Computational protocol: Discriminating the Difference between Remote and Close Association with Relation to White-Matter Structural Connectivity

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[…] The preprocessing of the image information includes removing the images of the skull and other non-brain materials []. This study corrected and readjusted the deviations caused by the head movements of the participants and the scanning distortion; fitted and decomposed the local diffusion tensor; and calculated the fractional anisotropy index. The researchers used PANDA (pipeline tool for diffusion MRI) [] to conduct preprocessing of all of the image information. This software can utilize related tools, such as FMRIB Software Library (FSL) [], Pipeline System for Octave and Matlab (PSOM) [], Diffusion Toolkit [], and MRIcron ( [...] In this study, the automated anatomical labeling (AAL, []) atlas was used to segment the cerebral cortex of each subject into 90 regions (45 for each hemisphere) without the cerebellum. Each region represents a node of the DTI-based WM network. The detailed parceling processes were implemented according to the procedure proposed by Gong and colleagues []. Briefly, the T1-weighted image was first non-linearly normalized to the MNI space. Next, the fractional anisotropy image of each subject was co-registered to the individual T1-weighted image. Finally, the inverse transformations from the previous two steps were applied to the atlas, which resulted in native-space GM parcellations for each subject. The deterministic fiber assignment continuous tracking (FACT) algorithm was applied to reconstruct whole-brain WM tracts [] using the Diffusion toolkit (, which is embedded in PANDA []. Specifically, the tracking procedure terminated if the turn angle of the fiber was greater than 45° or the fiber entered a voxel with a fractional anisotropy of less than 0.2. Two region pairs, A and B, were considered to be structurally connected (i.e., having an edge) if there existed at least three tracts with terminal points in both regions A and B []. Combining the above definitions of the nodes and edges, we attained for each subject a 90⊆90 binary network whose elements indicated the existence/absence of an edge between any pair-wise regions. […]

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