Electrophysiological Brain Connectivity: Theory and Implementation

How do the resting EEG preprocessing states affect the outcomes of postprocessing?

Plenty of artifact removal tools and pipelines have been developed to correct the resting EEG waves and discover scientific values behind. Without expertised visual inspection, it is susceptible to derive improper preprocessing, resulting in either insufficient preprocessed EEG (IPE) or excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on postprocessing in the temporal, frequency, and spatial domains, particularly as to the spectra and the functional connectivity analysis. Here, the clean EEG (CE) with linear and quasi-stationary assumption was synthesized as ground truth based on the New-York head model and the multivariate autoregressive model. Later, IPE and EPE were simulated by injecting Gaussian noise and losing brain components, respectively. Spectral homogeneities of all EEGs were evaluated by the proposed Parallel LOg Spectra index (PaLOSi). Then, the impacts on postprocessing were quantified by the IPE/EPE deviation from CE as to the temporal statistics, multichannel power, cross spectra, scalp EEG network properties, and source dispersion. Lastly, the association between PaLOSi and varying trends of postprocessing outcomes was analyzed with evolutionary preprocessing states. We found that compared with CE: 1) IPE (EPE) temporal statistics deviated more greatly with more noise injected (brain activities discarded); 2) IPE (EPE) power was higher (lower), and IPE power was almost parallel to that of CE across frequencies, while EPE power deviation decreased with higher frequencies; IPE cross spectra deviated more greatly than EPE, except for β band; 3) derived from 7 coupling measures, IPE (EPE) network had lower (higher) transmission efficiency and worse (better) integration ability; 4) IPE sources distributed more dispersedly with greater strength while EPE sources activated more focally with lower amplitudes; 5) PaLOSi was consistently correlated with varying trends of investigated postprocessing for both simulated and real data. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi is a promising quality control metric for creating normative EEG databases.

Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome

Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.

Exploration of working memory retrieval stage for mild cognitive impairment: time-varying causality analysis of electroencephalogram based on dynamic brain networks

BACKGROUND

Mild Cognitive Impairment (MCI) is an intermediate stage between the expected cognitive decline of normal aging and Alzheimer's disease (AD). Its management is crucial for it helps intervene and slow the progression of cognitive decline to AD. However, the understanding of the MCI mechanism is not completely clear. As working memory (WM) damage is a common symptom of MCI, this study focused on the core stage of WM, i.e., the memory retrieval stage, to investigate information processing and the causality relationships among brain regions based on electroencephalogram (EEG) signals.

METHOD

21 MCI and 20 normal cognitive control (NC) participants were recruited. The delayed matching sample paradigm with two different loads was employed to evaluate their WM functions. A time-varying network based on the Adaptive transfer function (ADTF) was constructed on the EEG of the memory retrieval trials.to perform the dynamic brain network analysis.

RESULTS

Our results showed that: (a) Behavioral data analysis: there were significant differences in accuracy and accuracy / reaction time between MCI and NC in tasks with memory load capacity of low load-four and high load-six, especially in tasks with memory load capacity of four. (b) Dynamic brain network analysis: there were significant differences in the dynamic changes of brain network patterns between the two groups during the memory retrieval stage of the WM task. Specifically, in low load WM tasks, the dynamic brain network changes of NC were more regular to accommodate for efficient information processing, with important core nodes showing a transition from bottom to up, while MCI did not display a regular dynamic brain network pattern. Further, the brain functional areas associated with low load WM disorders were mainly located in the left prefrontal lobe (FC1) and right occipital lobe (PO8). Compared with low load WM task, during the high load WM task, the dynamic brain network changes of NC during the memory retrieval stage were regular, and the core nodes exhibited a consistent transition phenomenon from up to bottom to up, which were not observed in MCI.

