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Champsas D, Zhang X, Rosch R, Ioannidou E, Gilmour K, Cooray G, Woodhall G, Pujar S, Kaliakatsos M, Wright SK. NORSE/FIRES: how can we advance our understanding of this devastating condition? Front Neurol 2024; 15:1426051. [PMID: 39175762 PMCID: PMC11338801 DOI: 10.3389/fneur.2024.1426051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/29/2024] [Indexed: 08/24/2024] Open
Abstract
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.
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Affiliation(s)
- Dimitrios Champsas
- Department of Neurology, Great Ormond Street Hospital (GOSH), London, United Kingdom
- Institute of Health and Neurodevelopment, School of Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Xushuo Zhang
- Institute of Health and Neurodevelopment, School of Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Richard Rosch
- Department of Clinical Neurophysiology, King’s College Hospital London NHS Foundation Trust, London, United Kingdom
- Departments of Neurology and Pediatrics, Columbia University, New York, NY, United States
| | - Evangelia Ioannidou
- Department of Neurology, Great Ormond Street Hospital (GOSH), London, United Kingdom
| | - Kimberly Gilmour
- Department of Immunology, Great Ormond Street Hospital (GOSH), London, United Kingdom
- Biomedical Research Centre (BRC), London, United Kingdom
- Institute of Child Health, University College London, London, United Kingdom
| | - Gerald Cooray
- Department of Neurophysiology, Great Ormond Street Hospital (GOSH), London, United Kingdom
- Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Gavin Woodhall
- Institute of Health and Neurodevelopment, School of Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Suresh Pujar
- Department of Neurology, Great Ormond Street Hospital (GOSH), London, United Kingdom
- Institute of Child Health, University College London, London, United Kingdom
| | - Marios Kaliakatsos
- Department of Neurology, Great Ormond Street Hospital (GOSH), London, United Kingdom
- Institute of Child Health, University College London, London, United Kingdom
| | - Sukhvir K. Wright
- Institute of Health and Neurodevelopment, School of Health and Life Sciences, Aston University, Birmingham, United Kingdom
- Birmingham Women’s and Children’s Hospital NHS Trust, Birmingham, United Kingdom
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Cooray GK, Rosch RE, Friston KJ. Global dynamics of neural mass models. PLoS Comput Biol 2023; 19:e1010915. [PMID: 36763644 PMCID: PMC9949652 DOI: 10.1371/journal.pcbi.1010915] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 02/23/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity. This complexity allows neural mass models to describe the itinerant features of brain dynamics. However, expressive, nonlinear neural mass models are often difficult to fit to empirical data without additional simplifying assumptions: e.g., that the system can be modelled as linear perturbations around a fixed point. In this study we offer a mathematical analysis of neural mass models, specifically the canonical microcircuit model, providing analytical solutions describing slow changes in the type of cortical activity, i.e. dynamical itinerancy. We derive a perturbation analysis up to second order of the phase flow, together with adiabatic approximations. This allows us to describe amplitude modulations in a relatively simple mathematical format providing analytic proof-of-principle for the existence of semi-stable states of cortical dynamics at the scale of a cortical column. This work allows for model inversion of neural mass models, not only around fixed points, but over regions of phase space that encompass transitions among semi or multi-stable states of oscillatory activity. Crucially, these theoretical results speak to model inversion in the context of multiple semi-stable brain states, such as the transition between interictal, pre-ictal and ictal activity in epilepsy.
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Affiliation(s)
- Gerald Kaushallye Cooray
- GOS-UCL Institute of Child Health, University College London, London, United Kingdom
- Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
- Karolinska Institutet, Stockholm, Sweden
- * E-mail:
| | - Richard Ewald Rosch
- Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
- The Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Karl John Friston
- The Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, United Kingdom
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Jha J, Hashemi M, Vattikonda AN, Wang H, Jirsa V. Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac9037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
Virtual brain models are data-driven patient-specific brain models integrating individual brain imaging data with neural mass modeling in a single computational framework, capable of autonomously generating brain activity and its associated brain imaging signals. Along the example of epilepsy, we develop an efficient and accurate Bayesian methodology estimating the parameters linked to the extent of the epileptogenic zone. State-of-the-art advances in Bayesian inference using Hamiltonian Monte Carlo (HMC) algorithms have remained elusive for large-scale differential-equations based models due to their slow convergence. We propose appropriate priors and a novel reparameterization to facilitate efficient exploration of the posterior distribution in terms of computational time and convergence diagnostics. The methodology is illustrated for in-silico dataset and then, applied to infer the personalized model parameters based on the empirical stereotactic electroencephalography (SEEG) recordings of retrospective patients. This improved methodology may pave the way to render HMC methods sufficiently easy and efficient to use, thus applicable in personalized medicine.
