Brain Activation Patterns Research Articles

Steady-State Visual Evoked Potential-Based Brain–Computer Interface System for Enhanced Human Activity Monitoring and Assessment

Advances in brain–computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.

Pediatric Neural Changes to Physical and Emotional Pain After Intensive Interdisciplinary Pain Treatment

Objective: Brain areas activated during pain can contribute to enhancing or reducing the pain experience, showing a potential connection between chronic pain and the neural response to pain in adolescents and youth. Methods: This study examined changes in brain activation associated with experiencing physical pain and observing physical and emotional pain in others by using functional magnetic resonance imaging (fMRI) before and after intensive interdisciplinary pain treatment (IIPT). Eighteen youths (age 14 to 18) with widespread chronic pain completed fMRI testing before and after IIPT to assess changes in brain activation in response to physical and emotional pain. Results: Broadly, brain activation changes were observed in frontal, somatosensory, and limbic regions. These changes may suggest improvements in descending pain modulation via thalamus and caudate, and the different pattern of brain activation after treatment suggests potentially better discrimination between physical and emotional pain. Brain activation changes were also correlated with improvements in clinical outcomes of catastrophizing (reduced activation in right caudate, right mid-cingulate, and postcentral gyrus) and pain-related disability (increased activation in precentral gyrus, left hippocampus, right middle occipital cortex, and left superior frontal gyrus). Discussion: These changes could indicate that reduced brain protective responses to pain were associated with treatment-related improvements. This pilot study highlights the need for larger trials designed to better understand the brain mechanisms involved in pediatric widespread pain treatment.

Shared Patterns of Cognitive Control Behavior and Electrophysiological Markers in Adolescence

Abstract Behavioral parameters obtained from cognitive control tasks have been linked to electrophysiological markers. Yet, most previous research has investigated only a few specific behavioral parameters at a time. An integrated approach with simultaneous consideration of multiple aspects of behavior may better elucidate the development and function of cognitive control. Here, we aimed to identify shared patterns between cognitive control behavior and electrophysiological markers using stop-signal task data and EEG recordings from an adolescent sample (n = 193, aged 11–25 years). We extracted behavioral variables covering various aspects of RT, accuracy, inhibition, and decision-making processes, as well as amplitude and latency of the ERPs N1, N2, and P3. To identify shared patterns between the two sets of variables, we employed a principal component analysis and a canonical correlation analysis. First, we replicated previously reported associations between various cognitive control behavioral parameters. Next, results from the canonical correlation analysis showed that overall good task performance was associated with fast and strong neural processing. Furthermore, the canonical correlation was affected by age, indicating that the association varies depending on age. The present study suggests that although distributional and computational methods can be applied to extract specific behavioral parameters, they might not capture specific patterns of cognitive control or electrophysiological brain activity in adolescents.

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model’s performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.

Resting-state brain activation patterns and network topology distinguish human sign and goal trackers

The “Sign-tracker/Goal-tracker” (ST/GT) is an animal model of individual differences in learning and motivational processes attributable to distinctive conditioned responses to environmental cues. While GT rats value the reward-predictive cue as a mere predictor, ST rats attribute it with incentive salience, engaging in aberrant reward-seeking behaviors that mirror those of impulse control disorders. Given its potential clinical value, the present study aimed to map such model onto humans and investigated resting state functional magnetic resonance imaging correlates of individuals categorized as more disposed to sign-tracking or goal-tracking behavior. To do so, eye-tracking was used during a translationally informed Pavlovian paradigm to classify humans as STs (n = 36) GTs (n = 35) or as Intermediates (n = 33), depending on their eye-gaze towards the reward-predictive cue or the reward location. Using connectivity and network-based approach, measures of resting state functional connectivity and centrality (role of a node as a hub) replicated preclinical findings, suggesting a major involvement of subcortical areas in STs, and dominant cortical involvement in GTs. Overall, the study strengthens the translational value of the ST/GT model, with important implications for the early identification of vulnerable phenotypes for psychopathological conditions such as substance use disorder.

Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e. brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach using real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants (n = 16) in a double-blinded decoded fMRI neurofeedback experiment to up- or downregulate the SIIPS. Our results indicate that participants can learn to downregulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Neurocognitive dynamics of preparatory and adaptive cognitive control: Insights from mass-univariate and multivariate pattern analysis of EEG data.

