Identification of autism spectrum disorder using deep learning and the ABIDE dataset
Pontifícia Universidade Católica do Rio Grande do Sul · Instituto do Cérebro · +2 more institutions
Abstract
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns…
Citation impact
- FWCI
- 27.92
- Percentile
- 100%
- References
- 62
Authors
5- ASAnibal Sólon Heinsfeld
Pontifícia Universidade Católica do Rio Grande do Sul
- ARAlexandre R. Franco
Instituto do Cérebro, Pontifícia Universidade Católica do Rio Grande do Sul
- RCR. Cameron Craddock
Nathan Kline Institute for Psychiatric Research, Child Mind Institute
- ABAugusto Buchweitz
Instituto do Cérebro, Pontifícia Universidade Católica do Rio Grande do Sul
- FMFelipe MeneguzziCorresponding
Instituto do Cérebro, Pontifícia Universidade Católica do Rio Grande do Sul
Topics & keywords
- Autism spectrum disorder
- Autism
- Psychology
- Neuroimaging
- Neuroscience
- Functional connectivity
- Brain function
- Neurodevelopmental disorder