Brain Activation Studies Research Articles

A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns

Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. Results: The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development.

Update on the Neurobiology of Borderline Personality Disorder: A Review of Structural, Resting-State and Task-Based Brain Imaging Studies.

This review summarizes recent advances in research on the neurobiology of borderline personality disorder (BPD) according to structural brain imaging investigations and resting-state and task-based functional brain activation studies. Extending established findings on differences in regional brain volumes and cortical thickness between BPD and healthy controls, recent research illuminates shared and distinct brain structural characteristics compared to other psychiatric diagnoses, and uncovers relations of these brain structures with transdiagnostic symptoms and clinical features. Resting-state functional brain imaging studies reveal disruptions among adolescents and adults with BPD in frontolimbic and default-mode networks, which primarily underlie affect regulation and self-referential processes, respectively. Recent task-based functional brain imaging research builds on existing neurobiological understanding of emotion and cognition in BPD by revealing novel intersections with interpersonal- and stress-related processes. Studies of psychological and pharmacological interventions suggest possible effects on neural regions underlying emotion processing and behavioral control.

Toward a more comprehensive understanding of network centrality disruption in amnestic mild cognitive impairment: a MEG multilayer approach

BackgroundAlzheimer’s Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies.MethodsOur study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI.ResultsThe results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions’ activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions’ activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions.ConclusionsOur findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Investigating the effect of template head models on Event-Related Potential source localization: a simulation and real-data study.

Event-Related Potentials (ERPs) are valuable for studying brain activity with millisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the head model used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks. Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset. The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiform gyrus, a known face processing area, using the subject-specific models. Apart from the fusiform gyrus, the template models also reconstructed several other sources, illustrating the localization inaccuracies. While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors.

An exploratory study of functional brain activity associated with gross motor function improvement in children with unilateral cerebral palsy

ABSTRACT Purpose Identify relations of gross motor function and primary motor cortex (M1) functional activity pre and post gross motor interventions for children with unilateral cerebral palsy (UCP). Methods Thirteen children with UCP completed a gross motor intervention. Pre/post-intervention functional MRI outcomes included the laterality index (LI), activation volume, and spatial overlap of M1 activation during active ankle dorsiflexion. Advanced gross motor function (Challenge) was assessed pre/post-intervention, and 2–6 months later. Bivariate correlations and linear regression assessed relations between neuroimaging and motor function. Results Mean pre-intervention M1 activity was contralateral during dominant (LI = +0.85, SD 0.21) but variable during the affected (LI = +0.43, SD 0.57) ankle dorsiflexion. Changes in motor function and neuroimaging outcomes were not significantly associated. However, smaller affected ankle activation and less spatial overlap between ankle activations pre-intervention predicted Challenge improvements post-intervention (adjusted R2 = 0.74, p = .001.) Conclusions This exploratory study identified pre-intervention neuroimaging predictors of post-intervention improvements in advanced gross motor function.

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Brain encoding of naturalistic, continuous, and unpredictable tactile events.

Studies employing EEG to measure somatosensory responses have been typically optimized to compute event-related potentials in response to discrete events (ERPs). However, tactile interactions involve continuous processing of non-stationary inputs that change in location, duration, and intensity. To fill this gap, this study aims to demonstrate the possibility of measuring the neural tracking of continuous and unpredictable tactile information. Twenty-seven young adults (females = 15) were continuously and passively stimulated with a random series of gentle brushes on single fingers of each hand, which were covered from view. Thus, tactile stimulations were unique for each participant, and stimulated fingers. An encoding model measured the degree of synchronization between brain activity and continuous tactile input, generating a temporal response function (TRF). Brain topographies associated with the encoding of each finger stimulation showed a contralateral response at central sensors starting at 50 ms and peaking at about 140 ms of lag, followed by a bilateral response at about 240 ms. A series of analyses highlighted that reliable tactile TRF emerged after just 3 minutes of stimulation. Strikingly, topographical patterns of the TRF allowed discriminating digit lateralization across hands and digit representation within each hand. Our results demonstrated for the first time the possibility of using EEG to measure the neural tracking of a naturalistic, continuous, and unpredictable stimulation in the somatosensory domain. Crucially, this approach allows the study of brain activity following individualized, idiosyncratic tactile events to the fingers.Significant Statement This study expands the current research conducted on neural tracking, opening the exploration of idiosyncratic tactile events and overcoming constraints of laboratory tasks that typically rely on discrete events. We validated a protocol for the ecological investigations of continuous, slow, tactile processing of the hands. The employed approach enriches the possible use of the EEG to characterize somatosensory neural representations of tactile events. Findings unravel coherent neural responses to continuous and naturalistic touch, with sensitivity for digit lateralization and representation.

Source imaging method based on diagonal covariance bases and its applications to OPM-MEG

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework.

