EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing

Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan

BACKGROUND AND OBJECTIVE

Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability.

METHODS

We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants' history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan).

RESULTS

The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature.

CONCLUSION

In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics.

Performance of game sessions in VR vs standard 2D monitor environment. an EEG study

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants' performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes.

EEG synchronization patterns during a Go/No-Go task in individuals with aphasia in subacute and chronic phases of stroke

Stroke and subsequent neuroregenerative processes cause changes in neural organization of attentional functions. In this study, we attempted to identify differences in neural synchronization patterns during a visual Go/No-Go task in people with post-stroke aphasia in both subacute and chronic stroke phases. To identify neuronal underpinnings of the behavioral differences we investigated pairwise connectivity patterns using corrected imaginary phase locking value and graph-theoretic measures (efficiency, modularity and clustering coefficient) at global and local level in subacute (n = 13) and chronic stroke phases (n = 14) during a Go/No-Go task. We observed significantly lower phase synchronization in the Subacute Group in the alpha band in the connections spanning frontal and central areas of both hemispheres alongside lower local efficiency and clustering coefficient in the left frontal region. Additionally, we observed higher modularity in the beta band in the unaffected right parietal region in the Subacute Group which may denote inhibition of motor and attention functions. Those mechanisms could serve to align cognitive abilities between the damaged and healthy hemispheres, harmonizing the activity of the neuronal networks of both hemispheres disrupted by the effects of the stroke. Our findings have potential implications for rehabilitation therapies, which should take into account the pattern of connectivity changes during different phases of reovery.

Corticocortical and corticomuscular connectivity dynamics in standing posture: electroencephalography study

Cortical mechanism is necessary for human standing control. Previous research has demonstrated that cortical oscillations and corticospinal excitability respond flexibly to postural demands. However, it is unclear how corticocortical and corticomuscular connectivity changes dynamically during standing with spontaneous postural sway and over time. This study investigated the dynamics of sway- and time-varying connectivity using electroencephalography and electromyography. Electroencephalography and electromyography were recorded in sitting position and 3 standing postures with varying base-of-support: normal standing, one-leg standing, and standing on a piece of wood. For sway-varying connectivity, corticomuscular connectivity was calculated based on the timing of peak velocity in anteroposterior sway. For time-varying connectivity, corticocortical connectivity was measured using the sliding-window approach. This study found that corticomuscular connectivity was strengthened at the peak velocity of postural sway in the γ- and β-frequency bands. For time-varying corticocortical connectivity, the θ-connectivity in all time-epoch was classified into 7 clusters including posture-relevant component. In one of the 7 clusters, strong connectivity pairs were concentrated in the mid-central region, and the proportion of epochs under narrow-base standing conditions was significantly higher, indicating a functional role for posture balance. These findings shed light on the connectivity dynamics and cortical oscillation that govern standing balance.

Effective Connectivity Network of Aberrant Prediction Error Processing in Auditory Phantom Perception

Introduction: Prediction error (PE) is key to perception in the predictive coding framework. However, previous studies indicated the varied neural activities evoked by PE in tinnitus patients. Here, we aimed to reconcile the conflict by (1) a more nuanced view of PE, which could be driven by changing stimulus (stimulus-driven PE [sPE]) and violation of current context (context-driven PE [cPE]) and (2) investigating the aberrant connectivity networks that are engaged in the processing of the two types of PEs in tinnitus patients. Methods: Ten tinnitus patients with normal hearing and healthy controls were recruited, and a local-global auditory oddball paradigm was applied to measure the electroencephalographic difference between the two groups during sPE and cPE conditions. Results: Overall, the sPE condition engaged bottom-up and top-down connections, whereas the cPE condition engaged mostly top-down connections. The tinnitus group showed decreased sensitivity to the sPE and increased sensitivity to the cPE condition. Particularly, the auditory cortex and posterior cingulate cortex were the hubs for processing cPE in the control and tinnitus groups, respectively, showing the orientation to an internal state in tinnitus. Furthermore, tinnitus patients showed stronger connectivity to the parahippocampus and pregenual anterior cingulate cortex for the establishment of the prediction during the cPE condition. Conclusion: These results begin to dissect the role of changes in stimulus characteristics versus changes in the context of processing the same stimulus in mechanisms of tinnitus generation. Impact Statement This study delves into the number dynamics of prediction error (PE) in tinnitus, proposing a dual framework distinguishing between stimulus-driven PE (sPE) and context-driven PE (cPE). Electroencephalographic data from tinnitus patients and controls revealed distinct connectivity patterns during sPE and cPE conditions. Tinnitus patients exhibited reduced sensitivity to sPE and increased sensitivity to cPE. The auditory cortex and posterior cingulate cortex emerged as pivotal regions for cPE processing in controls and tinnitus patients, indicative of an internal state orientation in tinnitus. Enhanced connectivity to the parahippocampus and pregenual anterior cingulate cortex underscores the role of context in tinnitus pathophysiology.

