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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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2
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Dallmer-Zerbe I, Jajcay N, Chvojka J, Janca R, Jezdik P, Krsek P, Marusic P, Jiruska P, Hlinka J. Computational modeling allows unsupervised classification of epileptic brain states across species. Sci Rep 2023; 13:13436. [PMID: 37596382 PMCID: PMC10439162 DOI: 10.1038/s41598-023-39867-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023] Open
Abstract
Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Nikola Jajcay
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- National Institute of Mental Health, 250 67, Klecany, Czech Republic
| | - Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Petr Jezdik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Pavel Krsek
- Department of Paediatric Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic.
- National Institute of Mental Health, 250 67, Klecany, Czech Republic.
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3
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Dura-Bernal S, Neymotin SA, Suter BA, Dacre J, Moreira JVS, Urdapilleta E, Schiemann J, Duguid I, Shepherd GMG, Lytton WW. Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics. Cell Rep 2023; 42:112574. [PMID: 37300831 PMCID: PMC10592234 DOI: 10.1016/j.celrep.2023.112574] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 02/27/2023] [Accepted: 05/12/2023] [Indexed: 06/12/2023] Open
Abstract
Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, Grossman School of Medicine, New York University (NYU), New York, NY, USA
| | - Benjamin A Suter
- Department of Physiology, Northwestern University, Evanston, IL, USA
| | - Joshua Dacre
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Eugenio Urdapilleta
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Julia Schiemann
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK; Center for Integrative Physiology and Molecular Medicine, Saarland University, Saarbrücken, Germany
| | - Ian Duguid
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | | | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA; Department of Neurology, Kings County Hospital Center, Brooklyn, NY, USA
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4
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Perl YS, Pallavicini C, Piccinini J, Demertzi A, Bonhomme V, Martial C, Panda R, Alnagger N, Annen J, Gosseries O, Ibañez A, Laufs H, Sitt JD, Jirsa VK, Kringelbach ML, Laureys S, Deco G, Tagliazucchi E. Low-dimensional organization of global brain states of reduced consciousness. Cell Rep 2023; 42:112491. [PMID: 37171963 PMCID: PMC11220841 DOI: 10.1016/j.celrep.2023.112491] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/19/2023] [Accepted: 04/24/2023] [Indexed: 05/14/2023] Open
Abstract
Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unclear. We combine whole-brain modeling, data augmentation, and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to patients with brain injury is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, increasing alongside loss of consciousness. Finally, we investigate the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.
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Affiliation(s)
- Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Paris Brain Institute (ICM), Paris, France.
| | - Carla Pallavicini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI), Buenos Aires, Argentina
| | - Juan Piccinini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
| | - Athena Demertzi
- Physiology of Cognition Research Lab, GIGA CRC-In Vivo Imaging Center, GIGA Institute, University of Liège, Liège, Belgium
| | - Vincent Bonhomme
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liège, Liège, Belgium; University Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Régional de la Citadelle (CHR Citadelle), Liège, Belgium; Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Naji Alnagger
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Agustin Ibañez
- National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California-San Francisco (UCSF), San Francisco, CA, USA; Trinity College, Dublin, Ireland
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany; Department of Neurology, Christian Albrechts University, Kiel, Germany
| | - Jacobo D Sitt
- Paris Brain Institute (ICM), Paris, France; INSERM U 1127, Paris, France; CNRS UMR 7225, Paris, France
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Århus, Denmark; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium.
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5
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Sanz Perl Y, Fittipaldi S, Gonzalez Campo C, Moguilner S, Cruzat J, Fraile-Vazquez ME, Herzog R, Kringelbach ML, Deco G, Prado P, Ibanez A, Tagliazucchi E. Model-based whole-brain perturbational landscape of neurodegenerative diseases. eLife 2023; 12:e83970. [PMID: 36995213 PMCID: PMC10063230 DOI: 10.7554/elife.83970] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/15/2023] [Indexed: 03/31/2023] Open
Abstract
The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.
