Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis

Synaptic inhibitory dynamics drive benzodiazepine response in pediatric status epilepticus

OBJECTIVE

Pediatric status epilepticus (SE) is a medical emergency associated with significant morbidity. Benzodiazepines (BZPs) are the current first-line treatment, but do not work in more than one third of children presenting with SE. Animal studies have shown that SE can cause changes in synaptic inhibition signaling that can ultimately lead to BZPs becoming ineffective. However, the relevance of these mechanisms in pediatric patients with SE remains unknown.

METHODS

To test this hypothesis, we combine clinical electroencephalographic (EEG) recordings with dynamic causal modeling (DCM). This approach allows model-based inference of cortical synaptic coupling parameters based on EEG recorded across distinct oscillatory states.

RESULTS

Our DCM revealed that dynamic changes in inhibitory synaptic coupling explain differences in EEG power spectra associated with BZP treatment responsiveness and guide the transition from ictal to interictal state. Furthermore, in silico simulations demonstrate that there are alternative routes to seizure termination even in cortical circuit models unresponsive to BZPs.

SIGNIFICANCE

Together, our findings confirm that alterations in synaptic inhibition underlie BZP response during pediatric SE. More broadly, this work further demonstrates the utility of computational modeling to validate insights from basic science in clinically accessible recordings in neurological disorders characterized by abnormal brain states.

Computational Synaptic Modeling of Pitch and Duration Mismatch Negativity in First-Episode Psychosis Reveals Selective Dysfunction of the N-Methyl-D-Aspartate Receptor

Mismatch negativity (MMN) to pitch (pMMN) and to duration (dMMN) deviant stimuli is significantly more attenuated in long-term psychotic illness compared to first-episode psychosis (FEP). It was recently shown that source-modeling of magnetically recorded MMN increases the detection of left auditory cortex MMN deficits in FEP, and that computational circuit modeling of electrically recorded MMN also reveals left-hemisphere auditory cortex abnormalities. Computational modeling using dynamic causal modeling (DCM) can also be used to infer synaptic activity from EEG-based scalp recordings. We measured pMMN and dMMN with EEG from 26 FEP and 26 matched healthy controls (HCs) and used a DCM conductance-based neural mass model including α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid, N-methyl-D-Aspartate (NMDA), and Gamma-aminobutyric acid receptors to identify any changes in effective connectivity and receptor rate constants in FEP. We modeled MMN sources in bilateral A1, superior temporal gyrus, and inferior frontal gyrus (IFG). No model parameters distinguished groups for pMMN. For dMMN, reduced NMDA receptor activity in right IFG in FEP was detected. This finding is in line with literature of prefrontal NMDA receptor hypofunction in chronic schizophrenia and suggests impaired NMDA-induced synaptic plasticity may be present at psychosis onset where scalp dMMN is only moderately reduced. To the best of our knowledge, this is the first report of impaired NMDA receptor activity in FEP found through computational modeling of dMMN and shows the potential of DCM to non-invasively reveal synaptic-level abnormalities that underly subtle functional auditory processing deficits in early psychosis.

[A study on the effects of learning on the properties of rats hippocampal-prefrontal connections in a memory task]

The transmission and interaction of neural information between the hippocampus and the prefrontal cortex play an important role in learning and memory. However, the specific effects of learning memory-related tasks on the connectivity characteristics between these two brain regions remain inadequately understood. This study employed in vivo microelectrode recording to obtain local field potentials (LFPs) from the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) in eight rats during the performance of a T-maze task, assessed both before and after task learning. Additionally, dynamic causal modeling (DCM) was utilized to analyze alterations in causal connectivity between the vHPC and the mPFC during memory task execution pre- and post-learning. Results indicated the presence of forward connections from vHPC to mPFC and backward connections from mPFC to vHPC during the T-maze task. Moreover, the forward connection between these brain regions was slightly enhanced after task learning, whereas the backward connection was diminished. These changes in connectivity corresponded with the observed trends when the rats correctly performed the T-maze task. In conclusion, this study may facilitate future investigations into the underlying mechanisms of learning and memory from the perspective of connectivity characteristics between distinct brain regions.

Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol

BACKGROUND

Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine's promising results in treating depression, a significant number of patients do not respond to the treatment, and predicting who will benefit remains a challenge. Although its antidepressant effects are known to be linked to its action as an antagonist of the N-methyl-D-aspartate (NMDA) receptor, the precise mechanisms that determine why some patients respond and others do not are still unclear.

OBJECTIVE

This study aims to understand the computational mechanisms underlying changes in the auditory mismatch negativity (MMN) response following treatment with intravenous ketamine. Moreover, we aim to link the computational mechanisms to their underlying neural causes and use the parameters of the neurocomputational model to make individual treatment predictions.