Visual mismatch negativity in Parkinson's psychosis and potential for testing treatment mechanisms

Psychosis and visual hallucinations are a prevalent non-motor symptom of Parkinson's disease, negatively affecting patients' quality of life and constituting a greater risk for dementia. Understanding neural mechanisms associated to these symptoms is instrumental for treatment development. The mismatch negativity is an event-related potential evoked by a violation in a sequence of sensory events. It is widely considered an index of sensory change-detection. Reduced mismatch negativity response is one of the most replicated results in schizophrenia and has been suggested to be a superior psychosis marker. To understand whether this event-related potential component could be a similarly robust marker for Parkinson's psychosis, we used electroencephalography with a change-detection task to study the mismatch negativity in the visual modality in 20 participants with Parkinson's and visual hallucinations and 18 matched Parkinson's participants without hallucinations. We find that visual mismatch negativity is clearly present in participants with Parkinson's disease without hallucinations at both parieto-occipital and frontal sites, whereas participants with Parkinson's and visual hallucinations show reduced or no differences in the two waveforms, confirming the sensitivity of mismatch negativity to psychosis, even within the same diagnostic group. We also explored the relationship between hallucination severity and visual mismatch negativity amplitude, finding a negative correlation between visual hallucinations severity scores and visual mismatch negativity amplitude at a central frontal and a parieto-occipital electrodes, whereby the more severe or complex (illusions, formed visual hallucinations) the symptoms the smaller the amplitude. We have also tested the potential role of the serotonergic 5-HT2A cascade in visual hallucinations in Parkinson's with these symptoms, following the receptor trafficking hypothesis. We did so with a pilot study in healthy controls (N = 18) providing support for the role of the Gi/o-dependent pathway in the psychedelic effect and a case series in participants with Parkinson's and visual hallucinations (N = 5) using a double-blind crossover design. Positive results on psychosis scores and mismatch amplitude add further to the potential role of serotonergic modulation of visual hallucinations in Parkinson's disease.

Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing

Electroencephalography (EEG) is a medical engineering technique aimed at recording the electric activity of the human brain. Brain signals derived from an EEG device can be processed and analyzed through computers by using digital signal processing, computational statistics, and machine learning techniques, that can lead to scientifically-relevant results and outcomes about how the brain works. In the last decades, the spread of EEG devices and the higher availability of EEG data, of computational resources, and of software packages for electroencephalography analysis has made EEG signal processing easier and faster to perform for any researcher worldwide. This increased ease to carry out computational analyses of EEG data, however, has made it easier to make mistakes, as well. And these mistakes, if unnoticed or treated wrongly, can in turn lead to wrong results or misleading outcomes, with worrisome consequences for patients and for the advancements of the knowledge about human brain. To tackle this problem, we present here our ten quick tips to perform electroencephalography signal processing analyses avoiding common mistakes: a short list of guidelines designed for beginners on what to do, how to do it, and what not to do when analyzing EEG data with a computer. We believe that following our quick recommendations can lead to better, more reliable and more robust results and outcome in clinical neuroscientific research.

Early Family Experiences and Neural Activity in Rural Pakistani Children: The Differential Role of Gender

Adversity within low- and middle-income countries (LMICs) poses severe threats to neurocognitive development, which can be partially mitigated by high-quality early family experiences. Specifically, maternal scaffolding and home stimulation can buffer cognitive development in LMIC, possibly by protecting underlying neural functioning. However, the association between family experiences and neural activity remains largely unexplored in LMIC contexts. This study explored the relation of early family experiences to later cognitive skills and absolute gamma power (21-45 Hz), a neural marker linked to higher-order cognitive skills. Drawing data from the PEDS trial, a longitudinal study in rural Pakistan, we examined maternal scaffolding at 24 months and home stimulation quality at 18 months as predictors of verbal IQ, executive functions, and absolute gamma at 48 months for 105 mother-child dyads (52 girls). Maternal scaffolding interacted with gender to predict absolute gamma power, such that higher maternal scaffolding was related to higher gamma more strongly for girls. Maternal scaffolding also interacted with absolute gamma to predict executive functions, such that higher gamma was related to better executive functions only when maternal scaffolding was average to high. Individual differences in early family experiences may partially buffer the neural underpinnings of cognitive skills from adversity in LMIC.

Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors

Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.

Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.

Neural effects of dopaminergic compounds revealed by multi-site electrophysiology and interpretable machine-learning

Background

Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis.

Methods

We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown.

Results

Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamo-hippocampal network entails a larger share of D1-as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs.

Conclusion

Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies.

EEG connectivity in functional brain networks supporting visuomotor integration processes in dominant and non-dominant hand movements

Objective.This study explores the changes in the organization of functional brain networks induced by performing a visuomotor integration task, as revealed by noninvasive spontaneous electroencephalographic traces (EEG).Approach.EEG data were acquired during the execution of the Nine Hole Peg Test (NHPT) with the dominant and non-dominant hands in a group of 44 right-handed volunteers. Both spectral analysis and phase-based connectivity analysis were performed in the theta (ϑ), mu (μ) and beta (ß) bands. Graph Theoretical Analysis (GTA) was also performed to investigate the topological reorganization induced by motor task execution.Main results.Spectral analysis revealed an increase of frontoparietal ϑ power and a spatially diffused reduction ofµand ß contribution, regardless of the hand used. GTA showed a significant increase in network integration induced by movement performed with the dominant limb compared to baseline in the ϑ band. Theµand ß bands were associated with a reduction in network integration during the NHPT. In theµrhythm, this result was more evident for the right-hand movement, while in the ß band, results did not show dependence on the laterality. Finally, correlation analysis highlighted an association between frequency-specific topology measures and task performance for both hands.Significance.Our results show that functional brain networks reorganize during visually guided movements in a frequency-dependent manner, differently depending on the hand used (dominant/non dominant).

