ArticleHuman brain mapping2016
Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields.
Article in Human brain mapping, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Neurophysiological Progression in Alzheimer's Disease: Insights From Dynamic Causal Modelling of Longitudinal Magnetoencephalography.Human brain mapping · 2025Article
- Peripheral contributions to resting state brain dynamics.Nature communications · 2024Article
- Interhemispheric interplay between the left and right premotor cortex during grasping as assessed by dynamic causal modelling.Scientific reports · 2023Article
- Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex.PLoS computational biology · 2022Article
- Toward biophysical markers of depression vulnerability.Frontiers in psychiatry · 2022Article
- Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity.Communications biology · 2020Article
- A guide to group effective connectivity analysis, part 2: Second level analysis with PEB.NeuroImage · 2019Article
- Optimizing Data for Modeling Neuronal Responses.Frontiers in neuroscience · 2018Article
- Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2018Article
- Directed network discovery with dynamic network modelling.Neuropsychologia · 2017Article
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Authors and funding
5 authors.
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Abstract
This article describes the first application of a generic (empirical) Bayesian analysis of between-subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non-invasive (MEG) data can be used to characterize subject-specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation-inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject-specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal-interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models-and optimize the corresponding connectivity estimates at the within and between-subject level. We also consider Bayesian cross-validation to obtain predictive estimates for gamma-response phenotypes, using a leave-one-out procedure. Hum Brain Mapp 37:4597-4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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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.