CONCLUSIONS

Behavioral data in the low load WM task paradigm and abnormal electrophysiological signals in the left prefrontal (FC1) and right occipital lobes (PO8) could be used for MCI diagnosis. This is the first time based on large-scale dynamic network methods to investigate the dynamic network patterns of MCI memory retrieval stages under different load WM tasks, providing a new perspective on the neural mechanisms of WM deficits in MCI patients and providing some reference for the clinical intervention treatment of MCI-WM memory disorders.

Non-parametric full cross mapping (NFCM): a highly-stable measure for causal brain network and a pilot application

Objective.Measuring causal brain network from neurophysiological signals has recently attracted much attention in the field of neuroinformatics. Traditional data-driven algorithms are computationally time-consuming and unstable due to parameter settings.Approach.To resolve these limits, we proposed a novel parameter-free technique, called 'non-parametric full cross mapping (NFCM)'. The NFCM adapts current convergent cross-mapping concept, and makes two improvements: (1) an improved phase-space reconstruction with constant embedding parameters and (2) cross-mapping estimate of all embedding vectors on manifolds following simplex projection.Main results.Numerical experiments verify that our NFCM has the highest quantization stability even when perturbed by system noise, and its coefficient of variation is almost lower than that of the six baseline methods. The developed NFCM is finally used in stereoelectroencephalogram analysis of drug-resistant epilepsy in children (DREC). A total of 36 seizures, comprising 18 surgical successes and 18 failures, were included to explore the brain network dynamics. The average causal coupling in epileptogenic zones of successful surgery (0.81 ± 0.04) is significantly higher than that in non-epileptogenic zones (0.40 ± 0.03) withP<0.001via Mann-Whitney-U-test. While there is no significant difference among the 18 failed surgeries.Significance.The causal brain network measured by our NFCM is confirmed as a credible biomarker for localizing epileptogenic zones in DREC. These findings promise to advance precision medicine for DREC.

Brain-scale theta band functional connectome as signature of slow breathing and breath-hold phases

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain have been investigated during attentive breathing at 2 cycles per minute (cpm). The study presents its novelty through three main aspects. First, it explores the complex breathing circuitry beyond the brain stem, specifically examining how higher brain regions interact with respiratory cycles. Second, unlike previous studies that treated respiratory phases as a singular phenomenon, this research analyses inhalation, exhalation, and breath-holds separately, providing a deeper understanding of their individual dynamics and FC in the brain. Finally, the breathing protocol is designed to include inhale-hold and exhale-hold sessions alongside symmetric breathing, allowing for testing on healthy subjects rather than specialized cohorts, which were used in earlier studies. An experimental protocol involving equal durations of inhale, inhale-hold, exhale, and exhale-hold conditions, synchronized to a visual metronome, was designed and administered to 20 healthy subjects (9 females and 11 males, age: 32.0 ± 9.5 years (mean ± SD)). EEG data were collected during these sessions using the 64-channel eego™ mylab system from ANT Neuro. Further, FC was estimated for all possible pairs of EEG time series data, for 7 EEG bands. Feature selection using a genetic algorithm (GA) was performed to identify a subset of functional connections that would best distinguish the inhale, inhale-hold, exhale, and exhale-hold phases using a random committee classifier. The best accuracy of 95.056% was obtained when 403 theta-band functional connections were fed as input to the classifier, highlighting the efficacy of the theta-band functional connectome in distinguishing these phases of the respiratory cycle. This functional network was further characterized using graph measures, and observations illustrated a statistically significant difference in the efficiency of information exchange through the network during different respiratory phases.

