1
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Yang J, Tang C. Causal relationship between imaging-derived phenotypes and neurodegenerative diseases: a Mendelian randomization study. Mamm Genome 2024:10.1007/s00335-024-10065-0. [PMID: 39180568 DOI: 10.1007/s00335-024-10065-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 08/14/2024] [Indexed: 08/26/2024]
Abstract
Neurodegenerative diseases are incurable conditions that lead to gradual and progressive deterioration of brain function in patients. With the aging population, the prevalence of these diseases is expected to increase, posing a significant economic burden on society. Imaging techniques play a crucial role in the diagnosis and monitoring of neurodegenerative diseases. This study utilized a two-sample Mendelian randomization (MR) analysis to assess the causal relationship between different imaging-derived phenotypes (IDP) in the brain and neurodegenerative diseases. Multiple MR methods were employed to minimize bias and obtain reliable estimates of the potential causal relationship between the variable exposures of interest and the outcomes. The study found potential causal relationships between different IDPs and Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and frontotemporal dementia (FTD). Specifically, the study identified potential causal relationships between 2 different types of IDPs and AD, 8 different types of IDPs and PD, 11 different types of imaging-derived phenotypes and ALS, 1 type of IDP and MS, and 1 type of IDP and FTD. This study provides new insights for the prevention, diagnosis, and treatment of neurodegenerative diseases, offering important clues for understanding the pathogenesis of these diseases and developing relevant intervention strategies.
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Affiliation(s)
- Jiaxin Yang
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550000, Guizhou, China
| | - Chao Tang
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550000, Guizhou, China.
- School of Clinical Medicine, Guizhou Medical University, No.28, Guiyi Street, Yunyan District, Guiyang, 550004, Guizhou, China.
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2
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Calabrese M, Preziosa P, Scalfari A, Colato E, Marastoni D, Absinta M, Battaglini M, De Stefano N, Di Filippo M, Hametner S, Howell OW, Inglese M, Lassmann H, Martin R, Nicholas R, Reynolds R, Rocca MA, Tamanti A, Vercellino M, Villar LM, Filippi M, Magliozzi R. Determinants and Biomarkers of Progression Independent of Relapses in Multiple Sclerosis. Ann Neurol 2024; 96:1-20. [PMID: 38568026 DOI: 10.1002/ana.26913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/04/2024] [Accepted: 02/15/2024] [Indexed: 06/20/2024]
Abstract
Clinical, pathological, and imaging evidence in multiple sclerosis (MS) suggests that a smoldering inflammatory activity is present from the earliest stages of the disease and underlies the progression of disability, which proceeds relentlessly and independently of clinical and radiological relapses (PIRA). The complex system of pathological events driving "chronic" worsening is likely linked with the early accumulation of compartmentalized inflammation within the central nervous system as well as insufficient repair phenomena and mitochondrial failure. These mechanisms are partially lesion-independent and differ from those causing clinical relapses and the formation of new focal demyelinating lesions; they lead to neuroaxonal dysfunction and death, myelin loss, glia alterations, and finally, a neuronal network dysfunction outweighing central nervous system (CNS) compensatory mechanisms. This review aims to provide an overview of the state of the art of neuropathological, immunological, and imaging knowledge about the mechanisms underlying the smoldering disease activity, focusing on possible early biomarkers and their translation into clinical practice. ANN NEUROL 2024;96:1-20.
