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Sorrentino P, Pathak A, Ziaeemehr A, Troisi Lopez E, Cipriano L, Romano A, Sparaco M, Quarantelli M, Banerjee A, Sorrentino G, Jirsa V, Hashemi M. The virtual multiple sclerosis patient. iScience 2024; 27:110101. [PMID: 38974971 PMCID: PMC11226980 DOI: 10.1016/j.isci.2024.110101] [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] [Received: 07/17/2023] [Revised: 03/09/2024] [Accepted: 05/22/2024] [Indexed: 07/09/2024] Open
Abstract
Multiple sclerosis (MS) diagnosis typically involves assessing clinical symptoms, MRI findings, and ruling out alternative explanations. While myelin damage broadly affects conduction speeds, traditional tests focus on specific white-matter tracts, which may not reflect overall impairment accurately. In this study, we integrate diffusion tensor immaging (DTI) and magnetoencephalography (MEG) data into individualized virtual brain models to estimate conduction velocities for MS patients and controls. Using Bayesian inference, we demonstrated a causal link between empirical spectral changes and inferred slower conduction velocities in patients. Remarkably, these velocities proved superior predictors of clinical disability compared to structural damage. Our findings underscore a nuanced relationship between conduction delays and large-scale brain dynamics, suggesting that individualized velocity alterations at the whole-brain level contribute causatively to clinical outcomes in MS.
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Affiliation(s)
- P. Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
| | - A. Pathak
- National Brain Research Centre, Manesar, Gurgaon, Haryana, India
| | - A. Ziaeemehr
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - E. Troisi Lopez
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - L. Cipriano
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - A. Romano
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - M. Sparaco
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Caserta, Italy
| | - M. Quarantelli
- Biostructure and Bioimaging Institute, National Research Council, Naples, Italy
| | - A. Banerjee
- National Brain Research Centre, Manesar, Gurgaon, Haryana, India
| | - G. Sorrentino
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - V. Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - M. Hashemi
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
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2
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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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3
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Tolley N, Rodrigues PLC, Gramfort A, Jones SR. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. PLoS Comput Biol 2024; 20:e1011108. [PMID: 38408099 PMCID: PMC10919875 DOI: 10.1371/journal.pcbi.1011108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 03/07/2024] [Accepted: 02/10/2024] [Indexed: 02/28/2024] Open
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
| | | | | | - Stephanie R. Jones
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
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4
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Shiraishi Y, Matsuya Y, Kusumoto T, Fukunaga H. Modeling for predicting survival fraction of cells after ultra-high dose rate irradiation. Phys Med Biol 2023; 69:015017. [PMID: 38056015 DOI: 10.1088/1361-6560/ad131b] [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: 06/09/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Objective. FLASH radiotherapy (FLASH-RT) with ultra-high dose rate (UHDR) irradiation (i.e. > 40 Gy s-1) spares the function of normal tissues while preserving antitumor efficacy, known as the FLASH effect. The biological effects after conventional dose rate-radiotherapy (CONV-RT) with ≤0.1 Gy s-1have been well modeled by considering microdosimetry and DNA repair processes, meanwhile modeling of radiosensitivities under UHDR irradiation is insufficient. Here, we developed anintegrated microdosimetric-kinetic(IMK)model for UHDR-irradiationenabling the prediction of surviving fraction after UHDR irradiation.Approach.TheIMK model for UHDR-irradiationconsiders the initial DNA damage yields by the modification of indirect effects under UHDR compared to CONV dose rate. The developed model is based on the linear-quadratic (LQ) nature with the dose and dose square coefficients, considering the reduction of DNA damage yields as a function of dose rate.Main results.The estimate by the developed model could successfully reproduce thein vitroexperimental dose-response curve for various cell line types and dose rates.Significance.The developed model would be useful for predicting the biological effects under the UHDR irradiation.
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Affiliation(s)
- Yuta Shiraishi
- Graduate school of Health Sciences, Hokkaido University, Kita-12, Nishi-5, Kita-ku, Sapporo, Hokkaido, 060-0812, Japan
- Faculty of Health Sciences, Japan Healthcare University, 3-11-1-50 Tsukisamu-higashi, Toyohira-ku, Sapporo, Hokkaido, 062-0053, Japan
| | - Yusuke Matsuya
- Faculty of Health Sciences, Hokkaido University, Kita-12, Nishi-5, Kita-ku, Sapporo, Hokkaido, 060-0812, Japan
- Nuclear Science and Engineering Center, Japan Atomic Energy Agency, 2-4 Shirakata, Tokai, Ibaraki, 319-1195, Japan
| | - Tamon Kusumoto
- National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan
| | - Hisanori Fukunaga
- Faculty of Health Sciences, Hokkaido University, Kita-12, Nishi-5, Kita-ku, Sapporo, Hokkaido, 060-0812, Japan
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5
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Lavanga M, Stumme J, Yalcinkaya BH, Fousek J, Jockwitz C, Sheheitli H, Bittner N, Hashemi M, Petkoski S, Caspers S, Jirsa V. The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging. Neuroimage 2023; 283:120403. [PMID: 37865260 DOI: 10.1016/j.neuroimage.2023.120403] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023] Open
Abstract
The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.
