1
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Ye C, Zhang Y, Ran C, Ma T. Recent Progress in Brain Network Models for Medical Applications: A Review. HEALTH DATA SCIENCE 2024; 4:0157. [PMID: 38979037 PMCID: PMC11227951 DOI: 10.34133/hds.0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 05/28/2024] [Indexed: 07/10/2024]
Abstract
Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.
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Affiliation(s)
- Chenfei Ye
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Yixuan Zhang
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Chen Ran
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Ting Ma
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
- Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology,
Harbin Institute of Technology at Shenzhen, China
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2
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Kavčič A, Demšar J, Georgiev D, Meglič NP, Šalamon AS. EEG functional connectivity after perinatal stroke. Cereb Cortex 2023; 33:9927-9935. [PMID: 37415237 DOI: 10.1093/cercor/bhad255] [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: 04/16/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
Impaired cognitive functioning after perinatal stroke has been associated with long-term functional brain network changes. We explored brain functional connectivity using a 64-channel resting-state electroencephalogram in 12 participants, aged 5-14 years with a history of unilateral perinatal arterial ischemic or haemorrhagic stroke. A control group of 16 neurologically healthy subjects was also included-each test subject was compared with multiple control subjects, matched by sex and age. Functional connectomes from the alpha frequency band were calculated for each subject and the differences in network graph metrics between the 2 groups were analyzed. Our results suggest that the functional brain networks of children with perinatal stroke show evidence of disruption even years after the insult and that the scale of changes appears to be influenced by the lesion volume. The networks remain more segregated and show a higher synchronization at both whole-brain and intrahemispheric level. Total interhemispheric strength was higher in children with perinatal stroke compared with healthy controls.
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Affiliation(s)
- Alja Kavčič
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Bohoričeva 20, 1000 Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Jure Demšar
- Faculty of Computer and Information Sciences, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
- Department of Psychology, Faculty of Arts, University of Ljubljana, Aškerčeva 2, 1000 Ljubljana, Slovenia
| | - Dejan Georgiev
- Faculty of Computer and Information Sciences, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
- Department of Neurology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Nuška Pečarič Meglič
- Department of Neuroradiology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Aneta Soltirovska Šalamon
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Bohoričeva 20, 1000 Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
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3
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Raj A, Verma P, Nagarajan S. Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging. Front Neurosci 2022; 16:959557. [PMID: 36110093 PMCID: PMC9468900 DOI: 10.3389/fnins.2022.959557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022] Open
Abstract
We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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4
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D'Angelo E, Jirsa V. The quest for multiscale brain modeling. Trends Neurosci 2022; 45:777-790. [PMID: 35906100 DOI: 10.1016/j.tins.2022.06.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/20/2022] [Accepted: 06/21/2022] [Indexed: 01/07/2023]
Abstract
Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, and Brain Connectivity Center, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy.
| | - Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale (INSERM) Unité 1106, Centre National de la Recherche Scientifique (CNRS), and University of Aix-Marseille, Marseille, France
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5
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Cohen AL. Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments. J Neurodev Disord 2022; 14:19. [PMID: 35279095 PMCID: PMC8918299 DOI: 10.1186/s11689-022-09433-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/03/2022] [Indexed: 11/20/2022] Open
Abstract
A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.
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Affiliation(s)
- Alexander Li Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA. .,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Laboratory for Brain Network Imaging and Modulation, Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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6
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Griffiths JD, Bastiaens SP, Kaboodvand N. Whole-Brain Modelling: Past, Present, and Future. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:313-355. [DOI: 10.1007/978-3-030-89439-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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7
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Task effects on functional connectivity measures after stroke. Exp Brain Res 2021; 240:575-590. [PMID: 34860257 DOI: 10.1007/s00221-021-06261-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 10/28/2021] [Indexed: 10/19/2022]
Abstract
Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.
