Clinical Relevance of Brain Atrophy Measures in Multiple Sclerosis

UČEBNÍ TEXTY UNIVERZITY KARLOVY V PRAZE KAROLINUM CLINICAL RELEVANCE OF BRAIN ATROPHY MEASURES IN MULTIPLE SCLEROSIS Tomáš Uher Clinical Relevance of Brain_obal_hrb 5mm.indd 1Clinical Relevance of Brain_obal_hrb 5mm.indd 1 02.10.2023 11:1102.10.2023 11:11 Clinical Relevance of Brain Atrophy Measures in Multiple Sclerosis Assoc. Prof. Tomáš Uher, MD, Ph.D. Reviewers: Prof. Zsigmond Tamás Kincses, MD, Ph.D., University of Szeged Prof. Guy Nagels, MD, Ph.D., Vrije Universiteit Brussel Assoc. Prof. Radomír Taláb, MD, CSc., Charles University Published by Charles University, Karolinum Press Prague 2023 Typeset by Karolinum Press First edition This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. © Charles University, 2023 © Tomáš Uher, 2023 The publication was supported by the Czech Republic multiple sclerosis patient registry ReMuS; institutional support of the hospital research RVO VFN 64165; Czech Ministry of Education – project Cooperatio LF1, research area Neuroscience; the project National Institute for Neurological Research (Programme EXCELES, ID project No LX22NPO5107)– funded by the European Union – Next Generation EU and by Czech Ministry of Health project – grant NU22-04-00193. ISBN 978-80-246-5692-2 ISBN 978-80-246-5196-5 (pdf) https://doi.org/10.14712/9788024651965 Charles University Karolinum Press www.karolinum.cz [email protected] Contents Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.1 Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2 Disease pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Genetic factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Environmental risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Clinical presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Neurological symptoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Cognitive symptoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.5 Paraclinical measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Magnetic resonance imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Biochemical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Optical coherent tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.6 Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.7 Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.8 Prediction of disease activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.9 Disease monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2. MAIN AIMS OF THE CURRENT WORK . . . . . . . . . . . . . . . . . . . . . . . . 22 3. METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.1 Brain MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 MRI acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 MRI analysis in BNAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 MRI analysis in Prague . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Clinical assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Neuropsychological assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Neurological assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4. SAMPLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1 SET study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Avonex-Steroid-Azathioprine (ASA) study . . . . . . . . . . . . . . . . . . . . 28 4.3 Grant Quantitative (GQ) study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.4 The quantitative magnetic resonance imaging (QMRI) program . . . 30 4.5 Healthy controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5. SUMMARY OF SELECTED STUDIES . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1 A novel semiautomated pipeline to measure brain atrophy . . . . . . 33 5.2 Evolution of brain volume loss rates in early stages of multiple sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.3 Pathological cut-offs of brain volume loss . . . . . . . . . . . . . . . . . . . . . 36 5.4 Interpretation of brain volume increase in multiple sclerosis . . . . . 38 5.5 Occurrence of non-linear brain volume loss in patients with MS . . 39 5.6 MRI phenotypes according to dissociation between thebrainatrophyandlesionburdenusinganewdefinition . . . . . . 43 5.7 The role of high-frequency MRI monitoring in the detection of brain atrophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.8 Early MRI and clinical predictors of disability progression over6yearsinpatientsafterfirstclinicaleventsuggestive of multiple sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.9 Early MRI predictors of clinical progression after 48 months in CIS patients treated with intramuscular interferon beta-1a . . . 48 5.10 Combining clinical and MRI markers enhances prediction of 12-year disability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.11IdentificationofMSpatientsathighestriskofcognitive impairment using integrated brain MRI assessment approach . . . 52 5.12 Cognitive clinico-radiological paradox in early stages of multiple sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.13 Pregnancy-induced brain MRI changes in women with multiple sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 6. SUMMARY AND FUTURE DIRECTIONS . . . . . . . . . . . . . . . . . . . . . . . 59 6.1 Time course and stability of brain atrophy . . . . . . . . . . . . . . . . . . . . 59 6.2 Relationship between brain atrophy and lesion burden accumulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.3 Prognostic role of MRI atrophy measures . . . . . . . . . . . . . . . . . . . . 