Comput. Biol. Med. - Effective connectivity analysis of fMRI and MEG data collected under identical paradigms.

Tópicos

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Resumo

Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.

Resumo Limpo

estim effect connect measur influenc among brain region can potenti reveal valuabl inform organ brain network effect connect usual evalu function data singl modal paper show may lead incorrect conclus effect connect paper use bayesian network estim connect two differ modal analyz structur estim effect connect network use aggreg statist field complex network studi conduct function mri magnetoencephalographi data collect subject ident paradigm result show similar also reveal strike differ conclus one make fmri data compar meg data strong support use multipl modal order gain complet pictur brain organ given limit inform one modal abl provid

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