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Affiliation(s)
- Massimiliano Calabrese
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Antonio Scalfari
- Centre of Neuroscience, Department of Medicine, Imperial College, London, UK
| | - Elisa Colato
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Damiano Marastoni
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Martina Absinta
- Translational Neuropathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Battaglini
- Siena Imaging S.r.l., Siena, Italy
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Owain W Howell
- Institute of Life Sciences, Swansea University Medical School, Swansea, UK
| | - Matilde Inglese
- Dipartimento di neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili - DINOGMI, University of Genova, Genoa, Italy
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Roland Martin
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Therapeutic Design Unit, Center for Molecular Medicine, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
- Cellerys AG, Schlieren, Switzerland
| | - Richard Nicholas
- Department of Brain Sciences, Faculty of Medicine, Burlington Danes, Imperial College London, London, UK
| | - Richard Reynolds
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Agnese Tamanti
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Marco Vercellino
- Multiple Sclerosis Center & Neurologia I U, Department of Neuroscience, University Hospital AOU Città della Salute e della Scienza di Torino, Turin, Italy
| | - Luisa Maria Villar
- Department of Immunology, Ramon y Cajal University Hospital. IRYCIS. REI, Madrid, Spain
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberta Magliozzi
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
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3
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Kenyon KH, Strik M, Noffs G, Morgan A, Kolbe S, Harding IH, Vogel AP, Boonstra FMC, van der Walt A. Volumetric and diffusion MRI abnormalities associated with dysarthria in multiple sclerosis. Brain Commun 2024; 6:fcae177. [PMID: 38846538 PMCID: PMC11154149 DOI: 10.1093/braincomms/fcae177] [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: 04/16/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Up to half of all people with multiple sclerosis experience communication difficulties due to dysarthria, a disorder that impacts the motor aspects of speech production. Dysarthria in multiple sclerosis is linked to cerebellar dysfunction, disease severity and lesion load, but the neuroanatomical substrates of these symptoms remain unclear. In this study, 52 participants with multiple sclerosis and 14 age- and sex-matched healthy controls underwent structural and diffusion MRI, clinical assessment of disease severity and cerebellar dysfunction and a battery of motor speech tasks. Assessments of regional brain volume and white matter integrity, and their relationships with clinical and speech measures, were undertaken. White matter tracts of interest included the interhemispheric sensorimotor tract, cerebello-thalamo-cortical tract and arcuate fasciculus, based on their roles in motor and speech behaviours. Volumetric analyses were targeted to Broca's area, Wernicke's area, the corpus callosum, thalamus and cerebellum. Our results indicated that multiple sclerosis participants scored worse on all motor speech tasks. Fixel-based diffusion MRI analyses showed significant evidence of white matter tract atrophy in each tract of interest. Correlational analyses further indicated that higher speech naturalness-a perceptual measure of dysarthria-and lower reading rate were associated with axonal damage in the interhemispheric sensorimotor tract and left arcuate fasciculus in people with multiple sclerosis. Axonal damage in all tracts of interest also correlated with clinical scales sensitive to cerebellar dysfunction. Participants with multiple sclerosis had lower volumes of the thalamus and corpus callosum compared with controls, although no brain volumetrics correlated with measures of dysarthria. These findings indicate that axonal damage, particularly when measured using diffusion metrics, underpin dysarthria in multiple sclerosis.
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Affiliation(s)
- Katherine H Kenyon
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
- Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC 3052, Australia
| | - Myrte Strik
- Spinoza Centre for Neuroimaging, Netherlands Institute for Neuroscience, Royal Academy for Arts and Sciences, KNAW, Amsterdam 1105 BK, The Netherlands
- Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Parkville, VIC 3052, Australia
| | - Gustavo Noffs
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
- Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC 3052, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC 3052, Australia
- Redenlab Inc, Melbourne, VIC 3000, Australia
| | - Angela Morgan
- Murdoch Children’s Research Institute, Genomic Medicine, Speech and Language Group, Parkville 3052, Australia
- Department of Speech Pathology and Audiology, University of Melbourne, Parkville 3052, Australia
| | - Scott Kolbe
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Ian H Harding
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Adam P Vogel
- Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC 3052, Australia
- Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Parkville, VIC 3052, Australia
- Redenlab Inc, Melbourne, VIC 3000, Australia
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
- Center for Neurology, University Hospital Tübingen, Tübingen 72076, Germany
- The Bionics Institute, East Melbourne, VIC 3002, Australia
| | - Frederique M C Boonstra
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Anneke van der Walt
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
- Spinoza Centre for Neuroimaging, Netherlands Institute for Neuroscience, Royal Academy for Arts and Sciences, KNAW, Amsterdam 1105 BK, The Netherlands
- The Bionics Institute, East Melbourne, VIC 3002, Australia
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4