Events in context-The HED framework for the study of brain, experience and behavior

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis

Background

At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.

Methods

We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.

Results

EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.

Conclusion

This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection

The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.

Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression

BACKGROUND

Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.

METHODS

This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.

RESULTS

Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.

CONCLUSIONS

Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation

Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PERSPECTIVE: This study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.

Dopamine improves defective cortical and muscular connectivity during bilateral control of gait in Parkinson's disease

Parkinson's Disease (PD)-typical declines in gait coordination are possibly explained by weakness in bilateral cortical and muscular connectivity. Here, we seek to determine whether this weakness and consequent decline in gait coordination is affected by dopamine levels. To this end, we compare cortico-cortical, cortico-muscular, and intermuscular connectivity and gait outcomes between body sides in people with PD under ON and OFF medication states, and in older adults. In our study, participants walked back and forth along a 12 m corridor. Gait events (heel strikes and toe-offs) and electrical cortical and muscular activities were measured and used to compute cortico-cortical, cortico-muscular, and intermuscular connectivity (i.e., coherences in the alpha, beta, and gamma bands), as well as features characterizing gait performance (e.g., the step-timing coordination, length, and speed). We observe that people with PD, mainly during the OFF medication, walk with reduced step-timing coordination. Additionally, our results suggest that dopamine intake in PD increases the overall cortico-muscular connectivity during the stance and swing phases of gait. We thus conclude that dopamine corrects defective feedback caused by impaired sensory-information processing and sensory-motor integration, thus increasing cortico-muscular coherences in the alpha bands and improving gait.

Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians

Using a creative production task, jazz improvisation, we tested alternative hypotheses about the flow experience: (A) that it is a state of domain-specific processing optimized by experience and characterized by minimal interference from task-negative default-mode network (DMN) activity versus (B) that it recruits domain-general task-positive DMN activity supervised by the fronto-parietal control network (FPCN) to support ideation. We recorded jazz guitarists' electroencephalograms (EEGs) while they improvised to provided chord sequences. Their flow-states were measured with the Core Flow State Scale. Flow-related neural sources were reconstructed using SPM12. Over all musicians, high-flow (relative to low-flow) improvisations were associated with transient hypofrontality. High-experience musicians' high-flow improvisations showed reduced activity in posterior DMN nodes. Low-experience musicians showed no flow-related DMN or FPCN modulation. High-experience musicians also showed modality-specific left-hemisphere flow-related activity while low-experience musicians showed modality-specific right-hemisphere flow-related deactivations. These results are consistent with the idea that creative flow represents optimized domain-specific processing enabled by extensive practice paired with reduced cognitive control.

Reproducible machine learning research in mental workload classification using EEG

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation

Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration

Objective. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC.Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials.Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error.Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks

Introduction

The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance declines that can arise from either work overload or under stimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments.

Methods

In this study, we developed a new six-cognitive-domain task protocol, coupling it with six biomedical monitoring modalities to concurrently capture performance and cognitive workload correlates across a longitudinal multi-day investigation. Utilizing two distinct modalities for each aspect of cardiac activity (ECG and PPG), ocular activity (EOG and eye-tracking), and brain activity (EEG and fNIRS), 23 participants engaged in four sessions over 4 weeks, performing tasks associated with working memory, vigilance, risk assessment, shifting attention, situation awareness, and inhibitory control.

Results

The results revealed varying levels of sensitivity to workload within each modality. While certain measures exhibited consistency across tasks, neuroimaging modalities, in particular, unveiled meaningful differences between task conditions and cognitive domains.

Discussion

This is the first comprehensive comparison of these six brain-body measures across multiple days and cognitive domains. The findings underscore the potential of wearable brain and body sensing methods for evaluating mental workload. Such comprehensive neuroergonomic assessment can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.

Graph-based Analysis to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Patients With Major Depressive Disorder Using EEG Signals

Introduction

Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment for drug-resistant major depressive disorder (MDD) patients. Since the success rate of rTMS treatment is about 50%-55%, it is essential to predict the treatment outcome before starting based on electroencephalogram (EEG) signals, leading to identifying effective biomarkers and reducing the burden of health care centers.

Methods

To this end, pretreatment EEG data with 19 channels in the resting state from 34 drug-resistant MDD patients were recorded. Then, all patients received 20 sessions of rTMS treatment, and a reduction of at least 50% in the total beck depression inventory (BDI-II) score before and after the rTMS treatment was defined as a reference. In the current study, effective brain connectivity features were determined by the direct directed transfer function (dDTF) method from patients' pretreatment EEG data in all frequency bands separately. Then, the brain functional connectivity patterns were modeled as graphs by the dDTF method and examined with the local graph theory indices, including degree, out-degree, in-degree, strength, out-strength, in-strength, and betweenness centrality.

Results

The results indicated that the betweenness centrality index in the Fp2 node and the δ frequency band are the best biomarkers, with the highest area under the receiver operating characteristic curve value of 0.85 for predicting the rTMS treatment outcome in drug-resistant MDD patients.

Conclusion

The proposed method investigated the significant biomarkers that can be used to predict the rTMS treatment outcome in drug-resistant MDD patients and help clinical decisions.

Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction

Previous studies have reported a role of alterations in the brain's inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate inhibitory control. Healthy controls and participants with Internet addiction were recruited to participate in the internet-related stop-signal task with 19-channel EEG signal recording, and the corresponding event-related potentials and spectral perturbations were analyzed. Brain effective connections were also evaluated using direct directed transfer function. The results showed that, relative to the healthy controls, participants with Internet addiction had increased Stop-P3 during inhibitory control, suggesting that they have an altered neural mechanism in impulsive control. Furthermore, participants with Internet addiction showed increased low-frequency synchronization and decreased alpha and beta desynchronization in the middle and right frontal regions compared to healthy controls. Aberrant brain effective connectivity was also observed, with increased occipital-parietal and intra-occipital connections, as well as decreased frontal-paracentral connection in participants with Internet addiction. These results suggest that physiological signals are essential in future implementations of cognitive assessment of Internet addiction to further investigate the underlying mechanisms and effective biomarkers.

Motor priming to enhance the effect of physical therapy in people with spinal cord injury

CONTEXT

Brain-Computer Interface (BCI) is an emerging neurorehabilitation therapy for people with spinal cord injury (SCI).

OBJECTIVE

The study aimed to test whether priming the sensorimotor system using BCI-controlled functional electrical stimulation (FES) before physical practice is more beneficial than physical practice alone.

METHODS

Ten people with subacute SCI participated in a randomized control trial where the experimental (N = 5) group underwent BCI-FES priming (∼15 min) before physical practice (30 min), while the control (N = 5) group performed physical practice (40 min) of the dominant hand. The primary outcome measures were BCI accuracy, adherence, and perceived workload. The secondary outcome measures were manual muscle test, grip strength, the range of motion, and Electroencephalography (EEG) measured brain activity.

RESULTS

The average BCI accuracy was 85%. The experimental group found BCI-FES priming mentally demanding but not frustrating. Two participants in the experimental group did not complete all sessions due to early discharge. There were no significant differences in physical outcomes between the groups. The ratio between eyes closed to eyes opened EEG activity increased more in the experimental group (theta Pθ = 0.008, low beta Plβ = 0.009, and high beta Phβ = 1.48e-04) indicating better neurological outcomes. There were no measurable immediate effects of BCI-FES priming.

CONCLUSION

Priming the brain before physical therapy is feasible but may require more than 15 min. This warrants further investigation with an increased sample size.

The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis

Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.

Response to experimental cold-induced pain discloses a resistant category among endurance athletes, with a distinct profile of pain-related behavior and GABAergic EEG markers: a case-control preliminary study

Pain is a major public health problem worldwide, with a high rate of treatment failure. Among promising non-pharmacological therapies, physical exercise is an attractive, cheap, accessible and innocuous method; beyond other health benefits. However, its highly variable therapeutic effect and incompletely understood underlying mechanisms (plausibly involving the GABAergic neurotransmission) require further research. This case-control study aimed to investigate the impact of long-lasting intensive endurance sport practice (≥7 h/week for the last 6 months at the time of the experiment) on the response to experimental cold-induced pain (as a suitable chronic pain model), assuming that highly trained individual would better resist to pain, develop advantageous pain-copying strategies and enhance their GABAergic signaling. For this purpose, clinical pain-related data, response to a cold-pressor test and high-density EEG high (Hβ) and low beta (Lβ) oscillations were documented. Among 27 athletes and 27 age-adjusted non-trained controls (right-handed males), a category of highly pain-resistant participants (mostly athletes, 48.1%) was identified, displaying lower fear of pain, compared to non-resistant non-athletes. Furthermore, they tolerated longer cold-water immersion and perceived lower maximal sensory pain. However, while having similar Hβ and Lβ powers at baseline, they exhibited a reduction between cold and pain perceptions and between pain threshold and tolerance (respectively -60% and - 6.6%; -179.5% and - 5.9%; normalized differences), in contrast to the increase noticed in non-resistant non-athletes (+21% and + 14%; +23.3% and + 13.6% respectively). Our results suggest a beneficial effect of long-lasting physical exercise on resistance to pain and pain-related behaviors, and a modification in brain GABAergic signaling. In light of the current knowledge, we propose that the GABAergic neurotransmission could display multifaceted changes to be differently interpreted, depending on the training profile and on the homeostatic setting (e.g., in pain-free versus chronic pain conditions). Despite limitations related to the sample size and to absence of direct observations under acute physical exercise, this precursory study brings into light the unique profile of resistant individuals (probably favored by training) allowing highly informative observation on physical exercise-induced analgesia and paving the way for future clinical translation. Further characterizing pain-resistant individuals would open avenues for a targeted and physiologically informed pain management.

Setting up EEG Source Imaging in Practice

SUMMARY

Adding EEG source imaging to a clinical practice has clear advantages over visual inspection of EEG. This article offers insight on incorporating EEG source imaging into an EEG laboratory and the best practices for producing optimal source analysis results.