Epilepsy Research in the Institute of Physiology of the Czech Academy of Sciences in Prague.

Starting from simple clinical statistics, the spectrum of methods used in epilepsy research in the Institute of Physiology of the Czechoslovak (now Czech) Academy of Sciences progressively increased. Professor Servít used electrophysiological methods for study of brain activity in lower vertebrates, neuropathology was focused on electronmicroscopic study of cortical epileptic focus and ion-sensitive microelectrodes were used for studies of cortical direct current potentials. Developmental studies used electrophysiological methods (activity and projection of cortical epileptic foci, EEG under the influence of convulsant drugs, hippocampal, thalamic and cortical electrical stimulation for induction of epileptic afterdischarges and postictal period). Extensive pharmacological studies used seizures elicited by convulsant drugs (at first pentylenetetrazol but also other GABA antagonists as well as agonists of glutamate receptors). Motor performance and behavior were also studied during brain maturation. The last but not least molecular biology was included into the spectrum of methods. Many original data were published making a background of position of our laboratory in the first line of laboratories interested in brain development.

Real-time fMRI neurofeedback of the anterior insula using arterial spin labelling.

Arterial spin labelling (ASL) is the only non-invasive technique that allows absolute quantification of perfusion and is increasingly used in brain activation studies. Contrary to the blood oxygen level-dependent (BOLD) effect ASL measures the cerebral blood flow (CBF) directly. However, the ASL signal has a lower signal-to-noise ratio (SNR), than the BOLD signal, which constrains its utilization in neurofeedback studies. If successful, ASL neurofeedback can be used to aid in the rehabilitation of health conditions with impaired blood flow, for example, stroke. We provide the first ASL-based neurofeedback study incorporating a double-blind, sham-controlled design. A pseudo-continuous ASL (pCASL) approach with background suppression and 3D GRASE readout was combined with a real-time post-processing pipeline. The real-time pipeline allows to monitor the ASL signal and provides real-time feedback on the neural activity to the subject. In total 41 healthy adults (19-56 years) divided into three groups underwent a neurofeedback-based emotion imagery training of the left anterior insula. Two groups differing only in the explicitness level of instruction received real training and a third group received sham feedback. Only those participants receiving real feedback with explicit instruction showed significantly higher absolute CBF values in the trained region during neurofeedback than participants receiving sham feedback. However, responder analyses of percent signal change values show no differences in activation between the three groups. Persisting limitations, such as the lower SNR, confounding effects of arterial transit time and partial volume effects still impact negatively the implementation of ASL neurofeedback.

Cognitive load in individuals with a transfemoral amputation during single- and dual-task walking: a pilot study of brain activity in people using a socket prosthesis or a bone-anchored prosthesis.

To explore cognitive load in people with transfemoral amputations fitted with socket or bone-anchored prostheses by describing activity in the left and right dorsolateral prefrontal cortices during single- and dual-task walking. Cross-sectional pilot study. 8 socket prosthesis users and 8 bone-anchored prosthesis users. All were fitted with microprocessor-controlled prosthetic knees. Participants answered self-report questionnaires and performed gait tests during 1 single-task walking condition and 2 dual-task walking conditions. While walking, activity in the dorsolateral prefrontal cortex was measured using functional near-infrared spectroscopy. Cognitive load was investigated for each participant by exploring the relative concentration of oxygenated haemoglobin in the left and right dorsolateral prefrontal cortex. Symmetry of brain activity was investigated by calculating a laterality index. Self-report measures and basic gait variables did not show differences between the groups. No obvious between-group differences were observed in the relative concentration of oxygenated haemoglobin for any walking condition. There was a tendency towards more right-side brain activity for participants using a socket prosthesis during dual-task conditions. This pilot study did not identify substantial differences in cognitive load or lateralization between socket prosthesis users and bone-anchored prosthesis users.

Alpha oscillation mediates the interaction between suicide risk and symptom severity in Major Depressive Disorder.

The aim of our study was to explore the relationship between changes in neural oscillatory power in the EEG, the severity of depressive-anxiety symptoms, and the risk of suicide in MDD. 350 MDD patients' demographic and clinical data were collected, and their depressive and anxious symptoms were evaluated using HDRS-17 and HAMA-14, along with a suicide risk assessment using the Nurses' Global Assessment of Suicide Risk (NGASR). EEG data were captured, processed, and analyzed to study brain activity patterns related to MDD. The participants were divided based on suicide risk levels, and statistical analyses, including chi-square, t-tests, Pearson's correlations were used to explore the associations between brain activity, symptom severity, and suicide risk. Closely related variables were identified and ultimately the optimal model was screened using stepwise regression analysis with a forward strategy, and mediation effects were further used to determine the possible interactions between the variables in the regression model. The regression model showed a significant effect of HDRS-17 and alpha power of Medial Occipital Cortex (MOC) on suicide risk, with elevated HDRS-17 increasing suicide risk and elevated alpha power decreasing suicide risk. Mediation effect analyses showed that MOC alpha power partially mediated the effect of depression level on suicide risk, and that an increase in depression severity may lead to a decrease in MOC alpha power, while a decrease in MOC alpha power may lead to an increase in suicide risk. The severity of depression directly increases suicide risk, whereas higher alpha power in the MOC serves as a protective factor, reducing this risk. Notably, MOC alpha power not only directly impacts suicide risk but also mediates the effects of both depression severity and anxiety levels on this risk. The relatively small sample size of this study may limit the representativeness of the overall MDD patient population and the detailed analysis of different subgroups. This study did not delve into the relationship between the severity of cognitive symptoms in MDD patients and suicide risk.

