Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network
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 bio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/6216 |
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| author | Aikaterini S. Karampasi Antonis D. Savva Vasileios Ch. Korfiatis Ioannis Kakkos George K. Matsopoulos |
| author_facet | Aikaterini S. Karampasi Antonis D. Savva Vasileios Ch. Korfiatis Ioannis Kakkos George K. Matsopoulos |
| author_sort | Aikaterini S. Karampasi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:52:58Z |
| format | Article |
| id | doaj.art-8efdfc153a6b437c813e49adfe3d9d0a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:52:58Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8efdfc153a6b437c813e49adfe3d9d0a2023-11-22T02:35:23ZengMDPI AGApplied Sciences2076-34172021-07-011113621610.3390/app11136216Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode NetworkAikaterini S. Karampasi0Antonis D. Savva1Vasileios Ch. Korfiatis2Ioannis Kakkos3George K. Matsopoulos4Laboratory of Biomedical Optics & Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceLaboratory of Biomedical Optics & Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceLaboratory of Biomedical Optics & Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceLaboratory of Biomedical Optics & Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceLaboratory of Biomedical Optics & Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceEffective 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.https://www.mdpi.com/2076-3417/11/13/6216ASDfMRIDMNbiomarkerdynamic functional connectivityfeature selection |
| spellingShingle | Aikaterini S. Karampasi Antonis D. Savva Vasileios Ch. Korfiatis Ioannis Kakkos George K. Matsopoulos Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network Applied Sciences ASD fMRI DMN biomarker dynamic functional connectivity feature selection |
| title | Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network |
| title_full | Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network |
| title_fullStr | Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network |
| title_full_unstemmed | Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network |
| title_short | Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network |
| title_sort | informative biomarkers for autism spectrum disorder diagnosis in functional magnetic resonance imaging data on the default mode network |
| topic | ASD fMRI DMN biomarker dynamic functional connectivity feature selection |
| url | https://www.mdpi.com/2076-3417/11/13/6216 |
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