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Affiliation(s)
- Yonatan Sanz Perl
- Department of Physics, University of Buenos AiresBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Sol Fittipaldi
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
| | - Cecilia Gonzalez Campo
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
| | - Sebastián Moguilner
- Global Brain Health Institute, University of California, San FranciscoSan FranciscoUnited States
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | - Josephine Cruzat
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | | | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | - Morten L Kringelbach
- Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus UniversityÅrhusDenmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBragaPortugal
- Centre for Eudaimonia and Human Flourishing, University of OxfordOxfordUnited Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
- Department of Information and Communication Technologies, Universitat Pompeu FabraBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA)BarcelonaSpain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological Sciences, Monash UniversityClaytonAustralia
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San SebastiánSantiagoChile
| | - Agustin Ibanez
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Global Brain Health Institute, University of California, San FranciscoSan FranciscoUnited States
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
- Trinity College Institute of Neuroscience (TCIN), Trinity College DublinDublinIreland
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos AiresBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
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6
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Firfilionis D, Hutchings F, Tamadoni R, Walsh D, Turnbull M, Escobedo-Cousin E, Bailey RG, Gausden J, Patel A, Haci D, Liu Y, LeBeau FEN, Trevelyan A, Constandinou TG, O'Neill A, Kaiser M, Degenaar P, Jackson A. A Closed-Loop Optogenetic Platform. Front Neurosci 2021; 15:718311. [PMID: 34566564 PMCID: PMC8462298 DOI: 10.3389/fnins.2021.718311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 08/16/2021] [Indexed: 01/11/2023] Open
Abstract
Neuromodulation is an established treatment for numerous neurological conditions, but to expand the therapeutic scope there is a need to improve the spatial, temporal and cell-type specificity of stimulation. Optogenetics is a promising area of current research, enabling optical stimulation of genetically-defined cell types without interfering with concurrent electrical recording for closed-loop control of neural activity. We are developing an open-source system to provide a platform for closed-loop optogenetic neuromodulation, incorporating custom integrated circuitry for recording and stimulation, real-time closed-loop algorithms running on a microcontroller and experimental control via a PC interface. We include commercial components to validate performance, with the ultimate aim of translating this approach to humans. In the meantime our system is flexible and expandable for use in a variety of preclinical neuroscientific applications. The platform consists of a Controlling Abnormal Network Dynamics using Optogenetics (CANDO) Control System (CS) that interfaces with up to four CANDO headstages responsible for electrical recording and optical stimulation through custom CANDO LED optrodes. Control of the hardware, inbuilt algorithms and data acquisition is enabled via the CANDO GUI (Graphical User Interface). Here we describe the design and implementation of this system, and demonstrate how it can be used to modulate neuronal oscillations in vitro and in vivo.
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Affiliation(s)
- Dimitrios Firfilionis
- Neuroprosthesis Lab, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Frances Hutchings
- Digital Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Reza Tamadoni
- Neuroprosthesis Lab, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Darren Walsh
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Mark Turnbull
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Enrique Escobedo-Cousin
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Richard G. Bailey
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Johannes Gausden
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Aaliyah Patel
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Dorian Haci
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Yan Liu
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
- Department of Micro-Nano Electronics, Shanghai Jiaotong University, Shanghai, China
| | - Fiona E. N. LeBeau
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew Trevelyan
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Timothy G. Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
- Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - Anthony O'Neill
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Patrick Degenaar
- Neuroprosthesis Lab, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew Jackson
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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7
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Klink PC, Aubry JF, Ferrera VP, Fox AS, Froudist-Walsh S, Jarraya B, Konofagou EE, Krauzlis RJ, Messinger A, Mitchell AS, Ortiz-Rios M, Oya H, Roberts AC, Roe AW, Rushworth MFS, Sallet J, Schmid MC, Schroeder CE, Tasserie J, Tsao DY, Uhrig L, Vanduffel W, Wilke M, Kagan I, Petkov CI. Combining brain perturbation and neuroimaging in non-human primates. Neuroimage 2021; 235:118017. [PMID: 33794355 PMCID: PMC11178240 DOI: 10.1016/j.neuroimage.2021.118017] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 03/07/2021] [Accepted: 03/22/2021] [Indexed: 12/11/2022] Open
Abstract
Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.
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Affiliation(s)
- P Christiaan Klink
- Department of Vision & Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands.
