Finding tau rhythms in EEG: An independent component analysis approach.

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

The Lab Streaming Layer for Synchronized Multimodal Recording.

Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.

A comparison of neuroelectrophysiology databases.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

A Comparison of Neuroelectrophysiology Databases.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data.

To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-analysis. Database URL: https://nemar.org.

Evaluation of skull conductivity using SCALE head tissue conductivity estimation using EEG.

Inaccurate estimation of skull conductivity is the largest impediment to high-resolution EEG source imaging because of its strong influence and wide variability across individuals. Nonetheless, there is yet no widely applied method for noninvasively measuring individual skull conductivity. We presented a skull conductivity and source location estimation algorithm (SCALE) for simultaneously estimating skull conductivity and the cortical distributions of 18-20 effective sources derived from the EEG data by independent component analysis (ICA). SCALE combines a realistic Finite Element Method (FEM) head model built from a magnetic resonance (MR) head image with the effective source scalp maps to estimate brain-to-skull conductivity ratio (BSCR) and to map the effective sources on the cortical surface. To estimate the robustness of SCALE BSCR estimates, we applied SCALE to MR image and high-density EEG data from ten participants, five having data from 2-3 different tasks and sessions. As expected, across participants SCALE BSCR estimates differed widely (mean 32.8, range 18-78). Within-participant SCALE BSCR estimates were far more consistent than between participants. By incorporating SCALE-optimized distributed EEG source localization, stable functional imaging of cortical EEG effective sources can become routine, giving relatively low-cost EEG imaging a spatial resolution compatible with other brain imaging results and uniquely capable for studying brain dynamics supporting thought and action in laboratory, virtual, and natural environments.

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined "grief" and "happiness" were more abrupt and better aligned with participant reports, while transitions for imagined "contentment" extended into adjoining "relaxation" periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.

Midfrontal Theta Activity in Psychiatric Illness: An Index of Cognitive Vulnerabilities Across Disorders.

There is an urgent need to identify the mechanisms that contribute to atypical thinking and behavior associated with psychiatric illness. Behavioral and brain measures of cognitive control are associated with a variety of psychiatric disorders and conditions as well as daily life functioning. Recognition of the importance of cognitive control in human behavior has led to intensive research into behavioral and neurobiological correlates. Oscillations in the theta band (4-8 Hz) over medial frontal recording sites are becoming increasingly established as a direct neural index of certain aspects of cognitive control. In this review, we point toward evidence that theta acts to coordinate multiple neural processes in disparate brain regions during task processing to optimize behavior. Theta-related signals in human electroencephalography include the N2, the error-related negativity, and measures of theta power in the (time-)frequency domain. We investigate how these theta signals are affected in a wide range of psychiatric conditions with known deficiencies in cognitive control: anxiety, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, and substance abuse. Theta-related control signals and their temporal consistency were found to differ in most patient groups compared with healthy control subjects, suggesting fundamental deficits in reactive and proactive control. Notably, however, clinical studies directly investigating the role of theta in the coordination of goal-directed processes across different brain regions are uncommon and are encouraged in future research. A finer-grained analysis of flexible, subsecond-scale functional networks in psychiatric disorders could contribute to a dimensional understanding of psychopathology.

Cortical mu rhythms during action and passive music listening.

Brain systems supporting body movement are active during music listening in the absence of overt movement. This covert motor activity is not well understood, but some theories propose a role in auditory timing prediction facilitated by motor simulation. One question is how music-related covert motor activity relates to motor activity during overt movement. We address this question using scalp electroencephalogram by measuring mu rhythms-cortical field phenomena associated with the somatomotor system that appear over sensorimotor cortex. Lateralized mu enhancement over hand sensorimotor cortex during/just before foot movement in foot versus hand movement paradigms is thought to reflect hand movement inhibition during current/prospective movement of another effector. Behavior of mu during music listening with movement suppressed has yet to be determined. We recorded 32-channel EEG (n = 17) during silence without movement, overt movement (foot/hand), and music listening without movement. Using an independent component analysis-based source equivalent dipole clustering technique, we identified three mu-related clusters, localized to left primary motor and right and midline premotor cortices. Right foot tapping was accompanied by mu enhancement in the left lateral source cluster, replicating previous work. Music listening was accompanied by similar mu enhancement in the left, as well as midline, clusters. We are the first, to our knowledge, to report, and also to source-resolve, music-related mu modulation in the absence of overt movements. Covert music-related motor activity has been shown to play a role in beat perception (Ross JM, Iversen JR, Balasubramaniam R. Neurocase 22: 558-565, 2016). Our current results show enhancement in somatotopically organized mu, supporting overt motor inhibition during beat perception.NEW & NOTEWORTHY We are the first to report music-related mu enhancement in the absence of overt movements and the first to source-resolve mu activity during music listening. We suggest that music-related mu modulation reflects overt motor inhibition during passive music listening. This work is relevant for the development of theories relating to the involvement of covert motor system activity for predictive beat perception.

Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED).

Human electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories. Manifold challenges must be addressed, most prominently ontological clarity, vocabulary extensibility, annotation tool availability, and overall usability, to allow and promote sharing of data with an effective level of descriptive detail for labeled events. Motivating data authors to perform the work needed to adequately annotate their data is a key challenge. This paper describes new developments in the Hierarchical Event Descriptor (HED) system for addressing these issues. We recap the evolution of HED and its acceptance by the Brain Imaging Data Structure (BIDS) movement, describe the recent release of HED-3G, a third generation HED tools and design framework, and discuss directions for future development. Given consistent, sufficiently detailed, tool-enabled, field-relevant annotation of the nature of recorded events, prospects are bright for large-scale analysis and modeling of aggregated time series data, both in behavioral and brain imaging sciences and beyond.

Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features.

The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on EEG data. Research remains open on the network architecture and the feature space that is most effective for EEG decoding. This paper compares deep learning using minimally processed EEG raw data versus deep learning using EEG spectral features using two different deep convolutional neural architectures. One of them from Putten et al. (2018) is tailored to process raw data; the other was derived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is designed to process EEG spectral features. We apply them to classify sex on 24-channel EEG from a large corpus of 1,574 participants. Not only do we improve on state-of-the-art classification performance for this type of classification problem, but we also show that in all cases, raw data classification leads to superior performance as compared to spectral EEG features. Interestingly we show that the neural network tailored to process EEG spectral features has increased performance when applied to raw data classification. Our approach suggests that the same convolutional networks used to process EEG spectral features yield superior performance when applied to EEG raw data.

Capturing the nature of events and event context using hierarchical event descriptors (HED).

Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made available an accompanying HED-annotated BIDS-formated edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645).

Brain Activity Response to Visual Cues for Gait Impairment in Parkinson's Disease: An EEG Study.

Background. Gait impairments are common in Parkinson's disease (PD) and increase falls risk. Visual cues can improve gait in PD, particularly freezing of gait (FOG), but mechanisms involved in visual cue response are unknown. This study aimed to examine brain activity in response to visual cues in people with PD who do (PD+FOG) and do not report FOG (PD-FOG) and explore relationships between attention, brain activity and gait. Methods. Mobile EEG measured brain activity during gait in 20 healthy older adults and 43 PD participants (n=22 PD+FOG, n=21 PD-FOG). Participants walked for 2-minutes with and without visual cues (transverse lines to step over). We report power spectral density (PSD) in Delta (1-4 Hz), Theta (4-7 Hz), Alpha (8-12 Hz), Beta (14-24 Hz) and Gamma (30-50 Hz) bands within clusters of similarly brain localized independent component sources. Results. PSDs within the parietal and occipital lobes were altered when walking with visual cues in PD, particularly in PD+FOG. Between group, differences suggested that parietal sources in PD, particularly with PD+FOG, had larger activity compared to healthy older adults when walking. Within group, visual cues altered brain activity in PD, particularly in PD+FOG, within visual processing brain regions. In PD participants, brain activity differences with cues correlated with gait improvements, and in PD+FOG those with worse attention required more visual attentional processing (reduced alpha PSD) in the occipital lobe. Conclusions. Visual cues improve gait and influence brain activity during walking in PD, particularly in PD+FOG. Findings may allow development of more effective therapeutics.

Inhibitory control in children with tic disorder: aberrant fronto-parietal network activity and connectivity.

Chronic tic disorders, including Tourette syndrome, are typically thought to have deficits in cognitive inhibition and top down cognitive control due to the frequent and repetitive occurrence of tics, yet studies reporting task performance results have been equivocal. Despite similar behavioural performance, individuals with chronic tic disorder have exhibited aberrant patterns of neural activation in multiple frontal and parietal regions relative to healthy controls during inhibitory control paradigms. In addition to these top down attentional control regions, widespread alterations in brain activity across multiple neural networks have been reported. There is a dearth, however, of studies examining event-related connectivity during cognitive inhibitory paradigms among affected individuals. The goal of this study was to characterize neural oscillatory activity and effective connectivity, using a case-control design, among children with and without chronic tic disorder during performance of a cognitive inhibition task. Electroencephalogram data were recorded in a cohort of children aged 8-12 years old (60 with chronic tic disorder, 35 typically developing controls) while they performed a flanker task. While task accuracy did not differ by diagnosis, children with chronic tic disorder displayed significant cortical source-level, event-related spectral power differences during incongruent flanker trials, which required inhibitory control. Specifically, attenuated broad band oscillatory power modulation within the anterior cingulate cortex was observed relative to controls. Whole brain effective connectivity analyses indicated that children with chronic tic disorder exhibit greater information flow between the anterior cingulate and other fronto-parietal network hubs (midcingulate cortex and precuneus) relative to controls, who instead showed stronger connectivity between central and posterior nodes. Spectral power within the anterior cingulate was not significantly correlated with any connectivity edges, suggesting lower power and higher connectivity are independent (versus resultant) neural mechanisms. Significant correlations between clinical features, task performance and anterior cingulate spectral power and connectivity suggest this region is associated with tic impairment (r = -0.31, P = 0.03) and flanker task incongruent trial accuracy (r's = -0.27 to -0.42, P's = 0.0008-0.04). Attenuated activation of the anterior cingulate along with dysregulated information flow between and among nodes within the fronto-parietal attention network may be neural adaptations that result from frequent engagement of neural pathways needed for inhibitory control in chronic tic disorder.