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8
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Shu P, Zhu H, Jin W, Zhou J, Tong S, Sun J. The Resilience and Vulnerability of Human Brain Networks Across the Lifespan. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1756-1765. [PMID: 34410925 DOI: 10.1109/tnsre.2021.3105991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Resilience, the ability for a system to maintain its basic functionality when suffering from lesions, is a critical property for human brain, especially in the brain aging process. This study adopted a novel metric of network resilience, the Resilience Index (RI), to assess human brain resilience with three different lifespan datasets. Based on the structural brain networks constructed from diffusion tensor imaging (DTI), we observed an inverted-U relationship between RI and age, that is, RI increased during development and early adulthood, reached a peak at about 35 years old, and then decreased during aging, which suggested that brain resilience could be quantified by RI. Furthermore, we studied brain network vulnerability by the decreases in RI when virtual lesions occurred to nodes (i.e., brain regions) or edges (i.e., structural brain connectivity). We found that the strong edges were markedly vulnerable, and the homotopic edges were the most prominent representatives of vulnerable edges. In other words, an arbitrary attack on homotopic edges would have a high probability to degrade brain network resilience. These findings suggest the change of human brain resilience across the lifespan and provide a new perspective for exploring human brain vulnerability.
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9
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Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
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Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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10
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Klein PC, Ettinger U, Schirner M, Ritter P, Rujescu D, Falkai P, Koutsouleris N, Kambeitz-Ilankovic L, Kambeitz J. Brain Network Simulations Indicate Effects of Neuregulin-1 Genotype on Excitation-Inhibition Balance in Cortical Dynamics. Cereb Cortex 2021; 31:2013-2025. [PMID: 33279967 DOI: 10.1093/cercor/bhaa339] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/01/2020] [Accepted: 10/11/2020] [Indexed: 11/14/2022] Open
Abstract
Neuregulin-1 (NRG1) represents an important factor for multiple processes including neurodevelopment, brain functioning or cognitive functions. Evidence from animal research suggests an effect of NRG1 on the excitation-inhibition (E/I) balance in cortical circuits. However, direct evidence for the importance of NRG1 in E/I balance in humans is still lacking. In this work, we demonstrate the application of computational, biophysical network models to advance our understanding of the interaction between cortical activity observed in neuroimaging and the underlying neurobiology. We employed a biophysical neuronal model to simulate large-scale brain dynamics and to investigate the role of polymorphisms in the NRG1 gene (rs35753505, rs3924999) in n = 96 healthy adults. Our results show that G/G-carriers (rs3924999) exhibit a significant difference in global coupling (P = 0.048) and multiple parameters determining E/I-balance such as excitatory synaptic coupling (P = 0.047), local excitatory recurrence (P = 0.032) and inhibitory synaptic coupling (P = 0.028). This indicates that NRG1 may be related to excitatory recurrence or excitatory synaptic coupling potentially resulting in altered E/I-balance. Moreover, we suggest that computational modeling is a suitable tool to investigate specific biological mechanisms in health and disease.
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Affiliation(s)
- Pedro Costa Klein
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany
| | - Ulrich Ettinger
- Department of Psychology, University of Bonn, Bonn, 53111, Germany
| | - Michael Schirner
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin 10115, Germany
| | - Petra Ritter
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin 10115, Germany
| | - Dan Rujescu
- University Clinic for Psychiatry, Psychotherapy and Psychosomatic, Martin-Luther-University, Halle-Wittenberg, 06112, Germany
| | - Peter Falkai
- Department of Psychiatry, Ludwig Maximilians Universität München, 80336, Germany
| | | | - Lana Kambeitz-Ilankovic
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany.,Department of Psychiatry, Ludwig Maximilians Universität München, 80336, Germany
| | - Joseph Kambeitz
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany
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11
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Zink N, Lenartowicz A, Markett S. A new era for executive function research: On the transition from centralized to distributed executive functioning. Neurosci Biobehav Rev 2021; 124:235-244. [PMID: 33582233 DOI: 10.1016/j.neubiorev.2021.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023]
Abstract
"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States.
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States
| | - Sebastian Markett
- Department of Psychology, Humboldt University Berlin, Berlin, Germany
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12
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Tao Y, Rapp B. Investigating the network consequences of focal brain lesions through comparisons of real and simulated lesions. Sci Rep 2021; 11:2213. [PMID: 33500494 PMCID: PMC7838400 DOI: 10.1038/s41598-021-81107-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
Given the increased interest in the functional human connectome, a number of computer simulation studies have sought to develop a better quantitative understanding of the effects of focal lesions on the brain’s functional network organization. However, there has been little work evaluating the predictions of this simulation work vis a vis real lesioned connectomes. One of the few relevant studies reported findings from real chronic focal lesions that only partially confirmed simulation predictions. We hypothesize that these discrepancies arose because although the effects of focal lesions likely consist of two components: short-term node subtraction and long-term network re-organization, previous simulation studies have primarily modeled only the short-term consequences of the subtraction of lesioned nodes and their connections. To evaluate this hypothesis, we compared network properties (modularity, participation coefficient, within-module degree) between real functional connectomes obtained from chronic stroke participants and “pseudo-lesioned” functional connectomes generated by subtracting the same sets of lesioned nodes/connections from healthy control connectomes. We found that, as we hypothesized, the network properties of real-lesioned connectomes in chronic stroke differed from those of the pseudo-lesioned connectomes which instantiated only the short-term consequences of node subtraction. Reflecting the long-term consequences of focal lesions, we found re-organization of the neurotopography of global and local hubs in the real but not the pseudo-lesioned connectomes. We conclude that the long-term network re-organization that occurs in response to focal lesions involves changes in functional connectivity within the remaining intact neural tissue that go well beyond the short-term consequences of node subtraction.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA.
