Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism
| Autor: | Christopher L. Keown, Ralph-Axel Müller, Colleen P. Chen, Mark E. Pflieger, Aarti Nair, Afrooz Jahedi, Barbara A. Bailey |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Male
Autism Spectrum Disorder Autism Somatosensory VIS visual lcsh:RC346-429 0302 clinical medicine Feature (machine learning) SMM somatosensory and motor [mouth] CEB cerebellum Child Default mode network Cerebral Cortex 0303 health sciences VA ventral attention DA dorsal attention Default mode Magnetic Resonance Imaging Random forest Neurology Autism spectrum disorder Connectome lcsh:R858-859.7 Female Psychology Visual Adult UN unknown Adolescent SMH somatosensory and motor [hand] Cognitive Neuroscience lcsh:Computer applications to medicine. Medical informatics Article SUB subcortical Young Adult 03 medical and health sciences SAL salience Machine learning medicine Humans Radiology Nuclear Medicine and imaging lcsh:Neurology. Diseases of the nervous system 030304 developmental biology FPTC frontal parietal task control Functional connectivity MRI MR memory retrieval medicine.disease Ensemble learning Support vector machine DMN default mode network Neurology (clinical) COTC cingulo-opercular task control Neuroscience 030217 neurology & neurosurgery AUD audio |
| Zdroj: | NeuroImage: Clinical, Vol 8, Iss C, Pp 238-245 (2015) NeuroImage : Clinical |
| ISSN: | 2213-1582 |
| 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 Highlights • Machine learning of resting fMRI attains high diagnostic accuracy for autism. • Peak accuracy is seen for a complex pattern of 100 connectivities. • Somatosensory regions are overall most informative. • Default mode and visual regions also contribute to diagnostic accuracy. |
| Databáze: | OpenAIRE |
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