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Xiang W, Karfoul A, Yang C, Shu H, Le Bouquin Jeannès R. Investigation of two neural mass models for DCM-based effective connectivity inference in temporal epilepsy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106840. [PMID: 35550455 DOI: 10.1016/j.cmpb.2022.106840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around β and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.
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Affiliation(s)
- Wentao Xiang
- Key Laboratory of Clinical and Medical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China; Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Ahmad Karfoul
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Chunfeng Yang
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Huazhong Shu
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Régine Le Bouquin Jeannès
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France.
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Saeidi M, Karwowski W, Farahani FV, Fiok K, Taiar R, Hancock PA, Al-Juaid A. Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sci 2021; 11:1525. [PMID: 34827524 PMCID: PMC8615531 DOI: 10.3390/brainsci11111525] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/04/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.
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Affiliation(s)
- Maham Saeidi
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
| | - Farzad V. Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Krzysztof Fiok
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
| | - Redha Taiar
- MATIM, Moulin de la Housse, Université de Reims Champagne Ardenne, CEDEX 02, 51687 Reims, France;
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA;
| | - Awad Al-Juaid
- Industrial Engineering Department, Taif University, Taif 26571, Saudi Arabia;
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6
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Pereira I, Frässle S, Heinzle J, Schöbi D, Do CT, Gruber M, Stephan KE. Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities. Neuroimage 2021; 245:118662. [PMID: 34687862 DOI: 10.1016/j.neuroimage.2021.118662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/12/2021] [Accepted: 10/17/2021] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland.
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Cao Tri Do
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Moritz Gruber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
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7
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Hashemi M, Vattikonda AN, Sip V, Diaz-Pier S, Peyser A, Wang H, Guye M, Bartolomei F, Woodman MM, Jirsa VK. On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread. PLoS Comput Biol 2021; 17:e1009129. [PMID: 34260596 PMCID: PMC8312957 DOI: 10.1371/journal.pcbi.1009129] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 07/26/2021] [Accepted: 05/29/2021] [Indexed: 11/18/2022] Open
Abstract
Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.
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Affiliation(s)
- Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Sandra Diaz-Pier
- SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Alexander Peyser
- SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Google, München, Germany
| | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | | | - Viktor K. Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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8
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Gefferie SR, Maric A, Critelli H, Gueden S, Kurlemann G, Kurth S, Nosadini M, Plecko B, Ringli M, Rostásy K, Sartori S, Schmitt B, Suppiej A, Van Bogaert P, Wehrle FM, Huber R, Bölsterli BK. Altered EEG markers of synaptic plasticity in a human model of NMDA receptor deficiency: Anti-NMDA receptor encephalitis. Neuroimage 2021; 239:118281. [PMID: 34147627 DOI: 10.1016/j.neuroimage.2021.118281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 03/15/2021] [Accepted: 06/16/2021] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
- Silvano R Gefferie
- Department of Neuropediatrics, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Stichting Epilepsie Instellingen Nederland (SEIN), 2103 SW, Heemstede, Netherlands
| | - Angelina Maric
- Department of Neurology, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland
| | - Hanne Critelli
- Department of Neuropediatrics, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
| | - Sophie Gueden
- Service de Pédiatrie, CHU d'Angers, 49933, Angers, France
| | | | - Salome Kurth
- Pulmonary Clinic, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland; Department of Psychology, University of Fribourg, 1700, Fribourg, Switzerland
| | - Margherita Nosadini
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children's Health, University of Padua, 35122, Padua, Italy
| | - Barbara Plecko
- Department of Neuropediatrics, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Department of Pediatrics and Adolescent Medicine, Division of General Pediatrics, Medical University of Graz, 8036, Graz, Austria
| | - Maya Ringli
- Department of Neurology, Inselspital, University Hospital Bern, 3010, Bern, Switzerland
| | - Kevin Rostásy
- Department of Pediatric Neurology, Children's Hospital Datteln, Witten/Herdecke University, 58448, Datteln/Witten, Germany
| | - Stefano Sartori
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children's Health, University of Padua, 35122, Padua, Italy
| | - Bernhard Schmitt
- Department of Neuropediatrics, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
| | - Agnese Suppiej
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children's Health, University of Padua, 35122, Padua, Italy; Department of Medical Sciences, Pediatric Section, University of Ferrara, 44121, Ferrara, Italy
| | | | - Flavia M Wehrle
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Child Development Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Department of Neonatology and Pediatric Intensive Care, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
| | - Reto Huber
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Child Development Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, 8091, Zurich, Switzerland
| | - Bigna K Bölsterli
- Department of Neuropediatrics, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland; Children's Research Center, University Children's Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland.
