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NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders
Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provide...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509081/ https://www.ncbi.nlm.nih.gov/pubmed/32961402 http://dx.doi.org/10.1016/j.nicl.2020.102375 |
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author | Du, Yuhui Fu, Zening Sui, Jing Gao, Shuang Xing, Ying Lin, Dongdong Salman, Mustafa Abrol, Anees Rahaman, Md Abdur Chen, Jiayu Hong, L. Elliot Kochunov, Peter Osuch, Elizabeth A. Calhoun, Vince D. |
author_facet | Du, Yuhui Fu, Zening Sui, Jing Gao, Shuang Xing, Ying Lin, Dongdong Salman, Mustafa Abrol, Anees Rahaman, Md Abdur Chen, Jiayu Hong, L. Elliot Kochunov, Peter Osuch, Elizabeth A. Calhoun, Vince D. |
author_sort | Du, Yuhui |
collection | PubMed |
description | Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. |
format | Online Article Text |
id | pubmed-7509081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75090812020-09-28 NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders Du, Yuhui Fu, Zening Sui, Jing Gao, Shuang Xing, Ying Lin, Dongdong Salman, Mustafa Abrol, Anees Rahaman, Md Abdur Chen, Jiayu Hong, L. Elliot Kochunov, Peter Osuch, Elizabeth A. Calhoun, Vince D. Neuroimage Clin Regular Article Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. Elsevier 2020-08-11 /pmc/articles/PMC7509081/ /pubmed/32961402 http://dx.doi.org/10.1016/j.nicl.2020.102375 Text en © 2020 The Author(s) http://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 | Regular Article Du, Yuhui Fu, Zening Sui, Jing Gao, Shuang Xing, Ying Lin, Dongdong Salman, Mustafa Abrol, Anees Rahaman, Md Abdur Chen, Jiayu Hong, L. Elliot Kochunov, Peter Osuch, Elizabeth A. Calhoun, Vince D. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
title | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
title_full | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
title_fullStr | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
title_full_unstemmed | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
title_short | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
title_sort | neuromark: an automated and adaptive ica based pipeline to identify reproducible fmri markers of brain disorders |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509081/ https://www.ncbi.nlm.nih.gov/pubmed/32961402 http://dx.doi.org/10.1016/j.nicl.2020.102375 |
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