Graph Theoretic Analysis of Brain Connectomics in Multiple Sclerosis: Reliability and Relationship to Cognition

Welton, Thomas; Constantinescu, Cris S; Auer, Dorothee P; Dineen, Robert A

Abstract

Research suggests that disruption of brain networks might explain cognitive deficits in multiple sclerosis (MS). The reliability and effectiveness of graph-theoretic network metrics as measures of cognitive performance were tested in 37 people with MS and 23 controls. Specifically, relationships to cognitive performance (linear regression against the Paced Auditory Serial Addition Test [PASAT-3], Symbol Digit Modalities Test [SDMT] and Attention Network Test [ANT]) and one-month reliability (using the intra-class correlation coefficient [ICC]) of network metrics were measured using both resting-state functional and diffusion MRI data. Cognitive impairment was directly related to measures of brain network segregation and inversely related to network integration (prediction of PASAT-3 by small-worldness, modularity, characteristic path length, R2=0.55; prediction of SDMT by small-worldness, global efficiency and characteristic path length, R2=0.60). Reliability of the measures over one month in a subset of 9 participants was mostly rated as good (ICC>0.6) for both controls and MS patients in both functional and diffusion data but was highly dependent on the chosen parcellation and graph density, with the 0.2-0.5 density range being the most reliable. This suggests that disrupted network organisation predicts cognitive impairment in MS and its measurement is reliable over a 1-month period. These new findings support the hypothesis of network disruption as a major determinant of cognitive deficits in MS and the future possibility of the application of derived metrics as surrogate outcomes in trials of therapies for cognitive impairment.

Citation

Welton, T., Constantinescu, C. S., Auer, D. P., & Dineen, R. A. (2020). Graph Theoretic Analysis of Brain Connectomics in Multiple Sclerosis: Reliability and Relationship to Cognition. Brain Connectivity, 10(2), 95-104. https://doi.org/10.1089/brain.2019.0717

• Journal Article Type: Article
• Acceptance Date: Feb 20, 2020
• Online Publication Date: Feb 20, 2020
• Publication Date: Mar 18, 2020
• Deposit Date: Feb 25, 2020
• Publicly Available Date: Jun 9, 2020
• Journal: Brain Connectivity
• Print ISSN: 2158-0014
• Electronic ISSN: 2158-0022
• Publisher: Mary Ann Liebert
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 2
• Pages: 95-104
• DOI: https://doi.org/10.1089/brain.2019.0717
• Keywords: General Neuroscience
• Public URL: https://nottingham-repository.worktribe.com/output/4032413
• Publisher URL: https://www.liebertpub.com/doi/10.1089/brain.2019.0717
• Additional Information: Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/brain.2019.0717.