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Biology subjects

Kathpalia, A.

Publications and source records attributed to Kathpalia, A..

2 recordsLinked to original sources

EEG connectivity changes in early response to antidepressant treatment

ObjectiveFinding indicators of early response to antidepressant treatment in EEG signals recorded from patients suffering from major depressive disorder. MethodsFunctional brain connectivity networks based on weighted imaginary coherence and weighted imaginary mean phase coherence were computed for 176 patients for 6 different EEG frequency bands. Cross-hemispheric connectivity (CH) and lateral asymmetry (LA) were estimated from these networks based on EEG signals recorded before the beginning of treatment (V is1) and one week after the start of the treatment (V is2). Repeated measures ANOVA was used to check for statistically significant changes in connectivity based on these measures at V is2 w.r.t. V is1. Post-hoc analysis was performed with multiple pairwise comparison tests to determine which group means were significantly different. ResultsIt was found that CHV is2 was significantly reduced w.r.t. CHV is1 in the {beta}1 [12.5 - 17.5 Hz] frequency band for the responders to treatment. Also, LAV is2 was significantly increased w.r.t. LAV is1 in the {beta}1 frequency band for the responders. No such significant changes were observed for the non-responders. Brain networks constructed using both weighted imaginary coherence and weighted imaginary mean phase coherence were found to exhibit these results. For the CH connectivity changes, binarized networks and for the LA connectivity changes, weighted networks were found to be more reliable. ConclusionsResponders were found to show a reduction in cross-hemispheric connectivity and an increase in lateral asymmetry, both in the {beta}1 band while no such change was observed for the non-responders. SignificanceDecrease in cross-hemispheric connectivity and increase in lateral asymmetry in the {beta}1 band may represent candidate neurophysiological indicators of early treatment response, but they require independent replication before any clinical application can be considered.

neuroscience↗

Distinguishing Different Levels Of Consciousness using a Novel Network Causal Activity Measure

Characterizing consciousness, the inner subjective feeling that is present in every experience, is a hard problem in neuroscience, but has important clinical implications. A leading neuro-scientific approach to understanding consciousness is to measure the complex causal neural interactions in the brain. Elucidating the complex causal interplay between cortical neural interactions and the subsequent network computations is very challenging. In this study, we propose a novel quantitative measure of consciousness - Network Causal Activity - using a recently proposed Compression-Complexity Causality measure to analyze electrocorticographic signals from the lateral cortex of four monkeys during two states of consciousness (awake and anaesthesia). Our results suggest that Network Causal Activity is consistently higher in the awake state as compared with anaesthesia state for all the monkeys.

bioengineering↗