Eyes-open Rest Research Articles

EEG activation in preschool children: characteristics and predictive value for current and future mental health status

BackgroundPrevious research has characterised EEG changes associated with resting activation in primary school children and adults, while task-related activation has only been considered in adults. The current study characterises physiological activation in preschool children and examines the potential value of activation indices for predicting mental health status at two time points. AimsTo investigate how resting activation and task-related activation are represented in 4- to 5-year-old preschool children and examine if these activation indices can predict current and future mental health status. Methods and proceduresFrontal EEG was recorded from 81 preschool children during eyes-closed resting, eyes-open resting, and an inhibitory control task to allow calculation of activation indices. The Child Behaviour Checklist was completed by the child’s parent at this time, and again 6-8 months later after the child’s transition to kindergarten. Outcomes and resultsResting activation was represented by reductions in frontal delta, theta, and alpha power in the eyes-open compared to eyes-closed condition, and an increase in frontal beta power. Task-related activation was represented by increases in frontal delta, theta, and alpha power and a decrease in beta power. Frontal delta and theta task-related activation significantly predicted externalising behaviours in both preschool and kindergarten, with stronger prediction in kindergarten. Conclusions and implicationsThis study characterised resting and task-related activation in preschool children, and reported similar effects to those found in older children and adults for resting activation, with novel effects for task-related activation. As task-related activation indices were predictive of externalising behaviours in both preschool and kindergarten, these results have implications for early identification of children who experience externalising behavioural problems across the transition to school period. What does this study add?This study provides new data on how the fundamental physiological processes of resting and task-related activation, both of which are theorized to contribute to “upstream” processes such as executive functions and broader behaviour, are represented in the frontal EEG of preschool aged children. We also learn that the top-down task-related activation indices for delta and theta activity were predictive of current mental health status and future status after the transition to kindergarten, while the bottom-up resting activation indices were not.

Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

A - 175 EEG Phase Can be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and SNR.

Electroencephalogram (EEG) phase can capture neural oscillation dynamics. Accordingly, EEG phase is a potential target for neurofeedback and brain-computer interfaces (BCIs). However, rigorous tests of the generalizability of real-time EEG phase are needed, requiring accurate phase estimates. We examined how cognitive states affect EEG phase prediction accuracy in the parieto-occipital alpha band. We identified datasets through public repositories. After preprocessing, we used the Educated Temporal Prediction Algorithm (ETP) to predict future peaks. We compared these predictions to the original signal, and defined accuracy from 0 to 100%. Our independent variable was cognitive state (eyes-open rest, eyes-closed rest, task), dependent variable was accuracy, and covariates were EEG instantaneous power and signal-to-noise ratio (SNR). We used a linear mixed-effects model, with random effects for dataset and individual. Across the 11 datasets, we had 543 participants and 1,641,074 predictions. There were no significant accuracy differences among the conditions (p < 1e-5), with a baseline accuracy of 59.07%. Power had an effect on accuracy, with a unit increase leading to a 13.1% increase in accuracy (p < 1e-5). SNR had an effect on accuracy, with an effect size of 0.46% (p < 1e-5), with a significant negative interaction with power with an effect size of -0.51% (p < 1e-5). Our results indicated that we could predict EEG phase accurately across cognitive conditions and datasets, with higher accuracy for high instantaneous band-power and SNR. Accordingly, real-time EEG phase experiments, closed-loop technologies, and BCIs should minimize external unwanted noise while targeting periods of high power, as opposed to manipulating experimental and cognitive conditions.

Normative Temporal Dynamics of Resting EEG Microstates.

