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Ding J, Thye M, Edmondson-Stait AJ, Szaflarski JP, Mirman D. Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia. Brain Commun 2024; 6:fcae313. [PMID: 39318782 PMCID: PMC11420983 DOI: 10.1093/braincomms/fcae313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 06/26/2024] [Accepted: 09/11/2024] [Indexed: 09/26/2024] Open
Abstract
Connectome-based lesion-symptom mapping relates behavioural impairments to disruption of structural brain connectivity. Connectome-based lesion-symptom mapping can be based on different approaches (diffusion MRI versus lesion mask), network scales (whole brain versus regions of interest) and measure types (tract-based, parcel-based, or network-based metrics). We evaluated the similarity of different connectome-based lesion-symptom mapping processing choices and identified factors that influence the results using multiverse analysis-the strategy of conducting and displaying the results of all reasonable processing choices. Metrics derived from lesion masks and diffusion-weighted images were tested for association with Boston Naming Test and Token Test performance in a sample of 50 participants with aphasia following left hemispheric stroke. 'Direct' measures were derived from diffusion-weighted images. 'Indirect' measures were derived by overlaying lesion masks on a white matter atlas. Parcel-based connectomes were constructed for the whole brain and regions of interest (14 language-relevant parcels). Numerous tract-based and network-based metrics were calculated. There was a high discrepancy across processing approaches (diffusion-weighted images versus lesion masks), network scales (whole brain versus regions of interest) and metric types. Results indicate weak correlations and different connectome-based lesion-symptom mapping results across the processing choices. Substantial methodological work is needed to validate the various decision points that arise when conducting connectome-based lesion-symptom mapping analyses. Multiverse analysis is a useful strategy for evaluating the similarity across different processing choices in connectome-based lesion-symptom mapping.
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Affiliation(s)
- Junhua Ding
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Melissa Thye
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | | | - Jerzy P Szaflarski
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Daniel Mirman
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
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Weigel K, Klingner CM, Brodoehl S, Wagner F, Schwab M, Güllmar D, Mayer TE, Güttler FV, Teichgräber U, Gaser C. Normative connectome-based analysis of sensorimotor deficits in acute subcortical stroke. Front Neurosci 2024; 18:1400944. [PMID: 39184327 PMCID: PMC11344269 DOI: 10.3389/fnins.2024.1400944] [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: 03/14/2024] [Accepted: 07/24/2024] [Indexed: 08/27/2024] Open
Abstract
The interrelation between acute ischemic stroke, persistent disability, and uncertain prognosis underscores the need for improved methods to predict clinical outcomes. Traditional approaches have largely focused on analysis of clinical metrics, lesion characteristics, and network connectivity, using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). However, these methods are not routinely used in acute stroke diagnostics. This study introduces an innovative approach that not only considers the lesion size in relation to the National Institutes of Health Stroke Scale (NIHSS score), but also evaluates the impact of disrupted fibers and their connections to cortical regions by introducing a disconnection value. By identifying fibers traversing the lesion and estimating their number within predefined regions of interest (ROIs) using a normative connectome atlas, our method bypasses the need for individual DTI scans. In our analysis of MRI data (T1 and T2) from 51 patients with acute or subacute subcortical stroke presenting with motor or sensory deficits, we used simple linear regression to assess the explanatory power of lesion size and disconnection value on NIHSS score. Subsequent hierarchical multiple linear regression analysis determined the incremental value of disconnection metrics over lesion size alone in relation to NIHSS score. Our results showed that models incorporating the disconnection value accounted for more variance than those based solely on lesion size (lesion size explained 44% variance, disconnection value 60%). Furthermore, hierarchical regression revealed a significant improvement (p < 0.001) in model fit when adding the disconnection value, confirming its critical role in stroke assessment. Our approach, which integrates a normative connectome to quantify disconnections to cortical regions, provides a significant improvement in assessing the current state of stroke impact compared to traditional measures that focus on lesion size. This is achieved by taking into account the lesion's location and the connectivity of the affected white matter tracts, providing a more comprehensive assessment of stroke severity as reflected in the NIHSS score. Future research should extend the validation of this approach to larger and more diverse populations, with a focus on refining its applicability to clinical assessment and long-term outcome prediction.
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Affiliation(s)
- Karolin Weigel
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Carsten M. Klingner
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Stefan Brodoehl
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Franziska Wagner
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Matthias Schwab
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Daniel Güllmar
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Thomas E. Mayer
- Section Neuroradiology, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Felix V. Güttler
- Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Ulf Teichgräber
- Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
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Zivadinov R, Jakimovski D, Burnham A, Kuhle J, Weinstock Z, Wicks TR, Ramanathan M, Sciortino T, Ostrem M, Suchan C, Dwyer MG, Reilly J, Bergsland N, Schweser F, Kennedy C, Young-Hong D, Eckert S, Hojnacki D, Benedict RHB, Weinstock-Guttman B. Neuroimaging assessment of facility-bound severely-affected MS reveals the critical role of cortical gray matter pathology: results from the CASA-MS case-controlled study. J Neurol 2024; 271:4949-4962. [PMID: 38758279 DOI: 10.1007/s00415-024-12420-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/24/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND A subgroup of people with multiple sclerosis (pwMS) will develop severe disability. The pathophysiology underlying severe MS is unknown. The comprehensive assessment of severely affected MS (CASA-MS) was a case-controlled study that compared severely disabled in skilled nursing (SD/SN) (EDSS ≥ 7.0) to less-disabled (EDSS 3.0-6.5) community dwelling (CD) progressive pwMS, matched on age-, sex- and disease-duration (DDM). OBJECTIVES To identify neuroimaging and molecular biomarker characteristics that distinguish SD/SN from DDM-CD progressive pwMS. METHODS This study was carried at SN facility and at a tertiary MS center. The study collected clinical, molecular (serum neurofilament light chain, sNfL and glial acidic fibrillary protein, sGFAP) and MRI quantitative lesion-, brain volume-, and tissue integrity-derived measures. Statistical analyses were controlled for multiple comparisons. RESULTS 42 SD/SN and 42 DDM-CD were enrolled. SD/SN pwMS showed significantly lower cortical volume (CV) (p < 0.001, d = 1.375) and thalamic volume (p < 0.001, d = 0.972) compared to DDM-CD pwMS. In a logistic stepwise regression model, the SD/SN pwMS were best differentiated from the DDM-CD pwMS by lower CV (p < 0.001) as the only significant predictor, with the accuracy of 82.3%. No significant differences between the two groups were observed for medulla oblongata volume, a proxy for spinal cord atrophy and white matter lesion burden, while there was a statistical trend for numerically higher sGFAP in SD/SN pwMS. CONCLUSIONS The CASA-MS study showed significantly more gray matter atrophy in severe compared to less-severe progressive MS.
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Affiliation(s)
- Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA.
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA.
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | | | - Jens Kuhle
- Neurologic Clinic and Policlinic, Department of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Zachary Weinstock
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | - Taylor R Wicks
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Tommaso Sciortino
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | | | - Christopher Suchan
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | | | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Cheryl Kennedy
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 77 Goodell Street, Suite 450, Buffalo, NY, 14203, USA
| | | | - Svetlana Eckert
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - David Hojnacki
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph H B Benedict
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA
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Herbet G, Duffau H, Mandonnet E. Predictors of cognition after glioma surgery: connectotomy, structure-function phenotype, plasticity. Brain 2024; 147:2621-2635. [PMID: 38573324 DOI: 10.1093/brain/awae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/19/2024] [Accepted: 03/09/2024] [Indexed: 04/05/2024] Open
Abstract
Determining preoperatively the maximal extent of resection that would preserve cognitive functions is the core challenge of brain tumour surgery. Over the past decade, the methodological framework to achieve this goal has been thoroughly renewed: the population-level topographically-focused voxel-based lesion-symptom mapping has been progressively overshadowed by machine learning (ML) algorithmics, in which the problem is framed as predicting cognitive outcomes in a patient-specific manner from a typically large set of variables. However, the choice of these predictors is of utmost importance, as they should be both informative and parsimonious. In this perspective, we first introduce the concept of connectotomy: instead of parameterizing resection topography through the status (intact/resected) of a huge number of voxels (or parcels) paving the whole brain in the Cartesian 3D-space, the connectotomy models the resection in the connectivity space, by computing a handful number of networks disconnection indices, measuring how the structural connectivity sustaining each network of interest was hit by the resection. This connectivity-informed reduction of dimensionality is a necessary step for efficiently implementing ML tools, given the relatively small number of patient-examples in available training datasets. We further argue that two other major sources of interindividual variability must be considered to improve the accuracy with which outcomes are predicted: the underlying structure-function phenotype and neuroplasticity, for which we provide an in-depth review and propose new ways of determining relevant predictors. We finally discuss the benefits of our approach for precision surgery of glioma.
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Affiliation(s)
- Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Praxiling lab, UMR5267 CNRS & Paul Valéry University, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Institut Universitaire de France, Paris 75000, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Team 'Plasticity of Central Nervous System, Stem Cells and Glial Tumors', U1191 Laboratory, Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM), University of Montpellier, Montpellier 34000, France
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, AP-HP, Paris 75010, France
- Frontlab, CNRS UMR 7225, INSERM U1127, Paris Brain Institute (ICM), Paris 75013, France
- Université de Paris Cité, UFR de médecine, Paris 75005, France
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5
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Pontillo G, Cepas MB, Broeders TAA, Koubiyr I, Schoonheim MM. Network Analysis in Multiple Sclerosis and Related Disorders. Neuroimaging Clin N Am 2024; 34:375-384. [PMID: 38942522 DOI: 10.1016/j.nic.2024.03.008] [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] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.
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Affiliation(s)
- Giuseppe Pontillo
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands; MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mar Barrantes Cepas
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
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6
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Olafson ER, Sperber C, Jamison KW, Bowren MD, Boes AD, Andrushko JW, Borich MR, Boyd LA, Cassidy JM, Conforto AB, Cramer SC, Dula AN, Geranmayeh F, Hordacre B, Jahanshad N, Kautz SA, Tavenner BP, MacIntosh BJ, Piras F, Robertson AD, Seo NJ, Soekadar SR, Thomopoulos SI, Vecchio D, Weng TB, Westlye LT, Winstein CJ, Wittenberg GF, Wong KA, Thompson PM, Liew SL, Kuceyeski AF. Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers. Brain Commun 2024; 6:fcae254. [PMID: 39171205 PMCID: PMC11336660 DOI: 10.1093/braincomms/fcae254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/27/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.
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Affiliation(s)
- Emily R Olafson
- Department of Radiology, Weill Cornell Medicine, New York City, NY 10021, USA
| | - Christoph Sperber
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern 3012, Switzerland
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, New York City, NY 10021, USA
| | - Mark D Bowren
- Department of Neurology, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Aaron D Boes
- Department of Neurology, Carver College of Medicine, Iowa City, IA 52242, USA
- Department of Psychiatry, Carver College of Medicine, Iowa City, IA 52242, USA
- Department of Pediatrics, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Justin W Andrushko
- Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Michael R Borich
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lara A Boyd
- Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Jessica M Cassidy
- Department of Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Adriana B Conforto
- Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paolo 05652-900, Brazil
- Hospital Israelita Albert Einstein, São Paulo 05652-900, Brazil
| | - Steven C Cramer
- Department Neurology, UCLA, California Rehabilitation Institute, Los Angeles, CA 90033, USA
| | - Adrienne N Dula
- Department of Neurology, Dell Medical School at The University of Texas Austin, Austin, TX 78712, USA
| | - Fatemeh Geranmayeh
- Clinical Language and Cognition Group, Department of Brain Sciences, Imperial College London, London W12 0HS, United Kingdom
| | - Brenton Hordacre
- Innovation, Implementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide 5000, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Charleston, SC 29425, USA
| | - Steven A Kautz
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC 29425, USA
- Ralph H. Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Bethany P Tavenner
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90033, USA
| | - Bradley J MacIntosh
- Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
- Computational Radiology and Artificial Intelligence (CRAI), Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo 0372, Norway
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome 00179, Italy
| | - Andrew D Robertson
- Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
- Schlegel-UW Research Institute for Aging, Waterloo, ON N2J 0E2, Canada
| | - Na Jin Seo
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC 29425, USA
- Ralph H. Johnson VA Health Care System, Charleston, SC 29425, USA
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Surjo R Soekadar
- Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité—Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Charleston, SC 29425, USA
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome 00179, Italy
| | - Timothy B Weng
- Department of Neurology, Dell Medical School at The University of Texas Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo 0372, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0372, Norway
| | - Carolee J Winstein
- Division of Biokinesiology and Physical Therapy, Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA 90033, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - George F Wittenberg
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA
- GRECC, HERL, Department of Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA 15213, USA
| | - Kristin A Wong
- Department of Physical Medicine & Rehabilitation, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Charleston, SC 29425, USA
| | - Sook-Lei Liew
- Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Amy F Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York City, NY 10021, USA
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Raj A, Torok J, Ranasinghe K. Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583407. [PMID: 38559176 PMCID: PMC10979926 DOI: 10.1101/2024.03.05.583407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
INTRODUCTION The interaction of amyloid and tau in neurodegenerative diseases is a central feature of AD pathophysiology. While experimental studies point to various interaction mechanisms, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. The aim of this study was to compare mathematical reaction-diffusion models encoding distinct cross-species couplings to identify which interactions were key to model success. METHODS We tested competing mathematical models of network spread, aggregation, and amyloid-tau interactions on publicly available data from ADNI. RESULTS Although network spread models captured the spatiotemporal evolution of tau and amyloid in human subjects, the model including a one-way amyloid-to-tau aggregation interaction performed best. DISCUSSION This mathematical exposition of the "pas de deux" of co-evolving proteins provides quantitative, whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.
