Dataset of brain functional connectome and its maturation in adolescents

• Type of Material: Article
• Authors: Zack Y Shan, University of the Sunshine Coast; Abdalla Z Mohamed, University of the Sunshine Coast; Paul Schwenn, University of the Sunshine Coast; Larisa T McLoughlin, University of the Sunshine Coast; Amanda Boyes, University of the Sunshine Coast; Dashiell D Sacks, University of the Sunshine Coast; Christina Driver, University of the Sunshine Coast; Vince Calhoun, Emory University; Jim Lagopoulos, University of the Sunshine Coast; Daniel F Hermens, University of the Sunshine Coast
• Language: English
• Date: 2022-08-01
• Publisher: Elsevier Inc
• Publication Version: Final Published Version
• Copyright Statement: © 2022 The Author(s)
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.dib.2022.108454
• Title of Journal or Parent Work: Data in Brief
• Volume: 43
• Start Page: 108454
• End Page: 108454
• Keywords: fMRI; Adolescent; Brain developmental changes; Longitudinal study; Functional connectivity
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

We provided the dataset of brain connectome matrices, their similarities measures to self and others longitudinally, and Kessler's psychological distress scales (K10) including the response to each question. The dataset can be used to replicate the results of the manuscript titled “A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence”. The functional connectome (whole-brain and 13 networks) matrices were calculated from the resting-state functional MRIs (rs-fMRIs). We collected rs-fMRI and Kessler's psychological distress scale (K10) in 77 adolescents longitudinally up to 9 times from 12 years of age every four months. After removal of data with excessive motion, 262 functional connectome matrices were provided with this paper. The 300 regions of interest (ROIs) were defined using the Greene lab brain atlas. The functional connectome matrices were calculated as correlations between time series from any pair of ROIs extracted from pre-processed fMRIs. This dataset could be potentially used to 1. Understand developmental changes in the functional brain connectivity, 2. As a normal control database of functional connectome matrices, 3. Develop and validate connectome and network-related analysing methods.