A novel approach for brain connectivity using recurrent neural networks and integrated gradients

Brain connectivity is an important tool for understanding the cognitive and perceptive neural mechanisms in the neuroimaging field. Many methods for estimating effective connectivity have relied on the linear regressive model. However, the linear regression approach might fail to account for the complexity inherent in brain connectivity. Due to the recent success of deep neural networks (DNNs), regressive data are able to be predicted with high accuracy. This study aimed to develop a connectivity method using the prediction performance of a DNN model and the parameters of the model. To this end, a method is proposed that utilizes integrated gradients in a recurrent neural network model. It is an extended application of explainable artificial intelligence in the multivariate autoregressive DNN model. It would be advantageous compared to the methods using the parameters of the linear regressive model or Granger's approach referring to the difference in error between the models. The performance of the connectivity estimation was tested by simulated datasets with various conditions. The overall performance was good on multiple metrics including recall (0.94), precision (0.90), F1-score (0.92), and accuracy (0.97). Compared with other conventional methods, the proposed method is robust and precise. The proposed method also demonstrates that it can be applied to estimate the actual brain connectivity in a magnetoencephalography study. In conclusion, the connectivity method based on integrated gradients provides an accurate estimation of brain connectivity by effectively capturing complex interactions, which is validated through high performance metrics such as recall, precision, F1-score, and accuracy across multiple simulated datasets. It introduces a novel framework to combine DNN and integrated gradients and to estimate effective connectivity by the explainable AI.

Cortex level connectivity between ACT-R modules during EEG-based n-back task

Finding the synchronization between Electroencephalography (EEG) and human cognition is an essential aspect of cognitive neuroscience. Adaptive Control of Thought-Rational (ACT-R) is a widely used cognitive architecture that defines the cognitive and perceptual operations of the human mind. This study combines the ACT-R and EEG-based cortex-level connectivity to highlight the relationship between ACT-R modules during the EEG-based n-back task (for validating working memory performance). Initially, the source localization method is performed on the EEG signal, and the mapping between ACT-R modules and corresponding brain scouts (on the cortex surface) is performed. Once the brain scouts are identified for ACT-R modules, then those scouts are called ACT-R scouts. The linear (Granger Causality: GC) and non-linear effective connectivity (Multivariate Transfer Entropy: MTE) methods are applied over the scouts' time series data. From the GC and MTE analysis, for all n-back tasks, information flow is observed from the visual-to-imaginal ACT-R scout for storing the visual stimuli (i.e., input letter) in short-term memory. For 2 and 3-back tasks, causal flow exists from imaginal to retrieval ACT-R scout and vice-versa. Causal flow from procedural to the imaginal ACT-R scout is also observed for all workload levels to execute the set of productions. Identifying the relationship among ACT-R modules through scout-level connectivity in the cortical surface facilitates the effects of human cognition in terms of brain dynamics.

Learning Interpretable Brain Functional Connectivity via Self-Supervised Triplet Network With Depth-Wise Attention

Brain functional connectivity has been widely explored to reveal the functional interaction dynamics between the brain regions. However, conventional connectivity measures rely on deterministic models demanding application-specific empirical analysis, while deep learning approaches focus on finding discriminative features for state classification, having limited capability to capture the interpretable connectivity characteristics. To address the challenges, this study proposes a self-supervised triplet network with depth-wise attention (TripletNet-DA) to generate the functional connectivity: 1) TripletNet-DA firstly utilizes channel-wise transformations for temporal data augmentation, where the correlated & uncorrelated sample pairs are constructed for self-supervised training, 2) Channel encoder is designed with a convolution network to extract the deep features, while similarity estimator is employed to generate the similarity pairs and the functional connectivity representations, 3) TripletNet-DA applies Triplet loss with anchor-negative similarity penalty for model training, where the similarities of uncorrelated sample pairs are minimized to enhance model's learning capability. Experimental results on pathological EEG datasets (Autism Spectrum Disorder, Major Depressive Disorder) indicate that 1) TripletNet-DA demonstrates superiority in both ASD discrimination and MDD classification than the state-of-the-art counterparts, where the connectivity features in beta & gamma bands have respectively achieved the accuracy of 97.05%, 98.32% for ASD discrimination, 89.88%, 91.80% for MDD classification in the eyes-closed condition and 90.90%, 92.26% in the eyes-open condition, 2) TripletNet-DA enables to uncover significant differences of functional connectivity between ASD EEG and TD ones, and the prominent connectivity links are in accordance with the empirical findings, thus providing potential biomarkers for clinical ASD analysis.