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8
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Mistri D, Margoni M, Pagani E, Valsasina P, Meani A, Moiola L, Filippi M, Rocca MA. Structural and functional imaging features of cognitive phenotypes in pediatric multiple sclerosis. Ann Clin Transl Neurol 2024; 11:1840-1851. [PMID: 38804116 PMCID: PMC11251463 DOI: 10.1002/acn3.52090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/29/2024] [Accepted: 05/04/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVE The present study aimed to identify the clinical and MRI features of the distinct cognitive phenotypes in pediatric multiple sclerosis (pedMS). METHODS PedMS patients (n = 73) and healthy controls (n = 30) underwent clinical examination and 3.0T MRI. All patients completed neuropsychological testing, and cognitive phenotypes were identified by performing K-means clustering on cognitive scores. MRI metrics included brain T2-hyperintese lesion volume and normalized brain volumes. Within seven cognitively relevant cortical networks, structural disconnectivity (i.e., the mean percentage of streamlines connecting each pair of cortical regions passing through a lesion) and resting-state (RS) functional connectivity (FC) were estimated. RESULTS Three cognitive phenotypes emerged: Preserved cognition (PC; n = 27, 37%), mild verbal learning and memory/semantic fluency involvement (MVS; n = 28, 38%), and multidomain involvement (MI; n = 18, 25%). Age, sex, and disease duration did not differ among groups. Compared with healthy subjects, PC patients had decreased RS FC within the default mode network (p = 0.045); MVS patients exhibited lower cortical volume and reduced RS FC within the frontoparietal network (all p = 0.045); and MI patients showed decreased volumes in all brain compartments except the hippocampus, and reduced RS FC within the frontoparietal network (all p ≤ 0.045). Compared to PC, MI patients had more severe disability and higher structural disconnectivity within four cortical networks (all p ≤ 0.045). Compared to PC and MVS, MI patients had lower intelligence quotient (all p ≤ 0.005). INTERPRETATION We identified three cognitive phenotypes in pedMS that demonstrate the existence of a spectrum of impairment. Such phenotypes showed distinct clinical and MRI characteristics that contributed to explain their cognitive profiles.
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Affiliation(s)
- Damiano Mistri
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Monica Margoni
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurorehabilitation UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Lucia Moiola
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurorehabilitation UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurophysiology ServiceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
| | - Maria A. Rocca
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
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9
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Hildesheim FE, Ophey A, Zumbansen A, Funck T, Schuster T, Jamison KW, Kuceyeski A, Thiel A. Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach. Neurorehabil Neural Repair 2024; 38:447-459. [PMID: 38602161 PMCID: PMC11097606 DOI: 10.1177/15459683241245410] [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] [Indexed: 04/12/2024]
Abstract
BACKGROUND The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients. OBJECTIVE To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function. METHODS Language function was assessed in 76 stroke patients from the Non-Invasive Repeated Therapeutic Stimulation for Aphasia Recovery trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures. Individual infarct masks were superimposed onto a diffusion tensor imaging tractogram reference set to calculate Change in Connectivity scores of language-relevant gray matter regions as estimates of structural connectivity disruption. Multivariable Random Forest models were derived to predict language function. RESULTS Random Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes. CONCLUSION Connectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.
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Affiliation(s)
- Franziska E. Hildesheim
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
- Department of Neurology & Neurosurgery, McGill University, Montréal, QC, Canada
- Canadian Platform for Trials in Non-Invasive Brain Stimulation (CanStim), Montréal, QC, Canada
| | - Anja Ophey
- Department of Medical Psychology | Neuropsychology and Gender Studies, Center for Neuropsychological Diagnostics and Intervention, University Hospital Cologne, Medical Faculty of the University of Cologne, Cologne, Germany
| | - Anna Zumbansen
- School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada
- Music and Health Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Thomas Funck
- Institute of Neurosciences and Medicine INM-1, Research Centre Jülich, Jülich, Germany
| | - Tibor Schuster
- Department of Family Medicine, McGill University, Montréal, QC, Canada
| | - Keith W. Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Alexander Thiel
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
- Department of Neurology & Neurosurgery, McGill University, Montréal, QC, Canada
- Canadian Platform for Trials in Non-Invasive Brain Stimulation (CanStim), Montréal, QC, Canada
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10
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Khalilian M, Roussel M, Godefroy O, Aarabi A. Predicting functional impairments with lesion-derived disconnectome mapping: Validation in stroke patients with motor deficits. Eur J Neurosci 2024; 59:3074-3092. [PMID: 38578844 DOI: 10.1111/ejn.16334] [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: 08/10/2023] [Revised: 02/24/2024] [Accepted: 03/07/2024] [Indexed: 04/07/2024]
Abstract
Focal structural damage to white matter tracts can result in functional deficits in stroke patients. Traditional voxel-based lesion-symptom mapping is commonly used to localize brain structures linked to neurological deficits. Emerging evidence suggests that the impact of structural focal damage may extend beyond immediate lesion sites. In this study, we present a disconnectome mapping approach based on support vector regression (SVR) to identify brain structures and white matter pathways associated with functional deficits in stroke patients. For clinical validation, we utilized imaging data from 340 stroke patients exhibiting motor deficits. A disconnectome map was initially derived from lesions for each patient. Bootstrap sampling was then employed to balance the sample size between a minority group of patients exhibiting right or left motor deficits and those without deficits. Subsequently, SVR analysis was used to identify voxels associated with motor deficits (p < .005). Our disconnectome-based analysis significantly outperformed alternative lesion-symptom approaches in identifying major white matter pathways within the corticospinal tracts associated with upper-lower limb motor deficits. Bootstrapping significantly increased the sensitivity (80%-87%) for identifying patients with motor deficits, with a minimum lesion size of 32 and 235 mm3 for the right and left motor deficit, respectively. Overall, the lesion-based methods achieved lower sensitivities compared with those based on disconnection maps. The primary contribution of our approach lies in introducing a bootstrapped disconnectome-based mapping approach to identify lesion-derived white matter disconnections associated with functional deficits, particularly efficient in handling imbalanced data.
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Affiliation(s)
- Maedeh Khalilian
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
| | - Martine Roussel
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
| | - Olivier Godefroy
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
- Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
- Neurology Department, Amiens University Hospital, Amiens, France
| | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
- Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
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11
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Zivadinov R, Bergsland N, Jakimovski D, Weinstock-Guttman B, Lorefice L, Schoonheim MM, Morrow SA, Ann Picone M, Pardo G, Zarif M, Gudesblatt M, Nicholas JA, Smith A, Hunter S, Newman S, AbdelRazek MA, Hoti I, Riolo J, Silva D, Fuchs TA, Dwyer MG, Hb Benedict R. Thalamic atrophy and dysconnectivity are associated with cognitive impairment in a multi-center, clinical routine, real-word study of people with relapsing-remitting multiple sclerosis. Neuroimage Clin 2024; 42:103609. [PMID: 38718640 PMCID: PMC11098945 DOI: 10.1016/j.nicl.2024.103609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/29/2024] [Accepted: 04/22/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Prior research has established a link between thalamic pathology and cognitive impairment (CI) in people with multiple sclerosis (pwMS). However, the translation of these findings to pwMS in everyday clinical settings has been insufficient. OBJECTIVE To assess which global and/or thalamic imaging biomarkers can be used to identify pwMS at risk for CI and cognitive worsening (CW) in a real-world setting. METHODS This was an international, multi-center (11 centers), longitudinal, retrospective, real-word study of people with relapsing-remitting MS (pwRRMS). Brain MRI exams acquired at baseline and follow-up were collected. Cognitive status was evaluated using the Symbol Digit Modalities Test (SDMT). Thalamic volume (TV) measurement was performed on T2-FLAIR, as well as on T1-WI, when available. Thalamic dysconnectivity, T2-lesion volume (T2-LV), and volumes of gray matter (GM), whole brain (WB) and lateral ventricles (LVV) were also assessed. RESULTS 332 pwMS were followed for an average of 2.8 years. At baseline, T2-LV, LVV, TV and thalamic dysconnectivity on T2-FLAIR (p < 0.016), and WB, GM and TV volumes on T1-WI (p < 0.039) were significantly worse in 90 (27.1 %) CI vs. 242 (62.9 %) non-CI pwRRMS. Greater SDMT decline over the follow-up was associated with lower baseline TV on T2-FLAIR (standardized β = 0.203, p = 0.002) and greater thalamic dysconnectivity (standardized β = -0.14, p = 0.028) in a linear regression model. CONCLUSIONS PwRRMS with thalamic atrophy and worse thalamic dysconnectivity present more frequently with CI and experience greater CW over mid-term follow-up in a real-world setting.
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Affiliation(s)
- Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States; Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, NY, United States.
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York and Kaleida Health, BGH, Buffalo, NY, United States
| | - Lorena Lorefice
- Department of Medical Sciences and Public Health, Multiple Sclerosis Center, Binaghi Hospital, ASL Cagliari, University of Cagliari, Cagliari, Italy
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy & Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Sarah A Morrow
- Schulich School of Medicine and Dentistry, London Health Sciences Centre, University Hospital, London, Ontario, CA, Canada; Department of Clinical Neurological Sciences, Hotchkiss Brain Institute, University of Calgary, Canada
| | | | - Gabriel Pardo
- Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Myassar Zarif
- South Shore Neurologic Associates NYU Langone, Patchogue, NY, United States
| | - Mark Gudesblatt
- South Shore Neurologic Associates NYU Langone, Patchogue, NY, United States
| | | | - Andrew Smith
- OhioHealth MS Center, Riverside Methodist Hospital, Columbus, OH, United States
| | - Samuel Hunter
- Advanced Neurosciences Institute, Franklin, TN, United States
| | - Stephen Newman
- Island Neurological Association, Plainview, NY, United States
| | - Mahmoud A AbdelRazek
- Mount Auburn Hospital, Harvard Medical School, United States; Atrium Health Neurosciences Institute, Wake Forest University School of Medicine, United States
| | - Ina Hoti
- Mount Auburn Hospital, Harvard Medical School, United States
| | - Jon Riolo
- Bristol Myers Squibb, Summit, NJ, United States
| | - Diego Silva
- Bristol Myers Squibb, Summit, NJ, United States
| | - Tom A Fuchs
- MS Center Amsterdam, Anatomy & Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States; Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, NY, United States
| | - Ralph Hb Benedict
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York and Kaleida Health, BGH, Buffalo, NY, United States
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12
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Zheng H, Zhai T, Lin X, Dong G, Yang Y, Yuan TF. The resting-state brain activity signatures for addictive disorders. MED 2024; 5:201-223.e6. [PMID: 38359839 PMCID: PMC10939772 DOI: 10.1016/j.medj.2024.01.008] [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: 08/10/2023] [Revised: 10/20/2023] [Accepted: 01/17/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Addiction is a chronic and relapsing brain disorder. Despite numerous neuroimaging and neurophysiological studies on individuals with substance use disorder (SUD) or behavioral addiction (BEA), currently a clear neural activity signature for the addicted brain is lacking. METHODS We first performed systemic coordinate-based meta-analysis and partial least-squares regression to identify shared or distinct brain regions across multiple addictive disorders, with abnormal resting-state activity in SUD and BEA based on 46 studies (55 contrasts), including regional homogeneity (ReHo) and low-frequency fluctuation amplitude (ALFF) or fractional ALFF. We then combined Neurosynth, postmortem gene expression, and receptor/transporter distribution data to uncover the potential molecular mechanisms underlying these neural activity signatures. FINDINGS The overall comparison between addiction cohorts and healthy subjects indicated significantly increased ReHo and ALFF in the right striatum (putamen) and bilateral supplementary motor area, as well as decreased ReHo and ALFF in the bilateral anterior cingulate cortex and ventral medial prefrontal cortex, in the addiction group. On the other hand, neural activity in cingulate cortex, ventral medial prefrontal cortex, and orbitofrontal cortex differed between SUD and BEA subjects. Using molecular analyses, the altered resting activity recapitulated the spatial distribution of dopaminergic, GABAergic, and acetylcholine system in SUD, while this also includes the serotonergic system in BEA. CONCLUSIONS These results indicate both common and distinctive neural substrates underlying SUD and BEA, which validates and supports targeted neuromodulation against addiction. FUNDING This work was supported by the National Natural Science Foundation of China and Intramural Research Program of the National Institute on Drug Abuse, National Institutes of Health.
