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Image Search Results
Journal: Human Brain Mapping
Article Title: A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data
doi: 10.1002/hbm.24729
Figure Lengend Snippet: Processing features of fully automated toolboxes
Article Snippet: Platform dependencies , MATLAB, SPM5+ ,
Techniques:
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 ) ,
Techniques: Labeling