Comput Math Methods Med - A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification.

Tópicos

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Resumo

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

Resumo Limpo

recent year grow need analyz function connect human brain previous studi focus extract static timeindepend function network describ longterm behavior brain activ howev static network general suffici repres long term communic pattern brain consid unreli snapshot function connect paper propos dynam network summar approach describ timevari evolut connect pattern function brain activ propos approach base first identifi key event interv quantifi chang connect pattern across time summar activ event interv extract inform network use princip compon decomposit propos method evalu character timevari network dynam eventrel potenti erp data index errorrel negat ern compon relat cognit control statist signific connect pattern interv present illustr dynam natur function connect

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