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Affiliation(s)
- Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Co-Innovation Center of Neuroregeneration, Nantong University, Nantong, China
| | - Tianye Zhai
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
| | - Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Guangheng Dong
- Department of Psychology, Yunnan Normal University, Kunming 650092, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Co-Innovation Center of Neuroregeneration, Nantong University, Nantong, China; Institute of Mental Health and Drug Discovery, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325000, China.
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13
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Comanducci A, Casarotto S, Rosanova M, Derchi CC, Viganò A, Pirastru A, Blasi V, Cazzoli M, Navarro J, Edlow BL, Baglio F, Massimini M. Unconsciousness or unresponsiveness in akinetic mutism? Insights from a multimodal longitudinal exploration. Eur J Neurosci 2024; 59:860-873. [PMID: 37077023 DOI: 10.1111/ejn.15994] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/02/2023] [Accepted: 04/17/2023] [Indexed: 04/21/2023]
Abstract
The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioural responses to standardised sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behaviour-based diagnoses. One such comorbidity is akinetic mutism (AM), a rare neurological syndrome characterised by the inability to initiate volitional motor responses, sometimes associated with clinical presentations that overlap with those of DoC. In this paper, we describe the case of a patient with large bilateral mesial frontal lesions, showing prolonged behavioural unresponsiveness and severe disorganisation of electroencephalographic (EEG) background, compatible with a vegetative state/unresponsive wakefulness syndrome (VS/UWS). By applying an unprecedented multimodal battery of advanced imaging and electrophysiology-based techniques (AIE) encompassing spontaneous EEG, evoked potentials, event-related potentials, transcranial magnetic stimulation combined with EEG and structural and functional MRI, we provide the following: (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of AM, (ii) a plausible neurophysiological explanation for behavioural unresponsiveness and its subsequent recovery during rehabilitation stay and (iii) novel insights into the relationships between DoC, AM and parkinsonism. The present case offers proof-of-principle evidence supporting the clinical utility of a multimodal hierarchical workflow that combines AIEs to detect covert signs of consciousness in unresponsive patients.
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Affiliation(s)
| | - Silvia Casarotto
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
- Department Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Mario Rosanova
- Department Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | | | | | | | - Valeria Blasi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Marta Cazzoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Jorge Navarro
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Marcello Massimini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
- Department Biomedical and Clinical Sciences, University of Milan, Milan, Italy
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14
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Browndyke JN, Tomalin LE, Erus G, Overbey JR, Kuceyeski A, Moskowitz AJ, Bagiella E, Iribarne A, Acker M, Mack M, Mathew J, O'Gara P, Gelijns AC, Suarez‐Farinas M, Messé SR. Infarct-related structural disconnection and delirium in surgical aortic valve replacement patients. Ann Clin Transl Neurol 2024; 11:263-277. [PMID: 38155462 PMCID: PMC10863920 DOI: 10.1002/acn3.51949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 10/28/2023] [Indexed: 12/30/2023] Open
Abstract
OBJECTIVE Although acute brain infarcts are common after surgical aortic valve replacement (SAVR), they are often unassociated with clinical stroke symptoms. The relationship between clinically "silent" infarcts and in-hospital delirium remains uncertain; obscured, in part, by how infarcts have been traditionally summarized as global metrics, independent of location or structural consequence. We sought to determine if infarct location and related structural connectivity changes were associated with postoperative delirium after SAVR. METHODS A secondary analysis of a randomized multicenter SAVR trial of embolic protection devices (NCT02389894) was conducted, excluding participants with clinical stroke or incomplete neuroimaging (N = 298; 39% female, 7% non-White, 74 ± 7 years). Delirium during in-hospital recovery was serially screened using the Confusion Assessment Method. Parcellation and tractography atlas-based neuroimaging methods were used to determine infarct locations and cortical connectivity effects. Mixed-effect, zero-inflated gaussian modeling analyses, accounting for brain region-specific infarct characteristics, were conducted to examine for differences within and between groups by delirium status and perioperative neuroprotection device strategy. RESULTS 23.5% participants experienced postoperative delirium. Delirium was associated with significantly increased lesion volumes in the right cerebellum and temporal lobe white matter, while diffusion weighted imaging infarct-related structural disconnection (DWI-ISD) was observed in frontal and temporal lobe regions (p-FDR < 0.05). Fewer brain regions demonstrated DWI-ISD loss in the suction-based neuroprotection device group, relative to filtration-based device or standard aortic cannula. INTERPRETATION Structural disconnection from acute infarcts was greater in patients who experienced postoperative delirium, suggesting that the impact from covert perioperative infarcts may not be as clinically "silent" as commonly assumed.
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Affiliation(s)
- Jeffrey N. Browndyke
- Division of Behavioral Medicine and Neurosciences, Department of Psychiatry and Behavioral SciencesDuke University Medical CenterDurhamNorth CarolinaUSA
- Division of Cardiovascular and Thoracic Surgery, Department of SurgeryDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Lewis E. Tomalin
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Guray Erus
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jessica R. Overbey
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell Medical CollegeNew YorkNew YorkUSA
- Brain and Mind Research InstituteWeill Cornell Medical CollegeNew YorkNew YorkUSA
| | - Alan J. Moskowitz
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Emilia Bagiella
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Alexander Iribarne
- Department of Cardiothoracic SurgeryStaten Island University Hospital, Northwell Health Staten IslandNew YorkNew YorkUSA
| | - Michael Acker
- Division of Cardiovascular Surgery, Department of SurgeryUniversity of Pennsylvania School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Michael Mack
- Department of Cardiothoracic SurgeryBaylor Research Institute, Baylor Scott and White HealthPlanoTexasUSA
| | - Joseph Mathew
- Department of AnesthesiologyDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Patrick O'Gara
- Cardiovascular Division, Department of MedicineBrigham and Women's HospitalBostonMassachusettsUSA
| | - Annetine C. Gelijns
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Mayte Suarez‐Farinas
- Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Steven R. Messé
- Department of NeurologyUniversity of Pennsylvania School of MedicinePhiladelphiaPennsylvaniaUSA
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15
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Tozlu C, Olafson E, Jamison KW, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. Brain Commun 2023; 5:fcad332. [PMID: 38107503 PMCID: PMC10724045 DOI: 10.1093/braincomms/fcad332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023] Open
Abstract
Prediction of disease progression is challenging in multiple sclerosis as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in multiple sclerosis. In a full cohort of 482 multiple sclerosis patients (age: 41.83 ± 11.63 years, 71.57% females), the Expanded Disability Status Scale was used to classify patients into (i) no or mild (Expanded Disability Status Scale <3) versus (ii) moderate or severe disability groups (Expanded Disability Status Scale ≥3). In 363 out of 482 patients, quantitative susceptibility mapping was used to identify paramagnetic rim lesions, which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects as being cognitively preserved or impaired. Network Modification Tool was used to estimate the regional structural disconnectivity due to multiple sclerosis lesions. Discriminative event-based modelling was applied to investigate the sequence of regional structural disconnectivity due to (i) all representative T2 fluid-attenuated inversion recovery lesions, (ii) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across disability groups ('no to mild disability' to 'moderate to severe disability'), (iii) all representative T2 fluid-attenuated inversion recovery lesions and (iv) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across cognitive status ('cognitively preserved' to 'cognitively impaired'). In the full cohort, structural disconnection in the ventral attention and subcortical networks, particularly in the supramarginal and putamen regions, was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to paramagnetic rim lesions in the motor-related regions. Subcortical structural disconnection, particularly in the ventral diencephalon and thalamus regions, was an early biomarker of cognitive impairment. Our data-driven model revealed that the structural disconnection in the subcortical regions, particularly in the thalamus, is an early biomarker for both disability and cognitive impairment in multiple sclerosis. Paramagnetic rim lesions-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in multiple sclerosis. Such information might be used to identify people with multiple sclerosis who have an increased risk of disability progression or cognitive decline in order to provide personalized treatment plans.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Emily Demmon
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Nara Michaelson
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Neha Safi
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
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16
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Koch PJ, Rudolf LF, Schramm P, Frontzkowski L, Marburg M, Matthis C, Schacht H, Fiehler J, Thomalla G, Hummel FC, Neumann A, Münte TF, Royl G, Machner B, Schulz R. Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke. Stroke 2023; 54:3081-3089. [PMID: 38011237 DOI: 10.1161/strokeaha.123.044221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/04/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND The indication for mechanical thrombectomy (MT) in stroke patients with large vessel occlusion has been constantly expanded over the past years. Despite remarkable treatment effects at the group level in clinical trials, many patients remain severely disabled even after successful recanalization. A better understanding of this outcome variability will help to improve clinical decision-making on MT in the acute stage. Here, we test whether current outcome models can be refined by integrating information on the preservation of the corticospinal tract as a functionally crucial white matter tract derived from acute perfusion imaging. METHODS We retrospectively analyzed 162 patients with stroke and large vessel occlusion of the anterior circulation who were admitted to the University Medical Center Lübeck between 2014 and 2020 and underwent MT. The ischemic core was defined as fully automatized based on the acute computed tomography perfusion with cerebral blood volume data using outlier detection and clustering algorithms. Normative whole-brain structural connectivity data were used to infer whether the corticospinal tract was affected by the ischemic core or preserved. Ordinal logistic regression models were used to correlate this information with the modified Rankin Scale after 90 days. RESULTS The preservation of the corticospinal tract was associated with a reduced risk of a worse functional outcome in large vessel occlusion-stroke patients undergoing MT, with an odds ratio of 0.28 (95% CI, 0.15-0.53). This association was still significant after adjusting for multiple confounding covariables, such as age, lesion load, initial symptom severity, sex, stroke side, and recanalization status. CONCLUSIONS A preinterventional computed tomography perfusion-based surrogate of corticospinal tract preservation or disconnectivity is strongly associated with functional outcomes after MT. If validated in independent samples this concept could serve as a novel tool to improve current outcome models to better understand intersubject variability after MT in large vessel occlusion stroke.