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9
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Frässle S, Aponte EA, Bollmann S, Brodersen KH, Do CT, Harrison OK, Harrison SJ, Heinzle J, Iglesias S, Kasper L, Lomakina EI, Mathys C, Müller-Schrader M, Pereira I, Petzschner FH, Raman S, Schöbi D, Toussaint B, Weber LA, Yao Y, Stephan KE. TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Front Psychiatry 2021; 12:680811. [PMID: 34149484 PMCID: PMC8206497 DOI: 10.3389/fpsyt.2021.680811] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/10/2021] [Indexed: 12/26/2022] Open
Abstract
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.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eduardo A. Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Saskia Bollmann
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Charlestown, MA, United States
| | - Kay H. Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Cao T. Do
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Olivia K. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- School of Pharmacy, University of Otago, Dunedin, New Zealand
| | - Samuel J. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sandra Iglesias
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lars Kasper
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Ekaterina I. Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Interacting Minds Center, Aarhus University, Aarhus, Denmark
| | - Matthias Müller-Schrader
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Frederike H. Petzschner
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sudhir Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Birte Toussaint
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lilian A. Weber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yu Yao
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
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10
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Shaw AD, Hughes LE, Moran R, Coyle-Gilchrist I, Rittman T, Rowe JB. In Vivo Assay of Cortical Microcircuitry in Frontotemporal Dementia: A Platform for Experimental Medicine Studies. Cereb Cortex 2021; 31:1837-1847. [PMID: 31216360 PMCID: PMC7869085 DOI: 10.1093/cercor/bhz024] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 01/07/2019] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Alexander D Shaw
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Medical Research Council, Cognition and Brain, Sciences Unit, Cambridge, UK
| | - Rosalyn Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Ian Coyle-Gilchrist
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Tim Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Medical Research Council, Cognition and Brain, Sciences Unit, Cambridge, UK
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11
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Shaw AD, Muthukumaraswamy SD, Saxena N, Sumner RL, Adams NE, Moran RJ, Singh KD. Generative modelling of the thalamo-cortical circuit mechanisms underlying the neurophysiological effects of ketamine. Neuroimage 2020; 221:117189. [PMID: 32711064 PMCID: PMC7762824 DOI: 10.1016/j.neuroimage.2020.117189] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Alexander D Shaw
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK.
| | - Suresh D Muthukumaraswamy
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Neeraj Saxena
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK; Department of Anaesthetics, Intensive Care and Pain Medicine, Cwm Taf Morgannwg University Health Board, Llantrisant CF72 8XR, UK
| | - Rachael L Sumner
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Natalie E Adams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Rosalyn J Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
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12
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Mesin L, Valerio M, Capizzi G. Automated diagnosis of encephalitis in pediatric patients using EEG rhythms and slow biphasic complexes. Phys Eng Sci Med 2020; 43:997-1006. [PMID: 32696434 DOI: 10.1007/s13246-020-00893-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/29/2020] [Indexed: 11/25/2022]
Abstract
Slow biphasic complexes (SBC) have been identified in the EEG of patients suffering for inflammatory brain diseases. Their amplitude, location and frequency of appearance were found to correlate with the severity of encephalitis. Other characteristics of SBCs and of EEG traces of patients could reflect the grade of pathology. Here, EEG rhythms are investigated together with SBCs for a better characterization of encephalitis. EEGs have been acquired from pediatric patients: ten controls and ten encephalitic patients. They were split by neurologists into five classes of different severity of the pathology. The relative power of EEG rhythms was found to change significantly in EEGs labeled with different severity scores. Moreover, a significant variation was found in the last seconds before the appearance of an SBC. This information and quantitative indexes characterizing the SBCs were used to build a binary classification decision tree able to identify the classes of severity. True classification rate of the best model was 76.1% (73.5% with leave-one-out test). Moreover, the classification errors were among classes with similar severity scores (precision higher than 80% was achieved considering three instead of five classes). Our classification method may be a promising supporting tool for clinicians to diagnose, assess and make the follow-up of patients with encephalitis.
