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Applying a Novel Integrated Persiste...

Catchings, Michael.

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Applying a Novel Integrated Persistent Feature to Understand Topographical Network Connectivity in Older Adults with Autism Spectrum Disorder.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying a Novel Integrated Persistent Feature to Understand Topographical Network Connectivity in Older Adults with Autism Spectrum Disorder./
Author:	Catchings, Michael.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	63 p.
Notes:	Source: Masters Abstracts International, Volume: 80-11.
Contained By:	Masters Abstracts International80-11.
Subject:	Neurosciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13865510
ISBN:	9781392142004

Applying a Novel Integrated Persistent Feature to Understand Topographical Network Connectivity in Older Adults with Autism Spectrum Disorder.
Catchings, Michael.

Applying a Novel Integrated Persistent Feature to Understand Topographical Network Connectivity in Older Adults with Autism Spectrum Disorder. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 63 p.

Source: Masters Abstracts International, Volume: 80-11.

Thesis (M.S.)--Arizona State University, 2019.

This item must not be sold to any third party vendors.

Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods in older men with ASD and matched neurotypical (NT) participants. Participants were 27 men with ASD (52 +/- 8.4 years) and 21 NT men (49.7 +/- 6.5 years). Resting-state functional MRI (rs-fMRI) scans were collected for six minutes (repetition time=3s) with eyes closed. Data was preprocessed in SPM12, and Data Processing Assistant for Resting-State fMRI (DPARSF) was used to extract 116 regions-of-interest defined by the automated anatomical labeling (AAL) atlas. AAL regions were separated into six large-scale brain networks. This proposed metric is the slope of a monotonically decreasing convergence function (Integrated Persistent Feature, IPF; Slope of the IPF, SIP). Results were analyzed in SPSS using ANCOVA, with IQ as a covariate. A reduced SIP was in older men with ASD, compared to NT men, in the Default Mode Network [F(1,47)=6.48; p=0.02; η2=0.13] and Executive Network [F(1,47)=4.40; p=0.04; η2=0.09], a trend in the Fronto-Parietal Network [F(1,47)=3.36; p=0.07; η2=0.07]. There were no differences in the non-prefrontal networks (Sensory motor network, auditory network, and medial visual network). The only other graph theory metric to reach significance was network diameter in the Default Mode Network [F(1,47)=4.31; p=0.04; η2=0.09]; however, the effect size for the SIP was stronger. Modularity, Betti number, characteristic path length, and eigenvalue centrality were all non-significant. These results provide empirical evidence of decreased functional network integration in pre-frontal networks of older adults with ASD and propose a useful biomarker for tracking prognosis of aging adults with ASD to enable more informed treatment, support, and care methods for this growing population.

ISBN: 9781392142004Subjects--Topical Terms:

588700
Neurosciences.

Applying a Novel Integrated Persistent Feature to Understand Topographical Network Connectivity in Older Adults with Autism Spectrum Disorder.
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520 $a Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods in older men with ASD and matched neurotypical (NT) participants. Participants were 27 men with ASD (52 +/- 8.4 years) and 21 NT men (49.7 +/- 6.5 years). Resting-state functional MRI (rs-fMRI) scans were collected for six minutes (repetition time=3s) with eyes closed. Data was preprocessed in SPM12, and Data Processing Assistant for Resting-State fMRI (DPARSF) was used to extract 116 regions-of-interest defined by the automated anatomical labeling (AAL) atlas. AAL regions were separated into six large-scale brain networks. This proposed metric is the slope of a monotonically decreasing convergence function (Integrated Persistent Feature, IPF; Slope of the IPF, SIP). Results were analyzed in SPSS using ANCOVA, with IQ as a covariate. A reduced SIP was in older men with ASD, compared to NT men, in the Default Mode Network [F(1,47)=6.48; p=0.02; η2=0.13] and Executive Network [F(1,47)=4.40; p=0.04; η2=0.09], a trend in the Fronto-Parietal Network [F(1,47)=3.36; p=0.07; η2=0.07]. There were no differences in the non-prefrontal networks (Sensory motor network, auditory network, and medial visual network). The only other graph theory metric to reach significance was network diameter in the Default Mode Network [F(1,47)=4.31; p=0.04; η2=0.09]; however, the effect size for the SIP was stronger. Modularity, Betti number, characteristic path length, and eigenvalue centrality were all non-significant. These results provide empirical evidence of decreased functional network integration in pre-frontal networks of older adults with ASD and propose a useful biomarker for tracking prognosis of aging adults with ASD to enable more informed treatment, support, and care methods for this growing population.
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