A Robust Coherence-Based Brain Connectivity Method with an Application to EEG Recordings

2016 ◽  
pp. 339-344
Author(s):  
Jiaqing Yan ◽  
Jianbin Wen ◽  
Yinghua Wang ◽  
Xianzeng Liu ◽  
Xiaoli Li
Keyword(s):  
Brain Connectivity ◽  
Eeg Recordings

scholarly journals CNN for a Connectivity Based Epilepsy Diagnosis with Resting-state EEG

2021 ◽  
Author(s):  
Berjo Rijnders ◽  
Emin Erkan Korkmaz ◽  
Funda Yildirim
Keyword(s):  
Resting State ◽  
Cognitive Abilities ◽  
Brain Connectivity ◽  
Clinical Decision ◽  
Control Group ◽  
Learning To Learn ◽  
Graph Metrics ◽  
Epilepsy Diagnosis ◽  
Eeg Recordings ◽  
Surface Eeg

Objective: This study investigates the performance of a CNN algorithm on epilepsy diagnosis. Without pathology, diagnosis involves long and costly electroencephalographic (EEG) monitoring. Novel approaches may overcome this by comparing brain connectivity using graph metrics. This study, however, uses deep learning to learn connectivity patterns directly from easily acquired EEG data. Approach: A convolutional neural network (CNN) algorithm was applied on directed Granger causality (GC) connectivity measures, derived from 50 seconds of resting-state surface EEG recordings from 30 subjects with epilepsy and a 30 subject control group. Main results: The learned CNN filters reflected reduced delta band connectivity in frontal regions and increased left lateralized frontal-posterior gamma band connectivity. A diagnosis accuracy of 85% (F1-score 85%) was achieved by an ensemble of CNN models, each trained on differently prepared data from different electrode combinations. Conclusions: Appropriate preparation of connectivity data enables generic CNN algorithms to be used for detection of multiple discriminative epileptic features. Differential patterns revealed in this study may help to shed light on underlying altered cognitive abilities in epilepsy patients. Significance: The accuracy achieved in this study shows that, in combination with other methods, this approach could prove a valuable clinical decision support system for epilepsy diagnosis.

Effective Brain Connectivity from Intracranial EEG Recordings: Identification of Epileptogenic Zone in Human Focal Epilepsies

2013 ◽  
pp. 87-101 ◽  
Author(s):  
Giulia Varotto ◽  
Laura Tassi ◽  
Fabio Rotondi ◽  
Roberto Spreafico ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Epileptogenic Zone ◽  
Intracranial Eeg ◽  
Eeg Recordings

scholarly journals Heritability of Resting State EEG Functional Connectivity Patterns

2013 ◽  
Vol 16 (5) ◽  
pp. 962-969 ◽  
Author(s):  
Nienke M. Schutte ◽  
Narelle K. Hansell ◽  
Eco J. C. de Geus ◽  
Nicholas G. Martin ◽  
Margaret J. Wright ◽  
...  
Keyword(s):  
Resting State ◽  
Cluster Coefficient ◽  
Path Length ◽  
Brain Connectivity ◽  
Network Clustering ◽  
Beta Band ◽  
Band Frequency ◽  
Functional Brain ◽  
Synchronization Likelihood ◽  
Eeg Recordings

We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis — network clustering and average path length — are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27–74%) and cluster coefficient and path length in the alpha and theta band (40–44% and 23–40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.

Dynamics of Epileptiform Discharges Induced by Transcranial Magnetic Stimulation in Genetic Generalized Epilepsy

2017 ◽  
Vol 27 (07) ◽  
pp. 1750037 ◽  
Author(s):  
Dimitris Kugiumtzis ◽  
Christos Koutlis ◽  
Alkiviadis Tsimpiris ◽  
Vasilios K. Kimiskidis
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Network ◽  
Brain State ◽  
Epileptiform Discharges ◽  
Eeg Recordings ◽  
Generalized Epilepsy ◽  
The Brain

Objective: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. Methods: EEG recordings from two GGE patients were analyzed. For hypothesis (a), the characteristics of the brain dynamics at the early ED stage are measured with univariate and multivariate EEG measures and the dependence of the ED duration on these measures is evaluated. For hypothesis (b), effective connectivity measures are combined with network indices so as to quantify the brain network characteristics and identify changes in brain connectivity. Results: A number of measures combined with specific channels computed on the first EEG segment post-TMS correlate with the ED duration. In addition, brain connectivity is altered from pre-ED to ED and post-ED and statistically significant changes were also detected across stages within the ED. Conclusion: ED duration is not purely stochastic, but depends on the dynamics of the post-TMS brain state. The brain network dynamics is significantly altered in the course of EDs.