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Koubiyr I, Krijnen EA, Eijlers AJC, Dekker I, Hulst HE, Uitdehaag BMJ, Barkhof F, Geurts JJG, Schoonheim MM. Longitudinal fibre-specific white matter damage predicts cognitive decline in multiple sclerosis. Brain Commun 2024; 6:fcae018. [PMID: 38344654 PMCID: PMC10853982 DOI: 10.1093/braincomms/fcae018] [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: 03/24/2023] [Revised: 12/21/2023] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
During the course of multiple sclerosis, many patients experience cognitive deficits which are not simply driven by lesion number or location. By considering the full complexity of white matter structure at macro- and microstructural levels, our understanding of cognitive impairment in multiple sclerosis may increase substantially. Accordingly, this study aimed to investigate specific patterns of white matter degeneration, the evolution over time, the manifestation across different stages of the disease and their role in cognitive impairment using a novel fixel-based approach. Neuropsychological test scores and MRI scans including 30-direction diffusion-weighted images were collected from 327 multiple sclerosis patients (mean age = 48.34 years, 221 female) and 95 healthy controls (mean age = 45.70 years, 55 female). Of those, 233 patients and 61 healthy controls had similar follow-up assessments 5 years after. Patients scoring 1.5 or 2 standard deviations below healthy controls on at least two out of seven cognitive domains (from the Brief Repeatable Battery of Neuropsychological Tests, BRB-N) were classified as mildly cognitively impaired or cognitively impaired, respectively, or otherwise cognitively preserved. Fixel-based analysis of diffusion data was used to calculate fibre-specific measures (fibre density, reflecting microstructural diffuse axonal damage; fibre cross-section, reflecting macrostructural tract atrophy) within atlas-based white matter tracts at each visit. At baseline, all fixel-based measures were significantly worse in multiple sclerosis compared with healthy controls (P < 0.05). For both fibre density and fibre cross-section, a similar pattern was observed, with secondary progressive multiple sclerosis patients having the most severe damage, followed by primary progressive and relapsing-remitting multiple sclerosis. Similarly, damage was least severe in cognitively preserved (n = 177), more severe in mildly cognitively impaired (n = 63) and worst in cognitively impaired (n = 87; P < 0.05). Microstructural damage was most pronounced in the cingulum, while macrostructural alterations were most pronounced in the corticospinal tract, cingulum and superior longitudinal fasciculus. Over time, white matter alterations worsened most severely in progressive multiple sclerosis (P < 0.05), with white matter atrophy progression mainly seen in the corticospinal tract and microstructural axonal damage worsening in cingulum and superior longitudinal fasciculus. Cognitive decline at follow-up could be predicted by baseline fixel-based measures (R2 = 0.45, P < 0.001). Fixel-based approaches are sensitive to white matter degeneration patterns in multiple sclerosis and can have strong predictive value for cognitive impairment. Longitudinal deterioration was most marked in progressive multiple sclerosis, indicating that degeneration in white matter remains important to characterize further in this phenotype.
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Affiliation(s)
- Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
| | - Eva A Krijnen
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Anand J C Eijlers
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
| | - Iris Dekker
- MS Center Amsterdam, Rehabilitation, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
| | - Hanneke E Hulst
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden 2333 AK, The Netherlands
| | - Bernard M J Uitdehaag
- MS Center Amsterdam, Neurology, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
| | - Frederik Barkhof
- MS Center Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - Jeroen J G Geurts
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam 1081 HV, The Netherlands
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5
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Tu Y, Li Z, Xiong F, Gao F. Progressive white matter degeneration in patients with spinocerebellar ataxia type 2. Neuroradiology 2024; 66:101-108. [PMID: 38040824 DOI: 10.1007/s00234-023-03260-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
PURPOSE Spinocerebellar ataxia type 2 (SCA2) is a progressive neurodegenerative disorder characterized by cerebellar atrophy. However, studies to elucidate the longitudinal progression of the neuropathology are limited. We sought to identify brain macrostructural and microstructural alterations in patients with SCA2 using fixel-based analysis (FBA) to better understand its distribution patterns and progression. METHODS We enrolled 9 patients with SCA2 and 16 age- and gender-matched controls. Longitudinal clinical and imaging data were collected at baseline, and 3.5 years later. Fiber density (FD), fiber-bundle cross-section (FC), and a combination of FD and FC (FDC) were calculated. The paired t-test was used to examine longitudinal differences. The associations between fixel-based metrics and clinical variables were explored in SCA2 patients. RESULTS At baseline, patients with SCA2 displayed multiple white matter tracts with significantly decreased FD, FC, and FDC in the corticospinal tract, cerebellar peduncles, brainstem, corpus callosum, thalamus, striatum, and prefrontal cortex, compared to controls. Over time, many of these macrostructural and microstructural alterations progressed, manifesting lower FD, FC, and FDC in corticospinal tract, middle cerebellar peduncle, brainstem, striatum, fornix, and cingulum. No significant brain white matter alterations were found in the healthy controls over time. There was no association between the FBA-derived metrics and clinical variables in SCA2. CONCLUSION This study provides evidence of brain macrostructural and microstructural alterations and of progression over time in SCA2. The FBA-derived metrics may serve as potential biomarkers of SCA2 progression.
