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...

Descripción completa

Detalles Bibliográficos
Autores principales: Katuwal, Gajendra J., Baum, Stefi A., Cahill, Nathan D., Michael, Andrew M.
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