| | - Jean-François Aubry
- Physics for Medicine Paris, Inserm U1273, CNRS UMR 8063, ESPCI Paris, PSL University, Paris, France
| | - Vincent P Ferrera
- Department of Neuroscience & Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Andrew S Fox
- Department of Psychology & California National Primate Research Center, University of California, Davis, CA, USA
| | | | - Béchir Jarraya
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France; Foch Hospital, UVSQ, Suresnes, France
| | - Elisa E Konofagou
- Ultrasound and Elasticity Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA
| | - Richard J Krauzlis
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Anna S Mitchell
- Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
| | - Michael Ortiz-Rios
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom; German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Hiroyuki Oya
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Neurosurgery, University of Iowa, Iowa city, IA, USA
| | - Angela C Roberts
- Department of Physiology, Development and Neuroscience, Cambridge University, Cambridge, United Kingdom
| | - Anna Wang Roe
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
| | | | - Jérôme Sallet
- Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Michael Christoph Schmid
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom; Faculty of Science and Medicine, University of Fribourg, Chemin du Musée 5, CH-1700 Fribourg, Switzerland
| | - Charles E Schroeder
- Nathan Kline Institute, Orangeburg, NY, USA; Columbia University, New York, NY, USA
| | - Jordy Tasserie
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France
| | - Doris Y Tsao
- Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience; Howard Hughes Medical Institute; Computation and Neural Systems, Caltech, Pasadena, CA, USA
| | - Lynn Uhrig
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Neurosciences Department, KU Leuven Medical School, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven Belgium; Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Melanie Wilke
- German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany; Department of Cognitive Neurology, University Medicine Göttingen, Göttingen, Germany
| | - Igor Kagan
- German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany.
| | - Christopher I Petkov
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom.
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8
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Saidi M, Firoozabadi SM. Glial cells have more important role in tDCS-induced brain activities rather than neurons. Med Hypotheses 2021; 153:110615. [PMID: 34214759 DOI: 10.1016/j.mehy.2021.110615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 05/03/2021] [Accepted: 05/25/2021] [Indexed: 10/21/2022]
Abstract
Transcranial Direct Current Stimulation (tDCS) is a non-invasive and sub-threshold method for altering the excitability of neurons to achieve target conditions. Many clinical applications of tDCS are in progress including treatment of neurological disease. Despite the therapeutic and practical potentials of tDCS, the precise mechanism of its impact is still unclear to a large extent. The initial perspective about the effect of tDCS on synaptic plasticity was that electric stimulation of the brain degrades the threshold of neural firing, thus facilitating electrical transmission and improving conductivity between neurons and ultimately leading to changes in the processing of information. However, new results of some experimental studies have changed the initial perspective. Recently, calcium imaging has shown sudden calcium fluctuations, followed by a change in cortical metaplasticity under tDCS. These pieces of evidence pinpoint the important role of astrocytes in describing the effect of tDCS. Astrocytes are glial cells that are traditionally viewed with only a supportive and nutritional role, but recent research expresses that they are active and contribute to processing and regulating synaptic information. We hypothesize that considering astrocytes as an important player in tDCS effect instead of the direct effect on voltage membrane of neurons is closer to the reality of tDCS effect mechanism. This hypothesis is examined here based on the experimental evidence published so far, which indicates astroglia involvement in tDCS-induced activities and results of our proposed computational model.
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Affiliation(s)
- Maryam Saidi
- Bioelectric Department, Computer and Electrical Engineering Faculty, Tarbiat Modares University, Tehran, Iran
| | - Seyed Mohammad Firoozabadi
- Bioelectric Department, Computer and Electrical Engineering Faculty, Tarbiat Modares University, Tehran, Iran.
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9
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Schroeder GM, Diehl B, Chowdhury FA, Duncan JS, de Tisi J, Trevelyan AJ, Forsyth R, Jackson A, Taylor PN, Wang Y. Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy. Proc Natl Acad Sci U S A 2020; 117:11048-11058. [PMID: 32366665 PMCID: PMC7245106 DOI: 10.1073/pnas.1922084117] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.
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Affiliation(s)
- Gabrielle M Schroeder
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - John S Duncan
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Andrew J Trevelyan
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Rob Forsyth
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Andrew Jackson
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom;
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
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10
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Çetin M. Model-based robust suppression of epileptic seizures without sensory measurements. Cogn Neurodyn 2019; 14:51-67. [PMID: 32015767 DOI: 10.1007/s11571-019-09555-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 08/06/2019] [Accepted: 09/12/2019] [Indexed: 12/15/2022] Open
Abstract
Uncontrolled seizures may lead to irreversible damages in the brain and various limitations in the patient's life. There exist experimental studies to stabilize the patient seizures. However, the experimental setups have many sensory devices to measure the dynamics of the brain cortex. These equipments prevent to produce small portable stabilizers for patients in everyday life. Recently, a comprehensive cortex model is introduced to apply model-based observers and controllers. However, this cortex model can be uncertain and have time-varying parameters. Therefore, in this paper, a robust Takagi-Sugeno (TS) controller and observer are designed to suppress the epileptic seizures without sensory measurements. The unavailable sensory measurements are provided by the designed nonlinear observer. The exponential convergence of the observer and controller is satisfied by the feedback parameter design using linear matrix inequalities. In addition, TS fuzzy observer-controller design has been compared with the conventional PID method in terms of control performance and design problem. The numerical computations show that the epileptic seizures are more effectively suppressed by the TS fuzzy observer-based controller under uncertain membrane potential dynamics.
