Evidence map›Paper›PMID 31216360›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2021

In Vivo Assay of Cortical Microcircuitry in Frontotemporal Dementia: A Platform for Experimental Medicine Studies.

Alexander D Shaw, Laura E Hughes, Rosalyn Moran, Ian Coyle-Gilchrist, Tim Rittman, James B Rowe

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alexander D ShawCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
Laura E HughesDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Rosalyn MoranDepartment of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Ian Coyle-GilchristDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Tim RittmanDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
James B RoweDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.

Funding

Medical Research Council MC_UU_00005/12Medical Research Council SUAG/004 RG91365Wellcome Trust 103838Wellcome Trust 104943
6 · The paper itself

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.

Indexed as

Machine LearningModels, NeurologicalAgedBiomedical ResearchBrainCerebral CortexFemaleFrontotemporal DementiaHumansMagnetoencephalographyMaleMiddle AgedNeural PathwaysSignal Processing, Computer-AssistedDCMdementiamachine learningMEGmicrocircuitry

Identifiers

PMID31216360
PMCPMC7869085

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.