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Affiliation(s)
- Ye Tu
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zheng Li
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fei Xiong
- Department of Radiology, General Hospital of Central Theater Command, Wuhan, China.
| | - Feng Gao
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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6
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Rocca MA, Margoni M, Battaglini M, Eshaghi A, Iliff J, Pagani E, Preziosa P, Storelli L, Taoka T, Valsasina P, Filippi M. Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice. Radiology 2023; 307:e221512. [PMID: 37278626 PMCID: PMC10315528 DOI: 10.1148/radiol.221512] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/07/2023]
Abstract
MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.
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Affiliation(s)
- Maria Assunta Rocca
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Monica Margoni
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Marco Battaglini
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Arman Eshaghi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Jeffrey Iliff
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Loredana Storelli
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Toshiaki Taoka
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paola Valsasina
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
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7
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Resting state effective connectivity abnormalities of the Papez circuit and cognitive performance in multiple sclerosis. Mol Psychiatry 2022; 27:3913-3919. [PMID: 35624146 DOI: 10.1038/s41380-022-01625-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/05/2022] [Accepted: 05/11/2022] [Indexed: 02/08/2023]
Abstract
The Papez circuit is central to memory and emotional processes. However, little is known about its involvement in multiple sclerosis (MS). We aimed to investigate abnormalities of resting state (RS) effective connectivity (EC) between regions of the Papez circuit in MS and their relationship with cognitive performances. Sixty-two MS patients and 64 healthy controls (HC) underwent neuropsychological assessment, 3D T1-weighted, and RS functional MRI. RS EC analysis was performed using SPM12 and dynamic causal modeling. RS EC abnormalities were investigated using parametric empirical Bayes models and were correlated with cognitive scores. Compared to HC, MS patients showed (posterior probability > 0.95) higher EC between the right entorhinal cortex and right subiculum, and lower EC from the anterior cingulate cortex (ACC) to the posterior cingulate cortex (PCC), from left to right subiculum, from left anterior thalamus to ACC, and within ACC and PCC. Lower RS EC from the ACC to the PCC correlated with worse global cognitive scores (rho = 0.19; p = 0.03), worse visuospatial memory (rho = 0.19; p = 0.03) and worse semantic fluency (rho = 0.21; p = 0.02). Lower RS EC from the left to the right subiculum correlated with worse verbal memory (rho = 0.20; p = 0.02), lower RS EC within the ACC correlated with worse attention (rho = -0.19; p = 0.04) and more severe brain atrophy (rho = -0.26; p = 0.003). Higher EC from the right entorhinal cortex to right subiculum correlated with worse semantic fluency (rho = 0.21; p = 0.02). In conclusion, MS patients showed altered RS EC within the Papez circuit. Abnormal RS EC involving cingulate cortices and hippocampal formation contributed to explain cognitive deficits.
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8
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Bells S, Longoni G, Berenbaum T, de Medeiros CB, Narayanan S, Banwell BL, Arnold DL, Mabbott DJ, Ann Yeh E. Patterns of white and gray structural abnormality associated with paediatric demyelinating disorders. Neuroimage Clin 2022; 34:103001. [PMID: 35381508 PMCID: PMC8980471 DOI: 10.1016/j.nicl.2022.103001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/21/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022]
Abstract
A multi-modal approach was used to evaluate the visual pathway from anterior (retina) to posterior (visual cortex) in both paediatric MOGAD and MS patients. MS patients exhibited more widespread white matter abnormalities; MOGAD patients exhibited white matter changes primarily within the optic radiation. The pattern of cortical thinning differed in MS and MOGAD patients. Reduced RNFLT was associated with lower axonal density in MOGAD and tortuosity in MS.