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Affiliation(s)
- Philipp J Koch
- Department of Neurology (P.J.K., M.M., G.R., B.M.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Germany (P.J.K., T.F.M., G.R., B.M.)
| | - Linda F Rudolf
- Department of Neuroradiology (L.F.R., P.S., H.S., A.N.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
| | - Peter Schramm
- Department of Neuroradiology (L.F.R., P.S., H.S., A.N.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
| | - Lukas Frontzkowski
- Department of Neurology (L.F., G.T., R.S.) University Medical Center Hamburg Eppendorf, Germany
| | - Maria Marburg
- Department of Neurology (P.J.K., M.M., G.R., B.M.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
| | - Christine Matthis
- Department of Social Medicine and Epidemiology (C.M.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
| | - Hannes Schacht
- Department of Neuroradiology (L.F.R., P.S., H.S., A.N.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
| | - Jens Fiehler
- Department of Neuroradiology (J.F.) University Medical Center Hamburg Eppendorf, Germany
| | - Götz Thomalla
- Department of Neurology (L.F., G.T., R.S.) University Medical Center Hamburg Eppendorf, Germany
| | - Friedhelm C Hummel
- Neuro-X Institute and Brain Mind Institute, Swiss Federal Institute of Technology, Geneva, Switzerland (F.C.H.)
- Neuro-X Institute and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland (F.C.H.)
- Clinical Neuroscience, University of Geneva Medical School, Switzerland (F.C.H.)
| | - Alexander Neumann
- Department of Neuroradiology (L.F.R., P.S., H.S., A.N.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
| | - Thomas F Münte
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Germany (P.J.K., T.F.M., G.R., B.M.)
| | - Georg Royl
- Department of Neurology (P.J.K., M.M., G.R., B.M.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Germany (P.J.K., T.F.M., G.R., B.M.)
| | - Björn Machner
- Department of Neurology (P.J.K., M.M., G.R., B.M.), University Hospital Schleswig-Holstein, Campus Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Germany (P.J.K., T.F.M., G.R., B.M.)
- Department of Neurology, Schoen Clinic Neustadt, Holstein, Germany (B.M.)
| | - Robert Schulz
- Department of Neurology (L.F., G.T., R.S.) University Medical Center Hamburg Eppendorf, Germany
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17
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Röhrig L, Rosenzopf H, Wöhrstein S, Karnath H. The need for hemispheric separation in pairwise structural disconnection studies. Hum Brain Mapp 2023; 44:5212-5220. [PMID: 37539793 PMCID: PMC10543104 DOI: 10.1002/hbm.26445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/05/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023] Open
Abstract
The development of new approaches indirectly measuring the structural disconnectome has recently led to an increase in studies investigating pairwise structural disconnections following brain damage. Previous studies jointly analyzed patients with left hemispheric and patients with right hemispheric lesions when investigating a behavior of interest. An alternative approach would be to perform analyses separated by hemisphere, which has been applied in only a minority of studies to date. The present simulation study investigated whether joint or separate analyses (or both equally) are appropriate to reveal the ground truth disconnections. In fact, both approaches resulted in very different patterns of disconnection. In contrast to analyses separated by hemisphere, joint analyses introduced a bias to the disadvantage of intra-hemispheric disconnections. Intra-hemispheric disconnections were statistically underpowered in the joint analysis and thus surpassed the significance threshold with more difficulty compared to inter-hemispheric disconnections. This statistical imbalance was also shown by a greater number of significant inter-hemispheric than significant intra-hemispheric disconnections. This bias from joint analyses is based on mechanisms similar to those underlying the "partial injury problem." We therefore conclude that pairwise structural disconnections in patients with unilateral left hemispheric and with unilateral right hemispheric lesions exhibiting a specific behavior (or disorder) of interest should be studied separately by hemisphere rather than in a joint analysis.
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Affiliation(s)
- Lisa Röhrig
- Center of Neurology, Division of Neuropsychology, Hertie‐Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
| | - Hannah Rosenzopf
- Center of Neurology, Division of Neuropsychology, Hertie‐Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
| | - Sofia Wöhrstein
- Center of Neurology, Division of Neuropsychology, Hertie‐Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
| | - Hans‐Otto Karnath
- Center of Neurology, Division of Neuropsychology, Hertie‐Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
- Department of PsychologyUniversity of South CarolinaColumbiaSouth CarolinaUSA
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18
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Olafson ER, Sperber C, Jamison KW, Bowren MD, Boes AD, Andrushko JW, Borich MR, Boyd LA, Cassidy JM, Conforto AB, Cramer SC, Dula AN, Geranmayeh F, Hordacre B, Jahanshad N, Kautz SA, Lo B, MacIntosh BJ, Piras F, Robertson AD, Seo NJ, Soekadar SR, Thomopoulos SI, Vecchio D, Weng TB, Westlye LT, Winstein CJ, Wittenberg GF, Wong KA, Thompson PM, Liew SL, Kuceyeski AF. Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.19.545638. [PMID: 37693419 PMCID: PMC10491132 DOI: 10.1101/2023.06.19.545638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.
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Affiliation(s)
- Emily R Olafson
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Christoph Sperber
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Mark D Bowren
- Department of Neurology, Carver College of Medicine, Iowa City, IA, USA
| | - Aaron D Boes
- Departments of Neurology, Psychiatry, and Pediatrics, Carver College of Medicine, Iowa City, IA, USA
| | - Justin W Andrushko
- Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Michael R Borich
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lara A Boyd
- Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Jessica M Cassidy
- Department of Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana B Conforto
- Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paolo, Brazil
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Steven C Cramer
- Dept. Neurology, UCLA; California Rehabilitation Institute, Los Angeles, CA, USA
| | - Adrienne N Dula
- Department of Neurology, Dell Medical School at The University of Texas Austin, Austin, TX, USA
| | - Fatemeh Geranmayeh
- Clinical Language and Cognition Group. Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Brenton Hordacre
- Innovation, Implementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Charleston, SC, USA
| | - Steven A Kautz
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
- Ralph H Johnson VA Health Care System, Charleston, SC, USA
| | - Bethany Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Bradley J MacIntosh
- Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Computational Radiology and Artificial Intelligence (CRAI), Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Andrew D Robertson
- Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Schlegel-UW Research Institute for Aging, Waterloo, ON, Canada
| | - Na Jin Seo
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
- Ralph H Johnson VA Health Care System, Charleston, SC, USA
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Surjo R Soekadar
- Dept. of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Charleston, SC, USA
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Timothy B Weng
- Department of Neurology, Dell Medical School at The University of Texas Austin, Austin, TX, USA
- Department of Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Carolee J Winstein
- Division of Biokinesiology and Physical Therapy, Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - George F Wittenberg
- Departments of Neurology, Bioengineering, Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- GRECC, HERL, Department of Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Kristin A Wong
- Department of Physical Medicine & Rehabilitation, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Charleston, SC, USA
| | - Sook-Lei Liew
- Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Amy F Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
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19
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Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat Neurosci 2023; 26:1613-1629. [PMID: 37580620 PMCID: PMC10471501 DOI: 10.1038/s41593-023-01404-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
| | - Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA
| | - Kevin Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- BrainKey Inc, Palo alto, CA, USA
| | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Martine Hoogman
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
| | - Christopher G Davey
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental, Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Victoria, Australia
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation School of Medicine, Deakin University, Geelong, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sue Cotton
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, UK
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
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20
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Li M, Habes M, Grabe H, Kang Y, Qi S, Detre JA. Disconnectome associated with progressive white matter hyperintensities in aging: a virtual lesion study. Front Aging Neurosci 2023; 15:1237198. [PMID: 37719871 PMCID: PMC10500060 DOI: 10.3389/fnagi.2023.1237198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/04/2023] [Indexed: 09/19/2023] Open
Abstract
Objective White matter hyperintensities (WMH) are commonly seen on T2-weighted magnetic resonance imaging (MRI) in older adults and are associated with an increased risk of cognitive decline and dementia. This study aims to estimate changes in the structural connectome due to age-related WMH by using a virtual lesion approach. Methods High-quality diffusion-weighted imaging data of 30 healthy subjects were obtained from the Human Connectome Project (HCP) database. Diffusion tractography using q-space diffeomorphic reconstruction (QSDR) and whole brain fiber tracking with 107 seed points was conducted using diffusion spectrum imaging studio and the brainnetome atlas was used to parcellate a total of 246 cortical and subcortical nodes. Previously published WMH frequency maps across age ranges (50's, 60's, 70's, and 80's) were used to generate virtual lesion masks for each decade at three lesion frequency thresholds, and these virtual lesion masks were applied as regions of avoidance (ROA) in fiber tracking to estimate connectivity changes. Connections showing significant differences in fiber density with and without ROA were identified using paired tests with False Discovery Rate (FDR) correction. Results Disconnections appeared first from the striatum to middle frontal gyrus (MFG) in the 50's, then from the thalamus to MFG in the 60's and extending to the superior frontal gyrus in the 70's, and ultimately including much more widespread cortical and hippocampal nodes in the 80's. Conclusion Changes in the structural disconnectome due to age-related WMH can be estimated using the virtual lesion approach. The observed disconnections may contribute to the cognitive and sensorimotor deficits seen in aging.
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Affiliation(s)
- Meng Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio, San Antonio, TX, United States
| | - Hans Grabe
- Department of Psychiatry and Psychotherapy, University of Greifswald, Stralsund, Germany
| | - Yan Kang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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21
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Brasanac J, Chien C. A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies. Front Hum Neurosci 2023; 17:1151531. [PMID: 37250694 PMCID: PMC10213782 DOI: 10.3389/fnhum.2023.1151531] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.
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Affiliation(s)
- Jelena Brasanac
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
| | - Claudia Chien
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Experimental and Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Neuroscience Clinical Research Center, Berlin, Germany
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22
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Bartnik A, Fuchs TA, Ashton K, Kuceyeski A, Li X, Mallory M, Oship D, Bergsland N, Ramasamy D, Jakimovski D, Benedict RHB, Weinstock-Guttman B, Zivadinov R, Dwyer MG. Functional alteration due to structural damage is network dependent: insight from multiple sclerosis. Cereb Cortex 2023; 33:6090-6102. [PMID: 36585775 PMCID: PMC10498137 DOI: 10.1093/cercor/bhac486] [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: 07/15/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/01/2023] Open
Abstract
Little is known about how the brain's functional organization changes over time with respect to structural damage. Using multiple sclerosis as a model of structural damage, we assessed how much functional connectivity (FC) changed within and between preselected resting-state networks (RSNs) in 122 subjects (72 with multiple sclerosis and 50 healthy controls). We acquired the structural, diffusion, and functional MRI to compute functional connectomes and structural disconnectivity profiles. Change in FC was calculated by comparing each multiple sclerosis participant's pairwise FC to controls, while structural disruption (SD) was computed from abnormalities in diffusion MRI via the Network Modification tool. We used an ordinary least squares regression to predict the change in FC from SD for 9 common RSNs. We found clear differences in how RSNs functionally respond to structural damage, namely that higher-order networks were more likely to experience changes in FC in response to structural damage (default mode R2 = 0.160-0.207, P < 0.001) than lower-order sensory networks (visual network 1 R2 = 0.001-0.007, P = 0.157-0.387). Our findings suggest that functional adaptability to structural damage depends on how involved the affected network is in higher-order processing.
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Affiliation(s)
- Alexander Bartnik
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Tom A Fuchs
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Kira Ashton
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Amy Kuceyeski
- Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, United States
| | - Xian Li
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Psychological and Brain Science Department, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Matthew Mallory
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Devon Oship
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Niels Bergsland
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan 20148, Italy
| | - Deepa Ramasamy
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Dejan Jakimovski
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Ralph H B Benedict
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Robert Zivadinov
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
| | - Michael G Dwyer
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14203, United States
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23
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Seghier ML. The elusive metric of lesion load. Brain Struct Funct 2023; 228:703-716. [PMID: 36947181 DOI: 10.1007/s00429-023-02630-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/15/2023] [Indexed: 03/23/2023]
Abstract
One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.