Inter-subject gamma oscillation synchronization as a biomarker of abnormal processing of social interaction in ADHD

Children with attention-deficit hyperactivity disorder (ADHD) have difficulties in social interactions. Studying brain activity during social interactions is difficult with conventional artificial stimuli. This pioneering study examined the neural correlates of social perception in children with ADHD and matched controls using naturalistic stimuli. We presented 20 children with ADHD and 20 age-and-sex-matched controls with tailored movies featuring high- or low-level social interactions while recording electroencephalographic signals. Both groups exhibited synchronized gamma-band oscillations, but controls demonstrated greater inter-subject correlations. Additionally, the difference in inter-subject correlations between high- and low-interaction movies was significantly larger in controls compared to ADHD patients. Between 55 and 75 Hz comparing viewing high interaction movies with low interaction moves, controls had a significantly larger weighting in the right parietal lobe, while ADHD patients had a significantly smaller weighting in the left occipital lobe. These findings reveal distinct spatiotemporal neural signatures in social interaction processing among children with ADHD and controls using naturalistic stimuli. These neural markers offer potential for group differentiation and assessing intervention efficacy, advancing our understanding ADHD-related social interaction mechanisms.

From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb.

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise in helping stroke patients regain lost motor functions. One potential avenue for increasing the understanding of how motor recovery occurs is to study brain activation during the movements that are targeted by therapy in healthy individuals. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging technique for examining neural underpinnings of motor function. This study aimed to investigate fNIRS neural correlates of complex lower limb movements in healthy subjects. Participants were asked to perform cycles of rest and movement for 6 min using a robotic device for motor rehabilitation. The task required coordinated knee and ankle joint movements to point to targets displayed on a computer screen. Two experimental conditions with different levels of movement assistance provided by the robot were explored. The results showed that the fNIRS protocol effectively detected brain regions associated with motor control during the task. Notably, all subjects exhibited greater activation in the contralateral premotor area during the no-assistance condition compared to the assisted condition. In conclusion, fNIRS appears to be a valuable approach for detecting changes in oxyhemoglobin concentration associated with multi-joint pointing movements of the lower limb. This research might contribute to the understanding of stroke motor recovery mechanisms and might pave the way for improved rehabilitation treatments for stroke patients. However, further research is needed to fully elucidate the potential of fNIRS in studying motor function and its applications in clinical settings.

Temporal summation of second pain is affected by cognitive load.

This work attempted to clarify the interaction of cognition and pain sensitization during a paradigm of Temporal Summation of Second Pain (TSSP). We analyzed pain ratings and electroencephalographic (EEG) activity obtained from 21 healthy participants during the presentation of four experimental conditions that differed in the manipulation of attention to painful stimuli or working memory load (Attention to hand & TSSP; 0-back & TSSP (low cognitive load); 2-back & TSSP (high cognitive load); 2-back (without pain)). We found that the TSSP was reduced when the attention was diverted and the cognitive load increased, and this reduction was accompanied by higher midfrontal theta activity and lower posterior alpha and central beta activity. Although it is well established that TSSP is a phenomenon that occurs at the spinal level, here we show that it is also affected by supraspinal attentional mechanisms. Delivery of painful repeated stimuli did not affect the performance of the 2-back task but was associated with smaller amplitudes of attentional event-related potentials (ERPs) after standard stimuli (not the target). The study of brain activity during TSSP allowed to clarify the role of top-down attentional modulation in pain sensitization processes. Results contribute to a better understanding of cognitive dysfunction in pain conditions and reinforce the use of therapeutic strategies based on distracting attention away from pain.

Opening the dialogue: A preliminary exploration of hair color, hair cleanliness, light, and motion effects on fNIRS signal quality.