The impact of multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein (MOG) - associated disorders (MOGAD) on brain structure in youth remains poorly understood. Reductions in cortical mantle thickness on structural MRI and abnormal diffusion-based white matter metrics (e.g., diffusion tensor parameters) have been well documented in MS but not in MOGAD. Characterizing structural abnormalities found in children with these disorders can help clarify the differences and similarities in their impact on neuroanatomy. Importantly, while MS and MOGAD affect the entire CNS, the visual pathway is of particular interest in both groups, as most patients have evidence for clinical or subclinical involvement of the anterior visual pathway. Thus, the visual pathway is of key interest in analyses of structural abnormalities in these disorders and may distinguish MOGAD from MS patients. In this study we collected MRI data on 18 MS patients, 14 MOGAD patients and 26 age- and sex-matched typically developing children (TDC). Full-brain group differences in fixel diffusion measures (fibre-bundle populations) and cortical thickness measures were tested using age and sex as covariates. Visual pathway analysis was performed by extracting mean diffusion measures within lesion free optic radiations, cortical thickness within the visual cortex, and retinal nerve fibre layer (RNFL) and ganglion cell layer thickness measures from optical coherence tomography (OCT). Fixel based analysis (FBA) revealed MS patients have widespread abnormal white matter within the corticospinal tract, inferior longitudinal fasciculus, and optic radiations, while within MOGAD patients, non-lesional impact on white matter was found primarily in the right optic radiation. Cortical thickness measures were reduced predominately in the temporal and parietal lobes in MS patients and in frontal, cingulate and visual cortices in MOGAD patients. Additionally, our findings of associations between reduced RNFLT and axonal density in MOGAD and TORT in MS patients in the optic radiations imply widespread axonal and myelin damage in the visual pathway, respectively. Overall, our approach of combining FBA, cortical thickness and OCT measures has helped evaluate similarities and differences in brain structure in MS and MOGAD patients in comparison to TDC.
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Affiliation(s)
- Sonya Bells
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Pediatric Neurology, Spectrum Health Helen Devos Children's Hospital, Grand Rapids, USA; Department of Pediatrics and Human Development, Michigan State University, East Lansing, USA
| | - Giulia Longoni
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Department of Neurology, Hospital for Sick Children, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada
| | - Tara Berenbaum
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada
| | - Cynthia B de Medeiros
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada
| | - Sridar Narayanan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Brenda L Banwell
- Division of Child Neurology, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, USA
| | - Douglas L Arnold
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Donald J Mabbott
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
| | - E Ann Yeh
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Department of Neurology, Hospital for Sick Children, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada.
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9
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Advanced diffusion-weighted imaging models better characterize white matter neurodegeneration and clinical outcomes in multiple sclerosis. J Neurol 2022; 269:4729-4741. [DOI: 10.1007/s00415-022-11104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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10
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Boonstra FM, Clough M, Strik M, van der Walt A, Butzkueven H, White OB, Law M, Fielding J, Kolbe SC. Longitudinal tracking of axonal loss using diffusion magnetic resonance imaging in multiple sclerosis. Brain Commun 2022; 4:fcac065. [PMID: 35425898 PMCID: PMC9006042 DOI: 10.1093/braincomms/fcac065] [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: 06/30/2021] [Revised: 10/27/2021] [Accepted: 03/15/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Axonal loss in the CNS is a key driver of progressive neurological impairments in people with multiple sclerosis. Currently, there are no established methods for tracking axonal loss clinically. This study aimed to determine the sensitivity of longitudinal diffusion MRI derived fibre specific measures of axonal loss in people with multiple sclerosis. Fibre measures were derived from diffusion MRI acquired as part of a standard radiological MRI protocol and were compared 1) to established measures of neuro-axonal degeneration: brain parenchymal fraction and retinal nerve fibre layer thickness and 2) between different disease stages: clinically isolated syndrome and early/late relapsing-remitting multiple sclerosis. Retrospectively identified data from fifty-nine people with multiple sclerosis (18 clinically isolated syndrome, 22 early and 19 late relapsing-remitting) who underwent diffusion MRI as part of their routine clinical monitoring were collated and analysed. Twenty-six patients had 1-year and 14 patients had 2-year follow-up. Brain parenchymal fraction was calculated from 3D MRI scans, and fibre-specific measures were calculated from diffusion MRI using multi-tissue constrained spherical deconvolution. At each study visit, patients underwent optical coherence tomography to determine retinal nerve fibre layer thickness, and standard neurological assessment expanded disability status scale. We found a significant annual fibre-specific neuro-axonal degeneration (mean ± SD = −3.49 ± 3.32%, p<0.001) that was approximately seven times larger than the annual change of brain parenchymal fraction (−0.53 ± 0.95%, p<0.001), and more than four times larger than annual retinal nerve fibre layer thinning (−0.75 ± 2.50% p=0.036). Only fibre-specific measures showed a significant difference in annual degeneration between the disease stages (p=0.029). Reduced brain parenchymal fraction, retinal nerve fibre layer thickness and fibre-specific measures were moderately related to higher expanded disability status scale (respectively rho=−0.368, rho=−0.408 and rho=−0.365). Fibre-specific measures can be measured from data collected within a standard radiological multiple sclerosis study and are substantially more sensitive to longitudinal change compared to brain atrophy and retinal nerve fibre layer thinning.