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Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
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24
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Civier O, Sourty M, Calamante F. MFCSC: Novel method to calculate mismatch between functional and structural brain connectomes, and its application for detecting hemispheric functional specialisations. Sci Rep 2023; 13:3485. [PMID: 36882426 PMCID: PMC9992688 DOI: 10.1038/s41598-022-17213-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/21/2022] [Indexed: 03/09/2023] Open
Abstract
We introduce a novel connectomics method, MFCSC, that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from functional MRI, at individual subject level. The MFCSC method is based on the fact that SC only broadly predicts FC, and for each connection in the brain, the method calculates a value that quantifies the mismatch that often still exists between the two modalities. To capture underlying physiological properties, MFCSC minimises biases in SC and addresses challenges with the multimodal analysis, including by using a data-driven normalisation approach. We ran MFCSC on data from the Human Connectome Project and used the output to detect pairs of left and right unilateral connections that have distinct relationship between structure and function in each hemisphere; we suggest that this reflects cases of hemispheric functional specialisation. In conclusion, the MFCSC method provides new information on brain organisation that may not be inferred from an analysis that considers SC and FC separately.
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Affiliation(s)
- Oren Civier
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia. .,Swinburne Neuroimaging, Swinburne University of Technology, Melbourne, VIC, Australia.
| | - Marion Sourty
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia.,Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
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25
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Schaechter JD, Kim M, Hightower BG, Ragas T, Loggia ML. Disruptions in Structural and Functional Connectivity Relate to Poststroke Fatigue. Brain Connect 2023; 13:15-27. [PMID: 35570655 PMCID: PMC9942175 DOI: 10.1089/brain.2022.0021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: Poststroke fatigue (PSF) is a disabling condition with unclear etiology. The brain lesion is thought to be an important causal factor in PSF, although focal lesion characteristics such as size and location have not proven to be predictive. Given that the stroke lesion results not only in focal tissue death but also in widespread changes in brain networks that are structurally and functionally connected to damaged tissue, we hypothesized that PSF relates to disruptions in structural and functional connectivity. Materials and Methods: Twelve patients who incurred an ischemic stroke in the middle cerebral artery (MCA) territory 1-3 years prior, and currently experiencing a range of fatigue severity, were enrolled. The patients underwent structural and resting-state functional magnetic resonance imaging (MRI). The structural MRI data were used to measure structural disconnection of gray matter resulting from lesion to white matter pathways. The functional MRI data were used to measure network functional connectivity. Results: The patients showed structural disconnection in varying cortical and subcortical regions. Fatigue severity correlated significantly with structural disconnection of several frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres. Fatigue-related structural disconnection was most severe in the IL rostral middle frontal cortex. Greater structural disconnection of a subset of fatigue-related frontal cortex regions, including the IL rostral middle frontal cortex, trended toward correlating significantly with greater loss in functional connectivity. Among identified fatigue-related frontal cortex regions, only the IL rostral middle frontal cortex showed loss in functional connectivity correlating significantly with fatigue severity. Conclusion: Our results provide evidence that loss in structural and functional connectivity of bihemispheric frontal cortex regions plays a role in PSF after MCA stroke, with connectivity disruptions of the IL rostral middle frontal cortex having a central role. Impact statement Poststroke fatigue (PSF) is a common disabling condition with unclear etiology. We hypothesized that PSF relates to disruptions in structural and functional connectivity secondary to the focal lesion. Using structural and resting-state functional connectivity magnetic resonance imaging (MRI) in patients with chronic middle cerebral artery (MCA) stroke, we found frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres with greater structural disconnection correlating with greater fatigue. Among these fatigue-related cortices, the IL rostral middle frontal cortex showed loss in functional connectivity correlating with fatigue. These findings suggest that disruptions in structural and functional connectivity play a role in PSF after MCA stroke.
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Affiliation(s)
- Judith D. Schaechter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Minhae Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Baileigh G. Hightower
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Trevor Ragas
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
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26
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Tozlu C, Olafson E, Jamison K, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525537. [PMID: 36747675 PMCID: PMC9900990 DOI: 10.1101/2023.01.26.525537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Objective Prediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS. Methods In a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment. Results Structural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment. Interpretation MS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Emily Demmon
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Nara Michaelson
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Neha Safi
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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27
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Rousseau PN, Chakravarty MM, Steele CJ. Mapping pontocerebellar connectivity with diffusion MRI. Neuroimage 2022; 264:119684. [PMID: 36252913 DOI: 10.1016/j.neuroimage.2022.119684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022] Open
Abstract
The cerebellum's involvement in cognitive, affective and motor functions is mediated by connections to different regions of the cerebral cortex. A distinctive feature of cortico-cerebellar loops that has been demonstrated in the animal work is a topographic organization that is preserved across its corticopontine, pontocerebellar, and cerebello-thalmo-cortical segments. Here we used tractography derived from diffusion imaging data to characterize the connections between the pons and the individual lobules of the cerebellum and generate a parcellation of the pons and middle cerebellar peduncle based on the pattern of connectivity. We identified a rostral to caudal gradient in the pons, similar to that observed in the animal work, such that rostral regions were preferentially connected to cerebellar lobules involved in non-motor, and caudal regions with motor regions. These findings advance our fundamental understanding of the cerebellum, and the parcellations we generated provide context for future research into the pontocerebellar tract's involvement in health and disease.
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Affiliation(s)
| | - M Mallar Chakravarty
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada; Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Christopher J Steele
- Department of Psychology, Concordia University, Montreal, QC, Canada; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; PERFORM Centre, Concordia University, Montreal, QC, Canada
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28
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Dulyan L, Talozzi L, Pacella V, Corbetta M, Forkel SJ, Thiebaut de Schotten M. Longitudinal prediction of motor dysfunction after stroke: a disconnectome study. Brain Struct Funct 2022; 227:3085-3098. [PMID: 36334132 PMCID: PMC9653357 DOI: 10.1007/s00429-022-02589-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 10/20/2022] [Indexed: 06/01/2023]
Abstract
Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.
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Affiliation(s)
- Lilit Dulyan
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
- Donders Centre for Brain Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Lia Talozzi
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
| | - Valentina Pacella
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
| | - Maurizio Corbetta
- Clinica Neurologica, Department of Neuroscience, University of Padova, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padua, Italy
- Venetian Institute of Molecular Medicine, VIMM, Padua, Italy
| | - Stephanie J Forkel
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Donders Centre for Brain Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
- Department of Neurosurgery, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
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29
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Hebert JR, Filley CM. Multisensory integration and white matter pathology: Contributions to cognitive dysfunction. Front Neurol 2022; 13:1051538. [PMID: 36408503 PMCID: PMC9668060 DOI: 10.3389/fneur.2022.1051538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/18/2022] [Indexed: 11/23/2022] Open
Abstract
The ability to simultaneously process and integrate multiple sensory stimuli is paramount to effective daily function and essential for normal cognition. Multisensory management depends critically on the interplay between bottom-up and top-down processing of sensory information, with white matter (WM) tracts acting as the conduit between cortical and subcortical gray matter (GM) regions. White matter tracts and GM structures operate in concert to manage both multisensory signals and cognition. Altered sensory processing leads to difficulties in reweighting and modulating multisensory input during various routine environmental challenges, and thus contributes to cognitive dysfunction. To examine the specific role of WM in altered sensory processing and cognitive dysfunction, this review focuses on two neurologic disorders with diffuse WM pathology, multiple sclerosis and mild traumatic brain injury, in which persistently altered sensory processing and cognitive impairment are common. In these disorders, cognitive dysfunction in association with altered sensory processing may develop initially from slowed signaling in WM tracts and, in some cases, GM pathology secondary to WM disruption, but also because of interference with cognitive function by the added burden of managing concurrent multimodal primary sensory signals. These insights promise to inform research in the neuroimaging, clinical assessment, and treatment of WM disorders, and the investigation of WM-behavior relationships.
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Affiliation(s)
- Jeffrey R. Hebert
- Physical Performance Laboratory, Marcus Institute for Brain Health, University of Colorado School of Medicine, Aurora, CO, United States
| | - Christopher M. Filley
- Behavorial Neurology Section, Department of Neurology and Psychiatry, Marcus Institute for Brain Health, University of Colorado School of Medicine, Aurora, CO, United States
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30
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Schlemm E, Jensen M, Kuceyeski A, Jamison K, Ingwersen T, Mayer C, Königsberg A, Boutitie F, Ebinger M, Endres M, Fiebach JB, Fiehler J, Galinovic I, Lemmens R, Muir KW, Nighoghossian N, Pedraza S, Puig J, Simonsen CZ, Thijs V, Wouters A, Gerloff C, Thomalla G, Cheng B. Early effect of thrombolysis on structural brain network organisation after anterior‐circulation stroke in the randomized
WAKE‐UP
trial. Hum Brain Mapp 2022; 43:5053-5065. [PMID: 36102287 PMCID: PMC9582379 DOI: 10.1002/hbm.26073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 07/11/2022] [Accepted: 08/22/2022] [Indexed: 11/08/2022] Open
Abstract
The symptoms of acute ischemic stroke can be attributed to disruption of the brain network architecture. Systemic thrombolysis is an effective treatment that preserves structural connectivity in the first days after the event. Its effect on the evolution of global network organisation is, however, not well understood. We present a secondary analysis of 269 patients from the randomized WAKE‐UP trial, comparing 127 imaging‐selected patients treated with alteplase with 142 controls who received placebo. We used indirect network mapping to quantify the impact of ischemic lesions on structural brain network organisation in terms of both global parameters of segregation and integration, and local disruption of individual connections. Network damage was estimated before randomization and again 22 to 36 h after administration of either alteplase or placebo. Evolution of structural network organisation was characterised by a loss in integration and gain in segregation, and this trajectory was attenuated by the administration of alteplase. Preserved brain network organization was associated with excellent functional outcome. Furthermore, the protective effect of alteplase was spatio‐topologically nonuniform, concentrating on a subnetwork of high centrality supported in the salvageable white matter surrounding the ischemic cores. This interplay between the location of the lesion, the pathophysiology of the ischemic penumbra, and the spatial embedding of the brain network explains the observed potential of thrombolysis to attenuate topological network damage early after stroke. Our findings might, in the future, lead to new brain network‐informed imaging biomarkers and improved prognostication in ischemic stroke.
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Affiliation(s)
- Eckhard Schlemm
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Märit Jensen
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Amy Kuceyeski
- Department of Radiology Weill Cornell Medicine New York New York USA
| | - Keith Jamison
- Department of Radiology Weill Cornell Medicine New York New York USA
| | - Thies Ingwersen
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Carola Mayer
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Alina Königsberg
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Florent Boutitie
- Department of Radiology Weill Cornell Medicine New York New York USA
- Hospices Civils de Lyon, Service de Biostatistique Lyon France
- Université Lyon 1 Villeurbanne France
- CNRS, UMR 5558 Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique‐Santé Villeurbanne France
| | - Martin Ebinger
- Centrum für Schlaganfallforschung Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
- Klinik für Neurologie Medical Park Berlin Humboldtmühle Berlin Germany
| | - Matthias Endres
- Centrum für Schlaganfallforschung Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
- Klinik und Hochschulambulanz für Neurologie Charité‐Universitätsmedizin Berlin Berlin Germany
- German Centre for Neurodegenerative Diseases (DZNE) Berlin Germany
- German Centre for Cardiovascular Research (DZHK) Berlin Germany
- ExcellenceCluster NeuroCure Berlin Germany
| | - Jochen B. Fiebach
- Centrum für Schlaganfallforschung Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Ivana Galinovic
- Centrum für Schlaganfallforschung Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
| | - Robin Lemmens
- Department of Neurology University Hospitals Leuven Leuven Belgium
- Department of Neurosciences Division of Experimental Neurology KU Leuven—University of Leuven Leuven Belgium
- VIB, Centre for Brain & Disease Research Laboratory of Neurobiology Leuven Belgium
| | - Keith W. Muir
- Institute of Neuroscience & Psychology University of Glasgow Glasgow UK
| | - Norbert Nighoghossian
- Department of Stroke Medicine, Université Claude Bernard Lyon 1 CREATIS CNRS UMR 5220‐INSERM U1206, INSA‐Lyon Lyon France
| | - Salvador Pedraza
- Department of Radiology, Institut de Diagnostic per la Image (IDI) Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI) Girona Spain
| | - Josep Puig
- Department of Radiology, Institut de Diagnostic per la Image (IDI) Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI) Girona Spain
| | | | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health University of Melbourne Heidelberg Victoria Australia
- Department of Neurology Austin Health Heidelberg Victoria Australia
| | - Anke Wouters
- Department of Neurology University Hospitals Leuven Leuven Belgium
- Department of Neurosciences Division of Experimental Neurology KU Leuven—University of Leuven Leuven Belgium
- VIB, Centre for Brain & Disease Research Laboratory of Neurobiology Leuven Belgium
- Department of Neurology Amsterdam UMC University of Amsterdam Amsterdam Netherlands
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
| | - Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf‐ und Neurozentrum University Medical Centre Hamburg‐Eppendorf Hamburg Germany
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31
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Sperber C, Griffis J, Kasties V. Indirect structural disconnection-symptom mapping. Brain Struct Funct 2022; 227:3129-3144. [PMID: 36048282 DOI: 10.1007/s00429-022-02559-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/24/2022] [Indexed: 01/01/2023]
Abstract
In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.
