Consistency of EEG source localization and connectivity estimates

Cortical activations induced by electrical versus vibrotactile finger stimulation using EEG

Somatosensory evoked potentials (SEPs) recorded with electroencephalography offer insights into cortical responses to tactile stimulation, typically elicited through temporally precise electrical stimulation. Although vibrotactile stimulation is more ecologically valid but less common, studies directly comparing EEG responses to both electrical and vibrotactile finger stimulation are limited. This study examines and compares (a) cortical responses, (b) connectivity patterns, and (c) somatotopic accuracy of these stimulation types on the fingers. In two experiments, SEPs were recorded from healthy participants' right-hand finger stimulation, using either electrical (experiment 1, n = 22) or vibrotactile (experiment 2, n = 22) stimulation. Vibrotactile stimuli were delivered at 10, 50, and 250 Hz, targeting different ranges of tactile mechanoreceptors activations. Electrical stimulation reliability was assessed across two days, showing consistent SEP amplitudes and latencies. Both stimulation types generated three early components (P1, N1, P2), but all vibrotactile components were increasingly delayed compared to electrical stimulation. Vibrotactile stimulation exhibited stronger localized connectivity in early components (P1, N1) in the left hemisphere, while electrical stimulation showed broader connectivity at P2. Electrical stimulation provided a clearer somatotopic organization in the postcentral gyrus than vibrotactile stimulation. These findings suggest distinct processing for electrical versus vibrotactile finger stimulation. These two stimulation methods are not interchangeable in somatosensory studies: the temporal shift in vibrotactile responses reflects selective activation of Pacinian corpuscles, whereas electrical stimulation yields stronger generalized cortical processing. Electrical stimulation may engage a serial processing pathway starting in the primary somatosensory cortex, while vibrotactile stimulation could involve parallel processing in both primary and secondary somatosensory cortices.

Direct Comparison of EEG Resting State and Task Functional Connectivity Patterns for Predicting Working Memory Performance Using Connectome-Based Predictive Modeling

Background: The integration of machine learning with advanced neuroimaging has emerged as a powerful approach for uncovering the relationship between neuronal activity patterns and behavioral traits. While resting-state neuroimaging has significantly contributed to understanding the neural basis of cognition, recent fMRI studies suggest that task-based paradigms may offer superior predictive power for cognitive outcomes. However, this hypothesis has never been tested using electroencephalography (EEG) data. Methods: We conducted the first experimental comparison of predictive models built on high-density EEG data recorded during both resting-state and an auditory working memory task. Multiple data processing pipelines were employed to ensure robustness and reliability. Model performance was evaluated by computing the Pearson correlation coefficient between predicted and observed behavioral scores, supplemented by mean absolute error and root mean square error metrics for each model configuration. Results: Consistent with prior fMRI findings, task-based EEG data yielded slightly better modeling performance than resting-state data. Both conditions demonstrated high predictive accuracy, with peak correlations between observed and predicted values reaching r = 0.5. Alpha and beta band functional connectivity were the strongest predictors of working memory performance, followed by theta and gamma bands. Additionally, the choice of parcellation atlas and connectivity method significantly influenced results, highlighting the importance of methodological considerations. Conclusion: Our findings support the advantage of task-based EEG over resting-state data in predicting cognitive performance, aligning with. The study underscores the critical role of frequency-specific functional connectivity and methodological choices in model performance. These insights should guide future experimental designs in cognitive neuroscience. Impact Statement This study provides the first direct comparison of EEG-based functional connectivity during rest and task conditions for predicting working memory performance using connectome-based predictive modeling (CPM). It demonstrates that task-based EEG data slightly outperforms resting-state data, with alpha and beta bands being the most predictive. The findings highlight the critical influence of methodological choices, such as parcellation atlases and connectivity metrics, on model outcomes. By bridging gaps in EEG research and validating CPM's applicability, this work advances the optimization of neuroimaging protocols for cognitive assessment, offering insights for future studies in cognitive neuroscience.

Cortical activation and functional connectivity in visual-cognitive-motor networks during motor-cognitive exercise

PURPOSE

When compared to computer-based brain training, motor-cognitive exercises and exergaming claim to provide stronger brain activation and better transfer due to the integration of a more complex motor task. To evaluate if this is supported by neural dynamics, this study compared event-related potentials and connectivity between a cognitive and motor-cognitive training task.

