Cargando…
Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry
Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827874/ https://www.ncbi.nlm.nih.gov/pubmed/27065101 http://dx.doi.org/10.1371/journal.pone.0153331 |
_version_ | 1782426526108614656 |
---|---|
author | Katuwal, Gajendra J. Baum, Stefi A. Cahill, Nathan D. Michael, Andrew M. |
author_facet | Katuwal, Gajendra J. Baum, Stefi A. Cahill, Nathan D. Michael, Andrew M. |
author_sort | Katuwal, Gajendra J. |
collection | PubMed |
description | Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD. |
format | Online Article Text |
id | pubmed-4827874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-48278742016-04-22 Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry Katuwal, Gajendra J. Baum, Stefi A. Cahill, Nathan D. Michael, Andrew M. PLoS One Research Article Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD. Public Library of Science 2016-04-11 /pmc/articles/PMC4827874/ /pubmed/27065101 http://dx.doi.org/10.1371/journal.pone.0153331 Text en © 2016 Katuwal et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Katuwal, Gajendra J. Baum, Stefi A. Cahill, Nathan D. Michael, Andrew M. Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry |
title | Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry |
title_full | Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry |
title_fullStr | Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry |
title_full_unstemmed | Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry |
title_short | Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry |
title_sort | divide and conquer: sub-grouping of asd improves asd detection based on brain morphometry |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827874/ https://www.ncbi.nlm.nih.gov/pubmed/27065101 http://dx.doi.org/10.1371/journal.pone.0153331 |
work_keys_str_mv | AT katuwalgajendraj divideandconquersubgroupingofasdimprovesasddetectionbasedonbrainmorphometry AT baumstefia divideandconquersubgroupingofasdimprovesasddetectionbasedonbrainmorphometry AT cahillnathand divideandconquersubgroupingofasdimprovesasddetectionbasedonbrainmorphometry AT michaelandrewm divideandconquersubgroupingofasdimprovesasddetectionbasedonbrainmorphometry |