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Mervyn Singh

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YNICL Journal 2026 Journal Article

Bridging mental health, cognition and the brain in mild traumatic brain injury: A multilayer network analysis of the TRACK-TBI study

  • Juan F. Domínguez D.
  • Mervyn Singh
  • Lyndon Firman-Sadler
  • Jade Guarnera
  • Ivan L. Simpson-Kent
  • Phoebe Imms
  • Andrei Irimia
  • Karen Caeyenberghs

BACKGROUND: People with mild traumatic brain injury (mTBI) suffer from several mental health symptoms (e.g., anxiety, depressive symptoms) and cognitive deficits (e.g., attentional deficits, slowed processing speed). However, symptoms in TBI are largely investigated in isolation, using univariate approaches, ignoring interactions between symptoms and the underlying large-scale brain networks. We constructed the first multilayer network in mTBI to examine relationships between networks of cognition, mental health and structural brain measures and to identify key variables bridging relationships across these networks. METHODS: Chronic phase cross-sectional data (6-month follow-up) from 457 mTBI participants was extracted from the TRACK-TBI Longitudinal study. We selected four variables from self-report mental health questionnaires (affective layer), eight cognitive test scores from the NIH toolbox (cognitive layer), and gray matter volumes from eight brain regions of the central executive and salience networks from anatomical MRI scans (brain layer). We used a multilayer network approach to examine the relationships (edges) between all variables (nodes) across layers. We then used the bridge strength centrality metric to identify nodes that 'bridge' the affective, cognitive, and brain layers. RESULTS: In this sample of mTBI participants, across all affective and cognitive layer nodes, only impairments in insomnia were noted. Multilayer network analysis revealed insomnia severity, immediate verbal memory, somatisation and processing speed nodes exceeded an a priori 80th percentile threshold on the bridge strength scores and may therefore be regarded as key nodes potentially bridging relationships across affective, cognitive and brain layers. CONCLUSIONS: The bridging nodes identified in our multilayer network analyses may suggest targets for future studies to develop more customized, efficient, and efficacious treatments to alleviate mental health symptoms and cognitive deficits in mTBI.

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YNIMG Journal 2021 Journal Article

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

  • Thijs Dhollander
  • Adam Clemente
  • Mervyn Singh
  • Frederique Boonstra
  • Oren Civier
  • Juan Dominguez Duque
  • Natalia Egorova
  • Peter Enticott

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

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