METHODS

This is a prospective study of 30 patients with TRD who are undergoing intravenous ketamine therapy. Prior to 3 out of 4 ketamine infusions, EEG will be recorded while patients complete the auditory MMN task. Depression, suicidality, and anxiety will be assessed throughout the study and a week after the last ketamine infusion. To translate the effects of ketamine on the MMN to computational mechanisms, we will model changes in the auditory MMN using the hierarchical Gaussian filter, a hierarchical Bayesian model. Furthermore, we will employ a conductance-based neural mass model of the electrophysiological data to link these computational mechanisms to their neural causes.

CONCLUSION

The findings of this study may improve understanding of the mechanisms underlying response and resistance to ketamine treatment in patients with TRD. The parameters obtained from fitting computational models to EEG recordings may facilitate single-patient treatment predictions, which could provide clinically useful prognostic information.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05464264. Registered June 24, 2022.

NORSE/FIRES: how can we advance our understanding of this devastating condition?

Introduction

New onset refractory status epilepticus (NORSE) is a rare and devastating condition characterised by the sudden onset of refractory status epilepticus (RSE) without an identifiable acute or active structural, toxic, or metabolic cause in an individual without a pre-existing diagnosis of epilepsy. Febrile infection-related epilepsy syndrome (FIRES) is considered a subcategory of NORSE and presents following a febrile illness prior to seizure onset. NORSE/FIRES is associated with high morbidity and mortality in children and adults.

Methods and results

In this review we first briefly summarise the reported clinical, paraclinical, treatment and outcome data in the literature. We then report on existing knowledge of the underlying pathophysiology in relation to in vitro and in vivo pre-clinical seizure and epilepsy models of potential relevance to NORSE/FIRES.

Discussion

We highlight how pre-clinical models can enhance our understanding of FIRES/NORSE and propose future directions for research.

Linking fast and slow: The case for generative models

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Anti-N-methyl-D-aspartate receptor-associated encephalitis: A review of clinicopathologic hallmarks and multimodal imaging manifestations

Anti-N-methyl-D-aspartate receptor-associated encephalitis (NMDARE) is a rare immune-mediated neuroinflammatory condition characterized by the rapid onset of neuropsychiatric symptoms and autonomic dysfunction. The mechanism of pathogenesis remains incompletely understood, but is thought to be related to antibodies targeting the GluN1 subunit of the NMDA receptor with resultant downstream dysregulation of dopaminergic pathways. Young adults are most frequently affected; the median age at diagnosis is 21 years. There is a strong female predilection with a female sex predominance of 4:1. NMDARE often develops as a paraneoplastic process and is most commonly associated with ovarian teratoma. However, NMDARE has also been described in patients with small cell lung cancer, clear cell renal carcinoma, and other benign and malignant neoplasms. Diagnosis is based on correlation of the clinical presentation, electroencephalography, laboratory studies, and imaging. Computed tomography, positron emission tomography, and magnetic resonance imaging are essential to identify an underlying tumor, exclude clinicopathologic mimics, and predict the likelihood of long-term functional impairment. Nuclear imaging may be of value for prognostication and to assess the response to therapy. Treatment may involve high-dose corticosteroids, intravenous immunoglobulin, and plasma exchange. Herein, we review the hallmark clinicopathologic features and imaging findings of this rare but potentially devastating condition and summarize diagnostic criteria, treatment regimens, and proposed pathogenetic mechanisms.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity

BACKGROUND

Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia.

METHODS

Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time.

RESULTS

Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters.

CONCLUSIONS

Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals' neurophysiological observations. At the second level, individuals' 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

The micro and macro interactions in acute autoimmune encephalitis: a study of resting-state EEG

Objective

Early recognition of autoimmune encephalitis (AIE) is often difficult and time-consuming. Understanding how the micro-level (antibodies) and macro-level (EEG) couple with each other may help rapidly diagnose and appropriately treat AIE. However, limited studies focused on brain oscillations involving micro- and macro-interactions in AIE from a neuro-electrophysiological perspective. Here, we investigated brain network oscillations in AIE using Graph theoretical analysis of resting state EEG.

Methods

AIE Patients (n = 67) were enrolled from June 2018 to June 2022. Each participant underwent a ca.2-hour 19-channel EEG examination. Five 10-second resting state EEG epochs with eyes closed were extracted for each participant. The functional networks based on the channels and Graph theory analysis were carried out.

Results

Compared with the HC group, significantly decreased FC across whole brain regions at alpha and beta bands were found in AIE patients. In addition, the local efficiency and clustering coefficient of the delta band was higher in AIE patients than in the HC group (P < 0.05). AIE patients had a smaller world index (P < 0.05) and higher shortest path length (P < 0.001) in the alpha band than those of the control group. Also, the AIE patients' global efficiency, local efficiency, and clustering coefficients decreased in the alpha band (P < 0.001). Different types of antibodies (antibodies against ion channels, antibodies against synaptic excitatory receptors, antibodies against synaptic inhibitory receptors, and multiple antibodies positive) showed distinct graph parameters. Moreover, the graph parameters differed in the subgroups by intracranial pressure. Correlation analysis revealed that magnetic resonance imaging abnormalities were related to global efficiency, local efficiency, and clustering coefficients in the theta, alpha, and beta bands, but negatively related to the shortest path length.

