Subspace-based Identification Algorithm for characterizing causal networks in resting brain
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TL;DR: A novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit cognitive task is introduced, and the Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI.
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About: This article is published in NeuroImage. The article was published on 02 Apr 2012. and is currently open access. The article focuses on the topics: Resting state fMRI & Causal inference.
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Citations
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
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TL;DR: In this article, a blind deconvolution technique for BOLD-fMRI signal is proposed, where point processes corresponding to signal fluctuations with a given signature are individuated, and a region-specific hemodynamic response function (HRF) is extracted and used to deconvolve BOLD signal.
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•Posted Content
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
Guo-Rong Wu,Wei Liao,Sebastiano Stramaglia,Jurong Ding,Jurong Ding,Huafu Chen,Daniele Marinazzo +6 more
TL;DR: This work considers resting fMRI as 'spontaneous event-related', individuate point processes corresponding to signal fluctuations with a given signature, extract a region-specific HRF and use it in deconvolution, after following an alignment procedure.
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Novel Experimental and Analysis Paradigms for Neuroimaging
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TL;DR: Results are relevant for the understanding of hemodynamic and neurochemical aberrations in ASD, as well as have methodological implications for resting state functional connectivity studies in Autism and more generally in disorders that are accompanied by neurochemical alterations that may impact HRF shape.
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