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Affiliation(s)
- Luca Mesin
- Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy.
| | - Massimo Valerio
- Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Giorgio Capizzi
- Ospedale Infantile Regina Margherita, Department of Child Neuropsychiatry, Universitá di Torino, Turin, Italy
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13
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Hamburg S, Rosch R, Startin CM, Friston KJ, Strydom A. Dynamic Causal Modeling of the Relationship between Cognition and Theta-alpha Oscillations in Adults with Down Syndrome. Cereb Cortex 2020; 29:2279-2290. [PMID: 30877793 PMCID: PMC6458903 DOI: 10.1093/cercor/bhz043] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 02/09/2019] [Indexed: 01/17/2023] Open
Abstract
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.
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Affiliation(s)
- Sarah Hamburg
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
| | - Richard Rosch
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - Carla Marie Startin
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
| | - Karl John Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - André Strydom
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
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14
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Chavez-Castillo M, Ruiz-Garcia M, Herrera-Mora P. Characterization and Outcomes of Epileptic Seizures in Mexican Pediatric Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis. Cureus 2020; 12:e8211. [PMID: 32577329 PMCID: PMC7305580 DOI: 10.7759/cureus.8211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Introduction Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is one of the most common autoimmune encephalitides. The frequency of anti-NMDAR encephalitis is known to exceed the frequency of any individual viral encephalitis in young subjects. Epileptic seizures are a cardinal symptom in anti-NMDAR encephalitis; a significant amount of pediatric patients exhibit seizures as the first symptom of the disease, and most of them will develop them during the acute phase. The use of antiepileptic drugs (AEDs) is a cornerstone of the treatment of these patients, but the choice of agent and duration of treatment is currently unknown. Materials and methods This was a single-center retrospective review case series of all pediatric patients with a confirmed diagnosis of anti-NMDAR encephalitis and epileptic seizures admitted to the National Institute of Pediatrics in Mexico City from January 2012 to July 2019. Results We included a total of 31 patients (males 64.5%, median age: 10 years). No patient showed evidence of teratoma; only 38% of cases had a viral prodrome. Most patients initially exhibited psychiatric symptoms (51%), but the leading cause in soliciting medical assistance was the presence of epileptic seizures (71%). About 85% of patients presented epileptic seizures during the course of the illness, predominantly focal onset seizures (42% focal to bilateral tonic-clonic seizures, 32% focal seizures with impaired awareness). Electroencephalogram (EEG) was abnormal in 97% of patients; the characteristic extreme delta brush pattern was found in 9% of patients. Two AEDs on average were required to control seizures during the acute stage. In six (19%) patients, human herpesvirus (HHV) was detected in cerebrospinal fluid (CSF); all of them had epileptic seizures, which were more resistant to pharmacological treatment during the acute phase, requiring a higher number of AED (median 2.5 vs. 2). The development of epilepsy after acute encephalitis was uncommon; at 24 months, only one patient continued to have epileptic seizures. One of the factors most closely related to the persistence of epileptic seizures was the inadequate response to immunotherapy after four weeks. The functional prognosis was generally good; at a two-year follow-up, only two (10%) patients had a significant disability [modified Rankin Scale (mRS) score: 3-5]; both patients had seizures at a one-year follow-up. Conclusions Sustained use of AEDs after the acute phase of anti-NMDAR encephalitis is controversial. We found that the continuation of AEDs after the acute phase could be considered in the following scenarios: status epilepticus (SE), inadequate response to immunotherapy at four weeks, and a high mRS score at discharge and during follow-up. In other cases, discontinuation of AED may be warranted. More studies are needed in our country to replicate these results.
