High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals
A highly targeted prediction model can identify a subset of functional connectivity alterations that confer high risk for ASD at the individual level, which may be masked by traditional machine learning models due to ASD heterogeneity. Results could help disentangle the multitude of etiological pathways and behavioral symptoms that challenge our understanding of ASD by focusing on highly penetrant connectivity signatures.
Authors
- Pierre Bellec (ORCID: https://orcid.org/0000-0002-9111-0699)
- Alan C. Evans (ORCID: https://orcid.org/0000-0003-3841-6098)
- Clara Moreau (ORCID: https://orcid.org/0000-0001-6217-731X)
- Hien D. Nguyen (ORCID: https://orcid.org/0000-0002-9958-432X)
- Sebastian Urchs (ORCID: https://orcid.org/0000-0001-5504-8579)
- Angela Tam (ORCID: https://orcid.org/0000-0001-6752-5707)
- Christian Dansereau (ORCID: https://orcid.org/0000-0003-3363-1901)
- Natasha Clarke (ORCID: https://orcid.org/0000-0003-2455-3614)
Institutions
- Montreal Neurological Institute and Hospital (CA)
- Kyushu University (JP)
- La Trobe University (AU)
- Centre Hospitalier Universitaire Sainte-Justine (CA)
- Institut Universitaire de Gériatrie de Montréal (CA)
- Université de Montréal (CA)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf091
- Citations
- 1
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Fondation Brain Canada
- Compute Canada
- Consortium canadien en neurodégénérescence associée au vieillissement
- Azrieli Foundation
- Institut de Valorisation des Données
- Australian Research Council