Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network
| Autor: | George K. Matsopoulos, Ioannis Kakkos, Aikaterini S. Karampasi, Antonis D. Savva, Vasileios Ch. Korfiatis |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Technology
QH301-705.5 Computer science QC1-999 Feature selection ASD 03 medical and health sciences feature selection 0302 clinical medicine medicine DMN General Materials Science Biology (General) QD1-999 Instrumentation Default mode network 030304 developmental biology Dynamic functional connectivity Fluid Flow and Transfer Processes 0303 health sciences medicine.diagnostic_test business.industry Physics Process Chemistry and Technology fMRI General Engineering Pattern recognition Engineering (General). Civil engineering (General) medicine.disease Computer Science Applications Chemistry Identification (information) medicine.anatomical_structure classification Autism spectrum disorder biomarker Autism dynamic functional connectivity Artificial intelligence TA1-2040 Functional magnetic resonance imaging business 030217 neurology & neurosurgery Parahippocampal gyrus |
| Zdroj: | Applied Sciences, Vol 11, Iss 6216, p 6216 (2021) Applied Sciences Volume 11 Issue 13 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11136216 |
| Popis: | Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|