Intersubject variability and induced gamma in the visual cortex: DCM with empirical <scp>B</scp>ayes and neural fields

Pinotsis, Dimitris A.; Perry, Gavin; Singh, Krish Devi; Friston, Karl J.

doi:10.1002/hbm.23331

Cited by 21 publications

(48 citation statements)

References 102 publications

(155 reference statements)

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“…In brief, coupled with a mapping from laminar dynamics to MEG sensors, the microscale model has been used to simulate MEG oscillations in different frequency bands [5], [14]. Following our earlier work [6], [10], we here used the fine tuned macroscale model and a similar mapping 4 to fit MEG data and explain individual variability in human brain oscillations. This is described below.…”

Section: Computational Models Of Brain Dynamics At the Micro And Macrmentioning

confidence: 99%

“…These quantify variability across subjects and overlap for model predictions and data. Model fits to individual subjects and conditions are shown in Figure 3B 5 .We fitted all three stimuli conditions simultaneously by modeling different sizes as condition-specific effects, see [10]. The three stimulus sizes correspond to red, blue and green lines.…”

Section: Variability Of Visually Induced Gamma Oscillations From Diffmentioning

confidence: 99%

“…Below, we illustrate our approach using two independent human MEG datasets analyzed earlier in [6] and [10]. These datasets contain visually induced oscillations recorded during perception tasks.…”

Section: Introductionmentioning

confidence: 99%

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Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Pinotsis

Miller

2020

Preprint

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Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject's hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

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Section: Computational Models Of Brain Dynamics At the Micro And Macrmentioning

confidence: 99%

Section: Variability Of Visually Induced Gamma Oscillations From Diffmentioning

confidence: 99%

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Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Pinotsis

Miller

2020

Preprint

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“…An alternative recently developed approach, Parametric Empirical Bayes (PEB) [45], does accommodate this uncertainty, and we apply it to our group level inferences. This PEB framework has been used with DCM, for example, to explain between-subject variability in visual gamma activity using MEG [46].…”

Section: Group and Family Inferencesmentioning

confidence: 99%

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer’s Disease

Penny

Iglesias-Fuster

Quiroz

et al. 2018

JAD

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Abstract. Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer's disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.

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“…DCM takes an approach similar to the second analysis: when a model including is selected, we can infer that each included connection improved the quality of fit enough to compensate for the additional complexity, but the parameter value for that connection might be quite different across participants. This observation has been recently raised in the MEG literature using DCM (Pinotsis et al [2016], Friston et al [2016]), where it has been addressed using an approach based on hierarchical Bayesian models. In DNM, we adopt an alternative solution: using random effect statistical tests on the parameter values.…”

Section: Introductionmentioning

confidence: 99%

Directed network discovery with dynamic network modelling

et al. 2017

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Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modeling technique for network discovery (Dynamic Network Modeling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modeling introducing three key changes: 1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, 2) the tests take into account the magnitude and sign of each influence, and 3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face.

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Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

Cited by 21 publications

References 102 publications

Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer’s Disease

Directed network discovery with dynamic network modelling

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