Functional near-infrared spectroscopy (fNIRS) is a promising tool for studying brain activity, offering advantages such as portability and affordability. However, challenges in data collection persist due to factors like participant physiology, environmental light, and gross-motor movements, with limited literature on their impact on fNIRS signal quality. This study addresses four potentially influential factors-hair color, hair cleanliness, environmental light, and gross-motor movements-on fNIRS signal quality. Our aim is to raise awareness and offer insights for future fNIRS research. Six participants (4 Females, 2 Males) took part in four different experiments investigating the effects of hair color, hair cleanliness, environmental light, and gross-motor movements on fNIRS signal quality. Participants in Experiment 1, categorized by hair color, completed a finger-tapping task in a between-subjects block design. Signal quality was compared between each hair color. Participants in Experiments 2 and 3 completed a finger-tapping task in a within-subjects block design, with signal quality being compared across hair cleanliness (i.e., five consecutive days without washing the hair) and environmental light (i.e., sunlight, artificial light, no light, etc.), respectively. Experiment 4 assessed three gross-motor movements (i.e., walking, turning and nodding the head) in a within-subjects block design. Motor movements were then compared to resting blocks. Signal quality was evaluated using Scalp Coupling Index (SCI) measurements. Lighter hair produced better signals than dark hair, while the impact of environmental light remains uncertain. Hair cleanliness showed no significant effects, but gross motor movements notably reduced signal quality. Our results suggest that hair color, environmental light, and gross-motor movements affect fNIRS signal quality while hair cleanliness does not. Nevertheless, future studies with larger sample sizes are warranted to fully understand these effects. To advance future research, comprehensive documentation of participant demographics and lab conditions, along with signal quality analyses, is essential.

Improvements of EEG Signal Quality: A Hybrid Method of Blind Source Separation and Variational Mode Destruction to Reduce Artifacts

The electroencephalogram (EEG) is a crucial tool for studying brain activity; yet it frequently encounters artifacts that distort meaningful neural signals. This paper addresses the challenge of artifact removal through a unique hybrid method, combining Variational Mode Decomposition (VMD) techniques with Blind Source Separation (BSS) algorithms. VMD, recognized for its adaptability to non-linear and non-stationary EEG data, as well as its ability to alleviate mode mixing and the “endpoint effect,” which serves as an effective preprocessing step. The paper evaluates the performance of two integrated BSS algorithms, AMICA and AMUSE, across various criteria. Comparisons across metrics such as Euclidean distance, Spearman correlation coefficient, and Root Mean Square Error reveal similar performance between AMICA and AMUSE. However, a distinct divergence is evident in the Signal to Artifact Ratio (SAR). When employed with VMD, AMICA demonstrates superiority in effectively discerning and segregating brain signals from artifacts, which gives a mean value of 1.0924. This study introduces a potent hybrid VMDBSS approach for enhancing EEG signal quality. The findings emphasize the notable impact of AMICA, particularly in achieving optimal results in artifact removal, as indicated by its superior performance in SAR. The abstract concludes by underlining the significance of these results, emphasizing AMICA’s pivotal role in achieving the highest measurable evaluation value, making it a compelling choice for researchers and practitioners in EEG signal processing.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

Cortical Sources And Autonomic Nervous System Response Of Listening To North Indian Classical Music

BACKGROUND: Music can affect the human brain and qEEG with its sub-second resolution can be used to study brain activity. The North Indian classical music’s (NICM) effect on brain activity is largely undiscovered. OBJECTIVE: To find out the cortical sources activated along with the autonomic nervous system response while listening to NICM. Methods: 30 Indian subjects (Mean age- 26.1 and (SD) - 1.4 years) were asked to listen to 4 NICM ragas of 5 minutes each with increasing minor note to major note ratio i.e., M1(0), M2(0.16), M3(0.4), M4(1.33).128 channel of EEG signal was recorded which was preprocessed and cortical source localization was done using sLORETA.The autonomic nervous system response while listening to each music excerpt was assessed using Heart Rate Variability parameters by measuring lead II electrocardiography. RESULTS: The cortical sources analysis showed increased activation in cortical regions associated with the extraction of acoustic features of music (superior, middle, and inferior temporal gyruses in addition to fusiform and supramarginal gyrus), emotion generation (precuneus and inferior frontal gyri) and Default Mode Network (posterior cingulate cortex, medial prefrontal cortex, and inferior parietal lobule) while listening to M1. There was a decreased activation in these areas while listening to M2, M3 & M4 (as the minor notes increased).The mean heart rate was significantly higher while listening to M4. There was a reduction in HF while listening to M4 and M3.There was a significant decrease in RMSSD while listening to M4 as compared to the resting state. CONCLUSION: Music can affect brain activity and autonomic function. Music with major notes causes activation of DMN areas causing mind wandering and self-referential mental activity than music with minor notes which decreases DMN activity.In terms of autonomic function increase in minor notes caused decrease in RMSSD and HF signifying an increase in stress and reduced parasympathetic tone. The authors declare no competing interest. The study was approved by the Institutional Review Board (IRB). No Grant was used. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.