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Huang S, Li M, Huang C, Liu J. Acute limbic system connectivity predicts chronic cognitive function in mild traumatic brain injury: An individualized differential structural covariance network study. Pharmacol Res 2024; 206:107274. [PMID: 38906205 DOI: 10.1016/j.phrs.2024.107274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024]
Abstract
Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.
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Affiliation(s)
- Sihong Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Mengjun Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Chuxin Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Department of Radiology Quality Control Center, Hunan Province, Changsha, Hunan 410011, China; Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan 410011, China.
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Imms P, Chowdhury NF, Chaudhari NN, Amgalan A, Poudel G, Caeyenberghs K, Irimia A. Prediction of cognitive outcome after mild traumatic brain injury from acute measures of communication within brain networks. Cortex 2024; 171:397-412. [PMID: 38103453 PMCID: PMC10922490 DOI: 10.1016/j.cortex.2023.10.022] [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: 11/07/2022] [Revised: 09/04/2023] [Accepted: 10/20/2023] [Indexed: 12/19/2023]
Abstract
A considerable but ill-defined proportion of patients with mild traumatic brain injury (mTBI) experience persistent cognitive sequelae; the ability to identify such individuals early can help their neurorehabilitation. Here we tested the hypothesis that acute measures of efficient communication within brain networks are associated with patients' risk for unfavorable cognitive outcome six months after mTBI. Diffusion and T1-weighted magnetic resonance imaging, alongside cognitive measures, were obtained to map connectomes both one week and six months post injury in 113 adult patients with mTBI (71 males). For task-related brain networks, communication measures (characteristic path length, global efficiency, navigation efficiency) were moderately correlated with changes in cognition. Taking into account the covariance of age and sex, more unfavorable communication within networks were associated with worse outcomes within cognitive domains frequently impacted by mTBI (episodic and working memory, verbal fluency, inductive reasoning, and processing speed). Individuals with more unfavorable outcomes had significantly longer and less efficient pathways within networks supporting verbal fluency (all t > 2.786, p < .006), highlighting the vulnerability of language to mTBI. Participants in whom a task-related network was relatively inefficient one week post injury were up to eight times more likely to have unfavorable cognitive outcome pertaining to that task. Our findings suggest that communication measures within task-related networks identify mTBI patients who are unlikely to develop persistent cognitive deficits after mTBI. Our approach and findings can help to stratify mTBI patients according to their expected need for follow-up and/or neurorehabilitation.
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Affiliation(s)
- Phoebe Imms
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA.
| | - Nahian F Chowdhury
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA.
| | - Nikhil N Chaudhari
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA.
| | - Anar Amgalan
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA.
| | - Govinda Poudel
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne Burwood Campus, Burwood, VIC, Australia.
| | - Andrei Irimia
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, 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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Pathak A, Roy D, Banerjee A. Whole-Brain Network Models: From Physics to Bedside. Front Comput Neurosci 2022; 16:866517. [PMID: 35694610 PMCID: PMC9180729 DOI: 10.3389/fncom.2022.866517] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.
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Affiliation(s)
| | - Dipanjan Roy
- Centre for Brain Science and Applications, School of Artificial Intelligence and Data Science, Indian Institute of Technology, Jodhpur, India
| | - Arpan Banerjee
- National Brain Research Centre, Gurgaon, India
- *Correspondence: Arpan Banerjee
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