the onset, t(21)=.706; p=.488; d=.29;

see Figure 4.

Mixed effects ANOVA for RT revealed a significant main effect of the load

(F=42.453, p=.000, ŋ2=.87), but no main effect of session (F=2.673, p=.117, ŋ

2=.113). A

significant interaction effect of session and group (F=5.665; p


21

Figure 5. The diagram presents the difference in response time during MOT tasks in the experimental (A) and control (B) group for each load condition. The bars represent the between-subjects SEM.

analysis for the experimental group was conducted by means of 4 paired t-tests and was

Bonferroni-corrected to a significance level of p


22

Pupillometry results

The data were tested for normality using the Shapiro-Wilk test and for the outliers

using the outlier detection rule. The Shapiro-Wilk test did not reach significance for any of

the groups, meaning that the data were normally distributed, and no outliers were detected.

An independent t-test was conducted in order to compare the pupil dilation during

MOT1 in the experimental and control groups to establish whether the groups were

comparable from the start. The analysis indicated that the groups did not differ by pupil

dilation during MOT1 (t(21)=.706; p=.196; d=.55; Figure 6).

The mixed repeated measures ANOVA indicated a significant main effect of load

(F=30.714; p=.0001; ŋ2=.594) and a significant main effect of session (F=6.815; p=.016;

ŋ2=.245). However, no significant interaction between load and group (F=.256; p=.857;

ŋ2=.012); session and group (F=1.040; p=.320; ŋ

2=.047) and load, session and group (F=.767;

p=.597; ŋ2=.035) were found.

A repeated measures ANOVA for the experimental group revealed a significant main

effect of load (F=21.860; p=.000; ŋ2=.665) and session (F=16.474; p=.002; ŋ

2=.600).

However, no significant interaction between load and session was shown (F=.330; p=.804;

ŋ2=.029). A planned comparison with paired t-tests was conducted in order to compare the

pupil dilation during MOT1 and MOT2 for the experimental group for each load and

Bonferroni-corrected to a significance level of p


23

significance for load3 in the MOT1 (M=.06; SD=.101) and MOT2 (M=-.021; SD=.107)

conditions; t(11)=2.347; p = .039, d=.768. However, no significant difference for load4

(p=.076) and load5 (p=.139) was detected.

A repeated measures ANOVA for the control group showed a significant main effect

of load (F=10.735; p=.000; ŋ2=.518), but no significant main effect of session (F=.739;

p=.410; ŋ2=.069) or interaction between session and load (F=.523; p=.670; ŋ

2=.05). Paired t-

tests in the control group (Figure 7B) were Bonferroni-corrected to a significance level of

p


24

Figure 8. The graphs present mean theta coherence across all resting baselines for the trained electrode pairs: F3-F4 (A), C3-C4 (B), P3-P4 (C). The regression lines are drawn with dotted lines. Bars represent SEM.


25

Full-spectrum analysis

In NeuroGuide the coherence between inter-hemispheric and intra-hemispheric

electrodes are calculated. The analysis output includes topographical maps with t-values and

the corresponding significance level for comparison. The interhemispheric and

intrahemispheric coherence in different frequency bands (delta, theta, alpha and beta

correspondingly) are presented with figures.

For a type of full spectrum analysis reported here, it is recommended to use a

Figure 9. Figure 9A. The absolute power comparisons during resting baseline EEG between sessions 1-3 and 8-10. Figure 9B and 9C. FFT coherence comparisons during resting baseline EEG for session 1-3 versus session 4-6 (9B) and session and session 1-3


26

statistical correction for the multiple comparisons made. However, there is a trade-off

between increasing the likelihood of finding a significant effect in topographically and

frequency specific regions and getting the full overview, even though the chance of a type 1

error increases. To follow Gruzelier’s (2014b) recommendation to do full spectrum analysis,

uncorrected data is reported in Figure 9, 10 and 11, while full p-value tables can be found in

the appendix.

Figure 9A displays absolute power comparisons during eyes-closed baseline

recordings for session 1-3 versus session 8-10. The Figure presents the likelihood of obtaining

the t-value for each of the comparisons. White color indicates no significant change. Except

for a change in Fz delta power, the neurofeedback protocol did not alter participant’s baseline

absolute power values. This is an important finding as it strengthens the validity of the

analysis of coherence, since power changes could confound the analysis of coherence (Fein et

al., 1988).