scholarly journals ANALYSIS OF ELECTROENCEPHALOGRAPHIC RECORDINGS FROM EPILEPSY PATIENTS USING RESTRICTED GRANGER CAUSALITY MEASURES

2015 ◽  
Author(s):  
Elsa Siggiridou ◽  
Vasilios Kimiskidis ◽  
D. Kugiumtzis
Keyword(s):  
Granger Causality ◽  
Brain Connectivity ◽  
Brain Regions ◽  
World Population ◽  
Var Models ◽  
Vector Autoregressive ◽  
Epileptiform Discharges ◽  
Eeg Recordings ◽  
Generalized Partial Directed Coherence ◽  
Patients With Epilepsy

Epilepsy is a chronic disorder of the brain that affects 1% of world population. The occurrence of epileptiform discharges (ED) in electroencephalographic (EEG) recordings of patients with epilepsy signifies a change in brain dynamics and particularly brain connectivity. In the last decade, many linear and nonlinear measures have been developed for the analysis of EEG recordings to detect the direct causal effects between brain regions. In many cases the number of EEG channels (the time series variables) is large and the analysis is based on short time intervals, resulting in unstable estimation of vector autoregressive models (VAR models) and subsequently unreliable Granger causality measure. For this, restricted VAR models have been proposed and in our recent study it was found that optimal restriction of VAR for the estimation of Granger causality was obtained by the backward-in-time selection method (BTS). We use the concept of restricted VAR models in measures both in time and frequency domain, namely restricted conditional Granger causality and restricted generalized partial directed coherence. We test the two measures in their ability of detecting changes in brain connectivity during an epileptiform discharge from multi-channel scalp electroencephalograms (EEG).

scholarly journals Nested oscillations and brain connectivity during sequential stages of feature-based attention

2020 ◽  
Author(s):  
Mattia F. Pagnotta ◽  
David Pascucci ◽  
Gijs Plomp
Keyword(s):  
Selective Attention ◽  
Brain Connectivity ◽  
Spatial Scales ◽  
Visual Selective Attention ◽  
Source Analysis ◽  
Network Efficiency ◽  
Eeg Source Analysis ◽  
Feature Based ◽  
Eeg Recordings ◽  
Oscillatory Rhythms

AbstractBrain mechanisms of visual selective attention involve both local and network-level activity changes at specific oscillatory rhythms, but their interplay remains poorly explored. Here, we investigate anticipatory and reactive effects of feature-based attention using separate fMRI and EEG recordings, while participants attended to one of two spatially overlapping visual features (motion and orientation). We focused on EEG source analysis of local nested oscillations and on graph analysis of connectivity changes in a network of fMRI-defined regions of interest, and characterized a cascade of attentional effects and their interplay at multiple spatial scales. We discuss how the results may reconcile several theories of selective attention, by showing how β rhythms support anticipatory information routing through increased network efficiency and β-γ coupling in functionally specialized regions (V1 for orientation, V5 for motion), while reactive α-band desynchronization patterns and increased α-γ coupling in V1 and V5 mediate stimulus-evoked processing of task-relevant signals.

scholarly journals Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 39 (5) ◽  
pp. 1571-1581
Author(s):  
Yifan Zhao ◽  
Yitian Zhao ◽  
Pholpat Durongbhan ◽  
Liangyu Chen ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Connectivity ◽  
Functional Brain ◽  
Eeg Recordings

REDUNDANCY IN FUNCTIONAL BRAIN CONNECTIVITY FROM EEG RECORDINGS

2012 ◽  
Vol 22 (07) ◽  
pp. 1250158 ◽  
Author(s):  
FABRIZIO DE VICO FALLANI ◽  
JLENIA TOPPI ◽  
CLAUDIA DI LANZO ◽  
GIOVANNI VECCHIATO ◽  
LAURA ASTOLFI ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Random Networks ◽  
Eeg Signals ◽  
Statistical Comparison ◽  
Natural Tendency ◽  
Critical Resource ◽  
Path Lengths ◽  
Eeg Recordings ◽  
The Brain

The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p < 0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p < 0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5–12.5 Hz), which is known to be the most responsive channel during resting state conditions.

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