METHODS

21 participants performed a choice-reaction task with either an upper extremity button press (cognitive condition) or lower extremity stepping movement (motor-cognitive condition) input using the SKILLCOURT technology. The visual stimulation and cognitive task were identical. In addition to reaction time, neural activity was recorded using a 64-channel EEG system. Time course of neural activation and event-related potential data in visual premotor, primary motor and sensory regions of interest were compared between conditions. In addition, connectivity was calculated to identify differences in functional communication.

RESULTS

Neural engagement was stronger in the motor-cognitive condition as reflected by a higher amplitude (p < 0.001) and longer latency (p = 0.02) of the BA6 negativity potential as well as higher activity in electrodes representing the foot region of the primary motor cortex (p < 0.001). This was accompanied by enhanced connectivity between electrodes covering the premotor cortex and frontal, primary motor and visual areas p < 0.05).

CONCLUSION

The findings suggest that the premotor cortex plays a key role in motor-cognitive training. This supports the assumption of stronger engagement of motor areas in motor-cognitive when compared to cognitive training and shed light on the neural processes that may underly superior training effects when compared to computer-based cognitive training.

Alpha rhythm and Alzheimer's disease: Has Hans Berger's dream come true?

In this "centenary" paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8-12 Hz) in 1924, his foresight of substantial clinical applications in patients with "senile dementia," and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients' quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.

Functional Connectivity in Chronic Schizophrenia: An EEG Resting-State Study with Corrected Imaginary Phase-Locking

Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations.

AIM

In the present study, we explored the network dynamics of schizophrenia using a novel functional connectivity metric-corrected imaginary phase locking value (ciPLV)-which is insensitive to changes in amplitude as well as interactions at zero-phase lag. This method, which is less prone to volume conduction effects, provides a more reliable estimate of sensor-space functional network connectivity in schizophrenia.

METHODS

We employed a cross-sectional design, utilizing resting state EEG recordings from two adult groups: individuals diagnosed with chronic schizophrenia (n = 30) and a control group of healthy participants (n = 30), all aged between 18 and 55 years old.

RESULTS

Our observations revealed that schizophrenia is characterized by a prevalence of excess theta (4-8 Hz) power localized to centroparietal electrodes. This was accompanied by significant alterations in inter- and intra-hemispheric functional network connectivity patterns, mainly between frontotemporal regions within the theta band and frontoparietal regions within beta/gamma bands.

CONCLUSIONS

Our findings suggest that patients with schizophrenia demonstrate long-range electrophysiological connectivity abnormalities that are independent of spectral power (i.e., volume conduction). Overall, distinct hemispheric differences were present in frontotemporo-parietal networks in theta and beta/gamma bands. While preliminary, these alterations could be promising new candidate biomarkers of chronic schizophrenia.

Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review

Background

Alzheimer's disease (AD) might be best conceptualized as a disconnection syndrome, such that symptoms may be largely attributable to disrupted communication between brain regions, rather than to deterioration within discrete systems. EEG is uniquely capable of directly and non-invasively measuring neural activity with precise temporal resolution; connectivity quantifies the relationships between such signals in different brain regions. EEG research on connectivity in AD and mild cognitive impairment (MCI), often considered a prodromal phase of AD, has produced mixed results and has yet to be synthesized for comprehensive review. Thus, we performed a systematic review of EEG connectivity in MCI and AD participants compared with cognitively healthy older adult controls.

Methods

We searched PsycINFO, PubMed, and Web of Science for peer-reviewed studies in English on EEG, connectivity, and MCI/AD relative to controls. Of 1,344 initial matches, 124 articles were ultimately included in the systematic review.

Results

The included studies primarily analyzed coherence, phase-locked, and graph theory metrics. The influence of factors such as demographics, design, and approach was integrated and discussed. An overarching pattern emerged of lower connectivity in both MCI and AD compared to healthy controls, which was most prominent in the alpha band, and most consistent in AD. In the minority of studies reporting greater connectivity, theta band was most commonly implicated in both AD and MCI, followed by alpha. The overall prevalence of alpha effects may indicate its potential to provide insight into nuanced changes associated with AD-related networks, with the caveat that most studies were during the resting state where alpha is the dominant frequency. When greater connectivity was reported in MCI, it was primarily during task engagement, suggesting compensatory resources may be employed. In AD, greater connectivity was most common during rest, suggesting compensatory resources during task engagement may already be exhausted.

