Int J Neural Syst - Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data.

Tópicos

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Resumo

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Resumo Limpo

increas recogn rest state brain connect deriv function magnet reson imag fmri data import marker brain function healthi clinic popul though linear correl extens use character brain connect limit detect first order depend studi propos framework phase synchron ps brain region character use new metric correl probabl recurr cpr subsequ graphtheoret analysi ensu network appli method rest state fmri data obtain human subject without administr propofol anesthet result show decreas ps anesthesia biolog plausibl communiti structur use cpr rather linear correl conclud cpr provid attract nonparametr method model interact brain network compar standard correl obtain physiolog meaning insight brain function

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