Conclusion

These findings add to our understanding of how brain FC and graph parameters change and how the micro- (antibodies) scales interact with the macro- (scalp EEG) scale in acute AIE. The clinical traits and subtypes of AIE may be suggested by graph properties. Further longitudinal cohort studies are needed to explore the associations between these graph parameters and recovery status, and their possible applications in AIE rehabilitation.

Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Recognizing New-Onset Sleep Disorders in Autoimmune Encephalitis Often Prompt Earlier Diagnosis

SUMMARY

Sleep/wake disorders are common in patients with autoimmune encephalitis, sometimes the most prominent or sole initial symptom, then delaying diagnosis. Sleep/wake disorders in autoimmune encephalitis vary and include severe sleeplessness, hypersomnia, central and/or obstructive sleep apnea, rapid eye movement sleep behavior disorder, indeterminate sleep/wake states, and loss of circadian sleep/wake rhythms. N-methyl- d aspartate receptor encephalitis (NMDAR) is often associated with insomnia, then hypersomnia and sleep-related central hypoventilation. Profound sleeplessness and rapid eye movement sleep behavior disorder are seen in patients with voltage-gated potassium channel-complex antibodies. Fragmented sleep and hypersomnia are common in paraneoplastic syndromes associated with anti-MA protein encephalitis; rapid eye movement sleep behavior disorder in those with antibodies against leucine-rich glioma inactivated protein (LGI1) or contactin-associated protein 2 (CASPR2) antibodies. Antibodies against a cell adhesion protein IGLON5 may result in obstructive sleep apnea, inspiratory stridor, disorganized nonrapid eye movement sleep, and excessive movements and parasomnias fragmenting nonrapid and rapid eye movement sleep. Recognizing a particular sleep/wake disorder is often a presenting or prominent feature in certain autoimmune encephalitis permit for earlier diagnosis. This is important because reduced morbidity and better short- and long-term outcomes are associated with earlier diagnosis and immunotherapies.

Cerebral Metabolic Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis on 18F-FDG PET Imaging

Background

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis (AE), and the prognosis may significantly be improved if identified earlier and immune-related treated more effectively. This study evaluated the brain metabolic network using fluorodeoxyglucose positron emission tomography (FDG PET).

Material and methods

FDG PET imaging of patients with NMDAR encephalitis was used to investigate the metabolic connectivity network, which was analyzed using the graph theory. The results in patients were compared to those in age- and sex-matched healthy controls.

Results

The hub nodes were mainly in the right frontal lobe in patients with NMDAR encephalitis. The global and local efficiencies in most brain regions were significantly reduced, and the shortest characteristic path length was significantly longer, especially in the temporal and occipital lobes. Significant network functions of topology properties were enhanced in the right frontal, caudate nucleus, and cingulate gyrus. In addition, the internal connection integration in the left cerebral hemisphere was poor, and the transmission efficiency of Internet information was low.

Conclusion

The present findings indicate that those characteristic and connections of metabolic network were changed in the brain by graph theory analysis quantitatively, which is helpful to better understand neuropathological and physiological mechanisms in patients with anti-NMDAR encephalitis.

GABAA Receptor Autoantibodies Decrease GABAergic Synaptic Transmission in the Hippocampal CA3 Network

Autoimmune encephalitis associated with antibodies (Abs) against α1, β3, and γ2 subunits of γ-aminobutyric acid receptor A (GABA_AR) represents a severe form of encephalitis with refractory seizures and status epilepticus. Reduction in inhibitory GABAergic synaptic activity is linked to dysfunction of neuronal networks, hyperexcitability, and seizures. The aim in this study was to investigate the direct pathogenic effect of a recombinant GABA_AR autoantibody (rAb-IP2), derived from the cerebrospinal fluid (CSF) of a patient with autoimmune GABA_AR encephalitis, on hippocampal CA1 and CA3 networks. Acute brain slices from C57BL/6 mice were incubated with rAb-IP2. The spontaneous synaptic GABAergic transmission was measured using electrophysiological recordings in voltage-clamp mode. The GABA_AR autoantibody rAb-IP2 reduced inhibitory postsynaptic signaling in the hippocampal CA1 pyramidal neurons with regard to the number of spontaneous inhibitory postsynaptic currents (sIPSCs) but did not affect their amplitude. In the hippocampal CA3 network, decreased number and amplitude of sIPSCs were detected, leading to decreased GABAergic synaptic transmission. Immunohistochemical staining confirmed the rAb-IP2 bound to hippocampal tissue. These findings suggest that GABA_AR autoantibodies exert direct functional effects on both hippocampal CA1 and CA3 pyramidal neurons and play a crucial role in seizure generation in GABA_AR autoimmune encephalitis.

Electroclinical biomarkers of autoimmune encephalitis

OBJECTIVE

To evaluate the utility of electroencephalography (EEG) changes as diagnostic and prognostic biomarkers in acute autoimmune encephalitis (AIE).