RSBagging: An ensemble classifier detecting the after-effects of ischemic stroke through EEG connectivity and microstates

BACKGROUND AND PURPOSE

Stroke can lead to significant after-effects, including motor function impairments, language impairments (aphasia), disorders of consciousness (DoC), and cognitive deficits. Computer-aided analysis of EEG connectivity matrices and microstates from bedside EEG monitoring can replace traditional clinical observation methods, offering an automatic approach to monitoring the progression of these after-effects. This EEG-based method also enables quicker and more efficient assessments for medical practitioners.

METHODS

In this study, we employed Functional Connectivity features that extract spatial representation and Microstate features that focus on the time domain representation to monitor the after-effects of ischemic stroke patients. As the dataset from stroke patients is heavily imbalanced across various clinical after-effects conditions, we designed an ensemble classifier, RSBagging, to address the issue of classifiers often favoring the majority classes in the classification of imbalanced datasets.

RESULTS

The experimental results demonstrate that different connectivity matrices are effective for three classification tasks: consciousness level, motor disturbance, and stroke location. Using our RSBagging model, all three tasks achieve over 98% accuracy, sensitivity, specificity, and F1-score, significantly outperforming the existing classifiers SVM, XGBoost, and Random Forest.

CONCLUSION

Therefore, the RSBagging classifier based on connectivity matrices offers an effective method for monitoring the after-effects in stroke patients.

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Assessment of TENS-Evoked Tactile Sensations for Transradial Amputees via EEG Investigation

Most of current prostheses can offer motor function restoration for limb amputees but usually lack natural and intuitive sensory feedback. Many studies have demonstrated that Transcutaneous Electrical Nerve Stimulation (TENS) is promising in non-invasive sensation evoking for amputees. However, the objective evaluation and mechanism analysis on sensation feedback are still limited. This work utilized multi-channel TENS with diverse stimulus patterns to evoke sensations on four non-disabled subjects and two transradial amputees. Meanwhile, electroencephalogram (EEG) was collected to objectively assess the evoked sensations, where event-related potentials (ERPs), brain electrical activity maps (BEAMs), and functional connectivity (FC) were computed. The results show that various sensations could be successfully evoked for both amputees and non-disabled subjects by customizing stimulus parameters. The ERP confirmed the sensation and revealed the sensory-processing-related components like N100 and P200; the BEAMs confirmed the corresponding regions of somatosensory cortex were activated by stimulation; the FC indicated an increase of interactions between the regions of sensorimotor cortex. This study may shed light on how the brain responds to external stimulation as sensory feedback and serve as a pilot for further bidirectional closed-loop prosthetic control.

Causal Brain Network Predicts Surgical Outcomes in Patients With Drug-Resistant Epilepsy: A Retrospective Comparative Study

Network neuroscience, especially causal brain network, has facilitated drug-resistant epilepsy (DRE) studies, while surgical success rate in patients with DRE is still limited, varying from 30%  ∼  70 %. Predicting surgical outcomes can provide additional guidance to adjust treatment plans in time for poorly predicted curative effects. In this retrospective study, we aim to systematically explore biomarkers for surgical outcomes by causal brain network methods and multicenter datasets. Electrocorticogram (ECoG) recordings from 17 DRE patients with 58 seizures were included. Ictal ECoG within clinically annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgeries. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α -frequency band ( 8  ∼  13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen's d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with [Formula: see text]. Based on the brain network features, machine learning models were developed to predict the surgical outcomes. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest average accuracy of 84.48% by 5-fold cross-validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE surgical outcomes.

Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy

Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40-200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

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

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

Attention-guided graph structure learning network for EEG-enabled auditory attention detection

Objective: Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. This study aims to overcome the challenges of extracting discriminative feature representations from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, particularly focusing on the intrinsic relationships between different EEG channels.Approach: We propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance. Specifically, AGSLnet is designed to dynamically capture latent relationships between channels and construct a graph structure of EEG signals.Main result: We evaluated AGSLnet on two publicly available AAD datasets and demonstrated its superiority and robustness over state-of-the-art models. Visualization of the graph structure trained by AGSLnet supports previous neuroscience findings, enhancing our understanding of the underlying neural mechanisms.Significance: This study presents a novel approach for examining brain functional connections, improving AAD performance in low-latency settings, and supporting the development of neuro-steered hearing aids.

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.

Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks

OBJECTIVE

Studying directed connectivity within spiking neuron networks can help understand neural mechanisms. Existing methods assume linear time-invariant neural dynamics with a fixed time lag in information transmission, while spiking networks usually involve complex dynamics that are nonlinear and nonstationary, and have varying time lags.

METHODS

We develop a Gated Recurrent Unit (GRU)-Point Process (PP) method to estimate directed connectivity within spiking networks. We use a GRU to describe the dependency of the target neuron's current firing rate on the source neurons' past spiking events and a PP to relate the target neuron's firing rate to its current 0-1 spiking event. The GRU model uses recurrent states and gate/activation functions to deal with varying time lags, nonlinearity, and nonstationarity in a parameter-efficient manner. We estimate the model using maximum likelihood and compute directed information as our measure of directed connectivity.

RESULTS

We conduct simulations using artificial spiking networks and a biophysical model of Parkinson's disease to show that GRU-PP systematically addresses varying time lags, nonlinearity, and nonstationarity, and estimates directed connectivity with high accuracy and data efficiency. We also use a non-human-primate dataset to show that GRU-PP correctly identifies the biophysically-plausible stronger PMd-to-M1 connectivity than M1-to-PMd connectivity during reaching. In all experiments, the GRU-PP consistently outperforms state-of-the-art methods.

CONCLUSION

The GRU-PP method efficiently estimates directed connectivity in varying time lag, nonlinear, and nonstationary spiking neuron networks.

SIGNIFICANCE

The proposed method can serve as a directed connectivity analysis tool for investigating complex spiking neuron network dynamics.

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Dementia classification using a graph neural network on imaging of effective brain connectivity

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task

Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.

Time course of effective connectivity associated with perspective taking in utterance comprehension

This study discusses the effective connectivity in the brain and its time course in realizing perspective taking in verbal communication through electroencephalogram (EEG) associated with the understanding of Japanese utterances. We manipulated perspective taking in a sentence with the Japanese subsidiary verbs -ageru and -kureru, which mean "to give". We measured the EEG during the auditory presentation of the sentences with a multichannel electroencephalograph, and the partial directed coherence and its temporal variations were analyzed using the source localization method to examine causal interactions between nineteen regions of interest in the brain. Three different processing stages were recognized on the basis of the connectivity hubs, direction of information flow, increase or decrease in flow, and temporal variation. We suggest that perspective taking in speech comprehension is realized by interactions between the mentalizing network, mirror neuron network, and executive control network. Furthermore, we found that individual differences in the sociality of typically developing adult speakers were systematically related to effective connectivity. In particular, attention switching was deeply concerned with perspective taking in real time, and the precuneus played a crucial role in implementing individual differences.

