Effective Connectivity Measures Research Articles

Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks

Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy. Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier’s features. Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis. Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.

Effective connectivity relates seizure outcome to electrode placement in responsive neurostimulation.

Responsive neurostimulation is a closed-loop neuromodulation therapy for drug resistant focal epilepsy. Responsive neurostimulation electrodes are placed near ictal onset zones so as to enable detection of epileptiform activity and deliver electrical stimulation. There is no standard approach for determining the optimal placement of responsive neurostimulation electrodes. Clinicians make this determination based on presurgical tests, such as MRI, EEG, magnetoencephalography, ictal single-photon emission computed tomography and intracranial EEG. Currently functional connectivity measures are not being used in determining the placement of responsive neurostimulation electrodes. Cortico-cortical evoked potentials are a measure of effective functional connectivity. Cortico-cortical evoked potentials are generated by direct single-pulse electrical stimulation and can be used to investigate cortico-cortical connections in vivo. We hypothesized that the presence of high amplitude cortico-cortical evoked potentials, recorded during intracranial EEG monitoring, near the eventual responsive neurostimulation contact sites is predictive of better outcomes from its therapy. We retrospectively reviewed 12 patients in whom cortico-cortical evoked potentials were obtained during stereoelectroencephalography evaluation and subsequently underwent responsive neurostimulation therapy. We studied the relationship between cortico-cortical evoked potentials, the eventual responsive neurostimulation electrode locations and seizure reduction. Directional connectivity indicated by cortico-cortical evoked potentials can categorize stereoelectroencephalography electrodes as either receiver nodes/in-degree (an area of greater inward connectivity) or projection nodes/out-degree (greater outward connectivity). The follow-up period for seizure reduction ranged from 1.3-4.8 years (median 2.7) after responsive neurostimulation therapy started. Stereoelectroencephalography electrodes closest to the eventual responsive neurostimulation contact site tended to show larger in-degree cortico-cortical evoked potentials, especially for the early latency cortico-cortical evoked potentials period (10-60 ms period) in six out of 12 patients. Stereoelectroencephalography electrodes closest to the responsive neurostimulation contacts (≤5 mm) also had greater significant out-degree in the early cortico-cortical evoked potentials latency period than those further away (≥10 mm) (P < 0.05). Additionally, significant correlation was noted between in-degree cortico-cortical evoked potentials and greater seizure reduction with responsive neurostimulation therapy at its most effective period (P < 0.05). These findings suggest that functional connectivity determined by cortico-cortical evoked potentials may provide additional information that could help guide the optimal placement of responsive neurostimulation electrodes.

Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI

Objective. New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC ( ML.FC ) which efficiently captures linear and nonlinear aspects. Approach. To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity ( ML.EC ), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality ( SP.GC ) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms of reproducibility and the ability to predict individual traits in order to demonstrate these measures’ internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits. Main results. The proposed new FC measure of ML.FC attains high reproducibility (mean intra-subject R 2 of 0.44), while the proposed EC measure of SP.GC attains the highest predictive power (mean R 2 across prediction tasks of 0.66). Significance. The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.

Influence of noninvasive brain stimulation on connectivity and local activation: a combined tDCS and fMRI study

The effect of transcranial direct current stimulation (tDCS) on neurobiological mechanisms underlying executive function in the human brain remains elusive. This study aims at examining the effect of anodal and cathodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) in comparison with sham stimulation on resting-state connectivity as well as functional activation and working memory performance. We hypothesized perturbed fronto-parietal resting-state connectivity during stimulation and altered working memory performance combined with modified functional working memory-related activation. We applied tDCS with 1 mA for 21 min over the DLPFC inside an fMRI scanner. During stimulation, resting-state fMRI was acquired and task-dependent fMRI during working memory task performance was acquired directly after stimulation. N = 36 healthy subjects were studied in a within-subject design with three different experimental conditions (anodal, cathodal and sham) in a double-blind design. Seed-based functional connectivity analyses and dynamic causal modeling were conducted for the resting-state fMRI data. We found a significant stimulation by region interaction in the seed-based ROI-to-ROI resting-state connectivity, but no effect on effective connectivity. We also did not find an effect of stimulation on task-dependent signal alterations in working memory activation in our regions of interest and no effect on working memory performance parameters. We found effects on measures of seed-based resting-state connectivity, while measures of effective connectivity and task-based connectivity did not show any stimulation effect. We could not replicate previous findings of tDCS stimulation effects on behavioral outcomes. We critically discuss possible methodological limitations and implications for future studies.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks. We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC). PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM). Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.

