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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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Verma P, Ranasinghe K, Prasad J, Cai C, Xie X, Lerner H, Mizuiri D, Miller B, Rankin K, Vossel K, Cheung SW, Nagarajan SS, Raj A. Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease. Alzheimers Res Ther 2024; 16:62. [PMID: 38504361 PMCID: PMC10953266 DOI: 10.1186/s13195-024-01426-7] [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: 12/07/2023] [Accepted: 03/04/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.
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Affiliation(s)
- Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Kamalini Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Hannah Lerner
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Katherine Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Mary S. Easton Center for Alzheimer's Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Surgical Services, Veterans Affairs, San Francisco, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Amato LG, Vergani AA, Lassi M, Fabbiani C, Mazzeo S, Burali R, Nacmias B, Sorbi S, Mannella R, Grippo A, Bessi V, Mazzoni A. Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12526. [PMID: 38371358 PMCID: PMC10870085 DOI: 10.1002/dad2.12526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 02/20/2024]
Abstract
INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.
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Affiliation(s)
- Lorenzo Gaetano Amato
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Alberto Arturo Vergani
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Michael Lassi
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Carlo Fabbiani
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Salvatore Mazzeo
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | - Benedetta Nacmias
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Sandro Sorbi
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | | | - Valentina Bessi
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Alberto Mazzoni
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
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Patow G, Stefanovski L, Ritter P, Deco G, Kobeleva X. Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer's disease. Alzheimers Res Ther 2023; 15:210. [PMID: 38053164 PMCID: PMC10696890 DOI: 10.1186/s13195-023-01349-9] [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: 05/16/2023] [Accepted: 11/07/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND Alzheimer's disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A[Formula: see text]) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact. METHODS We use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A[Formula: see text] and tau on the excitation-inhibition balance of the local nodes. RESULTS We found a clear dominance of A[Formula: see text] over tau in the early disease stages (MCI), while tau dominates over A[Formula: see text] in the latest stages (AD). We identify crucial roles for A[Formula: see text] and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD. CONCLUSIONS Our study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico.
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Affiliation(s)
- Gustavo Patow
- ViRVIG, Universitat de Girona, Girona, Spain.
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Center for Brain and Cognition, Computational Neuroscience Group, Barcelona, Spain.
| | - Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, 10117, Germany
| | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, 10117, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future Berlin, Berlin, Germany
| | - Gustavo Deco
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Center for Brain and Cognition, Computational Neuroscience Group, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Xenia Kobeleva
- Computational Neurology Research Group, Ruhr University Bochum, Bochum, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinic for Neurology, University Hospital Bonn, Bonn, Germany
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Verma P, Ranasinghe K, Prasad J, Cai C, Xie X, Lerner H, Mizuiri D, Miller B, Rankin K, Vossel K, Cheung SW, Nagarajan S, Raj A. Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2579392. [PMID: 36993350 PMCID: PMC10055509 DOI: 10.21203/rs.3.rs-2579392/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Alzheimer's disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify abnormal biophysical mechanisms underlying these abnormal electrophysiological patterns, we estimated the parameters of a spectral graph-theory model (SGM). SGM is an analytic model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. The long-range excitatory time scale was associated with greater deficits in global cognition and was able to distinguish AD patients from controls with high accuracy. These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the spatiospectral signatures and cognition in AD.
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Affiliation(s)
- Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kamalini Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Hannah Lerner
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Katherine Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Mary S. Easton Center for Alzheimer's Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Surgical Services, Veterans Affairs, San Francisco, USA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Verma P, Ranasinghe K, Prasad J, Cai C, Xie X, Lerner H, Mizuiri D, Miller B, Rankin K, Vossel K, Cheung SW, Nagarajan S, Raj A. Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2579392. [PMID: 36993350 PMCID: PMC10055509 DOI: 10.21203/rs.3.rs-2579392/v3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify abnormal biophysical mechanisms underlying these abnormal electrophysiological patterns, we estimated the parameters of a spectral graph-theory model (SGM). SGM is an analytic model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. The long-range excitatory time scale was associated with greater deficits in global cognition and was able to distinguish AD patients from controls with high accuracy. These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the spatiospectral signatures and cognition in AD.
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Affiliation(s)
- Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kamalini Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Hannah Lerner
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Katherine Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Mary S. Easton Center for Alzheimer's Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Surgical Services, Veterans Affairs, San Francisco, USA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Monteverdi A, Palesi F, Schirner M, Argentino F, Merante M, Redolfi A, Conca F, Mazzocchi L, Cappa SF, Cotta Ramusino M, Costa A, Pichiecchio A, Farina LM, Jirsa V, Ritter P, Gandini Wheeler-Kingshott CAM, D’Angelo E. Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias. Front Aging Neurosci 2023; 15:1204134. [PMID: 37577354 PMCID: PMC10419271 DOI: 10.3389/fnagi.2023.1204134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/10/2023] [Indexed: 08/15/2023] Open
Abstract
Introduction Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission. Methods We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. Results The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. Discussion These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.
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Affiliation(s)
- Anita Monteverdi
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michael Schirner
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Francesca Argentino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Mariateresa Merante
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Laura Mazzocchi
- Advanced Imaging and Artificial Intelligence Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- University Institute of Advanced Studies (IUSS), Pavia, Italy
| | | | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Artificial Intelligence Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, INSERM, INS, Aix Marseille University, Marseille, France
| | - Petra Ritter
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Egidio D’Angelo
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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