Slowing of EEG waves correlates with striatal [18 F]fluorodopa PET/CT uptake and executive dysfunction in Parkinson's disease

Parkinson's disease (PD) research on specific neuroimaging and neurophysiological biomarkers revealing executive dysfunction mechanisms is limited, necessitating validation. Thus, our study aimed to assess associations between electroencephalographic power spectral density (PSD-EEG), striatal [18 F]Fluorodopa uptake and neuropsychological executive function (EF) testing parameters in PD, while also estimating their diagnostic accuracy. We compared resting PSD-EEG, striatal [18 F]Fluorodopa uptake ratios based on positron emission computed tomography ([18 F]FDOPA PET/CT) and neuropsychological EF tests outcomes [Trail Making Test (TMT) and Stroop Test (ST)] between PD patients and healthy controls (HCO) and then calculated correlations among these measures separately for each group. Additionally, we estimated PD diagnostic accuracy of the PSD-EEG and [18 F]FDOPA PET/CT parameters. In PD patients, we observed the following: (i) slower EEG waves, reflected in increased power of the EEG theta and lower-alpha bands in frontal lobe areas; (ii) reduced [18 F]FDOPA PET/CT uptake in the putaminal and caudate nuclei, along with a decreased putamen-to-caudate ratio ([18 F]FDOPA PET/CT PCR); and (iii) longer performance times evident in nearly all EF tests' parameters. Slower EEG waves correlated negatively with [18 F]FDOPA PET/CT PCR and positively with most of the EF test parameters. Furthermore, we found negative correlations between [18 F]FDOPA PET/CT PCR and certain EF measures related to ST. [18 F]FDOPA PET/CT ratios and several PSD-EEG parameters, particularly those from the prefrontal cortex, demonstrated clinically reasonable diagnostic accuracy for PD. In conclusion, EEG waves slowing in the frontal lobe were correlated with striatal dopaminergic deficiency and impaired executive function in mild PD patients and showed promise as a biomarker of PD-related executive dysfunction.

EEG measurement for the effect of perceptual eye position and eye position training on comitant strabismus

One of the clinical features of comitant strabismus is that the deviation angles in the first and second eye positions are equal. However, there has been no report of consistency in the electroencephalography (EEG) signals between the 2 positions. In order to address this issue, we developed a new paradigm based on perceptual eye position. We collected steady-state visual evoked potentials (SSVEPs) signals and resting-state EEG data before and after the eye position training. We found that SSVEP signals could characterize the suppression effect and eye position effect of comitant strabismus, that is, the SSVEP response of the dominant eye was stronger than that of the strabismus eye in the first eye position but not in the second eye position. Perceptual eye position training could modulate the frequency band activities in the occipital and surrounding areas. The changes in the visual function of comitant strabismus after training could also be characterized by SSVEP. There was a correlation between intermodulation frequency, power of parietal electrodes, and perceptual eye position, indicating that EEG might be a potential indicator for evaluating strabismus visual function.

Pre-stimulus alpha activity modulates long-lasting unconscious feature integration

Pre-stimulus alpha (α) activity can influence perception of shortly presented, low-contrast stimuli. The underlying mechanisms are often thought to affect perception exactly at the time of presentation. In addition, it is suggested that α cycles determine temporal windows of integration. However, in everyday situations, stimuli are usually presented for periods longer than ∼100 ms and perception is often an integration of information across space and time. Moving objects are just one example. Hence, the question is whether α activity plays a role also in temporal integration, especially when stimuli are integrated over several α cycles. Using electroencephalography (EEG), we investigated the relationship between pre-stimulus brain activity and long-lasting integration in the sequential metacontrast paradigm (SQM), where two opposite vernier offsets, embedded in a stream of lines, are unconsciously integrated into a single percept. We show that increases in α power, even 300 ms before the stimulus, affected the probability of reporting the first offset, shown at the very beginning of the SQM. This effect was mediated by the systematic slowing of the α rhythm that followed the peak in α power. No phase effects were found. Together, our results demonstrate a cascade of neural changes, following spontaneous bursts of α activity and extending beyond a single moment, which influences the sensory representation of visual features for hundreds of milliseconds. Crucially, as feature integration in the SQM occurs before a conscious percept is elicited, this also provides evidence that α activity is linked to mechanisms regulating unconscious processing.

Semantic processing during continuous speech production: an analysis from eye movements and EEG

Introduction

Speech production involves neurological planning and articulatory execution. How speakers prepare for articulation is a significant aspect of speech production research. Previous studies have focused on isolated words or short phrases to explore speech planning mechanisms linked to articulatory behaviors, including investigating the eye-voice span (EVS) during text reading. However, these experimental paradigms lack real-world speech process replication. Additionally, our understanding of the neurological dimension of speech planning remains limited.

Methods

This study examines speech planning mechanisms during continuous speech production by analyzing behavioral (eye movement and speech) and neurophysiological (EEG) data within a continuous speech production task. The study specifically investigates the influence of semantic consistency on speech planning and the occurrence of "look ahead" behavior.

Results

The outcomes reveal the pivotal role of semantic coherence in facilitating fluent speech production. Speakers access lexical representations and phonological information before initiating speech, emphasizing the significance of semantic processing in speech planning. Behaviorally, the EVS decreases progressively during continuous reading of regular sentences, with a slight increase for non-regular sentences. Moreover, eye movement pattern analysis identifies two distinct speech production modes, highlighting the importance of semantic comprehension and prediction in higher-level lexical processing. Neurologically, the dual pathway model of speech production is supported, indicating a dorsal information flow and frontal lobe involvement. The brain network linked to semantic understanding exhibits a negative correlation with semantic coherence, with significant activation during semantic incoherence and suppression in regular sentences.

Discussion

The study's findings enhance comprehension of speech planning mechanisms and offer insights into the role of semantic coherence in continuous speech production. Furthermore, the research methodology establishes a valuable framework for future investigations in this domain.