Distinct dorsal and ventral streams for binocular rivalry dominance and suppression revealed by magnetoencephalography.

Binocular rivalry is an example of bistable visual perception extensively examined in neuroimaging. Magnetoencephalography can track brain responses to phasic visual stimulations of predetermined frequency and phase to advance our understanding of perceptual dominance and suppression in binocular rivalry. We used left and right eye stimuli that flickered at two tagging frequencies to track their respective oscillatory cortical evoked responses. We computed time-resolved measures of coherence to track brain responses phase locked with stimulus frequencies and with respect to the participants' indications of alternations of visual rivalry they experienced. We compared the brain maps obtained to those from a non-rivalrous control replay condition that used physically changing stimuli to mimic rivalry. We found stronger coherence within a posterior cortical network of visual areas during rivalry dominance compared with rivalry suppression and replay control. This network extended beyond the primary visual cortex to several retinotopic visual areas. Moreover, network coherence with dominant percepts in primary visual cortex peaked at least 50 ms prior to the suppressed percept nadir, consistent with the escape theory of alternations. Individual alternation rates were correlated with the rate of change in dominant evoked peaks, but not for the slope of response to suppressed percepts. Effective connectivity measures revealed that dominant (respectively, suppressed) percepts were expressed in dorsal (respectively ventral) streams. We thus demonstrate that binocular rivalry dominance and suppression engage distinct mechanisms and brain networks. These findings advance neural models of rivalry and may relate to more general aspects of selection and suppression in natural vision.

Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study.

Effective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health. In this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures. Women and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of p = 0.043). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3-3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of p = 0.047). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of p = 0.044). Women and non-drinkers had better FC-rest metrics (value of p = 0.004). In a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

Major depressive disorder (MDD) is a prevalent mental illness associated with abnormalities in structural and functional brain connectivity, and it has become a global public health problem. Early diagnosis is important and challenging for the treatment of MDD. Previous studies proposed the classification methods for MDD based on brain connectivity features through functional connectivity (FC) or effective connectivity (EC) measures. However, it requires prior knowledge and experience to manually select a algorithm to calculate the brain connectivity features. Given that the representation learning capabilities of deep learning (DL) models and the ability to capture correlations between data of self-attention mechanism, we proposed an end-to-end integrated DL model for classifying MDD patients and healthy controls (HCs) based on resting-state electroencephalography (EEG) data. This model first automatically learned the potential connectivity relationships among EEG channels through a multi-head self-attention mechanism, and then extracted higher-level features through a parallel two-branch convolution neural network (CNN) module, and finally completed the classification through a fully connected layer. A public resting-state EEG dataset was utilized to evaluate the validity of the proposed model. The experimental results indicated that the proposed model achieved 91.06% average accuracy that was better than those of comparison methods using the leave-one-subject-out cross-validation (LOSOCV) method. This study may provide a novel approach for brain connectivity modeling of MDD detection.

Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data

ObjectiveDistinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset. MethodsA 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects. Results14 connectivity features were selected by the proposed algorithm. Most of the selected features were related to the frontal, parietal, and temporal lobe electrodes. The major involved regions were the Broca region in the frontal lobe and the somatosensory association cortex in the parietal lobe. These regions are near electrodes FC5 and CPz and are involved in processing language and sensory information, respectively. The resulting classifier delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% of the data for training and evaluation and the remaining 20% for testing, respectively. ConclusionsThe high evaluation and test accuracies of our algorithm, derived from a large balanced training sample suggests that this method may hold significant promise as a clinical tool. SignificanceThe proposed MLA may provide an inexpensive and readily available tool that clinicians may use to enhance diagnostic accuracy and shorten time to effective treatment.

High-Definition Transcranial Infraslow Pink-Noise Stimulation Can Influence Functional and Effective Cortical Connectivity in Individuals With Chronic Low Back Pain: A Pilot Randomized Placebo-Controlled Study

IntroductionPain can be regarded as an emergent property of multiple interacting, dynamically changing brain networks and thus needs a targeted treatment approach. A novel high-definition transcranial infraslow pink-noise stimulation (HD-tIPNS) technique was developed to modulate the key hubs of the three main nociceptive pathways simultaneously, ie, the pregenual anterior cingulate cortex (pgACC) (descending inhibitory pathway), the dorsal anterior cingulate cortex (dACC) (medial nociceptive pathway), and both somatosensory cortices (S1) (lateral nociceptive pathway). This study aimed to evaluate safety and verify whether a single session of HD-tIPNS may disrupt functional and effective connectivity between targeted cortical regions. Materials and MethodsA pilot double-blind randomized two-arm placebo-controlled parallel trial was conducted. Participants (N = 30) with chronic low back pain were equally randomized to receive a single session of either sham stimulation or HD-tIPNS (targeting the pgACC, dACC, and bilateral S1). Primary outcomes included safety and electroencephalographic measures, and secondary outcomes included pain measures, collected after treatment. A Mann-Whitney U test was used to compare between-group differences in percentage changes with baseline for each outcome measures. A Wilcoxon signed-rank test was used to identify difference in effective connectivity measure before and after HD-tIPNS. ResultsNo serious adverse events were reported. A significant decrease in instantaneous functional connectivity was noted between the pgACC and dACC (U = 47.0, Z = −2.72, p = 0.007) and the pgACC and left S1 (U = 41.0, Z = −2.97, p = 0.003) in the infraslow band after HD-tIPNS when compared with sham stimulation. A significant decrease in instantaneous effective connectivity was noted in the direction of the dACC to the pgACC (Z = −2.10, p = 0.035), in the infraslow band after HD-tIPNS when compared with baseline. No changes in clinical pain measures were detected. ConclusionsHD-tIPNS can safely modulate the functional and effective connectivity between targeted pain-related cortical hubs. Further studies are warranted to evaluate whether repeated exposures to HD-tIPNS can incur clinical benefits through inducing changes in functional and effective connectivity at targeted cortical regions. Clinical Trial RegistrationThe Clinicaltrials.gov registration number for the study is ACTRN12621001438842.

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer.

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared tohealthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta anddelta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI

Objective. Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for the diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer-aided diagnosis system (CADS) for ASD which considers the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging. The proposed CADS classifies ASD males from normal males, and ASD females from normal females. Approach. After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity measures of full correlation and partial correlation and the effective connectivity measure of bivariate Granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group. Main results. In the female group, a classification accuracy of 93.3% was obtained using full correlation while in the male group, a classification accuracy of 86.7% was achieved using both full correlation and bivariate Granger causality. Also, in the mixed group, a classification accuracy of 83.3% was obtained using full correlation. Significance. This supports the importance of considering sex in diagnosing ASD patients from normal controls.

A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data.

Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. As a result, BD-depressed episode (BD-DE) is often misdiagnosed as MDD, and inappropriate therapy is given. Electroencephalography (EEG) has been widely studied as a potential source of biomarkers to differentiate these disorders. Previous attempts using machine learning (ML) methods have delivered insufficient sensitivity and specificity for clinical use, likely as a consequence of the small training set size, and inadequate ML methodology. We hope to overcome these limitations by employing a training dataset of resting-state EEG from 71 MDD and 71 BD patients. We introduce a robust 3 steps ML technique: 1) a multi-step preprocessing method is used to improve the quality of the EEG signal 2) symbolic transfer entropy (STE), which is an effective connectivity measure, is applied to the resultant EEG signals 3) the ML algorithm uses the extracted STE features to distinguish MDD from BD patients. Clinical Relevance--- The accuracy of our algorithm, derived from a large sample of patients, suggests that this method may hold significant promise as a clinical tool. The proposed method delivered total accuracy, sensitivity, and specificity of 84.9%, 83.4%, and 87.1%, respectively.

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.

Déjà-vu evoked by electrical stimulation of the insula: Stimulation-induced insular déjà-vu.

Déjà-vu is a mental phenomenon commonly experienced during temporal lobe seizures and can be evoked by electrical stimulation of the temporal lobe. We analyzed reproducible déjà-vu experiences evoked by stimulating the insula in two patients with pharmacoresistant temporal lobe epilepsy. We reviewed video-electroencephalography (EEG) recordings from extraoperative electrical cortical stimulation sessions. In addition, we performed the directed transfer function (DTF) effective connectivity measure of monopolar signals in Patient 1. To highlight elective changes due to each stimulation, we subtracted pre-stimulation DTF matrices from early poststimulation matrices. This analysis was performed for both non-inducing-déjàvu stimulation (control matrix) and déjà-vu-inducing stimulation (active matrix). Finally, the control matrix was subtracted from the active matrix. Comparison of effective connectivity during control stimulation versus déjà-vu-inducing stimulation revealed a reversal of connectivity levels in three main regions: the contralateral inferior insula (the ipsilateral insula could not be analyzed), bilateral mesiotemporal regions and the ipsilateral superior frontal gyrus. The drivers of evoked déjà-vu were the mesiotemporal regions (mainly ipsilateral) and the ipsilateral superior frontal gyrus. Although our findings are possibly anecdotal, the insula may (in rare instances) remotely generate unexpected déjà-vu. If confirmed by further studies, this might change the assessment strategy for possible causes of anterior temporal lobectomy failure.

A Nonlinear Effective Connectivity Measure Based on Granger Causality and Volterra Series

Estimating effective connectivity, especially in brain networks, is an important topic to find out the brain functions. Various effective connectivity measures are presented, but they have drawbacks, including bivariate structure, the problem in detecting nonlinear interactions, and high computational cost. In this paper, we have proposed a novel multivariate effective connectivity measure based on a hierarchical realization of the Volterra series model and Granger causality concept, namely hierarchical Volterra Granger causality (HVGC). HVGC is a multivariate connectivity measure that can detect linear and nonlinear causal effects. The performance of HVGC is compared with Granger causality index (GCI), conditional Granger causality index (CGCI), transfer entropy (TE), phase transfer entropy (Phase TE), and partial transfer entropy (Partial TE) in simulated and physiological datasets. In addition to accuracy, specificity, and sensitivity, the Matthews correlation coefficient (MCC) is used to evaluate the connectivity estimation in simulated datasets. Furthermore influence of different SNRs is investigated on the estimated connectivity. The obtained results show that HVGC with a minimum MCC of 0.76 performs well in the detection of both linear and nonlinear interactions in simulated data. HVGC is also applied to a physiological dataset that was cardiorespiratory interaction signals recorded during sleep from a patient suffering from sleep apnea. The results of this dataset also demonstrate the capability of the proposed method in the detection of causal interactions. Applying HVGC on the simulated fMRI dataset led to a high MCC of 0.78. Moreover, the results indicate that HVGC has slight changes in different SNRs. The results indicate that HVGC can estimate the causal effects of a linear and nonlinear system with a low computational cost and it is slightly affected by noise.

Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.

Temporal Profile of Descending Cortical Modulation of Spinal Excitability: Group and Individual-Specific Effects.