Furthermore, the neurofeedback protocol led to statistically significant changes in

participant’s baseline coherence values in all frequency bands, especially in the lower

frequencies including the trained theta band. Looking at the development from Figure 9B to

Figure 9C, it is evident that more significant changes in coherence took place with more

sessions of neurofeedback training. With the exception of a decrease in theta Fp2-F3

coherence between session 1-3 and 4-6, all significant changes were due to increased

coherence. More longitudinal coherence changes occurred only in the session 1-3 versus 8-10

comparison, with differences evident in occipital and frontal areas (O1-F7) theta, occipital

and central areas (O1-C3) beta in the left hemisphere and between frontal electrodes (F3-F4)

theta as examples. Coherence changes were most notably intrahemispheric, and most so in the

left hemisphere. Of the trained electrode pairs F3-F4, P3-P4 and C3-C4, only P3-P4 showed

significant changes in the trained theta band, occurring in the session 1-3 versus 8-10

comparison.

In summary, the neurofeedback training protocol led to the increases in coherence and

this could not be explained by absolute power changes. Changes were observed in the both

intra- and inter-hemispheric coherence. Increased central inter-hemispheric coherence was

observed in delta and beta frequencies, parietal increased coherence was found in delta, theta

and beta bands and there was increased frontal theta coherence. Regarding the intra-

hemispheric coherence an increase of occipito-central, occipito-parietal and occipito-frontal


27

Figure 10. Comparison of the coherence between MOT1 and MOT2 in MOT-task averaged across all loads for the experimental and control groups.

coherence in the left hemisphere was observed in all bands. Clearly, the NF protocol induced

changes in the resting EEG specifically related to coherence and not power.

Coherence during MOT1 and MOT2. Figure 10 shows the coherence changes for

MOT2 compared to MOT1, averaged across all loads.

Experimental group. For the experimental group, interhemispheric changes involved

decreasing coherence between parietal electrodes in delta frequency; occipital in theta;

temporal for beta and frontal for alpha and beta. The delta band showed reduction of the

coherence in frontal, central and parietal midline areas of the left hemisphere and across

frontal, central, parietal and occipital electrode in the right hemisphere. The intrahemispheric

changes for the theta frequency reveal increased longitudinal coherence across frontal, central,

parietal and occipital areas of both hemispheres. Similar patterns were detected for alpha and

beta bands, where increased coherence was shown between occipital, parieto-temporal,

central and frontal regions of the left hemisphere; and in frontal, central and parietal areas of

the right. In the beta frequency interhemispheric changes also involved increased coherence

between parieto-temporal electrodes.

Control group. For the control group, the interhemispheric results in delta band

revealed decreasing coherence between frontal, parieto-temporal and occipital regions, in

addition, coherence between frontal, central, parietal, temporal and occipital electrodes

decreased in both hemispheres. The theta and beta bands revealed decreased coherence

between temporal and parietal electrodes of the both hemispheres. Decreased coherence in

theta frequency was detected between the occipital and frontal areas of the both hemispheres.


28

The intrahemispheric coherence decreased between parietal, temporal and central electrodes

in the right and left hemispheres. At the same time, coherence in the theta band increased, as

seen in the frontal and central areas of the both hemispheres. Interhemispheric coherence

increased between the parietal electrodes in theta, alpha and beta frequency. The alpha band

showed increased coherence in the frontal regions of both hemispheres; decreased coherence

in the parietal, central and frontal areas of the left hemisphere and in occipital, parieto-

temporal and central areas in the right. The beta frequency decreased in occipital, parieto-

temporal, temporal regions in the left hemisphere and occipito-temporal areas in the right.

The intrahemispheric increased coherence was shown in the beta band between frontal,

central and parietal areas.

In Figure 11, the changes in task-related EEG coherence are presented for the

experimental and control group separately for different loads of the task. Only the EEG

recordings during correct responses were included.