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA
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13
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Wang HE, Scholly J, Triebkorn P, Sip V, Medina Villalon S, Woodman MM, Le Troter A, Guye M, Bartolomei F, Jirsa V. VEP atlas: An anatomic and functional human brain atlas dedicated to epilepsy patients. J Neurosci Methods 2020; 348:108983. [PMID: 33121983 DOI: 10.1016/j.jneumeth.2020.108983] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 09/27/2020] [Accepted: 10/18/2020] [Indexed: 01/15/2023]
Abstract
BACKGROUND Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research. NEW METHOD Here we present the Virtual Epileptic Patient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient's level. RESULTS It comprises 162 brain regions, including 73 cortical and 8 subcortical regions per hemisphere. We demonstrate the successful application of the VEP atlas in a cohort of 50 retrospective patients. The structural organization is complemented by the functional variation of stereotactic intracerebral EEG (SEEG) signal data features establishing brain region-specific 3d-maps. COMPARISON WITH EXISTING METHODS The VEP atlas integrates both anatomical and functional definitions in the same atlas, adapted to applications for epilepsy patients and individualizable. CONCLUSION The covariation of structural and functional organization is the basis for current efforts of patient-specific large-scale brain network modeling exploiting virtual brain technologies for the identification of the epileptogenic regions in an ongoing prospective clinical trial EPINOV.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
| | - Julia Scholly
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - Paul Triebkorn
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | | | | | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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14
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Toba MN, Godefroy O, Rushmore RJ, Zavaglia M, Maatoug R, Hilgetag CC, Valero-Cabré A. Revisiting 'brain modes' in a new computational era: approaches for the characterization of brain-behavioural associations. Brain 2020; 143:1088-1098. [PMID: 31764975 DOI: 10.1093/brain/awz343] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 08/07/2019] [Accepted: 08/28/2019] [Indexed: 11/12/2022] Open
Abstract
The study of brain-function relationships is undergoing a conceptual and methodological transformation due to the emergence of network neuroscience and the development of multivariate methods for lesion-deficit inferences. Anticipating this process, in 1998 Godefroy and co-workers conceptualized the potential of four elementary typologies of brain-behaviour relationships named 'brain modes' (unicity, equivalence, association, summation) as building blocks able to describe the association between intact or lesioned brain regions and cognitive processes or neurological deficits. In the light of new multivariate lesion inference and network approaches, we critically revisit and update the original theoretical notion of brain modes, and provide real-life clinical examples that support their existence. To improve the characterization of elementary units of brain-behavioural relationships further, we extend such conceptualization with a fifth brain mode (mutual inhibition/masking summation). We critically assess the ability of these five brain modes to account for any type of brain-function relationship, and discuss past versus future contributions in redefining the anatomical basis of human cognition. We also address the potential of brain modes for predicting the behavioural consequences of lesions and their future role in the design of cognitive neurorehabilitation therapies.