Cognitive control refers to humans' ability to willingly align thoughts and actions with internally represented goals. Research indicates that cognitive control is not one-dimensional but rather integrates multiple sub-processes to cope with task demands successfully. In particular, the dynamic interplay between preparatory (i.e., prior to goal-relevant events) and adaptive (i.e., in response to unexpected demands) recruitment of neural resources is believed to facilitate successful behavioural performance. However, whether preparatory and adaptive processes draw from independent or shared neural resources, and how these align in the information processing stream, remains unclear. To address these issues, we recorded electroencephalographic data from 52 subjects while they performed a computerised task. Using a combination of mass-univariate and multivariate pattern analysis procedures, we found that different types of control triggered distinct sequences of brain activation patterns, and that the order and temporal extent of these patterns were dictated by the type of control used by the participants. Stimuli that fostered preparatory recruitment of control evoked a sequence of transient occipital-parietal, sustained central-parietal, and sustained fronto-central responses. In contrast, stimuli that indicated the need for quick behavioural adjustments triggered a sequence of transient occipital-parietal, fronto-central, and central parietal responses. There was also a considerable degree of overlap in the temporal evolution of these brain activation patterns, with behavioural performance being mainly related to the magnitude of the central-parietal and fronto-central responses. Our results demonstrate how different neurocognitive mechanisms, such as early attentional allocation and subsequent behavioural selection processes, are likely to contribute to cognitive control. Moreover, our findings extend prior work by showing that these mechanisms are engaged (at least partly) in parallel, rather than independently of each other.

Valenced tactile information is evoked by neutral visual cues following emotional learning

Abstract Learning which stimuli in our environment co-occur with painful or pleasurable events is critical for survival. Previous research has established the basic neural and behavioral mechanisms of aversive and appetitive conditioning; however, it is unclear precisely what information content is learned. Here we examined the degree to which aspects of the unconditioned stimulus (US)—sensory information versus affective salience—are transferred to the conditioned stimulus (CS). To decode what stimuli features (e.g., valence vs. discriminative somatosensation) are represented in patterns of brain activation elicited during appetitive (soft touch) and aversive (painful touch) conditioning to faces, a novel approach to using modeling with representational similarity analysis (RSA) based on theoretically driven representational patterns of interest (POIs) was applied to fMRI data. Once associations were learned through conditioning, globally, the CS reactivated US representational patterns showing conditioning-dependent reactivation in specific high-order brain regions: In the dorsal anterior cingulate cortex, the CS reactivated patterns associated with the affective salience of the US—suggesting that, with affective conditioning, these regions carry forward the affective associations of the experience.

Age-related cerebral changes during different n-back tasks: a functional near-infrared spectroscopy study.

The n-back task is a widely used paradigm to assess working memory and is commonly applied in research on age-related cognitive decline. However, studies utilizing functional near-infrared spectroscopy (fNIRS) to explore this area are limited. This study aims to investigate age-related differences in brain activation during the n-back task using fNIRS. fNIRS data were collected from 18 elderly and 19 young participants while performing different n-back tasks. Brain activation patterns and peripheral performance were compared between the two groups. Significant differences in brain activation patterns were observed between elderly and young participants. Under the 3-back condition, the older group exhibited reduced activation in brain regions adjacent to prefrontal cognitive areas compared to the younger group. Additionally, the older group's performance plateaued at the 2-back level, along with a decline in prefrontal activation. These findings may suggest potential markers for cognitive decline, providing a new target for future screening.

Energy of functional brain states correlates with cognition in adolescent-onset schizophrenia and healthy persons.

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEM integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

Exposure to nicotine and withdrawal in Wistar rats: an electrophysiological study.