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15
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da Silva APB, Silva RBM, Goi LDS, Molina RD, Machado DC, Sato DK. Experimental Models of Neuroimmunological Disorders: A Review. Front Neurol 2020; 11:389. [PMID: 32477252 PMCID: PMC7235321 DOI: 10.3389/fneur.2020.00389] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 04/17/2020] [Indexed: 12/11/2022] Open
Abstract
Immune-mediated inflammatory diseases of the central nervous system (CNS) are a group of neurological disorders in which inflammation and/or demyelination are induced by cellular and humoral immune responses specific to CNS antigens. They include diseases such as multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSD), acute disseminated encephalomyelitis (ADEM) and anti-NMDA receptor encephalitis (NMDAR encephalitis). Over the years, many in vivo and in vitro models were used to study clinical, pathological, physiological and immunological features of these neuroimmunological disorders. Nevertheless, there are important aspects of human diseases that are not fully reproduced in the experimental models due to their technical limitations. In this review, we describe the preclinical models of neuroimmune disorders, and how they contributed to the understanding of these disorders and explore potential treatments. We also describe the purpose and limitation of each one, as well as the recent advances in this field.
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Affiliation(s)
- Ana Paula Bornes da Silva
- Neuroinflammation and Neuroimmunology Laboratory, Brain Institute, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil.,School of Medicine, Graduate Program in Pediatrics and Child Health, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil
| | - Rodrigo Braccini Madeira Silva
- Research Center in Toxicology and Pharmacology, School of Health and Life Sciences, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil
| | - Leise Daniele Sckenal Goi
- Neuroinflammation and Neuroimmunology Laboratory, Brain Institute, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil.,School of Medicine, Graduate Program in Medicine and Health Sciences, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil
| | - Rachel Dias Molina
- Neuroinflammation and Neuroimmunology Laboratory, Brain Institute, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil.,School of Medicine, Graduate Program in Medicine and Health Sciences, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil
| | - Denise Cantarelli Machado
- School of Medicine, Graduate Program in Medicine and Health Sciences, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil.,Molecular and Cellular Biology Laboratory, Brain Institute, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil
| | - Douglas Kazutoshi Sato
- Neuroinflammation and Neuroimmunology Laboratory, Brain Institute, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil.,School of Medicine, Graduate Program in Pediatrics and Child Health, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil.,School of Medicine, Graduate Program in Medicine and Health Sciences, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, Brazil
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16
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Hashemi M, Vattikonda AN, Sip V, Guye M, Bartolomei F, Woodman MM, Jirsa VK. The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. Neuroimage 2020; 217:116839. [PMID: 32387625 DOI: 10.1016/j.neuroimage.2020.116839] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 04/02/2020] [Accepted: 04/07/2020] [Indexed: 12/28/2022] Open
Abstract
Despite the importance and frequent use of Bayesian frameworks in brain network modeling for parameter inference and model prediction, the advanced sampling algorithms implemented in probabilistic programming languages to overcome the inference difficulties have received relatively little attention in this context. In this technical note, we propose a probabilistic framework, namely the Bayesian Virtual Epileptic Patient (BVEP), which relies on the fusion of structural data of individuals to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. To invert the individualized whole-brain model employed in this study, we use the recently developed algorithms known as No-U-Turn Sampler (NUTS) as well as Automatic Differentiation Variational Inference (ADVI). Our results indicate that NUTS and ADVI accurately estimate the degree of epileptogenicity of brain regions, therefore, the hypothetical brain areas responsible for the seizure initiation and propagation, while the convergence diagnostics and posterior behavior analysis validate the reliability of the estimations. Moreover, we illustrate the efficiency of the transformed non-centered parameters in comparison to centered form of parameterization. The Bayesian framework used in this work proposes an appropriate patient-specific strategy for estimating the epileptogenicity of the brain regions to improve outcome after epilepsy surgery.
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Affiliation(s)
- M Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
| | - A N Vattikonda
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - V Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - M Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - F Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - M M Woodman
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - V K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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17
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Zarghami TS, Friston KJ. Dynamic effective connectivity. Neuroimage 2019; 207:116453. [PMID: 31821868 DOI: 10.1016/j.neuroimage.2019.116453] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 10/29/2019] [Accepted: 12/06/2019] [Indexed: 01/17/2023] Open
Abstract
Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses - developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics-due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model's face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework - that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.
