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Dataset of brain functional connectome and its maturation in adolescents
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 longitudin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294043/ https://www.ncbi.nlm.nih.gov/pubmed/35864878 http://dx.doi.org/10.1016/j.dib.2022.108454 |
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author | Shan, Zack Y. Mohamed, Abdalla Z. Schwenn, Paul McLoughlin, Larisa T. Boyes, Amanda Sacks, Dashiell D. Driver, Christina Calhoun, Vince D. Lagopoulos, Jim Hermens, Daniel F. |
author_facet | Shan, Zack Y. Mohamed, Abdalla Z. Schwenn, Paul McLoughlin, Larisa T. Boyes, Amanda Sacks, Dashiell D. Driver, Christina Calhoun, Vince D. Lagopoulos, Jim Hermens, Daniel F. |
author_sort | Shan, Zack Y. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9294043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92940432022-07-20 Dataset of brain functional connectome and its maturation in adolescents Shan, Zack Y. Mohamed, Abdalla Z. Schwenn, Paul McLoughlin, Larisa T. Boyes, Amanda Sacks, Dashiell D. Driver, Christina Calhoun, Vince D. Lagopoulos, Jim Hermens, Daniel F. Data Brief Data Article 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. Elsevier 2022-07-08 /pmc/articles/PMC9294043/ /pubmed/35864878 http://dx.doi.org/10.1016/j.dib.2022.108454 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Data Article Shan, Zack Y. Mohamed, Abdalla Z. Schwenn, Paul McLoughlin, Larisa T. Boyes, Amanda Sacks, Dashiell D. Driver, Christina Calhoun, Vince D. Lagopoulos, Jim Hermens, Daniel F. Dataset of brain functional connectome and its maturation in adolescents |
title | Dataset of brain functional connectome and its maturation in adolescents |
title_full | Dataset of brain functional connectome and its maturation in adolescents |
title_fullStr | Dataset of brain functional connectome and its maturation in adolescents |
title_full_unstemmed | Dataset of brain functional connectome and its maturation in adolescents |
title_short | Dataset of brain functional connectome and its maturation in adolescents |
title_sort | dataset of brain functional connectome and its maturation in adolescents |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294043/ https://www.ncbi.nlm.nih.gov/pubmed/35864878 http://dx.doi.org/10.1016/j.dib.2022.108454 |
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