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Affiliation(s)
- Monica N Toba
- Laboratory of Functional Neurosciences (EA 4559), University Hospital of Amiens and University of Picardy Jules Verne, Amiens, France
| | - Olivier Godefroy
- Laboratory of Functional Neurosciences (EA 4559), University Hospital of Amiens and University of Picardy Jules Verne, Amiens, France
| | - R Jarrett Rushmore
- Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, Boston, MA 02118, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.,Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
| | - Melissa Zavaglia
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Focus Area Health, Jacobs University Bremen, Germany
| | - Redwan Maatoug
- Cerebral Dynamics, Plasticity and Rehabilitation Group, FRONTLAB Team, Brain and Spine Institute, ICM, Paris, France.,Sorbonne Université, INSERM UMR S 1127, CNRS UMR 7225, F-75013, and IHU-A-ICM, Paris, France
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Health Sciences Department, Boston University, 635 Commonwealth Ave. Boston, MA 02215, USA
| | - Antoni Valero-Cabré
- Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, Boston, MA 02118, USA.,Cerebral Dynamics, Plasticity and Rehabilitation Group, FRONTLAB Team, Brain and Spine Institute, ICM, Paris, France.,Sorbonne Université, INSERM UMR S 1127, CNRS UMR 7225, F-75013, and IHU-A-ICM, Paris, France.,Cognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC), Barcelona, Catalunya, Spain
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15
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Allegra Mascaro AL, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, von Arnim A, Formento E, Angelidis E, Blixhavn CH, Leergaard TB, Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO, Pavone FS. Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience. Front Syst Neurosci 2020; 14:31. [PMID: 32733210 PMCID: PMC7359878 DOI: 10.3389/fnsys.2020.00031] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 05/08/2020] [Indexed: 01/22/2023] Open
Abstract
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
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Affiliation(s)
- Anna Letizia Allegra Mascaro
- Neuroscience Institute, National Research Council, Pisa, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Egidio Falotico
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Spase Petkoski
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Maria Pasquini
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Núria Tort-Colet
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Emilia Conti
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Francesco Resta
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | | | | | | | - Emanuele Formento
- Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Emmanouil Angelidis
- Fortiss GmbH, Munich, Germany.,Chair of Robotics, Artificial Intelligence and Embedded Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | | | | | - Matteo Caleo
- Neuroscience Institute, National Research Council, Pisa, Italy.,Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Auke Ijspeert
- Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Cecilia Laschi
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Viktor Jirsa
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Francesco S Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
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16
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Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy. J Neurosci 2020; 40:5572-5588. [PMID: 32513827 PMCID: PMC7363471 DOI: 10.1523/jneurosci.0905-19.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 12/18/2022] Open
Abstract
Drug-resistant focal epilepsy is a large-scale brain networks disorder characterized by altered spatiotemporal patterns of functional connectivity (FC), even during interictal resting state (RS). Although RS-FC-based metrics can detect these changes, results from RS functional magnetic resonance imaging (RS-fMRI) studies are unclear and difficult to interpret, and the underlying dynamical mechanisms are still largely unknown. To better capture the RS dynamics, we phenomenologically extended the neural mass model of partial seizures, the Epileptor, by including two neuron subpopulations of epileptogenic and nonepileptogenic type, making it capable of producing physiological oscillations in addition to the epileptiform activity. Using the neuroinformatics platform The Virtual Brain, we reconstructed 14 epileptic and 5 healthy human (of either sex) brain network models (BNMs), based on individual anatomical connectivity and clinically defined epileptogenic heatmaps. Through systematic parameter exploration and fitting to neuroimaging data, we demonstrated that epileptic brains during interictal RS are associated with lower global excitability induced by a shift in the working point of the model, indicating that epileptic brains operate closer to a stable equilibrium point than healthy brains. Moreover, we showed that functional networks are unaffected by interictal spikes, corroborating previous experimental findings; additionally, we observed higher excitability in epileptogenic regions, in agreement with the data. We shed light on new dynamical mechanisms responsible for altered RS-FC in epilepsy, involving the following two key factors: (1) a shift of excitability of the whole brain leading to increased stability; and (2) a locally increased excitability in the epileptogenic regions supporting the mixture of hyperconnectivity and hypoconnectivity in these areas. SIGNIFICANCE STATEMENT Advances in functional neuroimaging provide compelling evidence for epilepsy-related brain network alterations, even during the interictal resting state (RS). However, the dynamical mechanisms underlying these changes are still elusive. To identify local and network processes behind the RS-functional connectivity (FC) spatiotemporal patterns, we systematically manipulated the local excitability and the global coupling in the virtual human epileptic patient brain network models (BNMs), complemented by the analysis of the impact of interictal spikes and fitting to the neuroimaging data. Our results suggest that a global shift of the dynamic working point of the brain model, coupled with locally hyperexcitable node dynamics of the epileptogenic networks, provides a mechanistic explanation of the epileptic processes during the interictal RS period. These, in turn, are associated with the changes in FC.