Throughout the world, smoking is one of the principal causes of preventable death. Nicotine, the primary active component of tabacco, acts as a psychostimulant and modulates the electrical activity of a number of the areas of the brain involved in addiction. Abstinence from nicotine will also impact the functional state of the brain, which is reflected in symptoms of craving and susceptibility to relapse. In addition, given the increase in the sympathetic tone of the heart and pulse rate promoted by nicotine, its consumption can contribute to tachyarrhythmia. The present study investigated the electroencephalographic (EEG) and electrocardiographic (ECG) patterns of Wistar rats submitted to acute or chronic exposure to nicotine, followed by withdrawal for 24 or 48 hours, and the re-administration (or not) of nicotine, to simulate episodes of relapse. The EEG data revealed an increase in all types of brainwaves, with emphasis on high-frequency (alpha, beta, and gamma) brain oscillations following both acute and chronic exposure to nicotine (14 days), whereas in withdrawal, there was a predominancy of delta waves. When exposure to nicotine was reinstated after withdrawal, the observed EEG profile was similar to that found in chronic exposure. The electrocardiogram reads showed that both acute and chronic exposure to nicotine caused abnormalities in the atrioventricular conduction and that, while these changes improve with substance withdrawal, relapse can worsen these parameters. The results of this study indicate that high-frequency brainwaves are correlated with nicotine dependence, while slow brain oscillations are consistent with drug craving, and episodes of nicotine relapse can reproduce brain activity patterns linked to dependence. Finally, exposure to nicotine predisposes the individual to heart rhythm abnormalities, which are attenuated by withdrawal, but may nevertheless be restored rapidly with re-exposure to the substance. This study demonstrated that nicotine increases high-frequency brain oscillations, which is associated with addiction, whereas withdrawal elevates the delta wave power, suggesting craving. Re-exposure to nicotine following withdrawal restores rapidly the EEG profile of chronic dependence. In addition, nicotine has deleterious impacts on cardiac activity, which are linked to fatal arrhythmias. This implies that stopping smoking is beneficial for the amelioration of the alterations in heart rhythm caused by nicotine addiction. This study elucidates the functional states of the brain and heart during both sporadic and chronic nicotine use, and the electrophysiological explanation for substance dependence and drug relapse after craving episodes.

Comparing Brain Activation Patterns in Stress-Induced and Post-Stress Recovery States of Highly and Moderately Stressed Individuals

Our study aimed to identify the mechanisms of stress-induced and post-stress recovery states by evaluating voxel type and the total number of voxels activated based on Perceived Stress Scale scores. Functional MRI (fMRI) was used to measure the brain activation patterns in participants grouped into moderate- and high-stress categories. The number of activation voxels in the parahippocampal gyrus of the limbic lobe was greater in the high-stress group than in the moderate-stress group. Moreover, only the high-stress group showed the characteristic activation of the left precuneus. The moderate-stress group showed a greater number of activation voxels than the high-stress group for the occipital, frontal, and limbic lobes, while the reverse was true for the parietal lobe. In the post-stress recovery phase, the left lingual, inferior frontal, and middle frontal gyri were activated, and the number of activation voxels in these areas was greater in the high-stress group than in the moderate-stress group. The characteristic activation of the declive in the left cerebellum was observed in the moderate-stress group, whereas the activation of the right cuneus was dominant in the high-stress group. Our findings suggest that perceived stress may play an important role in determining the neural mechanisms underlying stress and post-stress recovery, providing insights into the complex interplay between the psychological and neural processes occurring in response to stress.

The effects of deep TMS on purpose in life, quantitative EEG and event-related potentials in major depressive disorder

Background: Perceived purpose in life (PIL) is linked with many vital health outcomes, including Major Depressive Disorder (MDD). Methods: In this study, biomarkers associated with depression and PIL were investigated using Brain Network Activation (BNA) based quantitative electroencephalography (QEEG) and event-related potential (ERP) measures in a sample of individuals with MDD. Data were analyzed before and after a 36-session, Deep transcranial magnetic stimulation (TMS) program. Results: At baseline, the study observed that increased slow-frequency resting-state activity in the frontal and temporal regions correlated with higher levels of depression and reduced PIL. Additionally, a reduced P3b amplitude was found to predict elevated depressive symptoms. However, with the application of Deep TMS treatment notable improvements were observed in both depression (p < .001. d = 2.15) and PIL (p < .001, d = 1.59) levels. The treatment also successfully regulated resting-state QEEG (delta/beta) and ERP characteristics (P200 latency), bringing them closer to healthy levels. Conclusions: This attenuation of brain activity patterns relating to depression is encouraging as it suggests that effects were robust and are more likely to be sustained over time. This study represents the first exploration of the effects of Deep TMS on PIL and relevant QEEG and ERP biomarkers. The initial evidence suggests that Deep TMS holds promise in enhancing both PIL and neurophysiological health. Future investigations should continue exploring the utility of Deep TMS in targeting a wide range of neuropsychological and physical health conditions, leveraging objective biomarkers such as QEEG and ERP.

