Cargando…

Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network

INTRODUCTION: Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders. Yet, despite decades of research, the neurobiology of ASD is still poorly understood, as inconsistent findings preclude the identification of robust and interpretable neurobiological mar...

Descripción completa

Detalles Bibliográficos
Autores principales: Supekar, K., Ryali, S., Yuan, R., Kumar, D., De Los Angeles, C., Menon, V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471580/
http://dx.doi.org/10.1192/j.eurpsy.2021.397
_version_ 1784789110678880256
author Supekar, K.
Ryali, S.
Yuan, R.
Kumar, D.
De Los Angeles, C.
Menon, V.
author_facet Supekar, K.
Ryali, S.
Yuan, R.
Kumar, D.
De Los Angeles, C.
Menon, V.
author_sort Supekar, K.
collection PubMed
description INTRODUCTION: Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders. Yet, despite decades of research, the neurobiology of ASD is still poorly understood, as inconsistent findings preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms. OBJECTIVES: Identify robust and interpretable dynamic brain markers that distinguish children with ASD from typically-developing (TD) children and predict clinical symptom severity. METHODS: We leverage multiple functional brain imaging cohorts (ABIDE, Stanford; N = 1004) and exciting recent advances in explainable artificial intelligence (xAI), to develop a novel multivariate time series deep neural network model that extracts informative brain dynamics features that accurately distinguish between ASD and TD children, and predict clinical symptom severity. RESULTS: Our model achieved consistently high classification accuracies in cross-validation analysis of data from the ABIDE cohort. Crucially, despite the differences in symptom profiles, age, and data acquisition protocols, our model also accurately classified data from an independent Stanford cohort without additional training. xAI analyses revealed that brain features associated with the default mode network, and the human voice/face processing and communication systems, most clearly distinguished ASD from TD children in both cohorts. Furthermore, the posterior cingulate cortex emerged as robust predictor of the severity of social and communication deficits in ASD in both cohorts. CONCLUSIONS: Our findings, replicated across two independent cohorts, reveal robust and neurobiologically interpretable brain features that detect ASD and predict core phenotypic features of ASD, and have the potential to transform our understanding of the etiology and treatment of the disorder. DISCLOSURE: No significant relationships.
format Online
Article
Text
id pubmed-9471580
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Cambridge University Press
record_format MEDLINE/PubMed
spelling pubmed-94715802022-09-29 Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network Supekar, K. Ryali, S. Yuan, R. Kumar, D. De Los Angeles, C. Menon, V. Eur Psychiatry Abstract INTRODUCTION: Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders. Yet, despite decades of research, the neurobiology of ASD is still poorly understood, as inconsistent findings preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms. OBJECTIVES: Identify robust and interpretable dynamic brain markers that distinguish children with ASD from typically-developing (TD) children and predict clinical symptom severity. METHODS: We leverage multiple functional brain imaging cohorts (ABIDE, Stanford; N = 1004) and exciting recent advances in explainable artificial intelligence (xAI), to develop a novel multivariate time series deep neural network model that extracts informative brain dynamics features that accurately distinguish between ASD and TD children, and predict clinical symptom severity. RESULTS: Our model achieved consistently high classification accuracies in cross-validation analysis of data from the ABIDE cohort. Crucially, despite the differences in symptom profiles, age, and data acquisition protocols, our model also accurately classified data from an independent Stanford cohort without additional training. xAI analyses revealed that brain features associated with the default mode network, and the human voice/face processing and communication systems, most clearly distinguished ASD from TD children in both cohorts. Furthermore, the posterior cingulate cortex emerged as robust predictor of the severity of social and communication deficits in ASD in both cohorts. CONCLUSIONS: Our findings, replicated across two independent cohorts, reveal robust and neurobiologically interpretable brain features that detect ASD and predict core phenotypic features of ASD, and have the potential to transform our understanding of the etiology and treatment of the disorder. DISCLOSURE: No significant relationships. Cambridge University Press 2021-08-13 /pmc/articles/PMC9471580/ http://dx.doi.org/10.1192/j.eurpsy.2021.397 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstract
Supekar, K.
Ryali, S.
Yuan, R.
Kumar, D.
De Los Angeles, C.
Menon, V.
Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
title Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
title_full Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
title_fullStr Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
title_full_unstemmed Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
title_short Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
title_sort identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
topic Abstract
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471580/
http://dx.doi.org/10.1192/j.eurpsy.2021.397
work_keys_str_mv AT supekark identificationofrobustandinterpretablebrainsignaturesofautismandclinicalsymptomseverityusingadynamictimeseriesdeepneuralnetwork
AT ryalis identificationofrobustandinterpretablebrainsignaturesofautismandclinicalsymptomseverityusingadynamictimeseriesdeepneuralnetwork
AT yuanr identificationofrobustandinterpretablebrainsignaturesofautismandclinicalsymptomseverityusingadynamictimeseriesdeepneuralnetwork
AT kumard identificationofrobustandinterpretablebrainsignaturesofautismandclinicalsymptomseverityusingadynamictimeseriesdeepneuralnetwork
AT delosangelesc identificationofrobustandinterpretablebrainsignaturesofautismandclinicalsymptomseverityusingadynamictimeseriesdeepneuralnetwork
AT menonv identificationofrobustandinterpretablebrainsignaturesofautismandclinicalsymptomseverityusingadynamictimeseriesdeepneuralnetwork