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17
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Aerts H, Schirner M, Dhollander T, Jeurissen B, Achten E, Van Roost D, Ritter P, Marinazzo D. Modeling brain dynamics after tumor resection using The Virtual Brain. Neuroimage 2020; 213:116738. [DOI: 10.1016/j.neuroimage.2020.116738] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 02/24/2020] [Accepted: 03/11/2020] [Indexed: 11/28/2022] Open
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18
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Function Based Brain Modeling and Simulation of an Ischemic Region in Post-Stroke Patients using the Bidomain. J Neurosci Methods 2020; 331:108464. [PMID: 31738941 DOI: 10.1016/j.jneumeth.2019.108464] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 09/16/2019] [Accepted: 10/13/2019] [Indexed: 11/23/2022]
Abstract
BACKGROUND Several studies have shown that post-stroke patients develop divergent activity in the sensorimotor areas of the affected hemisphere of the brain compared to healthy people during motor tasks. Proper mathematical models will help us understand this activity and clarify the associated underlying mechanisms. New Method. This research describes an anatomically based brain computer model in post-stroke patients. We simulate an ischemic region for arm motion using the bidomain approach. Two scenarios are considered: a healthy subject and a post-stroke patient with motion impairment. Next, we limit the volume of propagation considering only the sensorimotor area of the brain. Comparison with existing methods. In comparison to existing methods, we combine the use of the bidomain for modeling the propagation of the electrical activity across the brain volume with functional information to limit the volume of propagation and the position of the expected stimuli, given a specific task. Whereas just using the bidomain without limiting the functional volume, propagates the electrical activity into non-expected areas. RESULTS To validate the simulation, we compare the activity with patient measurements using functional near-infrared spectroscopy during arm motion (n=5) against controls (n=3). The results are consistent with empirical measurements and previous research and show that there is a disparity between position and number of spikes in post-stroke patients in contrast to healthy subjects. CONCLUSIONS These results hold promise in improving the understanding of brain deterioration in stroke patients and the re-arrangement of brain networks. Furthermore, shows the use of functionality based brain modeling.
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19
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Stefanovski L, Triebkorn P, Spiegler A, Diaz-Cortes MA, Solodkin A, Jirsa V, McIntosh AR, Ritter P. Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease. Front Comput Neurosci 2019; 13:54. [PMID: 31456676 PMCID: PMC6700386 DOI: 10.3389/fncom.2019.00054] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/22/2019] [Indexed: 12/22/2022] Open
Abstract
Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer's Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.
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Affiliation(s)
- Leon Stefanovski
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Paul Triebkorn
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Andreas Spiegler
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Margarita-Arimatea Diaz-Cortes
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Institut für Informatik, Freie Universität Berlin, Berlin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | | | - Petra Ritter
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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20
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An S, Bartolomei F, Guye M, Jirsa V. Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP). PLoS Comput Biol 2019; 15:e1007051. [PMID: 31242177 PMCID: PMC6594587 DOI: 10.1371/journal.pcbi.1007051] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/17/2019] [Indexed: 01/18/2023] Open
Abstract
Studies to improve the efficacy of epilepsy surgery have focused on better refining the localization of the epileptogenic zone (EZ) with the aim of effectively resecting it. However, in a considerable number of patients, EZs are distributed across multiple brain regions and may involve eloquent areas that cannot be removed due to the risk of neurological complications. There is a clear need for developing alternative approaches to induce seizure relief, but minimal impact on normal brain functions. Here, we develop a personalized in-silico network approach, that suggests effective and safe surgical interventions for each patient. Based on the clinically identified EZ, we employ modularity analysis to identify target brain regions and fiber tracts involved in seizure propagation. We then construct and simulate a patient-specific brain network model comprising phenomenological neural mass models at the nodes, and patient-specific structural brain connectivity using the neuroinformatics platform The Virtual Brain (TVB), in order to evaluate effectiveness and safety of the target zones (TZs). In particular, we assess safety via electrical stimulation for pre- and post-surgical condition to quantify the impact on the signal transmission properties of the network. We demonstrate the existence of a large repertoire of efficient surgical interventions resulting in reduction of degree of seizure spread, but only a small subset of them proves safe. The identification of novel surgical interventions through modularity analysis and brain network simulations may provide exciting solutions to the treatment of inoperable epilepsies. We propose a personalized in-silico surgical approach able to suggest effective and safe surgical options for each epilepsy patient. In particular, we focus on deriving effective alternative methods for those cases where EZs are inoperable because of issues related with neurological complications. Based on modularity analysis using structural brain connectivity from each patient, TZs that would be considered as surgical sites are obtained. The acquired TZs are evaluated by personalized brain network simulations in terms of effectiveness and safety. Through the feedback approach combining modularity analysis and brain network simulations, the optimized TZ options that minimize seizure propagation while not affecting normal brain functions are obtained. Our study has a great importance in that it demonstrates the possibility of computational neuroscience field being able to construct a paradigm for personalized medicine by deriving innovative surgical options suitable for each patient and predicting the surgical outcomes.