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Affiliation(s)
- Frederique M. Boonstra
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
| | - Meaghan Clough
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
| | - Myrte Strik
- Department of Medicine and Radiology, University of Melbourne, Parkville, Australia
| | - Anneke van der Walt
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
| | - Helmut Butzkueven
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
| | - Owen B. White
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
- Department Radiology, Alfred Health, Prahran, Australia
| | - Joanne Fielding
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
| | - Scott C. Kolbe
- Department of Neuroscience, Central Clinical School, Monash University, Prahran, Australia
- Department Radiology, Alfred Health, Prahran, Australia
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11
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Finkelstein A, Faiyaz A, Weber MT, Qiu X, Uddin MN, Zhong J, Schifitto G. Fixel-Based Analysis and Free Water Corrected DTI Evaluation of HIV-Associated Neurocognitive Disorders. Front Neurol 2021; 12:725059. [PMID: 34803875 PMCID: PMC8600320 DOI: 10.3389/fneur.2021.725059] [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: 06/14/2021] [Accepted: 10/04/2021] [Indexed: 12/12/2022] Open
Abstract
Background: White matter (WM) damage is a consistent finding in HIV-infected (HIV+) individuals. Previous studies have evaluated WM fiber tract-specific brain regions in HIV-associated neurocognitive disorders (HAND) using diffusion tensor imaging (DTI). However, DTI might lack an accurate biological interpretation, and the technique suffers from several limitations. Fixel-based analysis (FBA) and free water corrected DTI (fwcDTI) have recently emerged as useful techniques to quantify abnormalities in WM. Here, we sought to evaluate FBA and fwcDTI metrics between HIV+ and healthy controls (HIV−) individuals. Using machine learning classifiers, we compared the specificity of both FBA and fwcDTI metrics in their ability to distinguish between individuals with and without cognitive impairment in HIV+ individuals. Methods: Forty-two HIV+ and 52 HIV– participants underwent MRI exam, clinical, and neuropsychological assessments. FBA metrics included fiber density (FD), fiber bundle cross section (FC), and fiber density and cross section (FDC). We also obtained fwcDTI metrics such as fractional anisotropy (FAT) and mean diffusivity (MDT). Tract-based spatial statistics (TBSS) was performed on FAT and MDT. We evaluated the correlations between MRI metrics with cognitive performance and blood markers, such as neurofilament light chain (NfL), and Tau protein. Four different binary classifiers were used to show the specificity of the MRI metrics for classifying cognitive impairment in HIV+ individuals. Results: Whole-brain FBA showed significant reductions (up to 15%) in various fiber bundles, specifically the cerebral peduncle, posterior limb of internal capsule, middle cerebellar peduncle, and superior corona radiata. TBSS of fwcDTI metrics revealed decreased FAT in HIV+ individuals compared to HIV– individuals in areas consistent with those observed in FBA, but these were not significant. Machine learning classifiers were consistently better able to distinguish between cognitively normal patients and those with cognitive impairment when using fixel-based metrics as input features as compared to fwcDTI metrics. Conclusion: Our findings lend support that FBA may serve as a potential in vivo biomarker for evaluating and monitoring axonal degeneration in HIV+ patients at risk for neurocognitive impairment.