METHODS

One hundred and thirty-one patients with AIE were recruited retrospectively across 7 hospitals. Clinical data were collected during admission and at 12 months. EEGs were reviewed using a standard reporting proforma. Associations between EEG biomarkers, AIE subtypes, and clinical outcomes were assessed using logistic regression modeling.

RESULTS

Presence of superimposed fast activity (OR 34.33; 95% CI 3.90, 4527.27; p < 0.001), fluctuating EEG abnormality (OR 6.60; 95% CI 1.60, 37.59; p = 0.008), and hemispheric focality (OR 28.48; 95% CI 3.14, 3773.14; p < 0.001) were significantly more common in N-methyl-d-aspartate receptor (NMDAR) antibody-associated patients with AIE compared to other AIE subtypes. Abnormal background rhythm was associated with a poor mRS (modified Rankin score) at discharge (OR 0.29; 95% CI 0.10, 0.75; p = 0.01) and improvement in mRS at 12 months compared with admission mRS (3.72; 95% CI 1.14, 15.23; p = 0.04).

SIGNIFICANCE

We have identified EEG biomarkers that differentiate NMDAR AIE from other subtypes. We have also demonstrated EEG biomarkers that are associated with poor functional outcomes.

Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia

BACKGROUND

Diminished synaptic gain-the sensitivity of postsynaptic responses to neural inputs-may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms.

METHODS

People with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits.

RESULTS

Classic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms.

CONCLUSIONS

First, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.

Technical note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasize speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice). In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.

Encephalitis patient-derived monoclonal GABAA receptor antibodies cause epileptic seizures

Autoantibodies targeting the GABAA receptor (GABAAR) hallmark an autoimmune encephalitis presenting with frequent seizures and psychomotor abnormalities. Their pathogenic role is still not well-defined, given the common overlap with further autoantibodies and the lack of patient-derived mAbs. Five GABAAR mAbs from cerebrospinal fluid cells bound to various epitopes involving the α1 and γ2 receptor subunits, with variable binding strength and partial competition. mAbs selectively reduced GABAergic currents in neuronal cultures without causing receptor internalization. Cerebroventricular infusion of GABAAR mAbs and Fab fragments into rodents induced a severe phenotype with seizures and increased mortality, reminiscent of encephalitis patients' symptoms. Our results demonstrate direct pathogenicity of autoantibodies on GABAARs independent of Fc-mediated effector functions and provide an animal model for GABAAR encephalitis. They further provide the scientific rationale for clinical treatments using antibody depletion and can serve as tools for the development of antibody-selective immunotherapies.

Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM.

Multimodal electrophysiological analyses reveal that reduced synaptic excitatory neurotransmission underlies seizures in a model of NMDAR antibody-mediated encephalitis

Seizures are a prominent feature in N-Methyl-D-Aspartate receptor antibody (NMDAR antibody) encephalitis, a distinct neuro-immunological disorder in which specific human autoantibodies bind and crosslink the surface of NMDAR proteins thereby causing internalization and a state of NMDAR hypofunction. To further understand ictogenesis in this disorder, and to test a potential treatment compound, we developed an NMDAR antibody mediated rat seizure model that displays spontaneous epileptiform activity in vivo and in vitro. Using a combination of electrophysiological and dynamic causal modelling techniques we show that, contrary to expectation, reduction of synaptic excitatory, but not inhibitory, neurotransmission underlies the ictal events through alterations in the dynamical behaviour of microcircuits in brain tissue. Moreover, in vitro application of a neurosteroid, pregnenolone sulphate, that upregulates NMDARs, reduced established ictal activity. This proof-of-concept study highlights the complexity of circuit disturbances that may lead to seizures and the potential use of receptor-specific treatments in antibody-mediated seizures and epilepsy.

Extreme Delta Brush in Anti-NMDAR Encephalitis Correlates With Poor Functional Outcome and Death

Objective: To characterize EEG findings in anti-NMDAR encephalitis patients looking for the proportion of EEGs that were abnormal, presence of extreme delta brush (EDB), and to relate EEG findings to clinical outcomes (Glasgow Outcome Scale (GOS) at 6 months, need for ICU admission, and death).

Methods: This retrospective cohort single center study included patients with anti-NMDAR encephalitis who had ≥1 EEGs obtained from 2014 to 2021. EEGs were retrospectively analyzed by 2 reviewers. Clinical outcomes of interest were extracted through hospital and clinic chart review.

Results: Twenty-one patients with anti-NMDAR encephalitis were included. Sixty-four EEGs were analyzed. Four EEGs (6.3%) were within normal limits. Focal or generalized slowing (without EDB) was seen on 44 EEGs (68.8%). EDB was seen on 16 EEGs (25.0%) in 9 of 21 patients (42.9%). The presence of EDB was significantly associated with need for ICU admission (p = 0.02), poorer outcome at 6 months as per the GOS (p = 0.002), and with death (p=0.02). EDB was present on ≥1 EEG of every patient who died.

Conclusions: The presence of EDB on EEG in anti-NMDAR encephalitis patients is associated with increased need for ICU admission, worse functional outcomes at 6 months, and risk of death.

