Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling

We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory-inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis

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

AbstractClinical assessments of brain function rely upon visual inspection of electroencephalographic waveform abnormalities in tandem with functional magnetic resonance imaging. However, no current technology proffers in vivo assessments of activity at synapses, receptors and ion-channels, the basis of neuronal communication. Using dynamic causal modeling we compared electrophysiological responses from two patients with distinct monogenic ion channelopathies and a large cohort of healthy controls to demonstrate the feasibility of assaying synaptic-level channel communication non-invasively. Synaptic channel abnormality was identified in both patients (100% sensitivity) with assay specificity above 89%, furnishing estimates of neurotransmitter and voltage-gated ion throughput of sodium, calcium, chloride and potassium. This performance indicates a potential novel application as an adjunct for clinical assessments in neurological and psychiatric settings. More broadly, these findings indicate that biophysical models of synaptic channels can be estimated non-invasively, having important implications for advancing human neuroimaging to the level of non-invasive ion channel assays

Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis

Using electroencephalography (EEG) dynamic brain function can be measured and its abnormalities identified and described. However, inferring pathological mechanisms from EEG recordings is an ill-posed, inverse problem. Here we illustrate the use of neural mass model based dynamic causal modelling to address this inverse problem. Using Bayesian model inversion and model comparison, DCM allows evaluation of different hypotheses regarding pathomechanisms leading to dynamic brain dysfunction in NMDA receptor encephalitis

Multiscale Modelling of Neuronal Dynamics and Their Dysfunction in the Developing Brain

Over the last few decades, an increasing number of neurodevelopmental disorders has been associated with molecular causes – such as genetic mutations, or autoantibodies affecting synaptic transmission. Yet understanding the pathophysiology that leads from particular molecular disruptions at the synapse to patients’ signs and symptoms remains challenging, even today. The work presented in this thesis illustrates how computational models can help bridge the explanatory gap between disruptions at the molecular scale and brain dysfunction at the level of integrated circuits. I utilise computational models at different scales of neuronal function, ranging from the neuronal membrane, to integrated cortical microcircuits and whole-brain sensory processing networks. These computational models are informed with, and further constrained by both empirical data derived from a number of model systems of neurodevelopmental disorders, and clinical patient data. The worked examples in this thesis include the biophysical characterisation of an epilepsy-causing mutation in the voltage-gated sodium channel gene SCN1A, calcium imaging in a larval zebrafish model of epileptic seizures in the immature brain, electrophysiological recordings from patients with NMDA receptor antibody encephalitis as well as from a mouse model of the disorder, and pharmacologically induced NMDA receptor blockade in young adults that captures features of acute psychosis and schizophrenia. The combination of this diverse range of empirical data and different computational models offers a mechanistic, multi-scale account of how specific phenotypic features in neurodevelopmental disorders emerge. This provides novel insights both in regard to the specific conditions included here, but also concerning the link between molecular determinants and their neurodevelopmental phenotypes more broadly

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms

Dynamic causal modelling of seizure activity in a rat model

This paper presents a physiological account of seizure activity and its evolution over time using a rat model of induced epilepsy. We analyse spectral activity recorded in the hippocampi of three rats who received kainic acid injections in the right hippocampus. We use dynamic causal modelling of seizure activity and Bayesian model reduction to identify the key synaptic and connectivity parameters that underlie seizure onset. Using recent advances in hierarchical modelling (parametric empirical Bayes), we characterise seizure onset in terms of slow fluctuations in synaptic excitability of specific neuronal populations. Our results suggest differences in the pathophysiology – of seizure activity in the lesioned versus the non-lesioned hippocampus – with pronounced changes in excitation-inhibition balance and temporal summation on the lesioned side. In particular, our analyses suggest that marked reductions in the synaptic time constant of the deep pyramidal cells and the self-inhibition of inhibitory interneurons (in the lesioned hippocampus) are sufficient to explain changes in spectral activity. Although these synaptic changes are consistent over rats, the resulting electrophysiological phenotype can be quite diverse

Generative modelling of the thalamo-cortical circuit mechanisms underlying the neurophysiological effects of ketamine

Cortical recordings of task-induced oscillations following subanaesthetic ketamine administration demonstrate alterations in amplitude, including increases at high-frequencies (gamma) and reductions at low frequencies (theta, alpha). To investigate the population-level interactions underlying these changes, we implemented a thalamo-cortical model (TCM) capable of recapitulating broadband spectral responses. Compared with an existing cortex-only 4-population model, Bayesian Model Selection preferred the TCM. The model was able to accurately and significantly recapitulate ketamine-induced reductions in alpha amplitude and increases in gamma amplitude. Parameter analysis revealed no change in receptor time-constants but significant increases in select synaptic connectivity with ketamine. Significantly increased connections included both AMPA and NMDA mediated connections from layer 2/3 superficial pyramidal cells to inhibitory interneurons and both GABAA and NMDA mediated within-population gain control of layer 5 pyramidal cells. These results support the use of extended generative models for explaining oscillatory data and provide in silico support for ketamine's ability to alter local coupling mediated by NMDA, AMPA and GABA-A

Computational neuroimaging strategies for single patient predictions

AbstractNeuroimaging 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

Characterising gene regulation during epileptogenesis in different models of epilepsy

As epilepsy develops an enormous range of changes occurs in neurons. This process, epileptogenesis, involves complex spatiotemporal alterations of neuronal homeostasis and neural networks. The molecular mechanism of epileptogenesis remains obscure and gene regulation during the epileptogenic process dynamically controls various signalling and functional pathways which play an important role in defining the mechanisms of epilepsy. This thesis explores gene regulation in different in vitro models of seizure like activity, and further focuses on the temporal profiles of molecular changes during an in vivo model of epilepsy. We seek to identify important regulators of epileptogenesis which may be the targets for further study of the mechanism of epilepsy in human. The High-K+, Low-Mg2+, Kainic acid, and Pentylenetetrazole models were used to elicit seizure like activity in cortical neuronal cultures. The tetanus toxin (TeNT) model of focal neocortical epilepsy in rats was utilised to characterise gene regulation in different time points: acute, subacute and chronic stages (48-72 hours, 2 weeks, and 30 days after first spontaneous seizure, respectively). A set of candidate genes relevant to epilepsy was selected to analyse changes in mRNA expression during these in vitro and in vivo models. The mRNA expression of the different candidate genes reveals diverse regulatory behaviours in different models, as well as at different time points during the process of epileptogenesis. The cell culture model treated with Low-Mg2+ for 72 hours displayed the most similar mRNA expression profile to the in vivo model of TeNT neocortical epilepsy during subacute to chronic stages. Furthermore, in the in vivo model, GFAP, mTOR, REST, and SNAP-25 are all temporarily apparently up-regulated during epileptogenesis, while CCL2 is strongly up-regulated later when epilepsy is established. Transient down-regulation of BDNF in the acute stage, and the distinctly lower expression of GABRA5 in late stage suggest that this GABAergic signalling pathway may be down-regulated in the late phase of epileptogenesis. Our work highlights how different candidate genes are differentially regulated during epileptogenesis, and how the regulation of individual genes changes as epileptogenesis progresses