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Affiliation(s)
- Meriç Çetin
- Department of Computer Engineering, Pamukkale University, Kinikli Campus, 20070 Denizli, Turkey
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11
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Douw L, van Dellen E, Gouw AA, Griffa A, de Haan W, van den Heuvel M, Hillebrand A, Van Mieghem P, Nissen IA, Otte WM, Reijmer YD, Schoonheim MM, Senden M, van Straaten ECW, Tijms BM, Tewarie P, Stam CJ. The road ahead in clinical network neuroscience. Netw Neurosci 2019; 3:969-993. [PMID: 31637334 PMCID: PMC6777944 DOI: 10.1162/netn_a_00103] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Alida A. Gouw
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alessandra Griffa
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn van den Heuvel
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Ida A. Nissen
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem M. Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Elisabeth C. W. van Straaten
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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12
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Papo D. Neurofeedback: Principles, appraisal, and outstanding issues. Eur J Neurosci 2019; 49:1454-1469. [PMID: 30570194 DOI: 10.1111/ejn.14312] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 11/21/2018] [Accepted: 11/27/2018] [Indexed: 12/16/2022]
Abstract
Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.
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Affiliation(s)
- David Papo
- SCALab, CNRS, Université de Lille, Villeneuve d'Ascq, France
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13
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Thornton C, Hutchings F, Kaiser M. The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) Version 2.0: Modelling in vitro electrical stimulation of brain tissue. Wellcome Open Res 2019; 4:20. [PMID: 30984877 PMCID: PMC6439485 DOI: 10.12688/wellcomeopenres.15058.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2019] [Indexed: 11/20/2022] Open
Abstract
Neuronal circuits can be modelled in detail allowing us to predict the effects of stimulation on individual neurons. Electrical stimulation of neuronal circuits in vitro and in vivo excites a range of neurons within the tissue and measurements of neural activity, e.g the local field potential (LFP), are again an aggregate of a large pool of cells. The previous version of our Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) allowed for the simulation of the LFP generated by a patch of brain tissue. Here, we extend VERTEX to simulate the effect of electrical stimulation through a focal electric field. We observe both direct changes in neural activity and changes in synaptic plasticity. Testing our software in a model of a rat neocortical slice, we determine the currents contributing to the LFP, the effects of paired pulse stimulation to induce short term plasticity (STP), and the effect of theta burst stimulation (TBS) to induce long term potentiation (LTP).
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Affiliation(s)
- Christopher Thornton
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK
| | - Frances Hutchings
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK
- Institute of Neuroscience, Newcastle University, UK, Newcastle upon Tyne, NE2 4HH, UK
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14
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Rubin JE. Computational models of basal ganglia dysfunction: the dynamics is in the details. Curr Opin Neurobiol 2017; 46:127-135. [PMID: 28888856 DOI: 10.1016/j.conb.2017.08.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 08/22/2017] [Indexed: 12/18/2022]
Abstract
The development, simulation, and analysis of mathematical models offer helpful tools for integrating experimental findings and exploring or suggesting possible explanatory mechanisms. As models relating to basal ganglia dysfunction have proliferated, however, there has not always been consistency among their findings. This work points out several ways in which biological details, relating to ionic currents and synaptic pathways, can influence the dynamics of models of the basal ganglia under parkinsonian conditions and hence may be important for inclusion in models. It also suggests some additional useful directions for future modeling studies relating to basal ganglia dysfunction.
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Affiliation(s)
- Jonathan E Rubin
- Department of Mathematics and Center for the Neural Basis of Cognition, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh, PA 15260, USA.
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15
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Beuter A. The Use of Neurocomputational Models as Alternatives to Animal Models in the Development of Electrical Brain Stimulation Treatments. Altern Lab Anim 2017; 45:91-99. [DOI: 10.1177/026119291704500203] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent publications call for more animal models to be used and more experiments to be performed, in order to better understand the mechanisms of neurodegenerative disorders, to improve human health, and to develop new brain stimulation treatments. In response to these calls, some limitations of the current animal models are examined by using Deep Brain Stimulation (DBS) in Parkinson's disease as an illustrative example. Without focusing on the arguments for or against animal experimentation, or on the history of DBS, the present paper argues that given recent technological and theoretical advances, the time has come to consider bioinspired computational modelling as a valid alternative to animal models, in order to design the next generation of human brain stimulation treatments. However, before computational neuroscience is fully integrated in the translational process and used as a substitute for animal models, several obstacles need to be overcome. These obstacles are examined in the context of institutional, financial, technological and behavioural lock-in. Recommendations include encouraging agreement to change long-term habitual practices, explaining what alternative models can achieve, considering economic stakes, simplifying administrative and regulatory constraints, and carefully examining possible conflicts of interest.