Experimental group. For the load2 in the experimental group there was increased

coherence in alpha and beta frequency between occipital and central areas of the left

hemisphere. For load3 changes involved increased coherence between occipital and frontal

areas (O1 and F7) in theta band, occipital and central areas (O1 and C3) in alpha and

occipital, parietal and frontal regions (O1-P3, O1-C3, O1-F3, O1-F7) of the left hemisphere in

beta frequency. For load4 coherence increased in frontal and central regions of the right

hemisphere in theta range, occipital and central regions in the left hemisphere and frontal

areas of the right in alpha frequency; and through the left frontal, central and occipital regions

in beta. The highest load involved increased coherence in occipital-frontal areas of the both

hemispheres and right frontal and central regions of the left hemisphere in theta frequency;

frontally (Fp2-F4) in alpha. The beta frequency showed a significant increase between frontal,

central and occipital regions of the left hemisphere and frontal-parietal in the right

hemispheres, while decreased coherence is disclosed in frontal areas (Fp1-F7) in theta, alpha

and beta frequency bands. Interestingly, with increasing of the task difficulty, there was a

parallel increase of the coherence between MOT1 and MOT2. For the easier load-levels of the

task, the enhancement of the coherence involved the left parieto-occipital regions and

included changes in alpha and beta bands. For the most difficult load level, increased

coherence was observed in theta and beta bands and consisted of increased fronto-occipital

coherence bilaterally (with the left hemisphere prevalence in beta band).

Control group. For the control group, the intrahemispheric changes involved decreases

coherence in the parietal areas in delta frequency and in occipital areas for theta. The delta


29

frequency coherence decreased also in temporal and parietal areas in the left hemisphere and

in the parietal and frontal areas in the right. There was an increase frontally in theta and in

central and parietal regions in beta. For load3 decreased coherence in delta frequency was

more expressed than in the lower load: it involved the decrease in intrahemispheric coherence

in the parietal areas and in interhemispheric coherence through the temporal, central, parietal

and frontal areas in both hemispheres. The theta, beta and alpha bands revealed decreased

coherence between frontal, temporal and parietal regions; at the same time, the alpha

frequency increased frontally and centrally and beta frequency showed increased coherence

between the parietal electrodes. The intrahemispheric results showed coherence decreases

between the parieto-temporal and occipital regions in delta, theta and beta. There was also

decrease in coherence in central and parietal areas in alpha frequency. Increased coherence

revealed in the frontal areas for theta, alpha and beta frequencies. Finally, for the load5, delta,

theta and beta bands decreased in coherence between hemispheres in the posterior regions;

there was a significant decrease in delta coherence between T6 and F4, T5 and C3, and other

electrodes in occipital, parietal and temporal regions of the left hemisphere. The theta

frequency showed decreases in parieto-temporal and frontal areas of the right hemisphere, as

well as occipital electrode between hemispheres. At the same time, significant increases in

theta coherence were detected within frontal regions in both hemispheres. The alpha

frequency coherence decreased in occipital and parieto-temporal areas of the right

hemisphere. There was an increase in coherence for the beta frequency in frontal and central

regions of both hemispheres.

As the sample size in the current experiment was small, it was not possible to match

the participants by EEG parameters and, therefore, reduce the impact of individual EEG

differences across groups. The comparison between task-related EEG recordings in the

experimental and control groups were made for MOT1 and revealed differences between the

groups. Therefore, similar comparisons for MOT2 were not conducted as the emphasis was

placed on within-group comparisons.


30

Fig

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31

Discussion

The current study sought to investigate a novel neurofeedback protocol involving

bidirectional, eyes-closed, theta coherence training in order to improve visual attention. At the

behavioral level, the experimental group showed improved accuracy scores and decreased RT

during a subset of load conditions of the MOT task (Figure 3A and Figure 5A). The control

group did not show such improvements in any of the measures (Figure 3B and Figure 5B).