Ketamine and Attentional Bias Toward Emotional Faces: Dynamic Causal Modeling of Magnetoencephalographic Connectivity in Treatment-Resistant Depression

The glutamatergic modulator ketamine rapidly reduces depressive symptoms in individuals with treatment-resistant major depressive disorder (TRD) and bipolar disorder. While its underlying mechanism of antidepressant action is not fully understood, modulating glutamatergically-mediated connectivity appears to be a critical component moderating antidepressant response. This double-blind, crossover, placebo-controlled study analyzed data from 19 drug-free individuals with TRD and 15 healthy volunteers who received a single intravenous infusion of ketamine hydrochloride (0.5 mg/kg) as well as an intravenous infusion of saline placebo. Magnetoencephalographic recordings were collected prior to the first infusion and 6-9 h after both drug and placebo infusions. During scanning, participants completed an attentional dot probe task that included emotional faces. Antidepressant response was measured across time points using the Montgomery-Asberg Depression Rating Scale (MADRS). Dynamic causal modeling (DCM) was used to measure changes in parameter estimates of connectivity via a biophysical model that included realistic local neuronal architecture and receptor channel signaling, modeling connectivity between the early visual cortex, fusiform cortex, amygdala, and inferior frontal gyrus. Clinically, ketamine administration significantly reduced depressive symptoms in TRD participants. Within the model, ketamine administration led to faster gamma aminobutyric acid (GABA) and N-methyl-D-aspartate (NMDA) transmission in the early visual cortex, faster NMDA transmission in the fusiform cortex, and slower NMDA transmission in the amygdala. Ketamine administration also led to direct and indirect changes in local inhibition in the early visual cortex and inferior frontal gyrus and to indirect increases in cortical excitability within the amygdala. Finally, reductions in depressive symptoms in TRD participants post-ketamine were associated with faster α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) transmission and increases in gain control of spiny stellate cells in the early visual cortex. These findings provide additional support for the GABA and NMDA inhibition and disinhibition hypotheses of depression and support the role of AMPA throughput in ketamine's antidepressant effects. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT00088699?term=NCT00088699&draw=2&rank=1, identifier NCT00088699.

Altered EEG markers of synaptic plasticity in a human model of NMDA receptor deficiency: Anti-NMDA receptor encephalitis

Plasticity of synaptic strength and density is a vital mechanism enabling memory consolidation, learning, and neurodevelopment. It is strongly dependent on the intact function of N-Methyl-d-Aspartate Receptors (NMDAR). The importance of NMDAR is further evident as their dysfunction is involved in many diseases such as schizophrenia, Alzheimer's disease, neurodevelopmental disorders, and epilepsies. Synaptic plasticity is thought to be reflected by changes of sleep slow wave slopes across the night, namely higher slopes after wakefulness at the beginning of sleep than after a night of sleep. Hence, a functional NMDAR deficiency should theoretically lead to altered overnight changes of slow wave slopes. Here we investigated whether pediatric patients with anti-NMDAR encephalitis, being a very rare but unique human model of NMDAR deficiency due to autoantibodies against receptor subunits, indeed show alterations in this sleep EEG marker for synaptic plasticity. We retrospectively analyzed 12 whole-night EEGs of 9 patients (age 4.3-20.8 years, 7 females) and compared them to a control group of 45 healthy individuals with the same age distribution. Slow wave slopes were calculated for the first and last hour of Non-Rapid Eye Movement (NREM) sleep (factor 'hour') for patients and controls (factor 'group'). There was a significant interaction between 'hour' and 'group' (p = 0.013), with patients showing a smaller overnight decrease of slow wave slopes than controls. Moreover, we found smaller slopes during the first hour in patients (p = 0.022), whereas there was no group difference during the last hour of NREM sleep (p = 0.980). Importantly, the distribution of sleep stages was not different between the groups, and in our main analyses of patients without severe disturbance of sleep architecture, neither was the incidence of slow waves. These possible confounders could therefore not account for the differences in the slow wave slope values, which we also saw in the analysis of the whole sample of EEGs. These results suggest that quantitative EEG analysis of slow wave characteristics may reveal impaired synaptic plasticity in patients with anti-NMDAR encephalitis, a human model of functional NMDAR deficiency. Thus, in the future, the changes of sleep slow wave slopes may contribute to the development of electrophysiological biomarkers of functional NMDAR deficiency and synaptic plasticity in general.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Immunoceptive inference: why are psychiatric disorders and immune responses intertwined?

There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a step back from a historical mind-body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems?