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Affiliation(s)
- Anne Beuter
- Institut Polytechnique de Bordeaux, Bordeaux, France
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16
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Wang Y, Trevelyan AJ, Valentin A, Alarcon G, Taylor PN, Kaiser M. Mechanisms underlying different onset patterns of focal seizures. PLoS Comput Biol 2017; 13:e1005475. [PMID: 28472032 PMCID: PMC5417416 DOI: 10.1371/journal.pcbi.1005475] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 03/23/2017] [Indexed: 02/07/2023] Open
Abstract
Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types-low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. We show that the two patterns are associated with different mechanisms at the spatial scale of a single ECoG electrode. The LAF onset is initiated by independent patches of localised activity, which slowly invade the surrounding tissue and coalesce over time. In contrast, the HAS onset is a global, systemic transition to a coexisting seizure state triggered by a local event. We find that such a global transition is enabled by an increase in the excitability of the "healthy" surrounding tissue, which by itself does not generate seizures, but can support seizure activity when incited. In our simulations, the difference in surrounding tissue excitability also offers a simple explanation of the clinically reported difference in surgical outcomes. Finally, we demonstrate in the model how changes in tissue excitability could be elucidated, in principle, using active stimulation. Taken together, our modelling results suggest that the excitability of the tissue surrounding the seizure core may play a determining role in the seizure onset pattern, as well as in the surgical outcome.
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Affiliation(s)
- Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neurology, University College London, London, United Kingdom
| | - Andrew J Trevelyan
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Gonzalo Alarcon
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Comprehensive Epilepsy Center, Neuroscience Institute, Academic Health Systems, Hamad Medical Corporation, Doha, Qatar
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neurology, University College London, London, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
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17
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Malerba P, Straudi S, Fregni F, Bazhenov M, Basaglia N. Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery. Front Neurol 2017; 8:58. [PMID: 28280482 PMCID: PMC5322214 DOI: 10.3389/fneur.2017.00058] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 02/09/2017] [Indexed: 12/27/2022] Open
Abstract
Stroke is a leading cause of worldwide disability, and up to 75% of survivors suffer from some degree of arm paresis. Recently, rehabilitation of stroke patients has focused on recovering motor skills by taking advantage of use-dependent neuroplasticity, where high-repetition of goal-oriented movement is at times combined with non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS). Merging the two approaches is thought to provide outlasting clinical gains, by enhancing synaptic plasticity and motor relearning in the motor cortex primary area. However, this general approach has shown mixed results across the stroke population. In particular, stroke location has been found to correlate with the likelihood of success, which suggests that different patients might require different protocols. Understanding how motor rehabilitation and stimulation interact with ongoing neural dynamics is crucial to optimize rehabilitation strategies, but it requires theoretical and computational models to consider the multiple levels at which this complex phenomenon operate. In this work, we argue that biophysical models of cortical dynamics are uniquely suited to address this problem. Specifically, biophysical models can predict treatment efficacy by introducing explicit variables and dynamics for damaged connections, changes in neural excitability, neurotransmitters, neuromodulators, plasticity mechanisms, and repetitive movement, which together can represent brain state, effect of incoming stimulus, and movement-induced activity. In this work, we hypothesize that effects of tDCS depend on ongoing neural activity and that tDCS effects on plasticity may be also related to enhancing inhibitory processes. We propose a model design for each step of this complex system, and highlight strengths and limitations of the different modeling choices within our approach. Our theoretical framework proposes a change in paradigm, where biophysical models can contribute to the future design of novel protocols, in which combined tDCS and motor rehabilitation strategies are tailored to the ongoing dynamics that they interact with, by considering the known biophysical factors recruited by such protocols and their interaction.
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Affiliation(s)
- Paola Malerba
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sofia Straudi
- Neuroscience and Rehabilitation Department, Ferrara University Hospital, Ferrara, Italy
| | - Felipe Fregni
- Center of Neuromodulation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Nino Basaglia
- Neuroscience and Rehabilitation Department, Ferrara University Hospital, Ferrara, Italy
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18
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Bestmann S. Computational neurostimulation in basic and translational research. PROGRESS IN BRAIN RESEARCH 2015; 222:xv-xx. [PMID: 26541385 DOI: 10.1016/s0079-6123(15)00159-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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