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Affiliation(s)
- Sora An
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM UMR 7339, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- * E-mail:
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21
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Gaillard-Bigot F, Zendjidjian XY, Kheloufi F, Casse-Perrot C, Guilhaumou R, Micallef J, Fakra E, Azorin JM, Blin O. Quantitative System Pharmacology (QSP): An Integrative Framework for paradigm change in the treatment of the first-episode schizophrenia. Encephale 2019; 44:S34-S38. [PMID: 30935485 DOI: 10.1016/s0013-7006(19)30077-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Despite the lack of progress in the curative treatment of mental illness, especially schizophrenia, the accumulation of neuroscience data over the past decade suggests the re-conceptualization of schizophrenia. With the advent of new biomarkers and cognitive tools, new neuroscience technologies such as functional dynamic connectivity and the identification of subtle clinical features; it is now possible to detect early stages at risk or prodromes of a first psychotic episode. Current concepts reconceptualizes schizophrenia as a neurodevelopmental disorder at early onset, with polygenic risk and only symptomatic treatment for positive symptoms at this time. The use of such technologies in the future suggests new diagnostic and therapeutic options. Next steps include new pharmacological perspectives and potential contributions of new technologies such as quantitative system pharmacology brain computational modeling approach.
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Affiliation(s)
- F Gaillard-Bigot
- Service de pharmacologie clinique et pharmacovigilance, CIC CPCET, assistance publique des hôpitaux de Marseille, Institut de neurosciences des systèmes, Inserm UMR 1106, université d'Aix-Marseille, France
| | - X-Y Zendjidjian
- Pôle psychiatrie centre, hôpital de la Conception, assistance publique des hôpitaux de Marseille, Marseille, France
| | - F Kheloufi
- Service de pharmacologie clinique et pharmacovigilance, CIC CPCET, assistance publique des hôpitaux de Marseille, Institut de neurosciences des systèmes, Inserm UMR 1106, université d'Aix-Marseille, France
| | - C Casse-Perrot
- Service de pharmacologie clinique et pharmacovigilance, CIC CPCET, assistance publique des hôpitaux de Marseille, Institut de neurosciences des systèmes, Inserm UMR 1106, université d'Aix-Marseille, France
| | - R Guilhaumou
- Service de pharmacologie clinique et pharmacovigilance, CIC CPCET, assistance publique des hôpitaux de Marseille, Institut de neurosciences des systèmes, Inserm UMR 1106, université d'Aix-Marseille, France
| | - J Micallef
- Service de pharmacologie clinique et pharmacovigilance, CIC CPCET, assistance publique des hôpitaux de Marseille, Institut de neurosciences des systèmes, Inserm UMR 1106, université d'Aix-Marseille, France
| | - E Fakra
- Department of Psychiatry, University Hospital of Saint-Etienne, Saint-Etienne, France, Inserm U1059, University of Lyon, Saint-Etienne F-42023, France
| | - J-M Azorin
- Department of Psychiatry, Sainte Marguerite University Hospital, Marseille, France
| | - O Blin
- Service de pharmacologie clinique et pharmacovigilance, CIC CPCET, assistance publique des hôpitaux de Marseille, Institut de neurosciences des systèmes, Inserm UMR 1106, université d'Aix-Marseille, France.
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22
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Zimmermann J, Griffiths J, Schirner M, Ritter P, McIntosh AR. Subject specificity of the correlation between large-scale structural and functional connectivity. Netw Neurosci 2018; 3:90-106. [PMID: 30793075 PMCID: PMC6326745 DOI: 10.1162/netn_a_00055] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/04/2018] [Indexed: 12/25/2022] Open
Abstract
Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.