GABAergic cortical network physiology in frontotemporal lobar degeneration

The clinical syndromes caused by frontotemporal lobar degeneration are heterogeneous, including the behavioural variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy. Although pathologically distinct, they share many behavioural, cognitive and physiological features, which may in part arise from common deficits of major neurotransmitters such as γ-aminobutyric acid (GABA). Here, we quantify the GABAergic impairment and its restoration with dynamic causal modelling of a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study. We analysed 17 patients with bvFTD, 15 patients with progressive supranuclear palsy, and 20 healthy age- and gender-matched controls. In addition to neuropsychological assessment and structural MRI, participants undertook two magnetoencephalography sessions using a roving auditory oddball paradigm: once on placebo and once on 10 mg of the oral GABA reuptake inhibitor tiagabine. A subgroup underwent ultrahigh-field magnetic resonance spectroscopy measurement of GABA concentration, which was reduced among patients. We identified deficits in frontotemporal processing using conductance-based biophysical models of local and global neuronal networks. The clinical relevance of this physiological deficit is indicated by the correlation between top-down connectivity from frontal to temporal cortex and clinical measures of cognitive and behavioural change. A critical validation of the biophysical modelling approach was evidence from parametric empirical Bayes analysis that GABA levels in patients, measured by spectroscopy, were related to posterior estimates of patients’ GABAergic synaptic connectivity. Further evidence for the role of GABA in frontotemporal lobar degeneration came from confirmation that the effects of tiagabine on local circuits depended not only on participant group, but also on individual baseline GABA levels. Specifically, the phasic inhibition of deep cortico-cortical pyramidal neurons following tiagabine, but not placebo, was a function of GABA concentration. The study provides proof-of-concept for the potential of dynamic causal modelling to elucidate mechanisms of human neurodegenerative disease, and explains the variation in response to candidate therapies among patients. The laminar- and neurotransmitter-specific features of the modelling framework, can be used to study other treatment approaches and disorders. In the context of frontotemporal lobar degeneration, we suggest that neurophysiological restoration in selected patients, by targeting neurotransmitter deficits, could be used to bridge between clinical and preclinical models of disease, and inform the personalized selection of drugs and stratification of patients for future clinical trials.

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

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.

Advances in the computational understanding of mental illness

Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.

Dynamic Causal Modeling of the Relationship between Cognition and Theta-alpha Oscillations in Adults with Down Syndrome

Individuals with Down syndrome (DS) show high inter-subject variability in cognitive ability and have an ultra-high risk of developing dementia (90% lifetime prevalence). Elucidating factors underlying variability in cognitive function can inform us about intellectual disability (ID) and may improve our understanding of factors associated with later cognitive decline. Increased neuronal inhibition has been posited to contribute to ID in DS. Combining electroencephalography (EEG) with dynamic causal modeling (DCM) provides a non-invasive method for investigating excitatory/inhibitory mechanisms. Resting-state EEG recordings were obtained from 36 adults with DS with no evidence of cognitive decline. Theta–alpha activity (4–13 Hz) was characterized in relation to general cognitive ability (raw Kaufmann’s Brief Intelligence Test second Edition (KBIT-2) score). Higher KBIT-2 was associated with higher frontal alpha peak amplitude and higher theta–alpha band power across distributed regions. Modeling this association with DCM revealed intrinsic self-inhibition was the key network parameter underlying observed differences in 4–13 Hz power in relation to KBIT-2 and age. In particular, intrinsic self-inhibition in right V1 was negatively correlated with KBIT-2. Results suggest intrinsic self-inhibition within the alpha network is associated with individual differences in cognitive ability in adults with DS, and may provide a potential therapeutic target for cognitive enhancement.

Changes in the Effective Connectivity of the Social Brain When Making Inferences About Close Others vs. the Self

Previous research showed that the ability to make inferences about our own and other’s mental states rely on common brain pathways; particularly in the case of close relationships (e.g., romantic relationships). Despite the evidence for shared neural representations of self and others, less is known about the distributed processing within these common neural networks, particularly whether there are specific patterns of internode communication when focusing on other vs. self. This study aimed to characterize context-sensitive coupling among social brain regions involved in self and other understanding. Participants underwent an fMRI while watching emotional video vignettes of their romantic partner and elaborated on their partner’s (other-condition) or on their own experience (self-condition). We used dynamic causal modeling (DCM) to quantify the associated changes in effective connectivity (EC) in a network of brain regions involved in social cognition including the temporoparietal junction (TPJ), the posterior cingulate (PCC)/precuneus and middle temporal gyrus (MTG). DCM revealed that: the PCC plays a central coordination role within this network, the bilateral MTG receives driving inputs from other nodes suggesting that social information is first processed in language comprehension regions; the right TPJ evidenced a selective increase in its sensitivity when focusing on the other’s experience, relative to focusing on oneself.

Magnetoencephalography in Cognitive Neuroscience: A Primer

Magnetoencephalography (MEG) is an invaluable tool to study the dynamics and connectivity of large-scale brain activity and their interactions with the body and the environment in functional and dysfunctional body and brain states. This primer introduces the basic concepts of MEG, discusses its strengths and limitations in comparison to other brain imaging techniques, showcases interesting applications, and projects exciting current trends into the near future, in a way that might more fully exploit the unique capabilities of MEG.

Magnetoencephalographic Correlates of Suicidal Ideation in Major Depression

BACKGROUND

Defining the neurobiological underpinnings of suicidal ideation (SI) is crucial to improving our understanding of suicide. This study used magnetoencephalographic gamma power as a surrogate marker for population-level excitation-inhibition balance to explore the underlying neurobiology of SI and depression. In addition, effects of pharmacological intervention with ketamine, which has been shown to rapidly reduce SI and depression, were assessed.