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Affiliation(s)
- Christoph Sperber
- University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany.
| | - Joseph Griffis
- University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany
| | - Vanessa Kasties
- Centre of Neurology, Hertie-Institute for Clinical Brain Research, University of Tubingen, Tubingen, Germany
- Child Development Center, University Childrens Hospital Zurich, University of Zurich, Zurich, Switzerland
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32
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Pandya S, Maia PD, Freeze B, Menke RAL, Talbot K, Turner MR, Raj A. Modeling seeding and neuroanatomic spread of pathology in amyotrophic lateral sclerosis. Neuroimage 2022; 251:118968. [PMID: 35143975 PMCID: PMC10729776 DOI: 10.1016/j.neuroimage.2022.118968] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 01/26/2022] [Accepted: 02/02/2022] [Indexed: 12/12/2022] Open
Abstract
The neurodegenerative disorder amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of upper and lower motor neurons, with pathological involvement of cerebral motor and extra-motor areas in a clinicopathological spectrum with frontotemporal dementia (FTD). A key unresolved issue is how the non-random distribution of pathology in ALS reflects differential network vulnerability, including molecular factors such as regional gene expression, or preferential spread of pathology via anatomical connections. A system of histopathological staging of ALS based on the regional burden of TDP-43 pathology observed in postmortem brains has been supported to some extent by analysis of distribution of in vivo structural MRI changes. In this paper, computational modeling using a Network Diffusion Model (NDM) was used to investigate whether a process of focal pathological 'seeding' followed by structural network-based spread recapitulated postmortem histopathological staging and, secondly, whether this had any correlation to the pattern of expression of a panel of genes implicated in ALS across the healthy brain. Regionally parcellated T1-weighted MRI data from ALS patients (baseline n=79) was studied in relation to a healthy control structural connectome and a database of associated regional cerebral gene expression. The NDM provided strong support for a structural network-based basis for regional pathological spread in ALS, but no simple relationship to the spatial distribution of ALS-related genes in the healthy brain. Interestingly, OPTN gene was identified as a significant but a weaker non-NDM contributor within the network-gene interaction model (LASSO). Intriguingly, the critical seed regions for spread within the model were not within the primary motor cortex but basal ganglia, thalamus and insula, where NDM recapitulated aspects of the postmortem histopathological staging system. Within the ALS-FTD clinicopathological spectrum, non-primary motor structures may be among the earliest sites of cerebral pathology.
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Affiliation(s)
- Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, 1300 York Avenue, New York, NY, United States.
| | - Pedro D Maia
- Department of Mathematics, University of Texas at Arlington, TX, United States
| | - Benjamin Freeze
- Scripps Health/MD Anderson Cancer Center, Department of Radiology, CA, United States
| | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, West Wing Level 6, Oxford OX2 7PZ, United Kingdom
| | - Kevin Talbot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, West Wing Level 6, Oxford OX2 7PZ, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Ashish Raj
- Department of Radiology, Weill Cornell Medicine, 1300 York Avenue, New York, NY, United States; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94121, United States.
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Carolus K, Fuchs TA, Bergsland N, Ramasamy D, Tran H, Uher T, Horakova D, Vaneckova M, Havrdova E, Benedict RHB, Zivadinov R, Dwyer MG. Time course of lesion-induced atrophy in multiple sclerosis. J Neurol 2022; 269:4478-4487. [PMID: 35394170 DOI: 10.1007/s00415-022-11094-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE White matter (WM) tract disruption impacts volume loss in connected deep gray matter (DGM) over 5 years in people with multiple sclerosis (PwMS). However, the timeline of this phenomenon remains poorly characterized. MATERIALS AND METHODS Annual serial MRI for 181 PwMS was retrospectively analyzed from a 10-year clinical trial database. Annualized thalamic atrophy, DGM atrophy, and disruption of connected WM tracts were measured. For time series analysis, ~700 epochs were collated using a sliding 5-year window, and regression models predicting 1-year atrophy were applied to characterize the influence of new tract disruption from preceding years, while controlling for whole brain atrophy and other relevant factors. RESULTS Disruptions of WM tracts connected to the thalamus were significantly associated with thalamic atrophy 1 year later (β: 0.048-0.103). This effect was not observed for thalamic tract disruption concurrent with the time of atrophy nor for thalamic tract disruption preceding the atrophy by 2-4 years. Similarly, disruptions of white matter tracts connected to the DGM were significantly associated with DGM atrophy 1 year later (β: 0.078-0.111), but not for tract disruption concurrent with, nor preceding the atrophy by 2-4 years. CONCLUSION Increased rates of thalamic and DGM atrophy were restricted to 1 year following newly developed disruption in connected WM tracts. In research and clinical settings, additional gray matter atrophy may be expected 1 year following new lesion growth in connected white matter.
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Affiliation(s)
- Keith Carolus
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Tom A Fuchs
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Deepa Ramasamy
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Hoan Tran
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University, General University Hospital, Prague, Czech Republic
| | - Eva Havrdova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Ralph H B Benedict
- Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
- Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA.
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Jaywant A, Dunlop K, Victoria LW, Oberlin L, Lynch CJ, Respino M, Kuceyeski A, Scult M, Hoptman MJ, Liston C, O’Dell MW, Alexopoulos GS, Perlis RH, Gunning FM. Estimated Regional White Matter Hyperintensity Burden, Resting State Functional Connectivity, and Cognitive Functions in Older Adults. Am J Geriatr Psychiatry 2022; 30:269-280. [PMID: 34412936 PMCID: PMC8799753 DOI: 10.1016/j.jagp.2021.07.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/24/2021] [Accepted: 07/22/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE White matter hyperintensities (WMH) are linked to deficits in cognitive functioning, including cognitive control and memory; however, the structural, and functional mechanisms are largely unknown. We investigated the relationship between estimated regional disruptions to white matter fiber tracts from WMH, resting state functional connectivity (RSFC), and cognitive functions in older adults. DESIGN Cross-sectional study. SETTING Community. PARTICIPANTS Fifty-eight cognitively-healthy older adults. MEASUREMENTS Tasks of cognitive control and memory, structural MRI, and resting state fMRI. We estimated the disruption to white matter fiber tracts from WMH and its impact on gray matter regions in the cortical and subcortical frontoparietal network, default mode network, and ventral attention network by overlaying each subject's WMH mask on a normative tractogram dataset. We calculated RSFC between nodes in those same networks. We evaluated the interaction of regional WMH burden and RSFC in predicting cognitive control and memory. RESULTS The interaction of estimated regional WMH burden and RSFC in cortico-striatal regions of the default mode network and frontoparietal network was associated with delayed recall. Models predicting working memory, cognitive inhibition, and set-shifting were not significant. CONCLUSION Findings highlight the role of network-level structural and functional alterations in resting state networks that are related to WMH and impact memory in older adults.
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Affiliation(s)
- Abhishek Jaywant
- Department of Psychiatry, Weill Cornell Medicine,Department of Rehabilitation Medicine, Weill Cornell Medicine
| | - Katharine Dunlop
- Department of Psychiatry, Weill Cornell Medicine,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine
| | - Lindsay W. Victoria
- Department of Psychiatry, Weill Cornell Medicine,Weill Cornell Institute of Geriatric Psychiatry
| | - Lauren Oberlin
- Department of Psychiatry, Weill Cornell Medicine,Weill Cornell Institute of Geriatric Psychiatry
| | - Charles J. Lynch
- Department of Psychiatry, Weill Cornell Medicine,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine
| | - Matteo Respino
- Department of Psychiatry, Weill Cornell Medicine,Weill Cornell Institute of Geriatric Psychiatry
| | | | | | - Matthew J. Hoptman
- Nathan Kline Institute for Psychiatric Research,Department of Psychiatry, New York University School of Medicine
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine
| | | | - George S. Alexopoulos
- Department of Psychiatry, Weill Cornell Medicine,Weill Cornell Institute of Geriatric Psychiatry
| | - Roy H. Perlis
- Harvard Medical School/Massachusetts General Hospital
| | - Faith M. Gunning
- Department of Psychiatry, Weill Cornell Medicine,Weill Cornell Institute of Geriatric Psychiatry
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35
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Fuchs TA, Vaughn CB, Benedict RHB, Weinstock-Guttman B, Bergsland N, Jakimovski D, Ramasamy D, Zivadinov R, Dwyer MG. Patient-Reported Outcome Severity and Emotional Salience Network Disruption in Multiple Sclerosis. Brain Imaging Behav 2022; 16:1252-1259. [PMID: 34985619 DOI: 10.1007/s11682-021-00614-5] [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] [Accepted: 12/02/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Overall burden of white matter damage is associated with increased self-report fatigue severity in people with multiple sclerosis. However, a paradoxically opposite association was reported for white matter damage to tracts in specific subnetworks including the amygdala, temporal pole, and insula. Based on neuroanatomical principles and other data from the literature, we hypothesized that these results might be indicative of a broader relationship between damage to these subnetworks and impaired recognition of negative emotional salience central to patient-reported outcomes. OBJECTIVE We examined whether damage in the same previously-identified subnetworks is also associated with lower self-report depressive symptoms, something which may be decreased in individuals with impaired recognition of negative emotional salience. Other patient characteristics were also explored. METHODS In a cohort of 137 people with multiple sclerosis, we measured location-specific network white matter tract damage in the proposed negative emotional salience network, along with self-report severity of depressive symptoms and cognitive problems, personality characteristics, objective cognitive performance, and physical disability. We applied regression analyses, accounting for lesion burden, to explore the relationship between damage in the proposed negative emotional salience network and these factors. RESULTS We found disruption within the negative emotional salience network is associated with lower self-report depressive symptoms (β = -0.277, p = 0.036), cognitive complaints (r = -0.196, p = 0.024) and personality trait Neuroticism (r = -0.179, p = 0.042). In contrast, damage within this network was not significantly associated with objective cognitive processing speed, personality trait Openness, or physical disability. CONCLUSION The identified network may be a generalizable network which corresponds to the recognition of negative emotional salience, but not to objective factors such as processing speed and physical disability. Damage to this network may paradoxically buffer against negative emotional perception of symptom severity, central to patient-reported outcomes.
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Affiliation(s)
- Tom A Fuchs
- Jacobs Multiple Sclerosis Center for Treatment and Research, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA.
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA.
| | - Caila B Vaughn
- Jacobs Multiple Sclerosis Center for Treatment and Research, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
| | - Ralph H B Benedict
- Jacobs Multiple Sclerosis Center for Treatment and Research, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center for Treatment and Research, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
- IRCSS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
| | - Deepa Ramasamy
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
| | - Robert Zivadinov
- Jacobs Multiple Sclerosis Center for Treatment and Research, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York (SUNY), Buffalo, NY, USA
| | - Michael G Dwyer
- Jacobs Multiple Sclerosis Center for Treatment and Research, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York (SUNY), 100 High St, Buffalo, NY, 14203, USA
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York (SUNY), Buffalo, NY, USA
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Liang L, Chen Z, Wei Y, Tang F, Nong X, Li C, Yu B, Duan G, Su J, Mai W, Zhao L, Zhang Z, Deng D. Fusion analysis of gray matter and white matter in subjective cognitive decline and mild cognitive impairment by multimodal CCA-joint ICA. Neuroimage Clin 2021; 32:102874. [PMID: 34911186 PMCID: PMC8605254 DOI: 10.1016/j.nicl.2021.102874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/30/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI. MATERIALS AND METHODS In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups. RESULTS A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups. CONCLUSIONS The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.
