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
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By Ksenia Folomeeva & Ove Mathias Langerud Nesheim
Submitted as a master thesis in Cognitive Neuroscience Department of Psychology
University of Oslo
May 2015
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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
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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).
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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.
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
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
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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
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
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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).
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’
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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-
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).
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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
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.
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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.
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.
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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);
load3 (3 targets, 9 distractors);
load4 (4 targets, 8 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
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).
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.
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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
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; ŋ2=.001). A significant interaction effect between Group and MOT session (F=6.622; p<0.01; ŋ2=.24) was observed. The group by session interaction indicated that the accuracy level across sessions differed between the experimental and the control group.
Figure 2. The graph presents the % of correct responses during MOT tasks in experimental and control groups averaged across load conditions. The bars represent the between-subjects standard error of the mean (SEM).
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<0.0125. There was a significant difference in accuracy.
The difference barely missed significance for the highest load between the MOT1 (M=.785;
SD=.095) and MOT2 (M=.86; SD=.072) conditions; t(11)=-2.652; p = .023, d=.88. No significant effects were observed for load 2 (p=.863), load 3 (p=.063) or load 4 (p=.770).
Repeated measures ANOVA for the control group showed a significant main effect of load (F=20.874; p=.000; ŋ2=.676). However, no significant main effect of session (F=3.954;
p=.075; ŋ2=.283) or interaction between session and load (F=.326; p=.806; ŋ2=.032) was revealed. Additionally, paired t-tests were conducted in order to compare accuracy during MOT1 and MOT2 for the control group for each load (Figure 3B) and Bonferroni-corrected to a significance level of p<0.0125. No significant difference was found in load2 (p=.402), load3 (p=.098), load4 (p=.094) or load5 (p=.39).
Figure 3. The diagram presents the % of correct responses during MOT tasks in the experimental (A) and control (B) group for each load condition. The bars represent the between-subjects SEM.
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, ŋ2=.113). A significant interaction effect of session and group (F=5.665; p<0.05; ŋ2=.221) was shown. The first interaction indicated that RT differed across sessions between the experimental and control group. The interaction was caused by the fact that the experimental group performed faster after NF training, whereas the control group performed slower in MOT2 (Figure 4).
In addition, a significant interaction between session and load (F=8.469; p=.001, ŋ2=.585) was revealed. The session and load interaction shows that RT level across different sessions differed between load conditions, irrespective of group. However, no significant interactions between load and group (F=.731; p=.547; ŋ2=.109); or session, load and group (F=.690; p=.570; ŋ2=.103) were found.
Repeated measures ANOVA for the experimental group revealed a main effect of the load (F=26.296; p=.000; ŋ2=.705) and session (F=11.272; p=.006; ŋ2=.506). A significant interaction between the session and load was shown (F=6.127, p=.002; ŋ2=.358). A planned
Figure 4. The diagram presents the difference in RT in the MOT tasks in experimental and control groups averaged across load conditions. The bars represent the between- subjects SEM.
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<0.0125. The analysis revealed a significant difference in RT (Figure 5A) for load5 between MOT1 (M=1053.84; SD=173.55) and MOT2 (M=933.15; SD=161.85) conditions; t(11)=4.952; p=.0001; d=.71. The differences barely missed significance level for load3 between MOT1 (M=934.93; SD=170.23) and MOT2 (M=880.72; SD=140.61) conditions; t(11)=2.537; p = .028, d=.34. No significant effect was shown for load2 (p=.09) or load4 (p=.04).
Repeated measures ANOVA for the control group revealed a main effect of load (F=35.320; p=.000; ŋ2=.779) and a significant interaction between session and load (F=3.635;
p=.024; ŋ2=.267). No significant effect of session (F=.333; p=.577; ŋ2=.032) was revealed.
Paired t-tests were conducted in order to compare control group RT between MOT1 and MOT2 for each load (Figure 5B) and Bonferroni-corrected to a significance level of p<0.0125.No significant difference was found in load2 (p=.116), load3 (p=.745), load4 (p=.116) or load5 (p=.304).
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; ŋ2=.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; ŋ2=.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<0.0125. There was a significant difference in pupil dilation (Figure 7A) for load2 between MOT1 (M=.009; SD=.092) and MOT2 (M=- .048; SD=.089) conditions; t(11)=3.387; p = .006, d=.63. The difference barely missed
Figure 6. The diagram presents the difference in pupil dilations during MOT tasks in experimental and control groups averaged across loads. The bars represent the between-subjects SEM.
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; ŋ2=.05). Paired t- tests in the control group (Figure 7B) were Bonferroni-corrected to a significance level of p<0.0125, and did not show any significant differences between MOT1 and MOT2 for load2 (p=.283), load3 (p=.877), load4 (p=.291) or load5 (p=.569).
EEG results
Regression analysis of resting baseline EEG. A simple linear regression was calculated to predict the mean theta coherence in trained electrodes pairs on a group level based on the number of NF training sessions. For electrode pair F3-F4 a significant regression equation was found (F(9)=6.079; p=.039), with an R2 of .432. Participants’ predicted theta coherence was equal to .8359*(number of sessions)+63.087 (Figure 8A). For electrode pair C3-C4 a significant regression equation was found (F(9)=5.816; p=.042), with an R2 of .421.
Participants’ predicted theta coherence was equal to 1.323 *(number of sessions)+52.211 (Figure 8B). For electrode pair P3-P4 a significant regression equation was found
(F(9)=11.680; p=.009), with an R2 of .593. Participants’ predicted theta coherence was equal to 1.037 *(number of sessions)+55.164 (Figure 8C).
Figure 7. The diagram presents the difference in pupil dilation during MOT1 and MOT2 for the experimental (A) and control (B) group for each load condition. The bars represent the between- subjects SEM.
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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.
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
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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
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.
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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
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.
