Cargando…
Predicting brain age during typical and atypical development based on structural and functional neuroimaging
Exploring typical and atypical brain developmental trajectories is very important for understanding the normal pace of brain development and the mechanisms by which mental disorders deviate from normal development. A precise and sex‐specific brain age prediction model is desirable for investigating...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596985/ https://www.ncbi.nlm.nih.gov/pubmed/34520078 http://dx.doi.org/10.1002/hbm.25660 |
_version_ | 1784600510156767232 |
---|---|
author | Wang, Qi Hu, Ke Wang, Meng Zhao, Yuxin Liu, Yong Fan, Lingzhong Liu, Bing |
author_facet | Wang, Qi Hu, Ke Wang, Meng Zhao, Yuxin Liu, Yong Fan, Lingzhong Liu, Bing |
author_sort | Wang, Qi |
collection | PubMed |
description | Exploring typical and atypical brain developmental trajectories is very important for understanding the normal pace of brain development and the mechanisms by which mental disorders deviate from normal development. A precise and sex‐specific brain age prediction model is desirable for investigating the systematic deviation and individual heterogeneity of disorders associated with atypical brain development, such as autism spectrum disorders. In this study, we used partial least squares regression and the stacking algorithm to establish a sex‐specific brain age prediction model based on T1‐weighted structural magnetic resonance imaging and resting‐state functional magnetic resonance imaging. The model showed good generalization and high robustness on four independent datasets with different ethnic information and age ranges. A predictor weights analysis showed the differences and similarities in changes in structure and function during brain development. At the group level, the brain age gap estimation for autistic patients was significantly smaller than that for healthy controls in both the ABIDE dataset and the healthy brain network dataset, which suggested that autistic patients as a whole exhibited the characteristics of delayed development. However, within the ABIDE dataset, the premature development group had significantly higher Autism Diagnostic Observation Schedule (ADOS) scores than those of the delayed development group, implying that individuals with premature development had greater severity. Using these findings, we built an accurate typical brain development trajectory and developed a method of atypical trajectory analysis that considers sex differences and individual heterogeneity. This strategy may provide valuable clues for understanding the relationship between brain development and mental disorders. |
format | Online Article Text |
id | pubmed-8596985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85969852021-12-02 Predicting brain age during typical and atypical development based on structural and functional neuroimaging Wang, Qi Hu, Ke Wang, Meng Zhao, Yuxin Liu, Yong Fan, Lingzhong Liu, Bing Hum Brain Mapp Research Articles Exploring typical and atypical brain developmental trajectories is very important for understanding the normal pace of brain development and the mechanisms by which mental disorders deviate from normal development. A precise and sex‐specific brain age prediction model is desirable for investigating the systematic deviation and individual heterogeneity of disorders associated with atypical brain development, such as autism spectrum disorders. In this study, we used partial least squares regression and the stacking algorithm to establish a sex‐specific brain age prediction model based on T1‐weighted structural magnetic resonance imaging and resting‐state functional magnetic resonance imaging. The model showed good generalization and high robustness on four independent datasets with different ethnic information and age ranges. A predictor weights analysis showed the differences and similarities in changes in structure and function during brain development. At the group level, the brain age gap estimation for autistic patients was significantly smaller than that for healthy controls in both the ABIDE dataset and the healthy brain network dataset, which suggested that autistic patients as a whole exhibited the characteristics of delayed development. However, within the ABIDE dataset, the premature development group had significantly higher Autism Diagnostic Observation Schedule (ADOS) scores than those of the delayed development group, implying that individuals with premature development had greater severity. Using these findings, we built an accurate typical brain development trajectory and developed a method of atypical trajectory analysis that considers sex differences and individual heterogeneity. This strategy may provide valuable clues for understanding the relationship between brain development and mental disorders. John Wiley & Sons, Inc. 2021-09-14 /pmc/articles/PMC8596985/ /pubmed/34520078 http://dx.doi.org/10.1002/hbm.25660 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Articles Wang, Qi Hu, Ke Wang, Meng Zhao, Yuxin Liu, Yong Fan, Lingzhong Liu, Bing Predicting brain age during typical and atypical development based on structural and functional neuroimaging |
title | Predicting brain age during typical and atypical development based on structural and functional neuroimaging |
title_full | Predicting brain age during typical and atypical development based on structural and functional neuroimaging |
title_fullStr | Predicting brain age during typical and atypical development based on structural and functional neuroimaging |
title_full_unstemmed | Predicting brain age during typical and atypical development based on structural and functional neuroimaging |
title_short | Predicting brain age during typical and atypical development based on structural and functional neuroimaging |
title_sort | predicting brain age during typical and atypical development based on structural and functional neuroimaging |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596985/ https://www.ncbi.nlm.nih.gov/pubmed/34520078 http://dx.doi.org/10.1002/hbm.25660 |
work_keys_str_mv | AT wangqi predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging AT huke predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging AT wangmeng predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging AT zhaoyuxin predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging AT liuyong predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging AT fanlingzhong predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging AT liubing predictingbrainageduringtypicalandatypicaldevelopmentbasedonstructuralandfunctionalneuroimaging |