data processing assistant for resting-state fmri v2.0 basic edition Search Results


ica of fmri toolbox (gift) software v2.0 c  (SourceForge net)
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SourceForge net ica of fmri toolbox (gift) software v2.0 c
The similarity between the independent components identified in each of the 100 iterations of the <t>group</t> <t>ICA</t> for both the low-(A) and high-dimensional (B) runs. Points represent individual runs, grey convex hulls represent a cluster of the same independent component (labeled with component number), cyan circles represent the best estimate of the independent component (centrotype), red shading indicates average intra-cluster similarity exceeds 0.9, and pink shading indicates average intra-cluster similarity is between 0.8 and 0.9. If intra-cluster similarity is bellow 0.9, red lines are drawn between runs that have similarity greater than 0.9. Compact clusters are indicative of high run-to-run similarity in the ICA solution for that component. 2D-two dimensional, CCA-curvilinear component analysis, ICA-independent component analysis.
Ica Of Fmri Toolbox (Gift) Software V2.0 C, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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The similarity between the independent components identified in each of the 100 iterations of the group ICA for both the low-(A) and high-dimensional (B) runs. Points represent individual runs, grey convex hulls represent a cluster of the same independent component (labeled with component number), cyan circles represent the best estimate of the independent component (centrotype), red shading indicates average intra-cluster similarity exceeds 0.9, and pink shading indicates average intra-cluster similarity is between 0.8 and 0.9. If intra-cluster similarity is bellow 0.9, red lines are drawn between runs that have similarity greater than 0.9. Compact clusters are indicative of high run-to-run similarity in the ICA solution for that component. 2D-two dimensional, CCA-curvilinear component analysis, ICA-independent component analysis.

Journal: PLoS ONE

Article Title: Non-Stationarity in the “Resting Brain’s” Modular Architecture

doi: 10.1371/journal.pone.0039731

Figure Lengend Snippet: The similarity between the independent components identified in each of the 100 iterations of the group ICA for both the low-(A) and high-dimensional (B) runs. Points represent individual runs, grey convex hulls represent a cluster of the same independent component (labeled with component number), cyan circles represent the best estimate of the independent component (centrotype), red shading indicates average intra-cluster similarity exceeds 0.9, and pink shading indicates average intra-cluster similarity is between 0.8 and 0.9. If intra-cluster similarity is bellow 0.9, red lines are drawn between runs that have similarity greater than 0.9. Compact clusters are indicative of high run-to-run similarity in the ICA solution for that component. 2D-two dimensional, CCA-curvilinear component analysis, ICA-independent component analysis.

Article Snippet: Preprocessing and data analysis was performed utilizing a combination of the Statistical Parametric Mapping (SPM5) software ( http://www.fil.ion.ucl.ac.uk/spm/software/spm5/ ) (Wellcome Department of Cognitive Neurology, University College London, UK), the Resting-State fMRI Data Analysis Toolkit (REST) v1.5 ( http://www.restfmri.net ) , Data Processing Assistant for Resting-State fMRI (DPARSF) v2.0 ( http://www.restfmri.net ) , group ICA of fMRI toolbox (GIFT) software v2.0 c ( http://icatb.sourceforge.net ) , brain connectivity toolbox ( http://www.brain-connectivity-toolbox.net ) , and in-house developed software implemented in MATLAB v7.11 (Mathworks Inc., Natick, MA, USA).

Techniques: Labeling