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Affiliation(s)
- J. Zimmermann
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario, Canada
| | - J. Griffiths
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario, Canada
| | - M. Schirner
- Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universitët Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - P. Ritter
- Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universitët Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt University, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - A. R. McIntosh
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario, Canada
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23
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Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain. eNeuro 2018; 5:eN-NWR-0083-18. [PMID: 29911173 PMCID: PMC6001263 DOI: 10.1523/eneuro.0083-18.2018] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 05/04/2018] [Accepted: 05/08/2018] [Indexed: 12/29/2022] Open
Abstract
Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.
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24
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Zimmermann J, Perry A, Breakspear M, Schirner M, Sachdev P, Wen W, Kochan NA, Mapstone M, Ritter P, McIntosh AR, Solodkin A. Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models. NEUROIMAGE-CLINICAL 2018; 19:240-251. [PMID: 30035018 PMCID: PMC6051478 DOI: 10.1016/j.nicl.2018.04.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 04/05/2018] [Accepted: 04/14/2018] [Indexed: 01/09/2023]
Abstract
Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes. Modeled local and global dynamics correlate with individual cognition in Alzheimer's. Proof of concept of The Virtual Brain to characterize individual dynamics Brain-behaviour relations depend on the network modeled (whole brain or limbic). Model parameters predict cognition better than metrics of neuroimaging data.
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Affiliation(s)
- J Zimmermann
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada.
| | - A Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - M Breakspear
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Service, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia
| | - M Schirner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - P Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - W Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - N A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - M Mapstone
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
| | - P Ritter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - A R McIntosh
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada
| | - A Solodkin
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
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Duncan ES, Small SL. Changes in dynamic resting state network connectivity following aphasia therapy. Brain Imaging Behav 2017; 12:1141-1149. [DOI: 10.1007/s11682-017-9771-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés JM, Laureys S, Marinazzo D. Ising model with conserved magnetization on the human connectome: Implications on the relation structure-function in wakefulness and anesthesia. CHAOS (WOODBURY, N.Y.) 2017; 27:047407. [PMID: 28456159 DOI: 10.1063/1.4978999] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Dynamical models implemented on the large scale architecture of the human brain may shed light on how a function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the critical state), the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between the structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of a homeostatic principle imposed to neural activity.
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Affiliation(s)
- S Stramaglia
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - M Pellicoro
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - L Angelini
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - E Amico
- Coma Science Group, University of Liège, Liège, Belgium
| | - H Aerts
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - J M Cortés
- Ikerbasque, The Basque Foundation for Science, E-48011 Bilbao, Spain
| | - S Laureys
- Coma Science Group, University of Liège, Liège, Belgium
| | - D Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
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27
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The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread. Neuroimage 2017; 145:377-388. [DOI: 10.1016/j.neuroimage.2016.04.049] [Citation(s) in RCA: 223] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 04/16/2016] [Accepted: 04/20/2016] [Indexed: 12/27/2022] Open
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A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain. Curr Opin Neurol 2016; 29:429-36. [PMID: 27224088 DOI: 10.1097/wco.0000000000000344] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
PURPOSE OF REVIEW An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called 'big data'), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine. RECENT FINDINGS Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke. SUMMARY These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. VIDEO ABSTRACT.
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MRI Biomarkers for Hand-Motor Outcome Prediction and Therapy Monitoring following Stroke. Neural Plast 2016; 2016:9265621. [PMID: 27747108 PMCID: PMC5056270 DOI: 10.1155/2016/9265621] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 08/23/2016] [Indexed: 01/01/2023] Open
Abstract
Several biomarkers have been identified which enable a considerable prediction of hand-motor outcome after cerebral damage already in the subacute stage after stroke. We here review the value of MRI biomarkers in the evaluation of corticospinal integrity and functional recruitment of motor resources. Many of the functional imaging parameters are not feasible early after stroke or for patients with high impairment and low compliance. Whereas functional connectivity parameters have demonstrated varying results on their predictive value for hand-motor outcome, corticospinal integrity evaluation using structural imaging showed robust and high predictive power for patients with different levels of impairment. Although this is indicative of an overall higher value of structural imaging for prediction, we suggest that this variation be explained by structure and function relationships. To gain more insight into the recovering brain, not only one biomarker is needed. We rather argue for a combination of different measures in an algorithm to classify fine-graded subgroups of patients. Approaches to determining biomarkers have to take into account the established markers to provide further information on certain subgroups. Assessing the best therapy approaches for individual patients will become more feasible as these subgroups become specified in more detail. This procedure will help to considerably save resources and optimize neurorehabilitative therapy.
