In vivo assay of cortical microcircuitry in frontotemporal dementia: a platform for experimental medicine studies
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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.
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Two small cross-sectional MEG studies suggested that disruption of sensory/auditory information processing in frontotemporal networks may be a useful biomarker to detect early disease progression.
“two small yet promising cross-sectional studies on disruption of sensory/auditory information processing, as measured by magnetoencephalography (MEG) analysis of cortical microcircuits, suggested these changes in frontotemporal networks may be a useful biomarker to detect (early) disease progression (2013, N = 12, 2019, N = 44) (Hughes and Rowe, 2013; Shaw et al., 2019).”
From The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials (2024) · cites this paper as Shaw et al., 2019
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- Authors
- Shaw, A. D., Hughes, L. E., Moran, R., Coyle-Gilchrist, I., Rittman, T., & Rowe, J. B.
- Affiliations
- Cardiff University (GB)
- MRC Cognition and Brain Sciences Unit (GB)
- King's College London (GB)
- University of Cambridge (GB)
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Corresponding author
- Rowe, James B
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-06-19
- Volume
- 31
- Issue
- 3
- Pages
- 1837-1847
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC7869085
- OpenAlex
- W2949471022
- Cited by
- 30
- References
- 66
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 9 · 2020: 4 · 2019: 1
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cerebral Cortex (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Funders
- Wellcome Trust
- Medical Research Council (MC-A060-5PQ30)
- NIHR Cambridge Biomedical Research Centre (104943/Z/14/Z)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- behavioral variant frontotemporal dementia, cortical microcircuitry, dynamic causal modeling, magnetoencephalography, local coupling, prediction error, sensory prediction, laminar specificity, case-control classification, neurodegeneration, experimental medicine
- MeSH terms
- Machine Learning, Aged, Brain, Cerebral Cortex, Female, Humans, Male, Middle Aged, Models, Neurological, Neural Pathways, Signal Processing, Computer-Assisted, Magnetoencephalography, Biomedical Research, Frontotemporal Dementia
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