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Links between excessive daytime sleepiness and EEG power and activation in two subtypes of ADHD

ObjectivesThis study aimed to replicate previously reported EEG characteristics between typically developing (TD) children and two subtypes of Attention Deficit Hyperactivity Disorder (ADHD) using a frontal, single-channel, dry-sensor portable EEG device, and explore whether differences are moderated by excessive daytime sleepiness (EDS). MethodsChildren with ADHD Inattentive (ADHD-I) and ADHD Combined presentation (ADHD-C) and typically-developing (TD) children (N = 34 in each group) had frontal EEG recorded during eyes-closed resting, eyes-open resting, and focus tasks. Participants also completed the Children’s Self-Report Sleep Patterns - Sleepiness Scale as a measure of EDS. ResultsConsistent with previous literature, there were increases in frontal delta and theta power in the ADHD-C compared to ADHD-I and TD groups, in all conditions. Novel power and activation effects in ADHD subtypes, as well as significant group and EDS interactions for alpha and beta power were also found. ConclusionsThese findings highlight the importance of considering ADHD subtypes and EDS when exploring EEG characteristics, and have important implications for the diagnosis and treatment of children with ADHD.

STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG.

STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.

Exploring Fatigue Effects on Performance Variation of Intensive Brain-Computer Interface Practice.

Motor imagery (MI) is an endogenous mental process and is commonly used as an electroencephalogram (EEG)-based brain–computer interface (BCI) strategy. Previous studies of P300 and MI-based (without online feedback) BCI have shown that mental states like fatigue can negatively affect participants’ EEG signatures. However, exogenous stimuli cause visual fatigue, which might have a different mechanism than endogenous tasks do. Furthermore, subjects could adjust themselves if online feedback is provided. In this sense, it is still unclear how fatigue affects online MI-based BCI performance. With this question, 12 healthy subjects are recruited to investigate this issue, and an MI-based online BCI experiment is performed for four sessions on different days. The first session is for training, and the other three sessions differ in rest condition and duration—no rest, 16-min eyes-open rest, and 16-min eyes-closed rest—arranged in a pseudo-random order. Multidimensional fatigue inventory (MFI) and short stress state questionnaire (SSSQ) reveal that general fatigue, mental fatigue, and distress have increased, while engagement has decreased significantly within certain sessions. However, the BCI performances, including percent valid correct (PVC) and information transfer rate (ITR), show no significant change across 400 trials. The results suggest that although the repetitive MI task has affected subjects’ mental states, their BCI performances and feature separability within a session are not affected by the task significantly. Further electrophysiological analysis reveals that the alpha-band power in the sensorimotor area has an increasing tendency, while event-related desynchronization (ERD) modulation level has a decreasing trend. During the rest time, no physiological difference has been found in the eyes-open rest condition; on the contrary, the alpha-band power increase and subsequent decrease appear in the eyes-closed rest condition. In summary, this experiment shows evidence that mental states can change dramatically in the intensive MI-BCI practice, but BCI performances could be maintained.

Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity.

During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions.

Decrease in brain complexity with methylphenidate treatment in boys diagnosed with attention deficit hyperactivity disorder: An entropy-based qeeg analysis

IntroductionAttention deficit hyperactivity (ADHD) disorder is a common childhood neurodevelopmental disorder, and Methylphenidate (MPH) is a first-line therapeutic option for treating ADHD.However, how brain complexity and entropy changes with methylphenidate treatment the clinical implications of possible changes in entropy and the clinical implications of possible changes in entropy have yet to be studied.ObjectivesThis study aimed to reveal how the MPH treatment affects the complexity in the brain of children with ADHD by entropy-based qEEG analysis. In addition, the presence of the relationship between possible neurophysiological changes to be detected with clinical variables and how they are two other important questions of this study to be answered.MethodsDuring eyes-open resting, EEG signals were recorded from 25 boys with ADHD-combined type before MPH administration and at the end of the 1st month of the treatment. Approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PermEn) were used to analyse.ResultsA statistically significant decrease in entropy level was found with MPH treatment in the F4 channel according to approximate entropy (ApEn) and sample entropy (SampEn) analysis (p&lt;0.05). In addition, according to permutation entropy (PermEn) analysis, the decrease in entropy with MPH treatment in the regions indicated by F3, F4, P4, T3, T6, and O2 channels was found to be statistically significant (p &lt;0.05).ConclusionsThis is the first study to investigate how MPH treatment affects the complexity in the brain of children with ADHD. Entropy-based qEEG analysis may be a new method that can be used in diagnostic, clinical and prognostic predictions in ADHD.DisclosureNo significant relationships.