A resting-state electroencephalographic microstates study in depressed adolescents with non-suicidal self-injury

Neuroimaging studies have revealed abnormal brain activities in depressed teenagers who engage in non-suicidal self-injury (NSSI). We used resting-state electroencephalography (EEG) microstate analysis, which indicates the brief overlap of brain network activation for exploring the characteristics of large-scale cortical activities in depressed adolescents engaged with NSSI to clarify the underlying temporal mechanism. A modified k-means cluster algorithm was used to segment 64-channel resting-state EEG data into microstates. Data from 27 healthy adolescents, 37 adolescents with major depressive disorder (MDD), and 53 adolescents with both MDD and NSSI were examined in this study. The resting-state microstate parameters were compared among groups using the one-way ANOVA and Spearman correlation. Then the associations between significantly different microstate parameters and the depressive severity and self-harming data in the patient groups were further analyzed. The MDD group had higher contribution (p < 0.01), occurrence (p < 0.01) of microstate A, and higher microstate E→A transition (p < 0.05) than the HC and the NSSI group. The MDD group showed a distinctly longer duration (p < 0.05) of microstate A and microstate A→C transition than the HC. The transition probability from B to C was increased in the NSSI group compared to the HC. In the MDD group, the HAMD correlated with the duration of microstate A (Spearman's rho = 0.34, p = 0.044), as the PHQ-9 correlated with its occurrence (Spearman's rho = 0.37, p = 0.028). This research revealed that whereas depressive adolescents with NSSI and MDD displayed similar patterns with healthy controls in EEG microstate, the MDD group did not. Additionally, the non-random transition from microstate E→A may protect against recent self-harm in adolescents with MDD.

Electrophysiological evidence for increased auditory crossmodal activity in adult ADHD

Background

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by core symptoms of inattention, and/or impulsivity and hyperactivity. In order to understand the basis for this multifaceted disorder, the investigation of sensory processing aberrancies recently reaches more interest. For example, during the processing of auditory stimuli comparable low sensory thresholds account for symptoms like higher distractibility and auditory hypersensitivity in patients with ADHD. It has further been shown that deficiencies not only exist on an intramodal, but also on a multimodal level. There is evidence that the visual cortex shows more activation during a focused auditory task in adults with ADHD than in healthy controls. This crossmodal activation is interpreted as the reallocation of more attentional resources to the visual domain as well as deficient sensory inhibition. In this study, we used, for the first time, electroencephalography to identify a potential abnormal regulated crossmodal activation in adult ADHD.

Methods

15 adult subjects with clinically diagnosed ADHD and 14 healthy controls comparable in age and gender were included. ERP components P50, P100, N100, P200 and N200 were measured during the performance of a unimodal auditory and visual discrimination task in a block design. Sensory profiles and ADHD symptoms were assessed with inattention as well as childhood ADHD scores. For evaluating intramodal and crossmodal activations, we chose four EEG channels for statistical analysis and group-wise comparison.

Results

At the occipital channel O2 that reflects possible crossmodal activations, a significantly enhanced P200 amplitude was measured in the patient group. At the intramodal channels, a significantly enhanced N200 amplitude was observed in the control group. Statistical analysis of behavioral data showed poorer performance of subjects with ADHD as well as higher discrimination thresholds. Further, the correlation of the assessed sensory profiles with the EEG parameters revealed a negative correlation between the P200 component and sensation seeking behavior.

Conclusion

Our findings show increased auditory crossmodal activity that might reflect an altered stimulus processing resource allocation in ADHD. This might induce consequences for later, higher order attentional deployment. Further, the enhanced P200 amplitude might reflect more sensory registration and therefore deficient inhibition mechanisms in adults with ADHD.

Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

Approaches for Hybrid Coregistration of Marker-Based and Markerless Coordinates Describing Complex Body/Object Interactions

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration.

Early visual perceptual processing is altered in obsessive-compulsive disorder

OBJECTIVE

Existing studies have shown changes in attention and emotion processing of disorder-relevant visual stimuli in those with obsessive compulsive disorder (OCD). However, early visual processing in OCD has not been assessed, as previous studies did not examine the entire time course of visual processing but instead assessed potential differences in pre-determined visual evoked potentials (VEPs). This study investigates the entire visual processing stream in OCD compared to healthy age-matched controls (HC) using emotionally-neutral visual stimuli and a data-driven rather than hypothesis-driven approach.

METHODS

35 HC and 26 participants with OCD underwent EEG recording while completing a modified Eriksen flanker task. Permutation-controlled t-tests were used to identify group differences in the data's full time course of visual evoked potentials. Baseline-corrected amplitudes at time points where the groups were significantly different were analyzed using ANCOVAs with BDI, BAI, and SNAP-inattentiveness scores included as covariates.

RESULTS

This analysis identified enhanced P1 amplitudes to two visual stimuli (the initial flanker and the stimulus), corresponding to time windows of 65-93 ms and 157-187 ms post-flanker presentation in the OCD group compared to controls. Group (OCD vs. HC) was the strongest predictor of VEP amplitude during both time windows, with no significant influences of any covariates.

CONCLUSIONS

This study showed an enhanced P1 component in people with OCD to neutral visual stimuli, potentially reflecting either inefficient or excessive early visual processing in this population. Additional inquiry is necessary to determine whether altered visual processing is associated with the sensory hypervigilance observed in those with OCD.

SIGNIFICANCE

This work identifies early visual processing alterations in OCD using neutral stimuli and a data-driven approach.