Conclusion

The review highlighted EEG connectivity as a powerful tool to advance understanding of AD-related changes in brain communication. We address the need for including demographic and methodological details, using source space connectivity, and extending this work to cognitively healthy older adults with AD risk toward advancing early AD detection and intervention.

Resting-State EEG Alpha Rhythms Are Related to CSF Tau Biomarkers in Prodromal Alzheimer's Disease

Patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show abnormally high delta (<4 Hz) and low alpha (8-12 Hz) rhythms measured from resting-state eyes-closed electroencephalographic (rsEEG) activity. Here, we hypothesized that the abnormalities in rsEEG activity may be greater in ADMCI patients than in those with MCI not due to AD (noADMCI). Furthermore, they may be associated with the diagnostic cerebrospinal fluid (CSF) amyloid-tau biomarkers in ADMCI patients. An international database provided clinical-demographic-rsEEG datasets for cognitively unimpaired older (Healthy; N = 45), ADMCI (N = 70), and noADMCI (N = 45) participants. The rsEEG rhythms spanned individual delta, theta, and alpha frequency bands. The eLORETA freeware estimated cortical rsEEG sources. Posterior rsEEG alpha source activities were reduced in the ADMCI group compared not only to the Healthy group but also to the noADMCI group (p < 0.001). Negative associations between the CSF phospho-tau and total tau levels and posterior rsEEG alpha source activities were observed in the ADMCI group (p < 0.001), whereas those with CSF amyloid beta 42 levels were marginal. These results suggest that neurophysiological brain neural oscillatory synchronization mechanisms regulating cortical arousal and vigilance through rsEEG alpha rhythms are mainly affected by brain tauopathy in ADMCI patients.

Measurement and Analyses of Olfactory Event-Related Potentials

Olfactory event-related potentials (OERPs) are brain electrical activities time-locked to olfactory stimuli, detectable via scalp electrodes. They offer a noninvasive means to study cortical olfactory processing in humans. Previous research suggests that olfactory cortical processing occurring within several seconds after the onset of an odor can be tracked using OERPs. This enables the investigation of the temporal dynamics of neural activity, spanning from those associated with odor properties to subjects' states. Moreover, OERPs are influenced by diseases, including olfactory, neurological, or psychiatric conditions, and have the potential to serve as biomarkers. This chapter describes the measurement and analysis methods required to obtain OERPs, with a particular focus on odor delivery, aiming to provide a primer for those unfamiliar with OERP measurement.

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study

Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research.

The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses

High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.

Cortical activations associated with spatial remapping of finger touch using EEG

The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100-125 ms in the parietal cortex.

Symptom dimensions of resting-state electroencephalographic functional connectivity in autism

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.

An evaluation of inter-brain EEG coupling methods in hyperscanning studies

EEG-based hyperscanning technology has been increasingly applied to analyze interpersonal interactions in social neuroscience in recent years. However, different methods are employed in various of studies without a complete investigation of the suitability of these methods. Our study aimed to systematically compare typical inter-brain EEG coupling methods, with simulated EEG data generated by real EEG data. In particular, two critical metrics of noise level and time delay were manipulated, and three different coupling models were tested. The results revealed that: (1) under certain conditions, various methods were leveraged by noise level and time delay, leading to different performances; (2) most algorithms achieved better experimental results and performance under high coupling degree; (3) with our simulation process, temporal and spectral models showed relatively good results, while data simulated with phase coupling model performed worse. This is the first systematic comparison of typical inter-brain EEG coupling methods, with simulated EEG data generated by real EEG data from different subjects. Existing methods mainly focused on intra-brain coupling. To our knowledge, there was only one previous study that compared five inter-brain EEG coupling methods (Burgess in Front Human Neurosci 7:881, 2013). However, the simulated data used in this study were generated time series with varied degrees of phase coupling without considering any EEG characteristics. For future research, appropriate methods need to be selected based on possible underlying mechanisms (temporal, spectral and phase coupling model hypothesis) of a specific study, as well as the expected coupling degree and conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09911-1.

Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models

State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions.