Pupil diameters were recorded during MOT to provide an estimate of mental effort, and a

significant main effect of session was found, revealing that processes related to learning

effects or familiarity with the task may have caused task-related pupil dilations to decrease

from before training to after the training (i.e., from MOT1 to MOT2). Planned t-tests showed

that the experimental group’s decrease in pupil dilations reached significance for a subset of

load conditions (i.e., the easiest, from MOT1 to MOT2; see Figure 6A), implying that the

experimental group spent less mental effort during MOT after NF. Furthermore, the theta

coherence training led to changes in the resting baseline EEG recorded before every training

session (Figure 8, Figure 9B and Figure 9C). Regression analysis revealed a significant linear

increase in theta coherence with more NF training over the trained electrode pairs. The full

spectrum analysis further revealed changes in all recorded frequency bands and outside the

trained electrode pairs. The analysis of task-related EEG in the experimental group showed

increased fronto-occipital, fronto-parietal, and fronto-central intrahemispheric coherence

during MOT2 (Figure 10 and Figure 11). The control group showed widespread decreased

coherence during MOT2 compared to MOT1 (Figure 10 and Figure 11). Despite the

limitations regarding the use of a passive (rather than active) control group and a small

sample size, which both warrant a cautious interpretation of the results, we discuss below

some possible mechanisms that could mediate the effect of NF on MOT performance.

We have trained 12 individuals with EEG based bidirectional theta coherence NF and

comparisons of successive pre-training baselines indicated a pattern of increased coherence in

all frequency bands recorded and, in fact, also outside the trained electrode pairs. To our

knowledge, bidirectional NF protocols have not been investigated in relation to improving

cognition in healthy participants and, therefore, we did not predict the direction of effects of

NF on the resting EEG. Although coherence changes outside the target of training could seem

surprising, they are often observed (Gruzelier, 2014b), and can be explained by reference to

Thatcher’s two-compartment model of coherence (Thatcher et al., 1986). That is, Thatcher

collected resting EEG data from 189 individuals and showed that interhemispheric coherence


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in the frontal areas (F3-F4) was closely linked to the intrahemispheric coherence in the

posterior areas (for example, between O1 and P3). At the same time, interhemispheric

interaction in central electrodes (C3-C4) was associated with the central and frontal

intrahemispheric coherence. Similar pattern have been shown in the coherence between

parietal electrodes. Thus, the present results might be interpreted in line with Thatcher’s

model, as training of the interhemispheric electrodes (F3-F4, C3-C4 and P3-P4) influenced

anterior-posterior coherence within each of the hemispheres.

Furthermore, as discussed in the introduction, some cortical theta rhythms have been

shown to originate from the thalamus (Steriade, 2005), and this subcortical structure has been

proposed to play an important role in EEG changes following NF training. Lubar (1997)

suggested that, in general, NF works through enhancing of the cortical rhythms generated by

several thalamic pace-makers (Steriade, 2005) which have their diverse connections with the

cortex. In this way, NF may alter widespread regions of the cortex by targeting a small

number of electrodes for training. Moreover, there are currently few NF studies reporting the

full spectrum of EEG changes following training, but the prevailing outcome is in line with

non-specificity (Gruzelier, 2014b), meaning that changes can also occur outside the trained

band. This is in line with the present findings, which supports the view that full spectrum

EEG analysis should be included in NF studies. However, in order to make conclusions about

the specific mechanism mediating NF training source-localization more data are required. For

example, an alternative source of Fm-theta may be in the ACC (Sauseng et al., 2007).

Given the large variety of different NF protocols that have been reported to have

beneficial effects on cognition (Gruzelier, 2014a), there might be a common mechanism, at

least across some protocols, by which NF leads to improved cognitive performance. Ghaziri

and colleagues (2013) investigated structural changes with diffusion tensor imaging after 3

months of NF training of beta amplitude, leading to improved performance on a sustained

attention test. Increased fractional anisotropy (FA) values were found in pathways related to

WM and sustained attention, perhaps indicating microstructural changes related to

myelination, axon caliber and fiber density. Correspondingly, myelination remains sensitive

to experience throughout adulthood (Young et al., 2013). In a recently proposed model

(Pajevic et al., 2014), conduction velocity variation supported by myelination works as a

major mechanism for neural plasticity. Activity-dependent modulation of myelin could

functionally influence the oscillatory coupling of distant brain regions (Pajevic et al., 2014).

Taken together, myelin plasticity might be a strong candidate common mechanism by which

some NF-induced cognitive improvement could be explained. All though speculative,


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mechanisms of myelin plasticity might have mediated the effects of NF on MOT in the

present experiment.