METHODS

Data were obtained from 29 drug-free patients with major depressive disorder who participated in an experiment comparing subanesthetic ketamine (0.5 mg/kg) with a placebo saline infusion. Magnetoencephalographic recordings were collected at baseline and after ketamine and placebo infusions. During scanning, patients rested with their eyes closed. SI and depression were assessed, and a linear mixed-effects model was used to identify brain regions where gamma power and both SI and depression were associated. Two regions of the salience network (anterior insula, anterior cingulate) were then probed using dynamic causal modeling to test for ketamine effects.

RESULTS

Clinically, patients showed significantly reduced SI and depression after ketamine administration. In addition, distinct regions in the anterior insula were found to be associated with SI compared with depression. In modeling of insula-anterior cingulate connectivity, ketamine lowered the membrane capacitance for superficial pyramidal cells. Finally, connectivity between the insula and anterior cingulate was associated with improvements in depression symptoms.

CONCLUSIONS

These findings suggest that the anterior insula plays a key role in SI, perhaps via its role in salience detection. In addition, transient changes in superficial pyramidal cell membrane capacitance and subsequent increases in cortical excitability might be a mechanism through which ketamine improves SI.

Predicting individual clinical trajectories of depression with generative embedding

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Patients with major depressive disorder (MDD) show variable clinical trajectories.

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Generative embedding (GE) is used to predict clinical trajectories in MDD patients.

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GE classifies patients with chronic depression vs. fast remission with 79% accuracy.

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GE provides mechanistic interpretability and outperforms conventional measures.

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Proof-of-concept that illustrates the potential of GE for clinical prediction.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy – generative embedding (GE) – which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.

GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography

To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked 'mismatch negativity' response and transiently induced oscillations. Here, we probe GABAergic influences in the networks using double-blind placebo-controlled randomized-crossover administration of the GABA reuptake inhibitor, tiagabine (oral, 10 mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both drug sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. We found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco- magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.SIGNIFICANCE STATEMENT Understanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach identified the effect of a test drug (GABA-reuptake inhibitor, tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain.

Anti-N-methyl-D-aspartate receptor encephalitis: A review of pathogenic mechanisms, treatment, prognosis

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a treatable autoimmune disorder characterized by prominent neuropsychiatric symptoms that predominantly affects children and young adults. In this review, we discuss the pathogenic mechanisms and immunologic triggers of anti-NMDAR encephalitis, and provide an overview of treatment and prognosis of this disorder, with specific focus on the management of common symptoms, complications, and patients during pregnancy. Most patients respond well to first-line treatment and surgical resection of tumors. When first-line immunotherapy fails, second-line immunotherapy can often improve outcomes. In addition, treatment with immunomodulators and tumor resection are effective treatment strategies for pregnant patients. Benzodiazepines are the preferred treatment for patients with catatonia, and electroconvulsive therapy (ECT) may be considered when pharmacological treatment is ineffective. Age, antibody titer, cerebellar atrophy, levels of biomarkers such as C-X-C motif chemokine 13 (CXCL13), cell-free mitochondrial (mt)DNA in cerebral serum fluid (CSF), and timing from symptom onset to treatment are the main prognostic factors. Patients without tumors or those who receive insufficient immunotherapy during the first episode are more likely to relapse.

Low-Frequency Brain Oscillations Track Motor Recovery in Human Stroke

OBJECTIVE

The majority of patients with stroke survive the acute episode and live with enduring disability. Effective therapies to support recovery of motor function after stroke are yet to be developed. Key to this development is the identification of neurophysiologic signals that mark recovery and are suitable and susceptible to interventional therapies. Movement preparatory low-frequency oscillations (LFOs) play a key role in cortical control of movement. Recent animal data point to a mechanistic role of motor cortical LFOs in stroke motor deficits and demonstrate neuromodulation intervention with therapeutic benefit. Their relevance in human stroke pathophysiology is unknown.

METHODS

We studied the relationship between movement-preparatory LFOs during the performance of a visuomotor grip task and motor function in a longitudinal (<5 days, 1 and 3 months) cohort study of 33 patients with motor stroke and in 19 healthy volunteers.

RESULTS

Acute stroke-lesioned brains fail to generate the LFO signal. Whereas in healthy humans, a transient occurrence of LFOs preceded movement onset at predominantly contralateral frontoparietal motor regions, recordings in patients revealed that movement-preparatory LFOs were substantially diminished to a level of 38% after acute stroke. LFOs progressively increased at 1 and 3 months. This re-emergence closely tracked the recovery of motor function across several movement qualities including grip strength, fine motor skills, and synergies and was frequency band specific.

INTERPRETATION

Our results provide the first human evidence for a link between movement-preparatory LFOs and functional recovery after stroke, promoting their relevance for movement control. These results suggest that it may be interesting to explore targeted, LFOs-restorative brain stimulation therapy in human stroke patients. ANN NEUROL 2019;86:853-865.

