Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions

Abstract

Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro-and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies.

Publication DOI: https://doi.org/10.1162/netn_a_00123
Divisions: Life & Health Sciences > Aston Brain Centre
Life & Health Sciences > Psychology
Additional Information: © 2019 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Uncontrolled Keywords: Cognition,Connectome,Morphology,Morphometric similarity networks,Structural MRI,Neuroscience(all),Computer Science Applications,Artificial Intelligence,Applied Mathematics
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.mit ... 62/netn_a_00123 (Publisher URL)
PURE Output Type: Article
Published Date: 2020-04-01
Accepted Date: 2019-12-16
Authors: King, Daniel J. ( 0000-0001-5797-9203)
Wood, Amanda G. ( 0000-0002-1537-6858)

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