Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects: Author name.
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| Title: | Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects: Author name. |
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| Authors: | Saha, Papri |
| Source: | Journal of Autism & Developmental Disorders. Jul2024, Vol. 54 Issue 7, p2757-2768. 12p. |
| Subjects: | Brain physiology, Diagnosis of autism, Sex distribution, Prefrontal cortex, Magnetic resonance imaging, Descriptive statistics, Mathematical models, Large-scale brain networks, Limbic system, Research methodology, Asperger's syndrome, Comparative studies, Theory, Machine learning, Confidence intervals, Brain mapping, Regression analysis, Algorithms |
| Abstract: | With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region wise network representations through fMRI diagnostic maps. The article investigates the suitability of network node centrality values to discriminate ASD subject groups compared to typically developing controls following a boxplot formalism and a classification and regression tree model. Region wise differences between normal and ASD subjects primarily belong to the frontoparietal, limbic, ventral attention, default mode and visual networks. The reduced number of regions-of-interests (ROI) clearly suggests the benefit of automated supervised machine learning algorithm over the manual classification method. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region wise network representations through fMRI diagnostic maps. The article investigates the suitability of network node centrality values to discriminate ASD subject groups compared to typically developing controls following a boxplot formalism and a classification and regression tree model. Region wise differences between normal and ASD subjects primarily belong to the frontoparietal, limbic, ventral attention, default mode and visual networks. The reduced number of regions-of-interests (ROI) clearly suggests the benefit of automated supervised machine learning algorithm over the manual classification method. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01623257 |
| DOI: | 10.1007/s10803-023-05922-x |
