Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal models: Case studies of single gene mutations

Gilbert, Jessica R.; Symmonds, Mkael; Hanna, Michael G.; Dolan, Raymond J.; Friston, Karl J.; Moran, Rosalyn J.

doi:10.1016/j.neuroimage.2015.08.057

Cited by 34 publications

(44 citation statements)

References 45 publications

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“…Ellipsoids represent 95% Bayesian confidence regions. Adapted, with permission, from (Gilbert et al, 2016).…”

Section: Figure 10mentioning

confidence: 99%

“…By contrast, recent work by Gilbert et al (2016) illustrates how model-based classification might contribute diagnostic relevant information. This study concerned genetically determined alterations of ion channel conductance (channelopathies) that play a possible role in numerous neuropsychiatric disorders, ranging from epilepsy to schizophrenia (Klassen et al, 2011).…”

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confidence: 94%

“…Beyond long-range connectivity, DCM has also proven useful for inferring detailed, low-level physiological (synaptic) mechanisms within local neuronal circuits of the human brain, exploiting the rich temporal information contained by EEG/MEG data. Examples include the detection of conductance changes in AMPA and NMDA receptors under dopaminergic modulation (Moran et al, 2011), changes in postsynaptic gain of supragranular pyramidal cells in auditory cortex under cholinergic stimulation (Moran et al, 2013), or the characterisation of changes in neuronal physiology in individuals with selective mutations of particular ion channels (Gilbert et al, 2016).…”

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confidence: 99%

“…On the other hand, generative models of fMRI data are indispensable for characterising the connectivity of networks involving deep sources (e.g., brain stem) while estimates of processes at synaptic and ion channel levels critically require generative models of M/EEG data (for examples, see Cooray et al 2015;Gilbert et al 2016;Moran et al 2008Moran et al , 2011a.…”

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confidence: 99%

“…Gilbert et al (2016) used a DCM representing a cortical microcircuit consisting of 3 different types of neurons (pyramidal cells, excitatory and inhibitory interneurons) equipped with 3 different ionotropic receptors (AMPA, NMDA and GABA A ) and associated sodium, potassium and calcium channels. Applying this generative model to MEG data from a large group of controls (N = 94), the authors established a reference distribution against which patients could be compared.…”

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confidence: 99%

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Computational neuroimaging strategies for single patient predictions

et al. 2017

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Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

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“…Ellipsoids represent 95% Bayesian confidence regions. Adapted, with permission, from (Gilbert et al, 2016).…”

Section: Figure 10mentioning

confidence: 99%

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confidence: 94%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Computational neuroimaging strategies for single patient predictions

et al. 2017

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Reliability of dynamic causal modelling of resting‐state magnetoencephalography

Jafarian,

Assem,

Kocagoncu

et al. 2024

Human Brain Mapping

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This study assesses the reliability of resting‐state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance‐based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting‐state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between‐subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within‐subject between‐session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first‐level DCMs, we compare model evidence associated with the covariance among subject‐specific free energies (i.e., the ‘quality’ of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within‐subject, within‐session, and between‐epochs; (ii) within‐subject between‐session; and (iii) within‐site between‐subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of ‘reliability’ and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance‐based DCMs for resting‐state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.

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Models of neuromodulation for computational psychiatry

Iglesias

Tomiello

Schneebeli

et al. 2016

WIRES Cognitive Science

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Psychiatry faces fundamental challenges: based on a syndrome-based nosology, it presently lacks clinical tests to infer on disease processes that cause symptoms of individual patients and must resort to trial-and-error treatment strategies. These challenges have fueled the recent emergence of a novel field-computational psychiatry-that strives for mathematical models of disease processes at physiological and computational (information processing) levels. This review is motivated by one particular goal of computational psychiatry: the development of 'computational assays' that can be applied to behavioral or neuroimaging data from individual patients and support differential diagnosis and guiding patient-specific treatment. Because the majority of available pharmacotherapeutic approaches in psychiatry target neuromodulatory transmitters, models that infer (patho)physiological and (patho)computational actions of different neuromodulatory transmitters are of central interest for computational psychiatry. This article reviews the (many) outstanding questions on the computational roles of neuromodulators (dopamine, acetylcholine, serotonin, and noradrenaline), outlines available evidence, and discusses promises and pitfalls in translating these findings to clinical applications. WIREs Cogn Sci 2017, 8:e1420. doi: 10.1002/wcs.1420 For further resources related to this article, please visit the WIREs website.

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Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal models: Case studies of single gene mutations

Cited by 34 publications

References 45 publications

Computational neuroimaging strategies for single patient predictions

Computational neuroimaging strategies for single patient predictions

Reliability of dynamic causal modelling of resting‐state magnetoencephalography

Models of neuromodulation for computational psychiatry

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