Sensorimotor control is modulated through complex interactions between descending corticomotor pathways and ascending sensory inputs. Pairing sub-threshold transcranial magnetic stimulation (TMS) with peripheral nerve stimulation (PNS) modulates the Hoffmann’s reflex (H-reflex), providing a neurophysiologic probe into the influence of descending cortical drive on spinal segmental circuits. However, individual variability in the timing and magnitude of H-reflex modulation is poorly understood. Here, we varied the inter-stimulus interval (ISI) between TMS and PNS to systematically manipulate the relative timing of convergence of descending TMS-induced volleys with respect to ascending PNS-induced afferent volleys in the spinal cord to: (1) characterize effective connectivity between the primary motor cortex (M1) and spinal circuits, mediated by both direct, fastest-conducting, and indirect, slower-conducting descending pathways; and (2) compare the effect of individual-specific vs. standard ISIs. Unconditioned and TMS-conditioned H-reflexes (24 different ISIs ranging from −6 to 12 ms) were recorded from the soleus muscle in 10 able-bodied individuals. The magnitude of H-reflex modulation at individualized ISIs (earliest facilitation delay or EFD and individual-specific peak facilitation) was compared with standard ISIs. Our results revealed a significant effect of ISI on H-reflex modulation. ISIs eliciting earliest-onset facilitation (EFD 0 ms) ranged from −3 to −5 ms across individuals. No difference in the magnitude of facilitation was observed at EFD 0 ms vs. a standardized short-interval ISI of −1.5 ms. Peak facilitation occurred at longer ISIs, ranging from +3 to +11 ms. The magnitude of H-reflex facilitation derived using an individual-specific peak facilitation was significantly larger than facilitation observed at a standardized longer-interval ISI of +10 ms. Our results suggest that unique insights can be provided with individual-specific measures of top-down effective connectivity mediated by direct and/or fastest-conducting pathways (indicated by the magnitude of facilitation observed at EFD 0 ms) and other descending pathways that encompass relatively slower and/or indirect connections from M1 to spinal circuits (indicated by peak facilitation and facilitation at longer ISIs). By comprehensively characterizing the temporal profile and inter-individual variability of descending modulation of spinal reflexes, our findings provide methodological guidelines and normative reference values to inform future studies on neurophysiological correlates of the complex array of descending neural connections between M1 and spinal circuits.

Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s α entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.

Effective connectivity differs between focal cortical dysplasia types I and II.

To determine whether brain connectivity differs between focal cortical dysplasia (FCD) types I and II. We compared cortico-cortical evoked potentials (CCEPs) as measures of effective brain connectivity in 25 FCD patients with drug-resistant focal epilepsy who underwent intracranial evaluation with stereo-electroencephalography (SEEG). We analyzed the amplitude and latency of CCEP responses following ictal-onset single-pulse electrical stimulation (iSPES). In comparison to FCD type II, patients with type I demonstrated significantly larger responses in the electrodes near the ictal-onset zone (<50mm). These findings persisted when controlling for the location of the epileptogenic zone, as noted in patients with temporal lobe epilepsies, as well as controlling for seizure type, as noted in patients with focal to bilateral tonic-clonic seizures (FBTCS). In type II, the root mean square (RMS) of CCEP responses dropped substantially from the early segment (10-60ms) to the middle and late segments (60-600ms). The middle and late CCEP latency segments showed the largest differences between FCD types I and II. Focal cortical dysplasia type I may have a greater degree of cortical hyperexcitability as compared with FCD type II. In addition, FCD type II displays a more restrictive area of hyperexcitability in both temporal and spatial domains. In patients with FBTCS and type I FCD, the increased amplitudes of RMS in the middle and late CCEP periods appear consistent with the cortico-thalamo-cortical network involvement of FBTCS. The notable differences in degree and extent of hyperexcitability may contribute to the different postsurgical seizure outcomes noted between these two pathological substrates.