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Affiliation(s)
- Mario Lavanga
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Bahar Hazal Yalcinkaya
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Jan Fousek
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hiba Sheheitli
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Nora Bittner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Meysam Hashemi
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Spase Petkoski
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France.
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Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, Safi SZ, Singh SK, Velmurugan D, Selvaraj C. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors. Methods 2023; 219:82-94. [PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010] [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: 08/07/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
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Affiliation(s)
| | | | - Baskaralingam Vaseeharan
- Department of Animal Health and Management, Science Block, Alagappa University, Karaikudi, Tamil Nadu 630 003, India
| | - Abdulaziz S Alothaim
- Department of Biology, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Esam S Al-Malki
- Department of Biology, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Babu Snekaa
- Laboratory for Artificial Intelligence and Molecular Modelling, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu 600077, India
| | - Sher Zaman Safi
- Faculty of Medicine, Bioscience and Nursing, MAHSA University, Jenjarom 42610, Selangor, Malaysia
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Devadasan Velmurugan
- Department of Biotechnology, College of Engineering & Technology, SRM Institute of Science & Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India
| | - Chandrabose Selvaraj
- Laboratory for Artificial Intelligence and Molecular Modelling, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu 600077, India; Laboratory for Artificial Intelligence and Molecular Modelling, Center for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu 602105, India.
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7
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Jirsa V, Wang H, Triebkorn P, Hashemi M, Jha J, Gonzalez-Martinez J, Guye M, Makhalova J, Bartolomei F. Personalised virtual brain models in epilepsy. Lancet Neurol 2023; 22:443-454. [PMID: 36972720 DOI: 10.1016/s1474-4422(23)00008-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023]
Abstract
Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.
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Affiliation(s)
- Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France.
| | - Huifang Wang
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Paul Triebkorn
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Meysam Hashemi
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Jayant Jha
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | | | - Maxime Guye
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Julia Makhalova
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Fabrice Bartolomei
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
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8
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Hashemi M, Vattikonda AN, Jha J, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Netw 2023; 163:178-194. [PMID: 37060871 DOI: 10.1016/j.neunet.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.
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9
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Wang HE, Woodman M, Triebkorn P, Lemarechal JD, Jha J, Dollomaja B, Vattikonda AN, Sip V, Medina Villalon S, Hashemi M, Guye M, Makhalova J, Bartolomei F, Jirsa V. Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy. Sci Transl Med 2023; 15:eabp8982. [PMID: 36696482 DOI: 10.1126/scitranslmed.abp8982] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.
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Affiliation(s)
- Huifang E Wang
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Marmaduke Woodman
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Paul Triebkorn
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery team, Paris F-75013, France
| | - Jayant Jha
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Borana Dollomaja
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Anirudh Nihalani Vattikonda
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Viktor Sip
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Samuel Medina Villalon
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Meysam Hashemi
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Julia Makhalova
- APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France.,Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Viktor Jirsa
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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10
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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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11
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Zhao Y, Boley M, Pelentritou A, Karoly PJ, Freestone DR, Liu Y, Muthukumaraswamy S, Woods W, Liley D, Kuhlmann L. Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm. Neuroimage 2022; 263:119592. [PMID: 36031185 DOI: 10.1016/j.neuroimage.2022.119592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/28/2022] Open
Abstract
Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes in different brain states.
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Affiliation(s)
- Yun Zhao
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Mario Boley
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Andria Pelentritou
- Swinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia
| | - Dean R Freestone
- Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, Australia
| | - Yueyang Liu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - William Woods
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - David Liley
- Swinburne University of Technology, Hawthorn, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia.
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12
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Wang C, Chen S, Huang L, Yu L. Prediction and control of focal seizure spread: Random walk with restart on heterogeneous brain networks. Phys Rev E 2022; 105:064412. [PMID: 35854502 DOI: 10.1103/physreve.105.064412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
Whole-brain models offer a promising method of predicting seizure spread, which is critical for successful surgical treatment of focal epilepsy. Existing methods are largely based on structural connectome, which ignores the effects of heterogeneity within the regional excitability of brains. In this study we used a whole-brain model to show that heterogeneity in nodal excitability had a significant impact on seizure propagation in the networks and compromised the prediction accuracy with structural connections. We then addressed this problem with an algorithm based on random walk with restart on graphs. We demonstrated that by establishing a relationship between the restarting probability and the excitability for each node, this algorithm could significantly improve the seizure spread prediction accuracy in heterogeneous networks and was more robust against the extent of heterogeneity. We also strategized surgical seizure control as a process to identify and remove the key nodes (connections) responsible for the early spread of seizures from the focal region. Compared to strategies based on structural connections, virtual surgery with a strategy based on a modified random walk with extended restart generated outcomes with a high success rate while maintaining low damage to the brain by removing fewer anatomical connections. These findings may have potential applications in developing personalized surgery strategies for epilepsy.
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Affiliation(s)
- Chen Wang
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Sida Chen
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Liang Huang
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
- Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Lianchun Yu
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
- Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou, Gansu 730000, China
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13
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Wischnewski KJ, Eickhoff SB, Jirsa VK, Popovych OV. Towards an efficient validation of dynamical whole-brain models. Sci Rep 2022; 12:4331. [PMID: 35288595 PMCID: PMC8921267 DOI: 10.1038/s41598-022-07860-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/22/2022] [Indexed: 12/12/2022] Open
Abstract
Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.
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14
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Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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