The AudioMaze: An EEG and motion capture study of human spatial navigation in sparse augmented reality.

Spatial navigation is one of the fundamental cognitive functions central to survival in most animals. Studies in humans investigating the neural foundations of spatial navigation traditionally use stationary, desk-top protocols revealing the hippocampus, parahippocampal place area (PPA), and retrosplenial complex to be involved in navigation. However, brain dynamics, while freely navigating the real world remain poorly understood. To address this issue, we developed a novel paradigm, the AudioMaze, in which participants freely explore a room-sized virtual maze, while EEG is recorded synchronized to motion capture. Participants (n = 16) were blindfolded and explored different mazes, each in three successive trials, using their right hand as a probe to "feel" for virtual maze walls. When their hand "neared" a virtual wall, they received directional noise feedback. Evidence for spatial learning include shortening of time spent and an increase of movement velocity as the same maze was repeatedly explored. Theta-band EEG power in or near the right lingual gyrus, the posterior portion of the PPA, decreased across trials, potentially reflecting the spatial learning. Effective connectivity analysis revealed directed information flow from the lingual gyrus to the midcingulate cortex, which may indicate an updating process that integrates spatial information with future action. To conclude, we found behavioral evidence of navigational learning in a sparse-AR environment, and a neural correlate of navigational learning was found near the lingual gyrus.

The open EEGLAB portal Interface: High-Performance computing with EEGLAB.

EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.

Electroencephalographic Study on Sensory Integration in Visually Induced Postural Sway.

A periodically reversing optic flow animation, experienced while standing, induces an involuntary sway termed visually induced postural sway (VIPS). Interestingly, VIPS is suppressed during light finger touch to a stationary object. Here, we explored whether VIPS is mediated by parietal field activity in the dorsal visual stream as well as by activity in early visual areas, as has been suggested. We performed a mobile brain/body imaging study using high-density electroencephalographic recording from human participants (11 men and 3 women) standing during exposure to periodically reversing optic flow with and without light finger touch to a stable surface. We also performed recording their visuo-postural tracking movements as a typical visually guided movement to explore differences of cortical process of VIPS from the voluntary visuomotor process involving the dorsal stream. In the visuo-postural tracking condition, the participants moved their center of pressure in time with a slowly oscillating (expanding, shrinking) target rectangle. Source-resolved results showed that alpha band (8-13 Hz) activity in the medial and right occipital cortex during VIPS was modulated by the direction and velocity of optic flow and increased significantly during light finger touch. However, source-resolved potentials from the parietal association cortex showed no such modulation. During voluntary postural sway with feedback (but no visual flow) in which the dorsal stream is involved, sensorimotor areas produced more theta band (4-7 Hz) and less beta band (14-35 Hz) activity than during involuntary VIPS. These results suggest that VIPS involves cortical field dynamic changes in the early visual cortex rather than in the posterior parietal cortex of the visual dorsal stream.

Bayesian models of human navigation behaviour in an augmented reality audiomaze.

We investigated Bayesian modelling of human whole-body motion capture data recorded during an exploratory real-space navigation task in an "Audiomaze" environment (see the companion paper by Miyakoshi et al. in the same volume) to study the effect of map learning on navigation behaviour. There were three models, a feedback-only model (no map learning), a map resetting model (single-trial limited map learning), and a map updating model (map learning accumulated across three trials). The estimated behavioural variables included step sizes and turning angles. Results showed that the estimated step sizes were constantly more accurate using the map learning models than the feedback-only model. The same effect was confirmed for turning angle estimates, but only for data from the third trial. We interpreted these results as Bayesian evidence of human map learning on navigation behaviour. Furthermore, separating the participants into groups of egocentric and allocentric navigators revealed an advantage for the map updating model in estimating step sizes, but only for the allocentric navigators. This interaction indicated that the allocentric navigators may take more advantage of map learning than do egocentric navigators. We discuss relationships of these results to simultaneous localization and mapping (SLAM) problem.