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Affiliation(s)
- Lingyan Liang
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, Guangxi, China
| | - Zaili Chen
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Department of Medical Instrument Measurement, Shenzhen Academy of Metrology and Quality Inspection, Shenzhen 518055, China.
| | - Yichen Wei
- Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Fei Tang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Department of Medical Instrument Measurement, Shenzhen Academy of Metrology and Quality Inspection, Shenzhen 518055, China.
| | - Xiucheng Nong
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Chong Li
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Bihan Yu
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Gaoxiong Duan
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, Guangxi, China
| | - Jiahui Su
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Wei Mai
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Lihua Zhao
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Peng Cheng Laboratory, Shenzhen 518055, China.
| | - Demao Deng
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, Guangxi, China.
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37
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Fuchs TA, Schoonheim MM, Broeders TAA, Hulst HE, Weinstock-Guttman B, Jakimovski D, Silver J, Zivadinov R, Geurts JJG, Dwyer MG, Benedict RHB. Functional network dynamics and decreased conscientiousness in multiple sclerosis. J Neurol 2021; 269:2696-2706. [PMID: 34713325 DOI: 10.1007/s00415-021-10860-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 10/15/2021] [Accepted: 10/17/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Conscientiousness is a personality trait that declines in people with multiple sclerosis (PwMS) and its decline predicts worse clinical outcomes. This study aims to investigate the neural underpinnings of lower Conscientiousness in PwMS by examining MRI anomalies in functional network dynamics. METHODS 70 PwMS and 50 healthy controls underwent personality assessment and resting-state MRI. Associations with dynamic functional network properties (i.e., eigenvector centrality) were evaluated, using a dynamic sliding-window approach. RESULTS In PwMS, lower Conscientiousness was associated with increased variability of centrality in the left insula (tmax = 4.21) and right inferior parietal lobule (tmax = 3.79); a relationship also observed in regressions accounting for handedness, disease duration, disability, and tract disruption in relevant structural networks (ΔR2 = 0.071, p = 0.003; ΔR2 = 0.094, p = 0.004). Centrality dynamics of the observed regions were not associated with Neuroticism (R2 < 0.001, p = 0.956; R2 < 0.001, p = 0.945). As well, higher Conscientiousness was associated with greater variability in connectivity for the left insula with the default-mode network (F = 3.92, p = 0.023) and limbic network (F = 5.66, p = 0.005). CONCLUSION Lower Conscientiousness in PwMS was associated with increased variability in network centrality, most prominently for the left insula and right inferior parietal cortex. This effect, specific to Conscientiousness and significant after accounting for disability and structural network damage, could indicate that overall stable network centrality is lost in patients with low Conscientiousness, especially for the insula and right parietal cortex. The positive relationship between Conscientiousness and variability of connectivity between left insula and default-mode network potentially affirms that dynamics between the salience and default-mode networks is related to the regulation of behavior.
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Affiliation(s)
- Tom A Fuchs
- Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Jacobs Multiple Sclerosis Center for Treatment and Research, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Hanneke E Hulst
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center for Treatment and Research, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Jacob Silver
- Department of Orthopedics, School of Medicine, University of Connecticut, Farmington, CT, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Jacobs Multiple Sclerosis Center for Treatment and Research, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Jacobs Multiple Sclerosis Center for Treatment and Research, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph H B Benedict
- Jacobs Multiple Sclerosis Center for Treatment and Research, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
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38
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Powell F, Tosun D, Raj A. Network-constrained technique to characterize pathology progression rate in Alzheimer's disease. Brain Commun 2021; 3:fcab144. [PMID: 34704025 PMCID: PMC8376686 DOI: 10.1093/braincomms/fcab144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/12/2021] [Accepted: 03/19/2021] [Indexed: 11/30/2022] Open
Abstract
Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer’s disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient’s longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer’s Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject’s degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.
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Affiliation(s)
- Fon Powell
- Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, AC-116, Parnassus, Box 0628, San Francisco, CA 94122, USA.,San Francisco Veterans Affairs Medical Center, San Francisco, CA 94121, USA
| | - Ashish Raj
- Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, AC-116, Parnassus, Box 0628, San Francisco, CA 94122, USA
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Olafson ER, Jamison KW, Sweeney EM, Liu H, Wang D, Bruss JE, Boes AD, Kuceyeski A. Functional connectome reorganization relates to post-stroke motor recovery and structural and functional disconnection. Neuroimage 2021; 245:118642. [PMID: 34637901 PMCID: PMC8805675 DOI: 10.1016/j.neuroimage.2021.118642] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/15/2021] [Accepted: 10/08/2021] [Indexed: 11/28/2022] Open
Abstract
Motor recovery following ischemic stroke is contingent on the ability of surviving brain networks to compensate for damaged tissue. In rodent models, sensory and motor cortical representations have been shown to remap onto intact tissue around the lesion site, but remapping to more distal sites (e.g. in the contralesional hemisphere) has also been observed. Resting state functional connectivity (FC) analysis has been employed to study compensatory network adaptations in humans, but mechanisms and time course of motor recovery are not well understood. Here, we examine longitudinal FC in 23 first-episode ischemic pontine stroke patients and utilize a graph matching approach to identify patterns of functional connectivity reorganization during recovery. We quantified functional reorganization between several intervals ranging from 1 week to 6 months following stroke, and demonstrated that the areas that undergo functional reorganization most frequently are in cerebellar/subcortical networks. Brain regions with more structural and functional connectome disruption due to the stroke also had more remapping over time. Finally, we show that functional reorganization is correlated with the extent of motor recovery in the early to late subacute phases, and furthermore, individuals with greater baseline motor impairment demonstrate more extensive early subacute functional reorganization (from one to two weeks post-stroke) and this reorganization correlates with better motor recovery at 6 months. Taken together, these results suggest that our graph matching approach can quantify recovery-relevant, whole-brain functional connectivity network reorganization after stroke.
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Affiliation(s)
- Emily R Olafson
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA.
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
| | - Elizabeth M Sweeney
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
| | - Hesheng Liu
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
| | - Danhong Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
| | - Joel E Bruss
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
| | - Aaron D Boes
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA; Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10021, USA; Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Department of Neurology, University of Iowa, Iowa, IA 52242, USA
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Tozlu C, Jamison K, Nguyen T, Zinger N, Kaunzner U, Pandya S, Wang Y, Gauthier S, Kuceyeski A. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain Behav 2021; 11:e2353. [PMID: 34498432 PMCID: PMC8553317 DOI: 10.1002/brb3.2353] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/28/2021] [Accepted: 08/19/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim- lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim- lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim- lesions. METHODS Ninety-six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim- or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim- lesions. Classification performance was assessed using the area under ROC curve (AUC). RESULTS The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim- lesions (AUC = 0.67 vs 0.63, p-value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories. CONCLUSION rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim- lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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Tozlu C, Jamison K, Gu Z, Gauthier SA, Kuceyeski A. Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups. Neuroimage Clin 2021; 32:102827. [PMID: 34601310 PMCID: PMC8488753 DOI: 10.1016/j.nicl.2021.102827] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Multiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain's anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain's structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls' connectivity networks. OBJECTIVE Here, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individual's lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups. MATERIALS AND METHODS One hundred pwMS (age:45.5 ± 11.4 years, 66% female, disease duration: 12.97 ± 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool's eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups. RESULTS The regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value < 0.05), while the pairwise eSC and SC performed similarly (p = 0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pairwise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson's r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability. DISCUSSION Here, for the first time, we use clinically acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool's estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Zijin Gu
- Electrical and Computer Engineering Department, Cornell University, Ithaca 14850, USA
| | - Susan A Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Neurology, Weill Cornell Medicine, New York, NY, USA; Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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Pontillo G, Tommasin S, Cuocolo R, Petracca M, Petsas N, Ugga L, Carotenuto A, Pozzilli C, Iodice R, Lanzillo R, Quarantelli M, Brescia Morra V, Tedeschi E, Pantano P, Cocozza S. A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis. AJNR Am J Neuroradiol 2021; 42:1927-1933. [PMID: 34531195 DOI: 10.3174/ajnr.a7274] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 07/12/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images. MATERIALS AND METHODS In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (n = 400) and test (n = 100) sets, while the site 2 cohort (n = 104) constituted the external test set. After feature selection of clinicodemographic and MR imaging-derived variables, different machine learning algorithms predicting disability as measured with the Expanded Disability Status Scale were trained and cross-validated on the training cohort and evaluated on the test sets. The effect of different algorithms on model performance was tested using the 1-way repeated-measures ANOVA. RESULTS The selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (r approaching 0.80) and excellent intra- and intersite generalizability (r ≥ 0.73). The machine learning algorithm had no relevant effect on the performance. CONCLUSIONS The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.
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Affiliation(s)
- G Pontillo
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.).,Electrical Engineering and Information Technology (G.P., M.Q.)
| | - S Tommasin
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy
| | - R Cuocolo
- Clinical Medicine and Surgery (R.C.) .,Laboratory of Augmented Reality for Health Monitoring (R.C.)
| | - M Petracca
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - N Petsas
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Mediterraneo (N.P., P.P.), Pozzilli, Italy
| | - L Ugga
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
| | - A Carotenuto
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - C Pozzilli
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy
| | - R Iodice
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - R Lanzillo
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - M Quarantelli
- Electrical Engineering and Information Technology (G.P., M.Q.).,Institute of Biostructure and Bioimaging (M.Q.), National Research Council, Naples, Italy
| | - V Brescia Morra
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
| | - P Pantano
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy.,Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Mediterraneo (N.P., P.P.), Pozzilli, Italy
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
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Raj A, Tora V, Gao X, Cho H, Choi JY, Ryu YH, Lyoo CH, Franchi B. Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography. Brain Connect 2021; 11:624-638. [PMID: 33947253 DOI: 10.1089/brain.2020.0841] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background: Alzheimer's disease involves widespread and progressive deposition of misfolded protein tau (τ), first appearing in the entorhinal cortex, coagulating in longer polymers and insoluble fibrils. There is mounting evidence for "prion-like" trans-neuronal transmission, whereby misfolded proteins cascade along neuronal pathways, giving rise to networked spread. However, the cause-effect mechanisms by which various oligomeric τ species are produced, aggregate, and disseminate are unknown. The question of how protein aggregation and subsequent spread lead to stereotyped progression in the Alzheimer brain remains unresolved. Materials and Methods: We address these questions by using mathematically precise parsimonious modeling of these pathophysiological processes, extrapolated to the whole brain. We model three key processes: τ monomer production; aggregation into oligomers and then into tangles; and the spatiotemporal progression of misfolded τ as it ramifies into neural circuits via the brain connectome. We model monomer seeding and production at the entorhinal cortex, aggregation using Smoluchowski equations; and networked spread using our prior Network-Diffusion model. Results: This combined aggregation-network-diffusion model exhibits all hallmarks of τ progression seen in human patients. Unlike previous theoretical studies of protein aggregation, we present here an empirical validation on in vivo imaging and fluid τ measurements from large datasets. The model accurately captures not just the spatial distribution of empirical regional τ and atrophy but also patients' cerebrospinal fluid phosphorylated τ profiles as a function of disease progression. Conclusion: This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
| | - Veronica Tora
- Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy
| | - Xiao Gao
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Jae Yong Choi
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, Republic of Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Bruno Franchi
- Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy
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Fuchs TA, Dwyer MG, Jakimovski D, Bergsland N, Ramasamy DP, Weinstock-Guttman B, Hb Benedict R, Zivadinov R. Quantifying disease pathology and predicting disease progression in multiple sclerosis with only clinical routine T2-FLAIR MRI. NEUROIMAGE-CLINICAL 2021; 31:102705. [PMID: 34091352 PMCID: PMC8182301 DOI: 10.1016/j.nicl.2021.102705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/12/2021] [Accepted: 05/19/2021] [Indexed: 12/17/2022]
Abstract
We explored five brain pathology measures from clinical-quality T2-FLAIR MRI in MS. These included LVV, thalamus volume, MOV, SCLV and network efficiency. T2-FLAIR measures predicted a majority of the variance in research-quality MRI. T2-FLAIR measures correlated with neurologic disability and cognitive function. T2-FLAIR measures predicted disability progression over five-years. T2-FLAIR measures can be used in legacy clinical datasets.