EEG responses induced by cerebellar TMS at rest and during visuomotor adaptation

BACKGROUND

Connections between the cerebellum and the cortex play a critical role in learning and executing complex behaviours. Dual-coil transcranial magnetic stimulation (TMS) can be used non-invasively to probe connectivity changes between the lateral cerebellum and motor cortex (M1) using the motor evoked potential as an outcome measure (cerebellar-brain inhibition, CBI). However, it gives no information about cerebellar connections to other parts of cortex.

OBJECTIVES

We used electroencephalography (EEG) to investigate whether it was possible to detect activity evoked in any areas of cortex by single-pulse TMS of the cerebellum (cerebellar TMS evoked potentials, cbTEPs). A second experiment tested if these responses were influenced by the performance of a cerebellar-dependent motor learning paradigm.

METHODS

In the first series of experiments, TMS was applied over either the right or left cerebellar cortex, and scalp EEG was recorded simultaneously. Control conditions that mimicked auditory and somatosensory inputs associated with cerebellar TMS were included to identify responses due to non-cerebellar sensory stimulation. We conducted a follow-up experiment that evaluated whether cbTEPs are behaviourally sensitive by assessing individuals before and after learning a visuomotor reach adaptation task.

RESULTS

A TMS pulse over the lateral cerebellum evoked EEG responses that could be distinguished from those caused by auditory and sensory artefacts. Significant positive (P80) and negative peaks (N110) over the contralateral frontal cerebral area were identified with a mirrored scalp distribution after left vs. right cerebellar stimulation. The P80 and N110 peaks were replicated in the cerebellar motor learning experiment and changed amplitude at different stages of learning. The change in amplitude of the P80 peak was associated with the degree of learning that individuals retained following adaptation. Due to overlap with sensory responses, the N110 should be interpreted with caution.

CONCLUSIONS

Cerebral potentials evoked by TMS of the lateral cerebellum provide a neurophysiological probe of cerebellar function that complements the existing CBI method. They may provide novel insight into mechanisms of visuomotor adaptation and other cognitive processes.

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.

Online Left-Hemispheric In-Phase Frontoparietal Theta tACS Modulates Theta-Band EEG Source-Based Large-Scale Functional Network Connectivity in Patients with Schizophrenia: A Randomized, Double-Blind, Sham-Controlled Clinical Trial

EEG studies indicated that schizophrenia patients had increased resting-state theta-band functional connectivity, which was associated with negative symptoms. We recently published the first study showing that theta (6 Hz) transcranial alternating current stimulation (tACS) over left prefrontal and parietal cortices during a working memory task for accentuating frontoparietal theta-band synchronization (in-phase theta-tACS) reduced negative symptoms in schizophrenia patients. Here, we hypothesized that in-phase theta-tACS can modulate theta-band large-scale networks connectivity in schizophrenia patients. In this randomized, double-blind, sham-controlled trial, patients received twice-daily, 2 mA, 20-min sessions of in-phase theta-tACS for 5 consecutive weekdays (n = 18) or a sham stimulation (n = 18). Resting-state electroencephalography data were collected at baseline, end of stimulation, and at one-week follow-up. Exact low resolution electromagnetic tomography (eLORETA) was used to compute intra-cortical activity. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity across 84 cortical regions at theta frequency (5-7 Hz). EEG data from 35 patients were analyzed. We found that in-phase theta-tACS significantly reduced the LPS between the posterior cingulate (PC) and the parahippocampal gyrus (PHG) in the right hemisphere only at the end of stimulation relative to sham (p = 0.0009, corrected). The reduction in right hemispheric PC-PHG LPS was significantly correlated with negative symptom improvement at the end of the stimulation (r = 0.503, p = 0.039). Our findings suggest that in-phase theta-tACS can modulate theta-band large-scale functional connectivity pertaining to negative symptoms. Considering the failure of right hemispheric PC-PHG functional connectivity to predict improvement in negative symptoms at one-week follow-up, future studies should investigate whether it can serve as a surrogate of treatment response to theta-tACS.

Early sleep after action observation and motor imagery training boosts improvements in manual dexterity

The systematic observation and imagination of actions promotes acquisition of motor skills. Furthermore, studies demonstrated that early sleep after practice enhances motor learning through an offline stabilization process. Here, we investigated behavioral effects and neurodynamical correlates of early sleep after action observation and motor imagery training (AO + MI-training) on motor learning in terms of manual dexterity. Forty-five healthy participants were randomized into three groups receiving a 3 week intervention consisting of AO + MI-training immediately before sleeping or AO + MI-training at least 12 h before sleeping or a control stimulation. AO + MI-training implied the observation and motor imagery of transitive manual dexterity tasks, whereas the control stimulation consisted of landscape video-clips observation. Manual dexterity was assessed using functional tests, kinematic and neurophysiological outcomes before and after the training and at 1-month follow-up. AO + MI-training improved manual dexterity, but subjects performing AO + MI-training followed by early sleep had significantly larger improvements than those undergoing the same training at least 12 h before sleeping. Behavioral findings were supported by neurodynamical correlates during motor performance and additional sleep-dependent benefits were also detected at 1 month follow-up. These findings introduce a new approach to enhance the acquisition of new motor skills or facilitate recovery in patients with motor impairments.

