| Autor: |
Colleen P. Chen, Christopher L. Keown, Afrooz Jahedi, Aarti Nair, Mark E. Pflieger, Barbara A. Bailey, Ralph-Axel Müller |
| Jazyk: |
angličtina |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
NeuroImage: Clinical, Vol 8, Iss C, Pp 238-245 (2015) |
| Druh dokumentu: |
article |
| ISSN: |
2213-1582 |
| DOI: |
10.1016/j.nicl.2015.04.002 |
| Popis: |
Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagnostic classification have reached only moderate accuracy. We selected 252 low-motion resting-state functional MRI (rs-fMRI) scans from the Autism Brain Imaging Data Exchange (ABIDE) including typically developing (TD) and ASD participants (n = 126 each), matched for age, non-verbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification, implementing several machine learning tools. While support vector machines in combination with particle swarm optimization and recursive feature elimination performed modestly (with accuracies for validation datasets |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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