Increase in brain connectivity with methylphenidate treatment in boys diagnosed with attention deficit hyperactivity disorder: A coherence-based qeeg analysis

IntroductionAttention deficit hyperactivity (ADHD) disorder is a common childhood neurodevelopmental disorder, and Methylphenidate (MPH) is a first-line therapeutic option for treating ADHD.However, how brain connectivity changes with methylphenidate treatment have yet to be studied.ObjectivesThis study investigates how the MPH treatment affects the connectivity in the brain of children with ADHD by coherence-based qEEG analysis during rest.MethodsDuring eyes-open resting, EEG signals were recorded from 25 boys with ADHD-combined type before MPH administration and at the end of the 1st month of the treatment. Mutual Information (MI),Coherence Function (COH) and Phase Locking Value (PLV) were used to analyse the changes in brain connectivity.ResultsA statistically significant increase in connectivity level was found with MPH treatment between the F3-F4 channels, P3-P4 channels, F7-F8 channels and T5-T6 channels according to PLV, COH and MI analysis (p&lt;0.001).ConclusionsThis is the first study to investigate how MPH treatment affects the connectivity of the brain of children with ADHD. Coherence-based qEEG analysis may be a new method that can be used in diagnostic, clinical and prognostic predictions in ADHD.DisclosureNo significant relationships.

EEG coherence during subjectively-rated psychological state variations

BackgroundNeurofeedback training aims to develop awareness and control of psychological states in order to self-regulate brain activity and while used widely therapeutically, important questions remain unanswered. Central to these aims is an assumed association between the live EEG-based feedback and the subjective experience of a psychological state. To date, there is little evidence to support this relationship. Previous studies examining the association between an EEG index and subjective experience have explored only the presence or absence of the state, or merely assumed state variations. The current study aims to examine this association by considering how different levels of a psychological state (i.e., attention) are reflected in EEG coherence. MethodsOur approach aims to allow comparisons of EEG coherence between psychological states (attention vs. rest), and also within subjectively-rated levels of a psychological state (attention) through a purpose-designed questionnaire. Thirty healthy adult participants performed a resting eyes-open (REO) and attention modulation task, while 28 channels of EEG were recorded. Levels within the psychological state were subjectively-attested by participants on a trial-by-trial basis. ResultsThe main analyses examined the effect of subjectively-rated attention levels (SRALs) on EEG coherence, with results suggesting that high and low SRALs may be represented by: 1) different levels of alpha and theta coherence at anterior and posterior electrodes of the frontal lobe bilaterally, and 2) different levels of alpha coherence between central and parietal lobes, also bilaterally. DiscussionThese findings provide partial, preliminary evidence for EEG correlates of SRALs. These findings may have implications for understanding underlying mechanisms of NFT, which is an underdeveloped area.

Troubled Hearts: Association Between Heart Rate Variability and Depressive Symptoms in Healthy Children.

Heart rate variability (HRV) captures the change in timing of consecutive heart beats and is reduced in individuals with depression and anxiety. The present study investigated whether typically-developing children without clinically recognized signs of depression or anxiety showed a relationship between HRV and depressive or anxiety symptoms. Children aged 9-14years (N = 104) provided three minutes of cardiac signal during eyes closed rest and eyes open rest. The association between high frequency HRV, low frequency HRV, root mean square of the successive differences (RMSSD), and pNN20 versus depressive symptoms (NIH Toolbox and Child Behavior Checklist) was investigated. Results partially confirm our hypothesis, with pNN20 positively correlated with the self-reported depression measure of loneliness while controlling for age, sex, social status, and physical activity. The association was stronger in male participants. However, there is no consensus in the literature about which HRV measures are associated with depressive symptoms in healthy children. Additional studies are needed which reliably account for variables that influence HRV to establish whether certain HRV measures can be used as an early marker for depression risk in children.