Brain imaging and clinical outcome of embodied VR-BCI training in chronic stroke patients: a longitudinal pilot study

ABSTRACT Introduction Restorative Brain–Computer Interfaces (BCIs) provide an alternative non-muscular channel for stroke patients lacking volitional movement by enhancing sensorimotor rhythm modulation through motor imagery (MI). MI practice can be augmented with embodied feedback via virtual reality (VR). However, the clinical impact of embodied VR-BCI training remains under-explored. Methods This study examines the effects of embodied VR-BCI training on brain activity patterns (measured through EEG and fMRI) and clinical outcomes (assessed by the Fugl–Meyer Assessment, FMA) in four chronic stroke patients. Over a 3-week MI-BCI intervention, patients performed a bimanual rowing task (NeuRow) in VR. EEG data were used to extract Event-related desynchronization (ERD) and lateralization indices, while fMRI data focused on the primary motor (M1) and supplementary motor (SMA) regions of interest. Results Results indicated that all patients significantly induced ERD power, though the affected side exhibited reduced ERD compared to baseline during contralateral MI. Post-intervention, significant ERD differences from both hemispheres were observed, with decreased ERD correlating with no clinical improvement. Patients showing decreased ERD lateralization had no FMA score improvement. Activity within ipsilesional M1 and SMA correlated with FMA scores. Conclusions The findings suggest a relationship between brain activity and clinical outcomes, highlighting that increased ERD lateralization is associated with clinical improvement.

Brain activity associated with emotion regulation predicts individual differences in working memory ability.

Previous behavioral research has found that working memory is associated with emotion regulation efficacy. However, there has been mixed evidence as to whether the neural mechanisms between emotion regulation and working memory overlap. The present study tested the prediction that individual differences on the working memory subtest of the Weschler Adult Intelligence Scale (WAIS-IV) could be predicted from the pattern of brain activity produced during emotion regulation in regions typically associated with working memory, such as the dorsal lateral prefrontal cortex (dlPFC). A total of 101 participants completed an emotion regulation fMRI task in which they either viewed or reappraised negative images. Participants also completed working memory test outside the scanner. A whole brain covariate analysis contrasting the reappraise negative and view negative BOLD response found that activity in the right dlPFC positively related to working memory ability. Moreover, a multivoxel pattern analysis approach using tenfold cross-validated support vector regression in regions-of-interest associated with working memory, including bilateral dlPFC, demonstrated that we could predict individual differences in working memory ability from the pattern of activity associated with emotion regulation. These findings support the idea that emotion regulation shares underlying cognitive processes and neural mechanisms with working memory, particularly in the dlPFC.

Exploring the combined impact of color and editing on emotional perception in authentic films: Insights from behavioral and neuroimaging experiments

Film color and editing are crucial elements in filmmaking, posing significant questions about their effective use in eliciting emotional responses from viewers. While prior research has explored the impacts of color and editing independently, their combined effect remains largely unexplored, leaving the open question of how these elements together affect emotional perception. This study investigates the combined impact of film color and editing on emotion perception, utilizing both subjective behavioral scales and objective functional magnetic resonance imaging (fMRI) techniques. It employs two experiments with a two-factor design to assess the interactions between film color (colored and black-and-white) and film editing (fearful, neutral, and happy levels) on emotional perceptions. Under the direction of a professional filmmaker, a series of film sequences was created for six experimental conditions. In Experiment 1, 117 participants watched the film sequences and subjectively rated the emotional valence of the neutral face in the sequence, uncovering a significant interaction effect between film color and editing on the valence rating of the neutral face. In Experiment 2, 67 participants watched similar film sequences in an MRI scanner to analyze their brain activation patterns. Distinct activations were identified in regions including the insula, anterior cingulate cortices (ACC), and middle frontal gyrus, where a region-of-interest (ROI) analysis of the left ACC revealed an interaction effect on neural responses. These results underscore the integral role of color and editing in influencing viewers’ emotion perception using two types of measurements. This research provides novel insights into the behavioral and neural responses to filmmaking elements that underpin film reception and offers practical implications for filmmakers, encouraging a more holistic approach to considering color and editing to further enrich the audience’s emotional experiences at the pre-production stage of filmmaking.

Multiple Brain Activation Patterns for the Same Perceptual Decision-Making Task.

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

Repetitive Transcranial Magnetic Stimulation (rTMS) Treatment Reduces Variability in Brain Function in Schizophrenia: Data From a Double-Blind, Randomized, Sham-Controlled Trial.

There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham. 83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group. We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected). Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.