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Affiliation(s)
- Alan Finkelstein
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
| | - Miriam T Weber
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States
| | - Md Nasir Uddin
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Jianhui Zhong
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States.,Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States.,Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States.,Department of Neurology, University of Rochester, Rochester, NY, United States.,Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
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12
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Seewoo BJ, Feindel KW, Won Y, Joos AC, Figliomeni A, Hennessy LA, Rodger J. White Matter Changes Following Chronic Restraint Stress and Neuromodulation: A Diffusion Magnetic Resonance Imaging Study in Young Male Rats. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:153-166. [PMID: 36325163 PMCID: PMC9616380 DOI: 10.1016/j.bpsgos.2021.08.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/27/2021] [Accepted: 08/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique, is an effective treatment for depression. However, few studies have used diffusion magnetic resonance imaging to investigate the longitudinal effects of rTMS on the abnormal brain white matter (WM) described in depression. Methods In this study, we acquired diffusion magnetic resonance imaging from young adult male Sprague Dawley rats to investigate 1) the longitudinal effects of 10- and 1-Hz low-intensity rTMS (LI-rTMS) in healthy animals; 2) the effect of chronic restraint stress (CRS), an animal model of depression; and 3) the effect of 10 Hz LI-rTMS in CRS animals. Diffusion magnetic resonance imaging data were analyzed using tract-based spatial statistics and fixel-based analysis. Results Similar changes in diffusion and kurtosis fractional anisotropy were induced by 10- and 1-Hz stimulation in healthy animals, although changes induced by 10-Hz stimulation were detected earlier than those following 1-Hz stimulation. Additionally, 10-Hz stimulation increased axial and mean kurtosis within the external capsule, suggesting that the two protocols may act via different underlying mechanisms. Brain maturation–related changes in WM, such as increased corpus callosum, fimbria, and external and internal capsule fiber cross-section, were compromised in CRS animals compared with healthy control animals and were rescued by 10-Hz LI-rTMS. Immunohistochemistry revealed increased myelination within the corpus callosum in LI-rTMS–treated CRS animals compared with those that received sham or no stimulation. Conclusions Overall, decreased WM connectivity and integrity in the CRS model corroborate findings in patients experiencing depression with high anxiety, and the observed LI-rTMS–induced effects on WM structure suggest that LI-rTMS might rescue abnormal WM by increasing myelination.
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13
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Winter M, Tallantyre EC, Brice TAW, Robertson NP, Jones DK, Chamberland M. Tract-specific MRI measures explain learning and recall differences in multiple sclerosis. Brain Commun 2021; 3:fcab065. [PMID: 33959710 PMCID: PMC8088789 DOI: 10.1093/braincomms/fcab065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/07/2021] [Accepted: 03/01/2021] [Indexed: 12/19/2022] Open
Abstract
Cognitive difficulties are common and a key concern for people with multiple sclerosis. Advancing knowledge of the role of white matter pathology in multiple sclerosis-related cognitive impairment is essential as both occur early in the disease with implications for early intervention. Consequently, this cross-sectional study asked whether quantifying the relationships between lesions and specific white matter structures could better explain co-existing cognitive differences than whole brain imaging measures. Forty participants with relapse-onset multiple sclerosis underwent cognitive testing and MRI at 3 Tesla. They were classified as cognitively impaired (n = 24) or unimpaired (n = 16) and differed across verbal fluency, learning and recall tasks corrected for intelligence and education (corrected P-values = 0.007-0.04). The relationships between lesions and white matter were characterized across six measures: conventional voxel-based T2 lesion load, whole brain tractogram load (lesioned volume/whole tractogram volume), whole bundle volume, bundle load (lesioned volume/whole bundle volume), Tractometry (diffusion-tensor and high angular resolution diffusion measures sampled from all bundle streamlines) and lesionometry (diffusion measures sampled from streamlines traversing lesions only). The tract-specific measures were extracted from corpus callosum segments (genu and isthmus), striato-prefrontal and -parietal pathways, and the superior longitudinal fasciculi (sections I, II and III). White matter measure-task associations demonstrating at least moderate evidence against the null hypothesis (Bayes Factor threshold < 0.2) were examined using independent t-tests and covariate analyses (significance level P < 0.05). Tract-specific measures were significant predictors (all P-values < 0.05) of task-specific clinical scores and diminished the significant effect of group as a categorical predictor in Story Recall (isthmus bundle load), Figure Recall (right striato-parietal lesionometry) and Design Learning (left superior longitudinal fasciculus III volume). Lesion load explained the difference in List Learning, whereas Letter Fluency was not associated with any of the imaging measures. Overall, tract-specific measures outperformed the global lesion and tractogram load measures. Variation in regional lesion burden translated to group differences in tract-specific measures, which in turn, attenuated differences in individual cognitive tasks. The structural differences converged in temporo-parietal regions with particular influence on tasks requiring visuospatial-constructional processing. We highlight that measures quantifying the relationships between tract-specific structure and multiple sclerosis lesions uncovered associations with cognition masked by overall tract volumes and global lesion and tractogram loads. These tract-specific white matter quantifications show promise for elucidating the relationships between neuropathology and cognition in multiple sclerosis.
