DOI: 10.5281/zenodo.8246344
title: Affinity matrices from fMRI data

description: This is a set of 27x27 matrices constructed from the fMRI data for 2 participants from the ABIDE data set [Craddock et al., 2013] and 10 from the HCP Young Adult data set [Van Essen et al., 2013, Glasser et al., 2013]. A subset of 118 matrices (saved in a folder titled 'Random') was produced by sampling uniformly from the interval [0,1]. Each matrix represents the affinity in the immediate neighbourhood of a randomly-selected vertex on the midthickness surface of the brain, and was generated with the VB toolbox as outlined in [Bajada et al., 2020]. Note, however, that the referenced work takes a surface-based approach, but we used a version of the toolbox that is newer and employs a hybrid method whereby the randomly-chosen vertex is first mapped to the corresponding voxel, a 27-voxel neighbourhood (which includes the original voxel) is identified, and the affinity in said neighbourhood calculated. The element <em>a</em><sub>ij</sub> of the matrix constructed for the neighbourhood indicates the degree of correlation between voxels<em> i </em>and<em> j</em> on the basis of their fMRI data. The particular version of the VB toolbox utilised for this work is available at https://github.com/VBIndex/py_vb_toolbox/tree/Local-gradients-paper (but please note that it would have to be modified to print out the affinity matrices for a set of randomly-selected vertices). The matrices are made available as numpy array files and are separated into folders according to the origin of the fMRI data used to construct them (the prefix ABIDE_ added to folder names indicates that the matrices contained inside were generated with the fMRI data of subjects from the ABIDE data set, while HCP is the Human Connectome Project counterpart. Each folder pertains to a different data subject, but note that the numbering scheme employed (DS1, DS2, etc.) has no correspondence to the original ABIDE or HCP participant numbers.
description: Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Derivatives of WU-Minn HCP Open Access data are distributed under the data use terms specified here: https://www.humanconnectome.org/storage/app/media/data_use_terms/DataUseTerms-HCP-Open-Access-26Apr2013.pdf. Data was also sourced from the ABIDE I (Autism Brain Imaging Data Exchange I) Preprocessed data set. ABIDE I funding acknowledgements are available at http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html. Adaptations of ABIDE I data are distributed under a CC BY-NC-SA 3.0 License (https://creativecommons.org/licenses/by-nc-sa/3.0/).
description: {"references": ["C. Craddock, Y. Benhajali, C. Chu, et al., The Neuro Bureau Preprocessing Initiative: Open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013, Stokholm, Sweden, 2013. Doi:10.3389/conf.fninf.2013.09.00041", "D. C. Van Essen, S. M. Smith, D. M. Barch, et al., The WU-Minn Human Connectome Project: An overview. NeuroImage, 80:62, 2013. Doi: 10.1016/j.neuroimage.2013.05.041.", "M. F. Glasser, S. N. Sotiropoulos, J. A. Wilson, et al., The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80:105, 2013. Doi: 10.1016/j.neuroimage.2013.04.127.", "C. J. Bajada, L. Q. Costa Campos, S. Caspers, et al., A tutorial and tool for exploring feature similarity gradients with MRI data. NeuroImage, 221:117140, 2020. Doi: 10.1016/j.neuroimage.2020.117140."]}