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Affiliation(s)
- Tahereh S Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, University of Tehran, Amirabad, Tehran, Iran.
| | - Karl J Friston
- The Wellcome Centre for Human Neuroimaging, University College London, Queen Square, London, WC1N 3AR, UK.
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18
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Cooray GK, Sundgren M, Brismar T. Mechanism of visual network dysfunction in relapsing-remitting multiple sclerosis and its relation to cognition. Clin Neurophysiol 2019; 131:361-367. [PMID: 31864125 DOI: 10.1016/j.clinph.2019.10.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 09/10/2019] [Accepted: 10/31/2019] [Indexed: 01/31/2023]
Abstract
OBJECTIVE To investigate if changes in brain network function and connectivity contribute to the abnormalities in visual event related potentials (ERP) in relapsing-remitting multiple sclerosis (RRMS), and explore their relation to a decrease in cognitive performance. METHODS We evaluated 72 patients with RRMS and 89 healthy control subjects in a cross-sectional study. Visual ERP were generated using illusory and non-illusory stimuli and recorded using 21 EEG scalp electrodes. The measured activity was modelled using Dynamic Causal Modelling. The model network consisted of 4 symmetric nodes including the primary visual cortex (V1/V2) and the Lateral Occipital Complex. Patients and controls were tested with a neuropsychological test battery consisting of 18 cognitive tests covering six cognitive domains. RESULTS We found reduced cortical connectivity in bottom-up and interhemispheric connections to the right lateral occipital complex in patients (p < 0.001). Furthermore, interhemispherical connections were related to cognitive dysfunction in several domains (attention, executive function, visual perception and organization, processing speed and global cognition) for patients (p < 0.05). No relation was seen between cortical network connectivity and cognitive function in the healthy control subjects. CONCLUSION Changes in the functional connectivity to higher cortical regions provide a neurobiological explanation for the changes of the visual ERP in RRMS. SIGNIFICANCE This study suggests that changes in connectivity to higher cortical regions partly explain visual network dysfunction in RRMS where a lower interhemispheric connectivity may contribute to impaired cognitive function.
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Affiliation(s)
- Gerald K Cooray
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Mathias Sundgren
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Neuro Department, Karolinska University Hospital, Stockholm, Sweden
| | - Tom Brismar
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
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19
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Mesin L, Valerio M, Beaumanoir A, Capizzi G. Automatic identification of slow biphasic complexes in EEG: an effective tool to detect encephalitis. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab2086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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20
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Rosch RE, Hunter PR, Baldeweg T, Friston KJ, Meyer MP. Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures. PLoS Comput Biol 2018; 14:e1006375. [PMID: 30138336 PMCID: PMC6124808 DOI: 10.1371/journal.pcbi.1006375] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 09/05/2018] [Accepted: 07/18/2018] [Indexed: 12/31/2022] Open
Abstract
Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of both synaptic connection strength and their temporal dynamics are required to explain observed seizure patterns. These findings highlight distinct changes in local (intrinsic) and long-range (extrinsic) synaptic transmission dynamics as a possible seizure pathomechanism and illustrate how our Bayesian model inversion approach can be used to link existing neural mass models of seizure activity and novel experimental methods.
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Affiliation(s)
- Richard E. Rosch
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Paul R. Hunter
- Department of Developmental Neurobiology & MRC Center for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Torsten Baldeweg
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Karl J. Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Martin P. Meyer
- Department of Developmental Neurobiology & MRC Center for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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21
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Abstract
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
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Affiliation(s)
- Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Geraint Rees
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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22
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The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream. J Neurosci 2017; 37:6751-6760. [PMID: 28607165 PMCID: PMC5508257 DOI: 10.1523/jneurosci.0291-17.2017] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 05/02/2017] [Accepted: 05/04/2017] [Indexed: 01/02/2023] Open
Abstract
Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography (MEG). Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N = 13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream. In auditory cortex, regular sequences with smaller alphabets induced greater gamma activity. Furthermore, sequence regularity shifted induced activity in frontal regions toward higher frequencies. To model these effects in terms of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and estimated slow fluctuations in neural (postsynaptic) gain. Using the model-based parameters, we accurately explain the sensor-level sustained field amplitude, demonstrating that slow changes in synaptic efficacy, combined with sustained sensory input, can result in profound and sustained effects on neural responses to predictable sensory streams. SIGNIFICANCE STATEMENT Brain activity can be strongly modulated by the predictability of stimuli it is currently processing. An example of such a modulation is a shift in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects. First, we establish the oscillatory neural correlates of independent predictability manipulations in hierarchically distinct areas of the auditory processing stream. Next, we use a biophysically realistic computational model to explain these effects in terms of the underlying neurophysiology. Finally, using the model-based parameters describing neural gain modulation, we can explain the previously unexplained effects observed at the sensor level. This demonstrates that slow modulations of synaptic gain can result in profound and sustained effects on neural activity.
