Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task

Abstract

Using spectral Granger causality (GC) we identified distinct spatio-temporal causal connectivity (CC) patterns in groups of control and epileptic children during the execution of a one-back matching visual discrimination working memory task. Differences between control and epileptic groups were determined for both GO and NOGO conditions. The analysis was performed on a set of 19-channel EEG cortical activity signals. We show that for the GO task, the highest brain activity in terms of the density of the CC networks is observed in a band for the control group while for the epileptic group the CC network seems disrupted as reflected by the small number of connections. For the NOGO task, the denser CC network was observed in h band for the control group while widespread differences between the control and the epileptic group were located bilaterally at the left temporal-midline and parietal areas. In order to test the discriminative power of our analysis, we performed a pattern analysis approach based on fuzzy classification techniques. The performance of the classification scheme was evaluated using permutation tests. The analysis demonstrated that, on average, 87.6 % of the subjects were correctly classified in control and epileptic. Thus, our findings may provide a helpful insight on the mechanisms pertaining to the cognitive response of children with well controlled epilepsy and could potentially serve as ''functional'' biomarkers for early diagnosis.

Simulation of retinal ganglion cell response using fast independent component analysis

“…Studies have shown that sparse neural coding patterns reflect the maximization of energy efficiency, that is, consume little energy to encode information (Levy and Baxter 1999;Laughlin 2001). Our study is mainly a simulation experiment without physiological studies, because biophysical mechanisms are too complicated and many mechanisms are not yet clear (Protopapa et al 2016;Momtaz and Daliri 2016). We are simply trying to simulate and perform a simple analysis on a simplified ganglion cell activity using an artificial neural network, so biophysical mechanisms were not investigated in the study.…”

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

“…This issue seems very difficult to circumvent for practical and theoretical reasons. Firstly, due to the high number of electrodes used in this study (which also seems to be a general trend), performing connectivity analysis on all electrodes is very time consuming [75], [76]. Additionally, since we simulated a network with finite number of nodes, in order to compare the results we needed to match the number of nodes in the estimated and simulated network, thus selecting a somewhat optimal set of electrodes was unavoidable.…”

Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson’s Disease

“…Moreover, GCA has recently also been applied to human fMRI data based on temporal order ( Friston, 2009 ; Seth et al, 2013 ). It has been widely used in exploring cognitive functions such as working memory ( Protopapa et al, 2014 , 2016 ), as well as other neurological disorders ( Brovelli et al, 2004 ; Jiao et al, 2011 ). The DN is the largest single structure linking the cerebellum to the rest of the brain.…”