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Duncan ES, Small SL. Increased Modularity of Resting State Networks Supports Improved Narrative Production in Aphasia Recovery. Brain Connect 2016; 6:524-9. [PMID: 27345466 PMCID: PMC5084363 DOI: 10.1089/brain.2016.0437] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The networks that emerge in the analysis of resting state functional magnetic resonance imaging (rsfMRI) data are believed to reflect the intrinsic organization of the brain. One key property of such complex biological networks is modularity, a measure of community structure. This topological characteristic changes in neurological disease and recovery. Nineteen subjects with language disorders after stroke (aphasia) underwent neuroimaging and behavioral assessment at multiple time points before (baseline) and after an imitation-based therapy. Language was assessed with a narrative production task. Group independent component analysis was performed on the rsfMRI data to identify resting state networks (RSNs). For each participant and each rsfMRI acquisition, we constructed a graph comprising all RSNs. We assigned nodal community based on a region's RSN membership, calculated the modularity score, and then correlated changes in modularity and therapeutic gains on the narrative task. We repeated this comparison controlling for pretherapy performance and using a community structure not based on RSN membership. Increased RSN modularity was positively correlated with improvement on the narrative task immediately post-therapy. This finding remained significant when controlling for pretherapy performance. There were no significant findings for network modularity and behavior when nodal community was assigned without consideration of RSN membership. We interpret these findings as support for the adaptive role of network segregation in behavioral improvement in aphasia therapy. This has important clinical implications for the targeting of noninvasive brain stimulation in poststroke remediation and suggests potential for further insight into the processes underlying such changes through computational modeling.
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Affiliation(s)
- E. Susan Duncan
- Department of Cognitive Sciences & Neurology, University of California, Irvine, Irvine, California
| | - Steven L. Small
- Department of Neurology, Neurobiology & Behavior, Cognitive Sciences, University of California, Irvine, Irvine, California
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31
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Aerts H, Fias W, Caeyenberghs K, Marinazzo D. Brain networks under attack: robustness properties and the impact of lesions. Brain 2016; 139:3063-3083. [PMID: 27497487 DOI: 10.1093/brain/aww194] [Citation(s) in RCA: 178] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 05/13/2016] [Accepted: 06/08/2016] [Indexed: 12/30/2022] Open
Abstract
A growing number of studies approach the brain as a complex network, the so-called 'connectome'. Adopting this framework, we examine what types or extent of damage the brain can withstand-referred to as network 'robustness'-and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer's disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions-and especially those connecting different subnetworks-was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research.
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Affiliation(s)
- Hannelore Aerts
- 1 Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
| | - Wim Fias
- 2 Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
| | - Karen Caeyenberghs
- 3 School of Psychology, Faculty of Health Sciences, Australian Catholic University, Australia
| | - Daniele Marinazzo
- 1 Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
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Reinkensmeyer DJ, Burdet E, Casadio M, Krakauer JW, Kwakkel G, Lang CE, Swinnen SP, Ward NS, Schweighofer N. Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J Neuroeng Rehabil 2016; 13:42. [PMID: 27130577 PMCID: PMC4851823 DOI: 10.1186/s12984-016-0148-3] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 04/13/2016] [Indexed: 01/19/2023] Open
Abstract
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.
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Affiliation(s)
- David J Reinkensmeyer
- Departments of Anatomy and Neurobiology, Mechanical and Aerospace Engineering, Biomedical Engineering, and Physical Medicine and Rehabilitation, University of California, Irvine, USA.
| | - Etienne Burdet
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
| | - Maura Casadio
- Department Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - John W Krakauer
- Departments of Neurology and Neuroscience, John Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gert Kwakkel
- Department of Rehabilitation Medicine, MOVE Research Institute Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Reade, Centre for Rehabilitation and Rheumatology, Amsterdam, The Netherlands
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USA
| | - Catherine E Lang
- Department of Neurology, Program in Physical Therapy, Program in Occupational Therapy, Washington University School of Medicine, St Louis, MO, USA
| | - Stephan P Swinnen
- Department of Kinesiology, KU Leuven Movement Control & Neuroplasticity Research Group, Leuven, KU, Belgium
- Leuven Research Institute for Neuroscience & Disease (LIND), KU, Leuven, Belgium
| | - Nick S Ward
- Sobell Department of Motor Neuroscience and UCLPartners Centre for Neurorehabilitation, UCL Institute of Neurology, Queen Square, London, UK
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, USA
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