Chapter title |
Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference
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Chapter number | 12 |
Book title |
Connectomics in NeuroImaging
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Published in |
Connectomics in neuroimaging : first International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec), September 2017
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DOI | 10.1007/978-3-319-67159-8_12 |
Pubmed ID | |
Book ISBNs |
978-3-31-967158-1, 978-3-31-967159-8
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Authors |
Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher, Bei Wang |
Abstract |
A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012). |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 17 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 3 | 18% |
Student > Ph. D. Student | 2 | 12% |
Researcher | 2 | 12% |
Student > Master | 2 | 12% |
Student > Doctoral Student | 1 | 6% |
Other | 0 | 0% |
Unknown | 7 | 41% |
Readers by discipline | Count | As % |
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Psychology | 2 | 12% |
Computer Science | 1 | 6% |
Social Sciences | 1 | 6% |
Physics and Astronomy | 1 | 6% |
Other | 0 | 0% |
Unknown | 8 | 47% |