Comput Math Methods Med - A computationally efficient, exploratory approach to brain connectivity incorporating false discovery rate control, a priori knowledge, and group inference.

Tópicos

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{ imag(2830) propos(1344) filter(1198) }
{ result(1111) use(1088) new(759) }
{ model(3404) distribut(989) bayesian(671) }
{ group(2977) signific(1463) compar(1072) }
{ framework(1458) process(801) describ(734) }
{ data(2317) use(1299) case(1017) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PC(fdr) algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PC(fdr) algorithm was unable to accommodate a priori information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects. Here we extend the original PC(fdr) algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity. In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold. When the proposed approach is applied to fMRI data in a Parkinson's disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication. The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.

Resumo Limpo

graphic model appear well suit infer brain connect fmri data can distinguish direct indirect brain connect nevertheless biolog interpret requir multivari time seri adequ model also accur errorcontrol infer edg pcfdr algorithm develop li wang provid comput effici mean control fals discoveri rate fdr comput edg asymptot origin pcfdr algorithm unabl accommod priori inform connect design infer connect singl subject rather group subject extend origin pcfdr algorithm propos multisubject errorratecontrol brain connect model approach allow incorpor prior knowledg connect simul show two propos extens can still control fdr around specifi threshold propos approach appli fmri data parkinson diseas studi find robust group evid diseaserel chang compensatori chang normal effect ldopa medic propos method provid robust accur practic method assess brain connect pattern function neuroimag data

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