Intact connectional morphometricity learning using multi-view morphological brain networks with application to autism spectrum disorder

Alaa Bessadok, Islem Rekik (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The morphology of anatomical brain regions can be affected by neurological disorders, including dementia and schizophrenia, to various degrees. Hence, identifying the morphological signature of a specific brain disorder can improve diagnosis and better explain how neuroanatomical changes associate with function and cognition. To capture this signature, a landmark study introduced, brain morphometricity, a global metric defined as the proportion of phenotypic variation that can be explained by brain morphology derived from structural brain MRI scans. However, this metric is limited to investigating morphological changes using low-order measurements (e.g., regional volumes) and overlooks how these changes can be related to each other (i.e., how morphological changes in region A are influenced by changes in region B). Furthermore, it is derived from a pre-defined anatomical similarity matrix using a Gaussian function, which might not be robust to outliers and constrains the locality of data to a fixed bandwidth. To address these limitations, we propose the intact connectional brain morphometricity (ICBM), a metric that captures the variation of connectional changes in brain morphology. In particular, we use multi-view morphological brain networks estimated from multiple cortical attributes (e.g., cortical thickness) to learn an intact space that first integrates the morphological network views into a unified space. Next, we learn a multi-view morphological similarity matrix in the intact space by adaptively assigning neighbors for each data sample based on local connectivity. The learned similarity capturing the shared traits across morphological brain network views is then used to derive our ICBM via a linear mixed effect model. Our framework shows the potential of the proposed ICBM in capturing the connectional neuroanatomical signature of brain disorders such as Autism Spectrum Disorder.

Original languageEnglish
Title of host publicationConnectomics in NeuroImaging
Subtitle of host publicationSecond International Workshop, CNI 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsGuorong Wu, Markus D. Schirmer, Ai Wern Chung, Islem Rekik, Brent Munsell
Place of PublicationSwitzerland
PublisherSpringer Verlag
Pages38-46
Number of pages9
Volume11083
ISBN (Electronic)9783030007553
ISBN (Print)9783030007546
DOIs
Publication statusPublished - 2018
Event2nd International Workshop on Connectomics in NeuroImaging, CNI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 20 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11083
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Connectomics in NeuroImaging, CNI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period20/09/1820/09/18

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  • Cite this

    Bessadok, A., & Rekik, I. (2018). Intact connectional morphometricity learning using multi-view morphological brain networks with application to autism spectrum disorder. In G. Wu, M. D. Schirmer, A. W. Chung, I. Rekik, & B. Munsell (Eds.), Connectomics in NeuroImaging: Second International Workshop, CNI 2018, Held in Conjunction with MICCAI 2018, Proceedings (Vol. 11083, pp. 38-46). (Lecture Notes in Computer Science; Vol. 11083). Springer Verlag. https://doi.org/10.1007/978-3-030-00755-3_5