Background Although quantitative measures from research-quality MRI provide a means to study multiple sclerosis (MS) pathology in vivo, these metrics are often unavailable in legacy clinical datasets. Objective To determine how well an automatically-generated quantitative snapshot of brain pathology, measured only on clinical routine T2-FLAIR MRI, can substitute for more conventional measures on research MRI in terms of capturing multi-factorial disease pathology and providing similar clinical relevance. Methods MRI with both research-quality sequences and conventional clinical T2-FLAIR was acquired for 172 MS patients at baseline, and neurologic disability was assessed at baseline and five-years later. Five measures (thalamus volume, lateral ventricle volume, medulla oblongata volume, lesion volume, and network efficiency) for quantifying disparate aspects of neuropathology from low-resolution T2-FLAIR were applied to predict standard research-quality MRI measures. They were compared in regard to association with future neurologic disability and disease progression over five years. Results The combination of the five T2-FLAIR measures explained most of the variance in standard research-quality MRI. T2-FLAIR measures were associated with neurologic disability and cognitive function five-years later (R2 = 0.279, p < 0.001; R2 = 0.382, p < 0.001), similar to standard research-quality MRI (R2 = 0.279, p < 0.001; R2 = 0.366, p < 0.001). They also similarly predicted disability progression over five years (%-correctly-classified = 69.8, p = 0.034), compared to standard research-quality MRI (%-correctly-classified = 72.4%, p = 0.022) in relapsing-remitting MS. Conclusion A set of five T2-FLAIR-only measures can substitute for standard research-quality MRI, especially in relapsing-remitting MS. When only clinical T2-FLAIR is available, it can be used to obtain substantially more quantitative information about brain pathology and disability than is currently standard practice.
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Affiliation(s)
- Tom A Fuchs
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Deepa P Ramasamy
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph Hb Benedict
- Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy.
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Kessner SS, Schlemm E, Gerloff C, Thomalla G, Cheng B. Grey and white matter network disruption is associated with sensory deficits after stroke. NEUROIMAGE-CLINICAL 2021; 31:102698. [PMID: 34023668 PMCID: PMC8163991 DOI: 10.1016/j.nicl.2021.102698] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 12/04/2022]
Abstract
Somatosensory deficits occur in about 60% of patients after ischaemic stroke. Clinical and imaging data of 101 ischaemic stroke patients were analysed. Stroke lesions may disrupt grey (GM) and/or white matter (WM) network. Lesion volume explains 23% of sensory deficit variance; GM / WM disruption adds 14% Subnetwork of postcentral, supramarginal, transverse temporal gyri involved.
Somatosensory deficits after ischaemic stroke are common and can occur in patients with lesions in the anterior parietal cortex and subcortical nuclei. It is less clear to what extent damage to white matter tracts within the somatosensory system may contribute to somatosensory deficits after stroke. We compared the roles of cortical damage and disruption of subcortical white matter tracts as correlates of somatosensory deficit after ischaemic stroke. Clinical and imaging data were assessed in incident stroke patients. Somatosensory deficits were measured using a standardized somatosensory test. Remote effects were quantified by projecting the MRI-based segmented stroke lesions onto a predefined atlas of white matter connectivity. Direct ischaemic damage to grey matter was computed by lesion overlap with grey matter areas. The association between lesion impact scores and sensory deficit was assessed statistically. In 101 patients, median sensory score was 188/193 (97.4%). Lesion volume was associated with somatosensory deficit, explaining 23.3% of variance. Beyond this, the stroke-induced grey and white matter disruption within a subnetwork of the postcentral, supramarginal, and transverse temporal gyri explained an additional 14% of the somatosensory outcome variability. On mutual comparison, white matter network disruption was a stronger predictor than grey matter damage. Ischaemic damage to both grey and white matter are structural correlates of acute somatosensory disturbance after ischaemic stroke. Our data suggest that white matter integrity of a somatosensory network of primary and secondary cortex is a prerequisite for normal processing of somatosensory inputs and might be considered as an additional parameter for stroke outcome prediction in the future.
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Affiliation(s)
- Simon S Kessner
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Eckhard Schlemm
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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46
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Schlemm E, Ingwersen T, Königsberg A, Boutitie F, Ebinger M, Endres M, Fiebach JB, Fiehler J, Galinovic I, Lemmens R, Muir KW, Nighoghossian N, Pedraza S, Puig J, Simonsen CZ, Thijs V, Wouters A, Gerloff C, Thomalla G, Cheng B. Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke. Nat Commun 2021; 12:2590. [PMID: 33972513 PMCID: PMC8110812 DOI: 10.1038/s41467-021-22786-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 03/29/2021] [Indexed: 12/12/2022] Open
Abstract
Thrombolysis with recombinant tissue plasminogen activator in acute ischemic stroke aims to restore compromised blood flow and prevent further neuronal damage. Despite the proven clinical efficacy of this treatment, little is known about the short-term effects of systemic thrombolysis on structural brain connectivity. In this secondary analysis of the WAKE-UP trial, we used MRI-derived measures of infarct size and estimated structural network disruption to establish that thrombolysis is associated not only with less infarct growth, but also with reduced loss of large-scale connectivity between grey-matter areas after stroke. In a causal mediation analysis, infarct growth mediated a non-significant 8.3% (CI95% [-8.0, 32.6]%) of the clinical effect of thrombolysis on functional outcome. The proportion mediated jointly through infarct growth and change of structural connectivity, especially in the border zone around the infarct core, however, was as high as 33.4% (CI95% [8.8, 77.4]%). Preservation of structural connectivity is thus an important determinant of treatment success and favourable functional outcome in addition to lesion volume. It might, in the future, serve as an imaging endpoint in clinical trials or as a target for therapeutic interventions.
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Affiliation(s)
- Eckhard Schlemm
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Thies Ingwersen
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Königsberg
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florent Boutitie
- Hospices Civils de Lyon, Service de Biostatistique, Lyon, France
- Université Lyon 1, Villeurbanne, France
- CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, Villeurbanne, France
| | - Martin Ebinger
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Klinik für Neurologie, Medical Park Berlin Humboldtmühle, Berlin, Germany
| | - Matthias Endres
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Klinik und Hochschulambulanz für Neurologie, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Berlin, Germany
- ExcellenceCluster NeuroCure, Berlin, Germany
| | - Jochen B Fiebach
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ivana Galinovic
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Robin Lemmens
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology, Leuven, Belgium
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, Campus Gasthuisberg, Leuven, Belgium
| | - Keith W Muir
- Institute of Neuroscience & Psychology, University of Glasgow, Glasgow, UK
| | - Norbert Nighoghossian
- Department of Stroke Medicine, Université Claude Bernard Lyon 1, CREATIS CNRS UMR 5220-INSERM U1206, INSA-Lyon; Hospices Civils de Lyon, Lyon, France
| | - Salvador Pedraza
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI), Parc Hospitalari Martí i Julià de Salt - Edifici M2, Salt, Girona, Spain
| | - Josep Puig
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI), Parc Hospitalari Martí i Julià de Salt - Edifici M2, Salt, Girona, Spain
| | - Claus Z Simonsen
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia
- Austin Health, Department of Neurology, Heidelberg, VIC, Australia
| | - Anke Wouters
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology, Leuven, Belgium
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, Campus Gasthuisberg, Leuven, Belgium
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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47
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Raj A, Powell F. Network model of pathology spread recapitulates neurodegeneration and selective vulnerability in Huntington's Disease. Neuroimage 2021; 235:118008. [PMID: 33789134 DOI: 10.1016/j.neuroimage.2021.118008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 12/12/2022] Open
Abstract
Huntington's Disease (HD), an autosomal dominant genetic disorder caused by a mutation in the Huntingtin gene (HTT), displays a stereotyped topography in the human brain and a stereotyped progression, initially appearing in the striatum. Like other degenerative diseases, spatial topography of HD is divorced from where implicated genes are expressed, a dissociation whose mechanistic underpinning is not currently understood. Cell autonomous molecular factors characterized by gene expression signatures, including proteolytic and post translational modifications, play a role in vulnerability to disease. Non-autonomous mechanisms, likely involving the brain's anatomic or functional connectivity patterns, might also be responsible for selective vulnerability in HD. Leveraging a large dataset of 635 subjects from a multinational study, this paper tests various cell-autonomous and non-autonomous models that can explain HD topography. We test whether the expression patterns of implicated genes is sufficient to explain regional HD atrophy, or whether the network transmission of protein products is required to explain them. We find that network models are capable of predicting, to a high degree, observed atrophy in human subjects. Lastly, we propose a model of anterograde network transmission, and show that it is the most parsimonious yet most likely to explain observed atrophy patterns in HD. Collectively, these data indicate that pathology spread in HD may be mediated by the brain's intrinsic structural network organization. This is the first study to systematically and quantitatively test multiple hypotheses of pathology spread in living human subjects with HD.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, USA; UCSF-UC Berkeley Graduate Program in BioEngineering, University of California at San Francisco, USA; Department of Radiology, Weill Cornell Medical College of Cornell University, 407 E. 61 Street, RR106, New York, NY 10065, USA.
| | - Fon Powell
- Department of Radiology, Weill Cornell Medical College of Cornell University, 407 E. 61 Street, RR106, New York, NY 10065, USA
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48
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Griffis JC, Metcalf NV, Corbetta M, Shulman GL. Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions. Neuroimage Clin 2021; 30:102639. [PMID: 33813262 PMCID: PMC8053805 DOI: 10.1016/j.nicl.2021.102639] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 12/19/2022]
Abstract
Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.
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Affiliation(s)
- Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center, Padua, Italy
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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49
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Wodeyar A, Cassidy JM, Cramer SC, Srinivasan R. Damage to the structural connectome reflected in resting-state fMRI functional connectivity. Netw Neurosci 2021; 4:1197-1218. [PMID: 33409436 PMCID: PMC7781612 DOI: 10.1162/netn_a_00160] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/21/2020] [Indexed: 11/04/2022] Open
Abstract
The relationship between structural and functional connectivity has been mostly examined in intact brains. Fewer studies have examined how differences in structure as a result of injury alters function. In this study we analyzed the relationship of structure to function across patients with stroke among whom infarcts caused heterogenous structural damage. We estimated relationships between distinct brain regions of interest (ROIs) from functional MRI in two pipelines. In one analysis pipeline, we measured functional connectivity by using correlation and partial correlation between 114 cortical ROIs. We found fMRI-BOLD partial correlation was altered at more edges as a function of the structural connectome (SC) damage, relative to the correlation. In a second analysis pipeline, we limited our analysis to fMRI correlations between pairs of voxels for which we possess SC information. We found that voxel-level functional connectivity showed the effect of structural damage that we could not see when examining correlations between ROIs. Further, the effects of structural damage on functional connectivity are consistent with a model of functional connectivity, diffusion, which expects functional connectivity to result from activity spreading over multiple edge anatomical paths.
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Affiliation(s)
- Anirudh Wodeyar
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
| | - Jessica M Cassidy
- Department of Allied Health Sciences, University of North Carolina, Chapel Hill, NC, USA
| | - Steven C Cramer
- Department of Neurology, University of California, Los Angeles, CA, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
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50
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Zayed A, Iturria-Medina Y, Villringer A, Sehm B, Steele CJ. Rapid Quantification of White Matter Disconnection in the Human Brain. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1701-1704. [PMID: 33018324 DOI: 10.1109/embc44109.2020.9176229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
With an estimated five million new stroke survivors every year and a rapidly aging population suffering from hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for assessing the impact of lesions consider only location, type, and extent, while ignoring how the affected region was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity. Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion-based white matter disconnection maps that require only moderate computational resources. We achieve this by creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small regions. We then interrupt these connectivity models by projecting patients' lesions into them to compute predicted white matter disconnection. A quantified disconnection map can be computed for an individual patient in approximately 35 seconds using a single core CPU-based computation. In comparison, a similar quantification performed with other tools provided by MRtrix3 takes 5.47 minutes.
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