Cerebellar EEG source localization reveals age-related compensatory activity moderated by genetic risk for Alzheimer's disease

The apolipoprotein-E (APOE) ε4 allele is the greatest genetic risk factor for late-onset Alzheimer's disease (AD), but alone it is not sufficiently predictive. Because neuropathological changes associated with AD begin decades before cognitive symptoms, neuroimaging of healthy, cognitively intact ε4 carriers (ε4+) may enable early characterization of patterns associated with risk for future decline. Research in the cerebral cortex highlights a period of compensatory recruitment in elders and ε4+, which serves to maintain cognitive functioning. Yet, AD-related changes may occur even earlier in the cerebellum. Advances in electroencephalography (EEG) source localization now allow effective modeling of cerebellar activity. Importantly, healthy aging and AD are associated with declines in both cerebellar functions and executive functioning (EF). However, it is not known whether cerebellar activity can detect pre-symptomatic AD risk. Thus, the current study analyzed cerebellar EEG source localization during an EF-dependent stop-signal task (i.e., inhibitory control) in healthy, intact older adults (Mage  = 80 years; 20 ε4+, 25 ε4-). Task performance was comparable between groups. Older age predicted greater activity in left crus II and lobule VIIb during the P300 window (i.e., performance evaluation), consistent with age-related compensation. Age*ε4 moderations specifically showed that compensatory patterns were evident only in ε4-, suggesting that cerebellar compensatory resources may already be depleted in healthy ε4+ elders. Thus, the posterolateral cerebellum is sensitive to AD-related neural deficits in healthy elders. Characterization of these patterns may be essential for the earliest possible detection of AD risk, which would enable critical early intervention prior to symptom onset.

WRA-MTSI: A Robust Extended Source Imaging Algorithm Based on Multi-Trial EEG

OBJECTIVE

Reconstructing brain activities from electroencephalography (EEG) signals is crucial for studying brain functions and their abnormalities. However, since EEG signals are nonstationary and vulnerable to noise, brain activities reconstructed from single-trial EEG data are often unstable, and significant variability may occur across different EEG trials even for the same cognitive task.

METHODS

In an effort to leverage the shared information across the EEG data of multiple trials, this paper proposes a multi-trial EEG source imaging method based on Wasserstein regularization, termed WRA-MTSI. In WRA-MTSI, Wasserstein regularization is employed to perform multi-trial source distribution similarity learning, and the structured sparsity constraint is enforced to enable accurate estimation of the source extents, locations and time series. The resulting optimization problem is solved by a computationally efficient algorithm based on the alternating direction method of multipliers (ADMM).

RESULTS

Both numerical simulations and real EEG data analysis demonstrate that WRA-MTSI outperforms existing single-trial ESI methods (e.g., wMNE, LORETA, SISSY, and SBL) in mitigating the influence of artifacts in EEG data. Moreover, WRA-MTSI yields superior performance compared to other state-of-the-art multi-trial ESI methods (e.g., group lasso, the dirty model, and MTW) in estimating source extents.

CONCLUSION AND SIGNIFICANCE

WRA-MTSI may serve as an effective robust EEG source imaging method in the presence of multi-trial noisy EEG data.

Visual Occlusions Result in Phase Synchrony Within Multiple Brain Regions Involved in Sensory Processing and Balance Control

There is a need to develop appropriate balance training interventions to minimize the risk of falls. Recently, we found that intermittent visual occlusions can substantially improve the effectiveness and retention of balance beam walking practice (Symeonidou & Ferris, 2022). We sought to determine how the intermittent visual occlusions affect electrocortical activity during beam walking. We hypothesized that areas involved in sensorimotor processing and balance control would demonstrate spectral power changes and inter-trial coherence modulations after loss and restoration of vision. Ten healthy young adults practiced walking on a treadmill-mounted balance beam while wearing high-density EEG and experiencing reoccurring visual occlusions. Results revealed spectral power fluctuations and inter-trial coherence changes in the visual, occipital, temporal, and sensorimotor cortex as well as the posterior parietal cortex and the anterior cingulate. We observed a prolonged alpha increase in the occipital, temporal, sensorimotor, and posterior parietal cortex after the occlusion onset. In contrast, the anterior cingulate showed a strong alpha and theta increase after the occlusion offset. We observed transient phase synchrony in the alpha, theta, and beta bands within the sensory, posterior parietal, and anterior cingulate cortices immediately after occlusion onset and offset. Intermittent visual occlusions induced electrocortical spectral power and inter-trial coherence changes in a wide range of frequencies within cortical areas relevant for multisensory integration and processing as well as balance control. Our training intervention could be implemented in senior and rehabilitation centers, improving the quality of life of elderly and neurologically impaired individuals.

Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems

Introduction

As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus.

Methods

In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA).

Results and discussion

Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.