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Affiliation(s)
- Mia Winter
- Department of Clinical Neuropsychology, University Hospital of Wales, Cardiff, CF14 4XW, UK
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Emma C Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
- Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, CF14 4XW, UK
| | - Thomas A W Brice
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
| | - Neil P Robertson
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
- Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, CF14 4XW, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria 3000, Australia
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
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14
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Strik M, Cofré Lizama LE, Shanahan CJ, van der Walt A, Boonstra FMC, Glarin R, Kilpatrick TJ, Geurts JJG, Cleary JO, Schoonheim MM, Galea MP, Kolbe SC. Axonal loss in major sensorimotor tracts is associated with impaired motor performance in minimally disabled multiple sclerosis patients. Brain Commun 2021; 3:fcab032. [PMID: 34222866 PMCID: PMC8244644 DOI: 10.1093/braincomms/fcab032] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/15/2020] [Accepted: 01/11/2021] [Indexed: 12/13/2022] Open
Abstract
Multiple sclerosis is a neuroinflammatory disease of the CNS that is associated with significant irreversible neuro-axonal loss, leading to permanent disability. There is thus an urgent need for in vivo markers of axonal loss for use in patient monitoring or as end-points for trials of neuroprotective agents. Advanced diffusion MRI can provide markers of diffuse loss of axonal fibre density or atrophy within specific white matter pathways. These markers can be interrogated in specific white matter tracts that underpin important functional domains such as sensorimotor function. This study aimed to evaluate advanced diffusion MRI markers of axonal loss within the major sensorimotor tracts of the brain, and to correlate the degree of axonal loss in these tracts to precise kinematic measures of hand and foot motor control and gait in minimally disabled people with multiple sclerosis. Twenty-eight patients (Expanded Disability Status Scale < 4, and Kurtzke Functional System Scores for pyramidal and cerebellar function ≤ 2) and 18 healthy subjects underwent ultra-high field 7 Tesla diffusion MRI for calculation of fibre-specific measures of axonal loss (fibre density, reflecting diffuse axonal loss and fibre cross-section reflecting tract atrophy) within three tracts: cortico-spinal tract, interhemispheric sensorimotor tract and cerebello-thalamic tracts. A visually guided force-matching task involving either the hand or foot was used to assess visuomotor control, and three-dimensional marker-based video tracking was used to assess gait. Fibre-specific axonal markers for each tract were compared between groups and correlated with visuomotor task performance (force error and lag) and gait parameters (stance, stride length, step width, single and double support) in patients. Patients displayed significant regional loss of fibre cross-section with minimal loss of fibre density in all tracts of interest compared to healthy subjects (family-wise error corrected p-value < 0.05), despite relatively few focal lesions within these tracts. In patients, reduced axonal fibre density and cross-section within the corticospinal tracts and interhemispheric sensorimotor tracts were associated with larger force tracking error and gait impairments (shorter stance, smaller step width and longer double support) (family-wise error corrected p-value < 0.05). In conclusion, significant gait and motor control impairments can be detected in minimally disabled people with multiple sclerosis that correlated with axonal loss in major sensorimotor pathways of the brain. Given that axonal loss is irreversible, the combined use of advanced imaging and kinematic markers could be used to identify patients at risk of more severe motor impairments as they emerge for more aggressive therapeutic interventions.
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Affiliation(s)
- Myrte Strik
- Department of Medicine and Radiology, University of Melbourne, Parkville 3010, Australia
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1081 HZ, the Netherlands
| | - L Eduardo Cofré Lizama
- Department of Medicine and Radiology, University of Melbourne, Parkville 3010, Australia
- School of Allied Health, Human Services and Sports, La Trobe University, Victoria 3086, Australia
| | - Camille J Shanahan
- Department of Medicine and Radiology, University of Melbourne, Parkville 3010, Australia
| | - Anneke van der Walt
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne 3004, Australia
| | - Frederique M C Boonstra
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne 3004, Australia
| | - Rebecca Glarin
- Department of Medicine and Radiology, University of Melbourne, Parkville 3010, Australia
| | - Trevor J Kilpatrick
- Florey Institute of Neuroscience and Mental Health, Parkville 3052, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville 3052, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville 3050, Australia
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1081 HZ, the Netherlands
| | - Jon O Cleary
- Department of Radiology, Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 7EH, UK
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1081 HZ, the Netherlands
| | - Mary P Galea
- Department of Medicine and Radiology, University of Melbourne, Parkville 3010, Australia
| | - Scott C Kolbe
- Department of Medicine and Radiology, University of Melbourne, Parkville 3010, Australia
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne 3004, Australia
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