Format

Send to

Choose Destination
Neuroimage. 2018 May 15;172:674-688. doi: 10.1016/j.neuroimage.2017.12.044. Epub 2017 Dec 21.

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

Author information

1
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland OR, 97239, USA. Electronic address: feczko@ohsu.edu.
2
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA.
3
Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA.
4
The University of Texas at Austin, Department of Psychology, Austin, TX 78713, USA.
5
Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Center for Spoken Language Understanding, Institute on Development & Disability, Oregon Health & Science University, Portland, OR 97239, USA.
6
Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA.
7
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA.

Abstract

DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.

KEYWORDS:

Autism; Functional connectivity; MRI; Random forests; Supervised learning

PMID:
29274502
PMCID:
PMC5969914
[Available on 2019-05-15]
DOI:
10.1016/j.neuroimage.2017.12.044

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center