Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects Author name
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 regi...
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| Published in | Journal of autism and developmental disorders Vol. 54; no. 7; pp. 2757 - 2768 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
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01.07.2024
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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. |
|---|---|
| AbstractList | 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. 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.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. 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. |
| Author | Saha, Papri |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37142901$$D View this record in MEDLINE/PubMed |
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| Keywords | Connectivity Regions of interest Autism spectrum disorder Eigenvector centrality fMRI |
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| SubjectTerms | Adolescent Adult Autism Spectrum Disorder - diagnostic imaging Autism Spectrum Disorder - physiopathology Behavioral Science and Psychology Brain - diagnostic imaging Brain - physiopathology Brain Mapping - methods Child and School Psychology Female Humans Magnetic Resonance Imaging - methods Male Nerve Net - diagnostic imaging Nerve Net - physiopathology Neurosciences Original Paper Pediatrics Psychology Public Health Sex Factors Young Adult |
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| Title | Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects |
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