Functional brain network features specify DBS outcome for patients with treatment resistant depression

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

Integration of interictal EEG source localization in presurgical epilepsy evaluation - A single-center prospective study

OBJECTIVE

To investigate cost in working hours for initial integration of interictal EEG source localization (ESL) into clinical practice of a tertiary epilepsy center, and to examine concordance of results obtained with three different ESL pipelines.

METHODS

This prospective study covered the first year of using ESL in the Epilepsy-Center Berlin-Brandenburg. Patients aged ≥14 years with drug-resistant focal epilepsy referred for noninvasive presurgical evaluation were included. Interictal ESL was based on low-density EEG and individual head models. Source maxima were obtained from two freely available software packages and one commercial provider. One physician and computer scientist documented their working hours for setting up and processing ESL. Additionally, a survey was conducted among epilepsy centers in Germany to assess the current role of ESL in presurgical evaluation.

RESULTS

Of 40 patients included, 22 (55%) had enough interictal spikes for ESL. The physician's working times decreased from median 4.7 hours [interquartile range 3.9-6.4] in the first third of cases to 2.0 hours [1.9-2.4] in the remaining two thirds; P < 0.01. In addition, computer scientist and physician spent a total of 35.5 and 33.0 working hours on setting up the digital infrastructure, and on training and testing. Sublobar agreement between all three pipelines was 20%, mean measurement of agreement (kappa) 0.13. Finally, the survey revealed that 53% of epilepsy centers in Germany currently use ESL for presurgical evaluation.

SIGNIFICANCE

This study provides information regarding expected effort and costs for integration of ESL into an epilepsy surgery program. Low result agreement across different ESL pipelines calls for further standardization.

Top-down reconfiguration of SMA cortical connectivity during action preparation

The Bereitschaftspotential (BP), a scalp potential recorded in humans during action preparation, is characterized by a slow amplitude increase over fronto-central regions as action execution approaches. We recorded TMS evoked-potentials (TEP) stimulating the supplementary motor area (SMA) at different time-points during a Go/No-Go task to assess whether and how cortical excitability and connectivity of this region change as the BP increases. When approaching BP peak, left SMA reactivity resulted greater. Concurrently, its effective connectivity increased with the left occipital areas, while it decreased with the right inferior frontal gyrus, indicating a fast reconfiguration of cortical networks during the preparation of the forthcoming action. Functional connectivity patterns supported these findings, suggesting a critical role of frequency-specific inter-areal interactions in implementing top-down mechanisms in the sensorimotor system prior to action. These findings reveal that BP time-course reflects quantitative and qualitative changes in SMA communication patterns that shape mechanisms involved in motor readiness.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Evaluating the Influence of Anatomical Accuracy and Electrode Positions on EEG Forward Solutions

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout. Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors. We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

An in-vivo validation of ESI methods with focal sources

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Spatiotemporal characteristics of cortical activities of REM sleep behavior disorder revealed by explainable machine learning using 3D convolutional neural network

Isolated rapid eye movement sleep behavior disorder (iRBD) is a sleep disorder characterized by dream enactment behavior without any neurological disease and is frequently accompanied by cognitive dysfunction. The purpose of this study was to reveal the spatiotemporal characteristics of abnormal cortical activities underlying cognitive dysfunction in patients with iRBD based on an explainable machine learning approach. A convolutional neural network (CNN) was trained to discriminate the cortical activities of patients with iRBD and normal controls based on three-dimensional input data representing spatiotemporal cortical activities during an attention task. The input nodes critical for classification were determined to reveal the spatiotemporal characteristics of the cortical activities that were most relevant to cognitive impairment in iRBD. The trained classifiers showed high classification accuracy, while the identified critical input nodes were in line with preliminary knowledge of cortical dysfunction associated with iRBD in terms of both spatial location and temporal epoch for relevant cortical information processing for visuospatial attention tasks.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning

Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.