Culture and gender modulate dlPFC integration in the emotional brain: evidence from dynamic causal modeling

Past research has recognized culture and gender variation in the experience of emotion, yet this has not been examined on a level of effective connectivity. To determine culture and gender differences in effective connectivity during emotional experiences, we applied dynamic causal modeling (DCM) to electroencephalography (EEG) measures of brain activity obtained from Chinese and American participants while they watched emotion-evoking images. Relative to US participants, Chinese participants favored a model bearing a more integrated dorsolateral prefrontal cortex (dlPFC) during fear v. neutral experiences. Meanwhile, relative to males, females favored a model bearing a less integrated dlPFC during fear v. neutral experiences. A culture-gender interaction for winning models was also observed; only US participants showed an effect of gender, with US females favoring a model bearing a less integrated dlPFC compared to the other groups. These findings suggest that emotion and its neural correlates depend in part on the cultural background and gender of an individual. To our knowledge, this is also the first study to apply both DCM and EEG measures in examining culture-gender interaction and emotion.

Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1-30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.

Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation.

EEG source derived salience network coupling supports real-world attention switching

While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.

A randomized controlled trial into the effects of probiotics on electroencephalography in preschoolers with autism

LAY ABSTRACT

This study investigates the effects of a probiotic on preschoolers' brain electrical activity with autism spectrum disorder. Autism is a disorder with an increasing prevalence characterized by an enormous individual, family, and social cost. Although the etiology of autism spectrum disorder is unknown, an interaction between genetic and environmental factors is implicated, converging in altered brain synaptogenesis and, therefore, connectivity. Besides deepening the knowledge on the resting brain electrical activity that characterizes this disorder, this study allows analyzing the positive central effects of a 6-month therapy with a probiotic through a randomized, double-blind placebo-controlled study and the correlations between electroencephalography activity and biochemical and clinical parameters. In subjects treated with probiotics, we observed a decrease of power in frontopolar regions in beta and gamma bands, and increased coherence in the same bands together with a shift in frontal asymmetry, which suggests a modification toward a typical brain activity. Electroencephalography measures were significantly correlated with clinical and biochemical measures. These findings support the importance of further investigations on probiotics' benefits in autism spectrum disorder to better elucidate mechanistic links between probiotics supplementation and changes in brain activity.

(A)symmetry during gait initiation in people with Parkinson's disease: A motor and cortical activity exploratory study

Background

Gait asymmetry and deficits in gait initiation (GI) are among the most disabling symptoms in people with Parkinson's disease (PwPD). Understanding if PwPD with reduced asymmetry during GI have higher asymmetry in cortical activity may provide support for an adaptive mechanism to improve GI, particularly in the presence of an obstacle.

Objective

This study quantified the asymmetry of anticipatory postural adjustments (APAs), stepping parameters and cortical activity during GI, and tested if the presence of an obstacle regulates asymmetry in PwPD.

Methods

Sixteen PwPD and 16 control group (CG) performed 20-trials in two conditions: unobstructed and obstructed GI with right and left limbs. We measured, through symmetry index, (i) motor parameters: APAs and stepping, and (ii) cortical activity: the PSD of the frontal, sensorimotor and occipital areas during APA, STEP-I (moment of heel-off of the leading foot in the GI until the heel contact of the same foot); and STEP-II (moment of the heel-off of the trailing foot in the GI until the heel contact of the same foot) phases.

Results

Parkinson's disease showed higher asymmetry in cortical activity during APA, STEP-I and STEP-II phases and step velocity (STEP-II phase) during unobstructed GI than CG. However, unexpectedly, PwPD reduced the level of asymmetry of anterior-posterior displacement (p < 0.01) and medial-lateral velocity (p < 0.05) of the APAs. Also, when an obstacle was in place, PwPD showed higher APAs asymmetry (medial-lateral velocity: p < 0.002), with reduced and increased asymmetry of the cortical activity during APA and STEP-I phases, respectively.

Conclusion

Parkinson's disease were not motor asymmetric during GI, indicating that higher cortical activity asymmetry can be interpreted as an adaptive behavior to reduce motor asymmetry. In addition, the presence of obstacle did not regulate motor asymmetry during GI in PwPD.

Measures of tonic and phasic activity of the locus coeruleus-norepinephrine system in children with autism spectrum disorder: An event-related potential and pupillometry study

A growing body of research suggests that locus coeruleus-norepinephrine (LC-NE) system may function differently in individuals with autism spectrum disorder (ASD). Understanding the dynamics of both tonic (resting pupil diameter) and phasic (pupil dilation response [PDR] and event-related potential [ERP]) indices may provide meaningful insights about the nature of LC-NE function in ASD. Twenty-four children with ASD and 27 age- and nonverbal-IQ matched typically developing (TD) children completed two experiments: (1) a resting eye-tracking task to measure tonic pupil diameter, and (2) a three-stimulus oddball paradigm to measure phasic responsivity using PDR and ERP. Consistent with prior reports, our results indicate that children with ASD exhibit increased tonic (resting pupil diameter) and reduced phasic (PDR and ERP) activity of the LC-NE system compared to their TD peers. For both groups, decreased phasic responsivity was associated with increased resting pupil diameter. Lastly, tonic and phasic LC-NE indices were primarily related to measures of attention-deficit/hyperactivity disorder (ADHD), and not ASD, symptomatology. These findings expand our understanding of neurophysiological differences present in ASD and demonstrate that aberrant LC-NE activation may be associated with atypical arousal and decreased responsivity to behaviorally-relevant information in ASD.