A Model to Study Time Lagged Interactions, Source Connectivity and Source Activities Using Multi-channel EEG

A computational model to examine time lagged interactions; identify number of interacting pairs of neuronal sources; and determine source activities from multi-channel EEG measurements is described. It is based on the imaginary part of the cross spectrum between the EEG channels. The imaginary part of the cross spectrum between the EEG channels provides the most suitable property that reflects the presence of interacting sources. The model assumes that not all sources are activated simultaneously and that there is a time lag amongst some of them. A new analytical expression derived for the imaginary part of cross spectrum between channels shows that it is different from the zero lag case. A method is then proposed to identify time lag interactions, by studying its variation as a function of frequency. Assuming pair wise interaction between sources, the model shows that simultaneous diagonalization at different frequencies of symmetric matrices formed by multiplying the anti-symmetric matrix of the imaginary part of cross spectrum with its transpose will provide information on the number of interacting source pairs as a function of frequency. The matrix that simultaneously diagonalizes all the symmetric matrices is identified as the mixing matrix. This can be used to obtain the source activities.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance

(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.

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.

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.

Automatic Quality Control of the numerical accuracy of EEG Lead fields

Precise individualized EEG source localization is predicated on having accurate subject-specific Lead Fields (LFs) obtained from their Magnetic Resonance Images (MRI). LF calculation is a complex process involving several error-prone steps that start with obtaining a realistic head model from the MRI and finalizing with computationally expensive solvers such as the Boundary Element Method (BEM) or Finite Element Method (FEM). Current Big-Data applications require the calculation of batches of hundreds or thousands of LFs. LF. Quality Control is conventionally checked subjectively by experts, a procedure not feasible in practice for larger batches. To facilitate this step, we introduce the Lead Field Automatic-Quality Control Index (LF-AQI) that flags LF with potential errors. We base our LF-AQI on the assumption that LFs obtained from simpler head models, i.e., the homogeneous head model LF (HHM-LF) or spherical head model LF (SHM-LF), deviate only moderately from a "good" realistic test LF. Since these simpler LFs are easier to compute and check for errors, they may serve as "reference LF" to detect anomalous realistic test LF. We investigated this assumption by comparing correlation-based channel ρ_min(ref,test)and source τ_min(ref,test) similarity indices (SI) between "gold standards," i.e., very accurate FEM and BEM LFs, and the proposed references (HHM-LF and SHM-LF). Surprisingly we found that the most uncomplicated possible reference, HHM-LF had high SI values with the gold standards-leading us to explore further use of the channel ρ_min(HHM-LF,test)and source τ_min(HHM-LF,test)_{SI as a basis for} our LF-AQI. Indeed, these SI successfully detected five simulated scenarios of LFs artifacts. This result encouraged us to evaluate the SI on a large dataset and thus define our LF-AQI. We thus computed the SI of 1251 LFs obtained from the Child Mind Institute (CMI) MRI dataset. When ρ_min(HHM-LF,test)and source τ_min(HHM-LF,test) were plotted for all test subjects on a 2D space, most were tightly clustered around the median of a high similarity centroid (HSC), except for a smaller proportion of outliers. We define the LF-AQI for a given LF as the log Euclidean distance between its SI and the HSC median. To automatically detect outliers, the threshold is at the 90^th percentile of the CMI LF-AQIs (-0.9755). LF-AQI greater than this threshold flag individual LF to be checked. The robustness of this LF-AQI screening was checked by repeated out-of-sample validation. Strikingly, minor corrections in re-processing the flagged cases eliminated their status as outliers. Furthermore, the "doubtful" labels assigned by LF-AQI were validated by neuroscience students using a Likert scale questionnaire designed to manually check the LF's quality. Item Response Theory (IRT) analysis was applied to the questionnaire results to compute an optimized model and a latent variable θ for that model. A linear mixed model (LMM) between the θ and LF-AQI resulted in an effect with a Cohen's d value of 1.3 and a p-value <0.001, thus validating the correspondence of LF-AQI with the visual quality control. We provide an open-source pipeline to implement both LF calculation and its quality control to allow further evaluation of our index.

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

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.

