Comput Math Methods Med - Independent component analysis of instantaneous power-based fMRI.

Tópicos

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Resumo

In functional magnetic resonance imaging (fMRI) studies using spatial independent component analysis (sICA) method, a model of "latent variables" is often employed, which is based on the assumption that fMRI data are linear mixtures of statistically independent signals. However, actual fMRI signals are nonlinear and do not automatically meet with the requirement of sICA. To provide a better solution to this problem, we proposed a novel approach termed instantaneous power based fMRI (ip-fMRI) for regularization of fMRI data. Given that the instantaneous power of fMRI signals is a scalar value, it should be a linear mixture that naturally satisfies the "latent variables" model. Based on our simulated data, the curves of accuracy and resulting receiver-operating characteristic curves indicate that the proposed approach is superior to the traditional fMRI in terms of accuracy and specificity by using sICA. Experimental results from human subjects have shown that spatial components of a hand movement task-induced activation reveal a brain network more specific to motor function by ip-fMRI than that by the traditional fMRI. We conclude that ICA decomposition of ip-fMRI may be used to localize energy signal changes in the brain and may have a potential to be applied to detection of brain activity.

Resumo Limpo

function magnet reson imag fmri studi use spatial independ compon analysi sica method model latent variabl often employ base assumpt fmri data linear mixtur statist independ signal howev actual fmri signal nonlinear automat meet requir sica provid better solut problem propos novel approach term instantan power base fmri ipfmri regular fmri data given instantan power fmri signal scalar valu linear mixtur natur satisfi latent variabl model base simul data curv accuraci result receiveroper characterist curv indic propos approach superior tradit fmri term accuraci specif use sica experiment result human subject shown spatial compon hand movement taskinduc activ reveal brain network specif motor function ipfmri tradit fmri conclud ica decomposit ipfmri may use local energi signal chang brain may potenti appli detect brain activ

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