Lateralization of orthographic processing in fixed-gaze and natural reading conditions

Lateralized processing of orthographic information is a hallmark of proficient reading. However, how this finding obtained for fixed-gaze processing of orthographic stimuli translates to ecologically valid reading conditions remained to be clarified. To address this shortcoming, here we assessed the lateralization of early orthographic processing in fixed-gaze and natural reading conditions using concurrent eye-tracking and EEG data recorded from young adults without reading difficulties. Sensor-space analyses confirmed the well-known left-lateralized negative-going deflection of fixed-gaze EEG activity throughout the period of early orthographic processing. At the same time, fixation-related EEG activity exhibited left-lateralized followed by right-lateralized processing of text stimuli during natural reading. A strong positive relationship was found between the early leftward lateralization in fixed-gaze and natural reading conditions. Using source-space analyses, early left-lateralized brain activity was obtained in lateraloccipital and posterior ventral occipito-temporal cortices reflecting letter-level processing in both conditions. In addition, in the same time interval, left-lateralized source activity was found also in premotor and parietal brain regions during natural reading. While brain activity remained left-lateralized in later stages representing word-level processing in posterior and middle ventral temporal regions in the fixed-gaze condition, fixation-related source activity became stronger in the right hemisphere in medial and more anterior ventral temporal brain regions indicating higher-level processing of orthographic information. Although our results show a strong positive relationship between the lateralization of letter-level processing in the two reading modes and suggest lateralized brain activity as a general marker for processing of orthographic information, they also clearly indicate the need for reading research in ecologically valid conditions to identify the neural basis of visuospatial attentional, oculomotor and higher-level processes specific to natural reading.

Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data

Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

Selective corticocortical connectivity suppression during propofol-induced anesthesia in healthy volunteers

We comprehensively studied directional feedback and feedforward connectivity to explore potential connectivity changes that underlie propofol-induced deep sedation. We further investigated the corticocortical connectivity patterns within and between hemispheres. Sixty-channel electroencephalographic data were collected from 19 healthy volunteers in a resting wakefulness state and propofol-induced deep unconsciousness state defined by a bispectral index value of 40. A source analysis was employed to locate cortical activity. The Desikan-Killiany atlas was used to partition cortices, and directional functional connectivity was assessed by normalized symbolic transfer entropy between higher-order (prefrontal and frontal) and lower-order (auditory, sensorimotor and visual) cortices and between hot-spot frontal and parietal cortices. We found that propofol significantly suppressed feedforward connectivity from the left parietal to right frontal cortex and bidirectional connectivity between the left frontal and left parietal cortex, between the frontal and auditory cortex, and between the frontal and sensorimotor cortex. However, there were no significant changes in either feedforward or feedback connectivity between the prefrontal and all the lower-order cortices and between the frontal and visual cortices or in feedback connectivity from the frontal to parietal cortex. Propofol anesthetic selectively decreased the unidirectional interaction between higher-order frontoparietal cortices and bidirectional interactions between the higher-order frontal cortex and lower-order auditory and sensorimotor cortices, which indicated that both feedback and feedforward connectivity were suppressed under propofol-induced deep sedation. Our findings provide critical insights into the connectivity changes underlying the top-down mechanism of propofol anesthesia at deep sedation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09775-x.

Electrophysiological evidence for inhibition hypothesis of micro-expressions based on tensor component analysis and Physarum network algorithm

The inhibition hypothesis advocated by Ekman (1985) states when an emotion is concealed or masked, the true emotion is manifested as a micro-expression (ME) which is a fleeting expression lasting for 40 to 500 ms. However, research about the inhibition hypothesis of ME from the perspective of electrophysiology is lacking. Here, we report the electrophysiological evidence obtained from an electroencephalography (EEG) data analysis method. Specifically, we designed an ME elicitation paradigm to collect data of MEs of positive emotions and EEG from 70 subjects, and proposed a method based on tensor component analysis (TCA) combined with the Physarum network (PN) algorithm to characterize the spatial, temporal, and spectral signatures of dynamic EEG data of MEs. The proposed TCA-PN methods revealed two pathways involving dorsal and ventral streams in functional brain networks of MEs, which reflected the inhibition processing and emotion arousal of MEs. The results provide evidence for the inhibition hypothesis from an electrophysiological standpoint, which allows us to better understand the neural mechanism of MEs.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

EEG functional connectivity analysis in the source space

There is a growing interest in using electroencephalography (EEG) and source modeling to investigate functional interactions among cortical processes, particularly when dealing with pediatric populations. This paper introduces two pipelines that have been recently used to conduct EEG FC analysis in the cortical source space. The analytic streams of these pipelines can be summarized into the following steps: 1) cortical source reconstruction of high-density EEG data using realistic magnetic resonance imaging (MRI) models created with age-appropriate MRI templates; 2) segmentation of reconstructed source activities into brain regions of interest; and 3) estimation of FC in age-related frequency bands using robust EEG FC measures, such as weighted phase lag index and orthogonalized power envelope correlation. In this paper we demonstrate the two pipelines with resting-state EEG data collected from children at 12 and 36 months of age. We also discuss the advantages and limitations of the methods/techniques integrated into the pipelines. Given there is a need in the research community for open-access analytic toolkits that can be used for pediatric EEG data, programs and codes used for the current analysis are made available to the public.