Relevance of Surface Neuronal Protein Autoantibodies as Biomarkers in Seizure-Associated Disorders

The detection of neuronal surface protein autoantibody-related disorders has contributed to several changes in our understanding of central nervous system autoimmunity. The clinical presentation of these disorders may be associated (or not) with tumors, and often patients develop an inexplicable onset of epilepsy, catatonic or autistic features, or memory and cognitive dysfunctions. The autoantigens in such cases have critical roles in synaptic transmission and plasticity, memory function, and process learning. For months, patients with such antibodies may be comatose or encephalopathic and yet completely recover with palliative care and immunotherapies. This paper reviews several targets of neuronal antibodies as biomarkers in seizure disorders, focusing mainly on autoantibodies, which target the extracellular domains of membrane proteins, namely leucine-rich glioma-inactivated-1 (LGI1), contactin-associated protein-like 2 (CASPR2), the N-methyl-D-aspartate receptor (NMDAR), γ-aminobutyric acid receptor-B (GABA_BR), the glycine receptor (GlyR), and a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs). In order to restore health status, limit hospitalization, and optimize results, testing these antibodies should be done locally, using internationally certified procedures for a precise and rapid diagnosis, with the possibility of initiating therapy as soon as possible.

Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis

Fatigue is one of the most common symptoms in multiple sclerosis (MS), with a major impact on patients' quality of life. Currently, treatment proceeds by trial and error with limited success, probably due to the presence of multiple different underlying mechanisms. Recent neuroscientific advances offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. However, development of these tools for differential diagnosis will require guidance by pathophysiological and cognitive theories that propose mechanisms which can be assessed in individual patients. This article provides an overview of contemporary pathophysiological theories of fatigue in MS and discusses how the mechanisms they propose may become measurable with emerging technologies and thus lay a foundation for future personalised treatments.

Transcranial direct current stimulation over the sensory-motor regions inhibits gamma synchrony

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique able to induce plasticity phenomena. Although tDCS application has been spreading over a variety of neuroscience domains, the mechanisms by which the stimulation acts are largely unknown. We investigated tDCS effects on cortical gamma synchrony, which is a crucial player in cortical function. We performed a randomized, sham-controlled, double-blind study on healthy subjects, combining tDCS and magnetoencephalography. By driving brain activity via 40 Hz auditory stimulation during magnetoencephalography, we experimentally tuned cortical gamma synchrony and measured it before and after bilateral tDCS of the primary sensory-motor hand regions (anode left, cathode right). We demonstrated that the stimulation induces a remarkable decrease of gamma synchrony (13 out of 15 subjects), as measured by gamma phase at 40 Hz. tDCS has strong remote effects, as the cortical region mostly affected was located far away from the stimulation site and covered a large area of the right centro-temporal cortex. No significant differences between stimulations were found for baseline gamma synchrony, as well as early transient auditory responses. This suggests a specific tDCS effect on externally driven gamma synchronization. This study sheds new light on the effect of tDCS on cortical function showing that the net effect of the stimulation on cortical gamma synchronization is an inhibition.

NMDA-receptor antibodies alter cortical microcircuit dynamics

Recently, autoantibodies against NMDA receptors (NMDARs) were identified as a major cause of autoimmune encephalitis. They cause abnormalities in brain function often associated with significant changes in patients’ brain dynamics. Here we use computational modeling to identify how NMDAR dysfunction causes abnormalities in brain dynamics using patient EEGs and local field potential recordings in a mouse model of NMDAR-Ab encephalitis. NMDAR autoantibodies cause a specific shift in excitatory coupling within cortical circuits that places the circuits closer to pathological transitions between dynamic brain states. Because of the proximity to these phase transitions, otherwise benign fluctuations in neuronal coupling cause abnormal EEG responses in the presence of the antibodies. Our modeling results thus explain fluctuating abnormalities in brain dynamics observed in patients.

NMDA-receptor antibodies (NMDAR-Abs) cause an autoimmune encephalitis with a diverse range of EEG abnormalities. NMDAR-Abs are believed to disrupt receptor function, but how blocking this excitatory synaptic receptor can lead to paroxysmal EEG abnormalities—or even seizures—is poorly understood. Here we show that NMDAR-Abs change intrinsic cortical connections and neuronal population dynamics to alter the spectral composition of spontaneous EEG activity and predispose brain dynamics to paroxysmal abnormalities. Based on local field potential recordings in a mouse model, we first validate a dynamic causal model of NMDAR-Ab effects on cortical microcircuitry. Using this model, we then identify the key synaptic parameters that best explain EEG paroxysms in pediatric patients with NMDAR-Ab encephalitis. Finally, we use the mouse model to show that NMDAR-Ab–related changes render microcircuitry critically susceptible to overt EEG paroxysms when these key parameters are changed, even though the same parameter fluctuations are tolerated in the in silico model of the control condition. These findings offer mechanistic insights into circuit-level dysfunction induced by NMDAR-Ab.