Development of Frontal EEG Differences Between Eyes-Closed and Eyes-Open Resting Conditions in Children: Data From a Single-Channel Dry-Sensor Portable Device.

Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

The effect of movie-watching on electroencephalographic responses to tactile stimulation

Movie-watching is becoming a popular acquisition method to increase compliance and enable neuroimaging data collection in challenging populations such as children, with potential to facilitate studying the somatosensory system. However, relatively little is known about the possible crossmodal (audiovisual) influence of movies on cortical somatosensory processing. In this study, we examined the impact of dynamic audiovisual movies on concurrent cortical somatosensory processing using electroencephalography (EEG). Forty healthy young adults (18–25 years) received passive tactile fingertip stimulation while watching an “entertaining” movie and a novel “low-demand” movie called ‘Inscapes’ compared to eyes-open rest. Watching a movie did not modulate properties of early or late somatosensory-evoked potentials (SEPs). Similarly, no crossmodal influence on somatosensory adaptation, denoted by a reduction in SEP amplitude with repetitive tactile stimulation, was found. The prominent oscillatory responses in the alpha and beta frequency bands following tactile stimulation differed as a function of viewing condition, with stronger alpha/beta event-related desynchronization (ERD) during movie-watching compared to rest. These findings highlight that movie-watching is a valid acquisition method during which SEPs can be measured in basic research and clinical studies, but that the attentional demands of movies need to be taken into account when performing oscillatory analyses.

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.

Not all imagery is created equal: A functional Magnetic resonance imaging study of internally driven and symbol driven musical performance imagery

Playing a musical instrument requires fast multimodal sensory-motor processing which can be activated by voluntary access to performance imagery. Musicians use different methods to activate imagery for the purpose of “mental practice”. The aim of the present study was to investigate cortical activation patterns in different methods of mental practice of musical performance. While undergoing functional magnetic resonance imaging (fMRI), 7 male oud (fretless lute) players engaged in performance imagery of a pre-memorized short excerpt from mainstream oud repertoire using three common imagery methods (task conditions): From memory (internally driven) 1)eyes closed, 2)eyes open, and while following the musical score (symbol driven). The study design consisted of a four-task 16-epoch block design where the 4th task was an eyes-open rest tasks (EOR) included as a control condition. Each task was repeated four times in a pseudorandomized sequence. The superior temporal gyrus and transvers temporal gyrus (Heschl) were active in the left and right hemispheres in all imagery conditions. The occipital cortex, specifically the fusiform gyrus was active in all three conditions. Symbol driven imagery resulted in less prominent activations in frontal and parietal lobes. The findings suggest that not all imagery modalities activate sensory and motor areas similarly.