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23
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Stephan KE, Manjaly ZM, Mathys CD, Weber LAE, Paliwal S, Gard T, Tittgemeyer M, Fleming SM, Haker H, Seth AK, Petzschner FH. Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression. Front Hum Neurosci 2016; 10:550. [PMID: 27895566 PMCID: PMC5108808 DOI: 10.3389/fnhum.2016.00550] [Citation(s) in RCA: 216] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 10/14/2016] [Indexed: 01/13/2023] Open
Abstract
This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.
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Affiliation(s)
- Klaas E Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College LondonLondon, UK; Max Planck Institute for Metabolism ResearchCologne, Germany
| | - Zina M Manjaly
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Department of Neurology, Schulthess ClinicZurich, Switzerland
| | - Christoph D Mathys
- Wellcome Trust Centre for Neuroimaging, University College London London, UK
| | - Lilian A E Weber
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Saee Paliwal
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Tim Gard
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Center for Complementary and Integrative Medicine, University Hospital ZurichZurich, Switzerland
| | | | - Stephen M Fleming
- Wellcome Trust Centre for Neuroimaging, University College London London, UK
| | - Helene Haker
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Anil K Seth
- Sackler Centre for Consciousness Science, School of Engineering and Informatics, University of Sussex Brighton, UK
| | - Frederike H Petzschner
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
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24
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Dynamic causal modelling of seizure activity in a rat model. Neuroimage 2016; 146:518-532. [PMID: 27639356 DOI: 10.1016/j.neuroimage.2016.08.062] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 07/27/2016] [Accepted: 08/30/2016] [Indexed: 11/22/2022] Open
Abstract
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.
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25
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Stephan KE, Schlagenhauf F, Huys QJM, Raman S, Aponte EA, Brodersen KH, Rigoux L, Moran RJ, Daunizeau J, Dolan RJ, Friston KJ, Heinz A. Computational neuroimaging strategies for single patient predictions. Neuroimage 2016; 145:180-199. [PMID: 27346545 DOI: 10.1016/j.neuroimage.2016.06.038] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 05/21/2016] [Accepted: 06/20/2016] [Indexed: 10/21/2022] Open
Abstract
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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Affiliation(s)
- K E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany
| | - F Schlagenhauf
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, 10115 Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 04130 Leipzig, Germany
| | - Q J M Huys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Department of Psychiatry, Psychosomatics and Psychotherapy, Hospital of Psychiatry, University of Zurich, Switzerland
| | - S Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - E A Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - K H Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - L Rigoux
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany
| | - R J Moran
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK; Virgina Institute of Technology, USA
| | - J Daunizeau
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; ICM Paris, France
| | - R J Dolan
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
| | - K J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK
| | - A Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, 10115 Berlin, Germany; Humboldt Universität zu Berlin, Berlin School of Mind and Brain, 10115 Berlin, Germany
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26
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Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal models: Case studies of single gene mutations. Neuroimage 2015; 124:43-53. [PMID: 26342528 PMCID: PMC4655917 DOI: 10.1016/j.neuroimage.2015.08.057] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Revised: 07/31/2015] [Accepted: 08/25/2015] [Indexed: 11/24/2022] Open
Abstract
Clinical 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 modeling (DCM) for M/EEG includes ion channel parameter estimates. Parameter estimates from patients with monogenic ion channelopathies were compared. Synaptic channel abnormality was identified in patients, with specificity above 89%. DCM could serve as a platform for non-invasively assaying brain molecular dynamics.
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