Evaluation of EEG dynamic connectivity around seizure onset with principal component analysis

Seizures represent a brain activity state charac-terised by extended synchronised firing in multiple regions that prevent normal brain functioning. It is important to develop methods to distinguish between normal and abnormal synchro-nisation in epilepsy, as well as to localise the networks involved in seizures. To this end, we perform a preliminary investigation in the use of principal components analysis (PCA) to assess the change in dynamic electroencephalogram (EEG) connectivity before and after seizure onset. Source estimation was performed for an openly available EEG dataset from 14 patients with epilepsy. By applying PCA onto the EEG data processed into dynamic connectivity (dFC) matrices, we identified a set of connectivity topologies (eigenconnectivities) that explain high levels of variance in the dynamic connectivity. We compare the dimensionality reduction results obtained on source-level vs. scalp-level connectivity. We identified eigenconnectivities with differences in preictal vs. ictal activity and the brain networks associated with these activations. The work illustrates a data-driven approach for identification of topologies of brain networks that change with seizure onset. Clinical relevance We identified networks that are signifi-cantly varying with preictal vs. ictal brain activity some of which verify preexistent epilepsy markers in a data-driven way.

Aging-Related Dissociation of Spatial and Temporal N400 in Sentence-Level Semantic Processing: Evidence From Source Analyses

Age-related differences in sentence-level lexical-semantic processes have been extensively studied, based on the N400 component of event-related potential (ERP). However, there is still a lack of understanding in this regard at the brain-region level. This study explores aging effects on sentence-level semantic processing by comparing the characteristics of the N400 ERP component and brain engagement patterns within individual N400 time windows for two age groups (16 younger adults aged 24.38 ± 3.88 years and 15 older adults aged 67.00 ± 5.04 years) during sentence processing with different plausibility conditions. Our results demonstrated that the N400 effect according to the plausibility condition occurred in different temporal windows in the two age groups, with a delay in the older group. Moreover, it was identified that there was a distinct difference between the groups in terms of the source location of the condition-dependent N400 effect even though no significant difference was derived in its magnitude itself at the sensor-level. Interestingly, the source analysis results indicated that the two groups involved different functional networks to resolve the same semantic violations: the younger group activated the regions corresponding to the typical lexical-semantic network more, whereas the older group recruited the regions belonging to the multiple-demand network more. The findings of this study could be used as a basis for understanding the aging brain in a linguistic context.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Methodological considerations for studying neural oscillations

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

Are there consistent abnormalities in event-related EEG oscillations in patients with Alzheimer's disease compared to other diseases belonging to dementia?

Cerebrospinal and structural-molecular neuroimaging in-vivo biomarkers are recommended for diagnostic purposes in Alzheimer's disease (AD) and other dementias; however, they do not explain the effects of AD neuropathology on neurophysiological mechanisms underpinning cognitive processes. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association reviewed the field literature and reached consensus on the event-related electroencephalographic oscillations (EROs) that show consistent abnormalities in patients with significant cognitive deficits due to Alzheimer's, Parkinson's (PD), Lewy body (LBD), and cerebrovascular diseases. Converging evidence from oddball paradigms showed that, as compared to cognitively unimpaired (CU) older adults, AD patients had lower amplitude in widespread delta (>4 Hz) and theta (4-7 Hz) phase-locked EROs as a function of disease severity. Similar effects were also observed in PD, LBD, and/or cerebrovascular cognitive impairment patients. Non-phase-locked alpha (8-12 Hz) and beta (13-30 Hz) oscillations were abnormally reduced (event-related desynchronization, ERD) in AD patients relative to CU. However, studies on patients with other dementias remain lacking. Delta and theta phase-locked EROs during oddball tasks may be useful neurophysiological biomarkers of cognitive systems at work in heuristic and intervention clinical trials performed in AD patients, but more research is needed regarding their potential role for other dementias.

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

Purpose

We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity.

Theory

The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β.

Methods

MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract.

Results

The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm^−α.

Conclusion

The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.

Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms

Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.