T143. NOT A NUISANCE ANY MORE: GLOBAL FMRI SIGNAL AT REST, PROCESSING SPEED AND SYMPTOM SEVERITY IN SCHIZOPHRENIA

BackgroundBefore data from resting-state functional magnetic resonance imaging (rs-fMRI) is analyzed, the global signal (GS) - average blood-oxygen level dependent (BOLD) signal across all voxels in the brain - is normally removed through global signal regression (GSR). This convention arose in order to control for changes in brain activity that are usually of no interest but may be caused by non-neuronal factors, including changes in respiratory rate, arterial CO2 levels or cardiac pulsation. However, recent studies have indicated that GS may systematically vary between patients with schizophrenia and healthy controls. In addition to testing this notion, we also studied if the dynamic variance of global signal across time carried any meaningful information that relates to overall symptom severity and information processing speed in patients with schizophrenia.MethodsData was collected from 39 in a clinically stable, medicated early stage of schizophrenia (median duration of illness= 6.5 years), and 34 sex, age and parental socioeconomic-status matched healthy controls over a 10-minute period of eyes-open rest at TR=2.5s (3T Philips Achieva, 240 time points, dual-echo, gradient-echo EPI). Scores were obtained from the Signs and Symptoms of Psychotic Illness (SSPI) scale and the Digit Symbol Substitution Test (DSST). Rs-fMRI time-series data were motion-corrected (using 6 parameters), slice-time corrected, reoriented with structural images, band-pass filtered (0.01–0.1 Hz), scrubbed using ArtRepair for framewise displacement and transformed to MNI space. SPM8 and the advanced version of the Data Processing Assistant for resting-state fMRI (DPARSFA) were used for this purpose. GS mean was computed from all grey matter voxels using a template mask in MNI space, using grand mean scaling to a base of 1000, averaged across all time-points. The variance of GS across time (dynamic GS variance) was computed from the entire 10-minutes of acquisition (240 time points).ResultsIndependent-sample t-tests used to compare GS mean between controls (mean[sd] = 3135.1[1244.4]) and the SCZ group (mean[sd] = 3207.8[1191.7]) yielded no significant results [t(71) = -0.14, p = .89]. The temporal variance of GS did not differ between controls (mean[sd] = 113.05[59.41]) and the SCZ group (mean[sd] = 118.02[57.93]) [t(71) = -0.36, p = .72]. In the SCZ group there was a significant correlation between the total SSPI score reflecting overall illness severity (□ = -.322, p < 0.05) and the mean GS. This relationship was especially pronounced for the syndrome of Reality Distortion (rho = -.344, p < 0.05) and Disorganization (rho = -.303, p = 0.065), where higher symptom severity was seen in patients with lower mean GS.Dynamic variance in GS was higher in healthy controls with lower mean DSST (r = -.364, p = 0.04), but no such relationship was seen in the SCZ group (r = .066, p = .694). Notably, when compared to controls (mean[sd] = 57.4[9.40]), patients (mean[sd]=42.4[9.97]) had significantly lower DSST scores [t(69) = 6.47, p < 0.001]. Neither GS nor GS variance related to root mean square of framewise displacement in the 2 groups.DiscussionWe did not find an overall increase or reduction in the global signal in patients with schizophrenia. Nevertheless, the strength of global signal obtained from resting fMRI is related to severity of persisting symptoms of schizophrenia, whereas the dynamic variance of this signal relates to the speed of processing ability assessed outside the scanner in healthy subjects. With emerging evidence relating global signal to cognitive vigilance and overall brain connectivity, our results indicate that global signal is a parameter of interest that should not be automatically discarded in resting fMRI studies of schizophrenia

Individual differences in functional connectivity during naturalistic viewing conditions

Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level.

EEG-Based Biometric Authentication Using Gamma Band Power During Rest State

Electroencephalography (EEG), one of the most effective noninvasive methods for recording brain’s electrical activity, has widely been employed in the diagnosis of brain diseases for a few decades. Recently, the promising biometric potential of EEG, for developing person identification and authentication systems, has also been explored. This paper presents the superior performance of power spectral density (PSD) features of gamma band (30–50 Hz) in biometric authentication, compared to delta, theta, alpha and beta band of EEG signals during rest state. The proposed authentication technique based on simple cross-correlation values of PSD features extracted from 19 EEG channels during eyes closed and eyes open rest state conditions among 109 subjects offers an equal error rate (EER) of 0.0196 which is better than the state-of-the-art method employing eigenvector centrality features extracted from gamma band of 64 EEG channels of the same dataset. The obtained results are promising, but further investigation is essential for exploring the subject-specific neural dynamics and stability of gamma waves and for optimizing the results.

Eyes-Open Resting EEG of Women with Endometriosis Versus Age-Matched Controls

Eyes-Open Resting EEG of Women with Endometriosis Versus Age-Matched Controls