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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.
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]
Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects: Author name.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Autism+%26+Developmental+Disorders%22">Journal of Autism & Developmental Disorders</searchLink>. Jul2024, Vol. 54 Issue 7, p2757-2768. 12p.
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  Data: 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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  Data: <i>Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=178677507
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        Value: 10.1007/s10803-023-05922-x
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      – SubjectFull: Brain physiology
        Type: general
      – SubjectFull: Diagnosis of autism
        Type: general
      – SubjectFull: Sex distribution
        Type: general
      – SubjectFull: Prefrontal cortex
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      – SubjectFull: Magnetic resonance imaging
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Large-scale brain networks
        Type: general
      – SubjectFull: Limbic system
        Type: general
      – SubjectFull: Research methodology
        Type: general
      – SubjectFull: Asperger's syndrome
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Theory
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Confidence intervals
        Type: general
      – SubjectFull: Brain mapping
        Type: general
      – SubjectFull: Regression analysis
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      – SubjectFull: Algorithms
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    Titles:
      – TitleFull: Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects: Author name.
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              M: 07
              Text: Jul2024
              Type: published
              Y: 2024
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