Distinctive modes of cortical communications in tactile temporal order judgment

Temporal order judgment of two successive tactile stimuli delivered to our hands is often inverted when we cross our hands. The present study aimed to identify time-frequency profiles of the interactions across the cortical network associated with the crossed-hand tactile temporal order judgment task using magnetoencephalography. We found that the interactions across the cortical network were channeled to a low-frequency band (5-10 Hz) when the hands were uncrossed. However, the interactions became activated in a higher band (12-18 Hz) when the hands were crossed. The participants with fewer inverted judgments relied mainly on the higher band, whereas those with more frequent inverted judgments (reversers) utilized both. Moreover, reversers showed greater cortical interactions in the higher band when their judgment was correct compared to when it was inverted. Overall, the results show that the cortical network communicates in two distinctive frequency modes during the crossed-hand tactile temporal order judgment task. A default mode of communications in the low-frequency band encourages inverted judgments, and correct judgment is robustly achieved by recruiting the high-frequency mode.

Electrical nerve stimulation for sensory-neural pathway reconstruction in upper-limb amputees

Introduction

The loss of the neural sensory function pathways between the stump limbs and the brain greatly impacts the rehabilitation of limb function and the daily lives of amputees. Non-invasive physical stressors, such as mechanical pressure and transcutaneous electrical nerve stimulation (TENS), could be potential solutions for recovering somatic sensations in amputees. Previous studies have shown that stimulating the residual or regenerated nerves in the stumps of some amputees can produce phantom hand sensations. However, the results are inconclusive due to unstable physiological responses caused by inaccurate stimulus parameters and positions.

Methods

In this study, we developed an optimal TENS strategy by mapping the distribution of the nerves in the stump skin that elicitsphantom sensations known as a "phantom hand map." We evaluated the effectiveness and stability of the confirmed stimulus configuration in a long-term experiment using single- and multi-stimulus paradigms. Additionally, we evaluated the evoked sensations by recording electroencephalograms (EEG) and analyzing brain activities.

Results

The results demonstrated that various types of intuitive sensations for amputees could be stably induced by adjusting TENS frequencies, particularly at 5 and 50 Hz. At these frequencies, 100% stability of sensory types was achieved when the stimuli were applied to two specific locations on the stump skin. Furthermore, at these locations, the stability of sensory positions was 100% across different days. Moreover, the evoked sensations were objectively supported by specific patterns of event-related potentials in brain responses.

Discussion

This study provides an effective method for developing and evaluating physical stressor stimulus strategies, which could play an important role in the somatosensory rehabilitation of amputees and other patients suffering from somatomotor sensory dysfunction. The paradigm developed in this study can provide effective guidelines for stimulus parameters in physical and electrical nerve stimulation treatments for a variety of symptoms related to neurological disorders.

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

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

Comparison of beamformer and ICA for dynamic connectivity analysis: A simultaneous MEG-SEEG study

Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.

Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG

Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.

Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings

The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.

Clinical neuroscience and neurotechnology: An amazing symbiosis

In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.

FiNN: A toolbox for neurophysiological network analysis

Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox and its documentation are freely available as Supporting Information. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis

Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

This article addresses the problem of estimating brain effective connectivity from electroencephalogram (EEG) signals using a Granger causality (GC) characterized on state-space models, extended from the conventional vector autoregressive (VAR) process. The scheme involves two main steps: model estimation and model inference to estimate brain connectivity. The model estimation performs a subspace identification and active source selection based on group-norm regularized least-squares. The model inference relies on the concept of state-space GC that requires solving a Riccati equation for the covariance of estimation error. We verify the performance on simulated datasets that represent realistic human brain activities under several conditions, including percentages and location of active sources, and the number of EEG electrodes. Our model's accuracy in estimating connectivity is compared with a two-stage approach using source reconstructions and a VAR-based Granger analysis. Our method achieved better performances than the two-stage approach under the assumptions that the true source dynamics are sparse and generated from state-space models. When the method was applied to a real EEG SSVEP dataset, the temporal lobe was found to be a mediating connection between the temporal and occipital areas, which agreed with findings in previous studies.

A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals

Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.

Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome

Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.