In vivo assay of cortical microcircuitry in frontotemporal dementia: A platform for experimental medicine studies

Shaw, Alexander D., Hughes, Laura E., Moran, Rosalyn, Coyle-Gilchrist, Ian ORCID: https://orcid.org/0000-0001-8887-1801, Rittman, Tim and Rowe, James B. (2021) In vivo assay of cortical microcircuitry in frontotemporal dementia: A platform for experimental medicine studies. Cerebral Cortex, 31 (3). 1837–1847. ISSN 1047-3211

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Abstract

The analysis of neural circuits can provide crucial insights into the mechanisms of neurodegeneration and dementias, and offer potential quantitative biological tools to assess novel therapeutics. Here we use behavioral variant frontotemporal dementia (bvFTD) as a model disease. We demonstrate that inversion of canonical microcircuit models to noninvasive human magnetoencephalography, using dynamic causal modeling, can identify the regional-and laminar-specificity of bvFTD pathophysiology, and their parameters can accurately differentiate patients from matched healthy controls. Using such models, we show that changes in local coupling in frontotemporal dementia underlie the failure to adequately establish sensory predictions, leading to altered prediction error responses in a cortical information-processing hierarchy. Using machine learning, this model-based approach provided greater case-control classification accuracy than conventional evoked cortical responses. We suggest that this approach provides an in vivo platform for testing mechanistic hypotheses about disease progression and pharmacotherapeutics.

Item Type: Article
Additional Information: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. The article was made available as a preprint on bioarxiv: https://www.biorxiv.org/content/10.1101/416388v1 Funding Information: the Medical Research Council (SUAG/004 RG91365).
Uncontrolled Keywords: dcm,dementia,machine learning,meg,microcircuitry,cellular and molecular neuroscience,cognitive neuroscience,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2800/2804
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 12 May 2023 12:31
Last Modified: 05 Jun 2023 08:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/92036
DOI: 10.1101/416388v1

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