In the present experiment, the experimental group showed higher levels of longitudinal

coherence during MOT2 compared to MOT1, as well as higher levels of accuracy and faster

RT. This finding is in line with other studies reporting correlations between high behavioral

performance and increased task-related coherence (Anokhin et al., 1999; Von Stein &

Sarnthein, 2000; Weiss et al., 2000). The communication-through-coherence (CTC)

hypothesis suggests that flexible coherence between oscillating areas is essential for cognitive

performance (Fries, 2005). It is argued that coherence provides a temporal window for

effective communication to interacting neuronal assemblies. Perhaps the increase in task-

related coherence reflects more effective communication between areas of the tracking

network, where information from occipital, parietal and frontal areas can be integrated (Howe

et al., 2009). Looking at Figure 11, it was evident that the experimental group recruited more

coherence during MOT2 between frontal, central, parietal and occipital cortices. Interestingly,

relatively more significant changes related to longitudinal coherence were shown comparing

the higher loads against lower loads. During the task-related EEG, the experimental group

showed a higher number of significant changes in the fronto-parietal, fronto-central and

fronto-occipital coherence in the beta band, compared to the theta band (Figure 10). To some

degree, there are overlaps between patterns of changes in the resting EEG and the task-related

EEG which raises the question of whether the task-related EEG was really “task-related”.

Task-related pupil dilations decreased from MOT1 to MOT2 as reflected by a main

effect of session in the mixed ANOVA. Paired t-tests indicated that only the experimental

group’s decrease in pupil dilations reached significance and for a subset of load conditions

from MOT1 to MOT2 (Figure 7A). A decrease in mental effort as a result of learning effects

(Sibley, Coyne, & Baldwin, 2011) could be expected in both groups, but as the experimental

group was the only group showing significant changes from MOT1 to MOT2, the findings

suggest that NF led task-related pupil dilations in the experimental group to decrease further.

However, as the interaction between group and session was not found in the mixed ANOVA

for pupillary responses, we cannot conclude that the experimental group showed significantly

lower pupillary responses than the control group. If the decrement observed in the

experimental group is caused by NF, our data suggest that NF might lead to cortical and

subcortical changes related to higher cognitive performance. Whether these changes are

actually caused by NF or not, it seems that both decreased task-related pupil dilations and

increased task-related coherence during MOT is associated with higher performance on a


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group level. As the NF protocol applied here was not specifically aimed at MOT related EEG

activity, our results indicate that the NF training might have led to higher performance

through enhancement of some general attentional/cognitive mechanism. If so, the medium

effect sizes (d=0.5 or higher; (Cohen, 1977) reported here are perhaps surprising. However,

the possibly NF induced improvement of the experimental group is likely mixed with learning

effects and motivational factors.

Alternatively, a slight non-significant decrease in MOT performance that coincided

with a significant decrease in coherence in all frequency bands for the control group, could

suggest that the control group had lost some motivation by MOT2. Typically, lower task-

related coherence is reported during lower levels of performance (Anokhin et al., 1999; Weiss

et al., 2000). In fact, participants in the control group returned to the re-test session only in

order to take part in the experiment and were generally interested in experiencing the EEG

recording and pupillometry. By the second session the novelty of the approach would be thus

lost on them. If motivation could cause a change in performance and coherence, the same

argument should be applied to increased performance and coherence for the experimental

group. In this case, the participants in the experimental group may have built up more

motivation by participating in the cost-free neurofeedback training sessions, which may have

given a particular meaning to the final MOT2 session that concluded the training.

Limitations and future directions

The present experiment has several limitations that should be addressed in further

research. First of all, there was only a passive control group and the inclusion of an active

control group would further strengthen the current results. It is plausible that the mere

participation in a brain training study significantly alters a person’s motivation and

performance level. For example, a control group could have received pseudo-neurofeedback,

as was done in Enriquez-Geppert and colleagues (2014) study. Typically, pseudo-

neurofeedback involves random feedback that should not alter the functioning of the

receiver’s brain. Including such an active control group, one could more confidently conclude

that changes in the experimental group were due to the specific NF training protocol. Given

the time limit and demand on data sampling, it was not feasible to include such an additional

group in the Master’s project. A possible follow up-study should address this problem.

However, based on findings from other NF-studies that have included pseudo-neurofeedback


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