61 6.4 Recommendations for use of brain atrophy measures in clinical practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.5 MRI phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.6 Summaryofthemainfindings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 – 5 – Acknowledgments I would like to express my appreciation to my research supervisors Prof . Dana Horáková and Prof . Eva Kubala Havrdová from the MS Center of the Department of Neurology at the General University Hospital and Charles University’s First Faculty of Medicine . My gratitude also extends to Prof . Manuela Vaněčková and Dr. Jan Krásenský from the MRI Department at the General University Hospital and the First Faculty of Medicine . I would like to thank the staff of our MS Center and MRI Department for their assistance with data collection and analysis . Furthermore, I wish to acknowledge my scientific collaboration with Robert Zivadinov, Murali Ramanathan, Niels Bergsland, Mike Dwyer, and Jesper Hagemeier from the University at Buffalo in the USA; Tomáš Kalinčík from the University of Melbourne in Australia; Petr Bob from the Department of Psychiatry at the General University Hospital and the First Faculty of Medicine, as well as statisticians Lukáš Sobíšek and Václav Čapek from Prague. Last but not least, I want to express my gratitude to my family . – 6 – Abbreviations 95% CI 95% Confidence Interval ASA Avonex–Steroid–Azathioprine study BICAMS Brief International Cognitive Assessment for Multiple Sclerosis BVMTR Brief Visuospatial Memory Test Revised CNS Central Nervous System CVLT2 California Verbal Learning Test, Second Edition EDSS Expanded Disability Status Scale FA Flip Angle FIRST FMRIB Integrated Registration and Segmentation Tool FLAIR Fluid-Attenuated Inversion Recovery FOV Field of View GQ Grant Quantitative study HLA Human Leucocyte Antigen HR Hazard Ratio ICV Intra-Cranial Volume lin-R2 coefficients of determination of individual linear model MACFIMS Minimal Assessment of Cognitive Function in Multiple Sclerosis MHC Major Histocompatibility Complex MMSE Mini Mental State Examination MOG Myelin Oligodendrocyte Glycoprotein MRI Magnetic Resonance Imaging MS Multiple Sclerosis MSNQ Multiple Sclerosis Neuropsychological Questionnaire NEDA-4 No Evidence of Disease Activity-4 OCT Optical Coherent Tomography OR Odds Ratio β – 7 – p (adjusted p) p-value adjusted by Benjamini-Hochberg procedure PBVC Percent Brain Volume Change measured by SIENA method QMRI Quantitative Magnetic Resonance Imaging quad-R2 coefficients of determination of individual quadratic model RNFL Retinal Nerve Fiber Layer PASAT Paced Auditory Serial Addition Test SET Study of Early interferon beta-1a Teatment SDMT Symbol Digit Modalities Test SDP Sustained Disability Progression SIENA Structural Image Evaluation using Normalization of Atrophy SIENAX Structural Image Evaluation using Normalization of Atrophy cross-sectional T1-WI/FFE 3D T1-Weighted Images 3-Dimensional Fast Field Echo TE Time to Echo THK Slice Thickness TI Inversion Time TR Time to Repetition – 8 – Preface Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) presenting with a wide spectrum of clinical and radiological phenotypes .1–4 Although MS was originally considered an inflammatory disease that predominantly affects the white matter,5, 6 neurodegeneration resulting in accelerated brain and spinal cord atrophy is now recognized as an important determinant of disability .1, 2, 4, 7–13 It is commonly understood that MS is a complex heterogeneous disease characterized by a broad spectrum of physical14, 15 and cognitive16, 17 symptoms, variable treatment response, radiological features, and neuropathology. This heterogeneous presentation of symptoms is likely attributable to complex interactions between external and hereditary factors,18 resulting in limited predictability of the disease and its response to treatment. Therefore, there is an urgent need for personalized treatment . Unfortunately, traditional clinical predictors are not sufficiently sensitive to reliably predict MS future and monitor ongoing disease activity . Contrastingly, abnormal magnetic resonance imaging (MRI) findings have been shown to be the most informative predictors and surrogate markers of disease activity .19, 20 Not only the accumulation of the lesion burden but also the atrophy of the brain and spinal cord are important determinants of disease progression and associated with the development of physical1, 4, 7–9, 21 and cognitive disabilities .16, 17, 22, 23 Therefore, the assessment of the course of brain atrophy within individual patients could facilitate the identification of those with current disease activity and those at the highest risk of accumulating permanent disability .14 In this context, some efforts have been made to bring measurements of brain atrophy into clinical practice .9, 24, 25 Unfortunately, the relatively high intra-individual variability of longitudinal brain atrophy measures renders the application of brain volumetric measures in individual patients with MS – 9 – challenging .7–9, 25, 26 Therefore, brain atrophy measures are yet to be utilized routinely in clinical practice . In this publication, we investigated how the high intra-individual variability of volumetric brain volume measures can be overcome and whether they have practical applications in clinical decision-making. We propose several approaches, including high-frequency MRI scanning, combined clinico-radiological composite scores, and the application of cross-sectional volumetric measures . This publication is intended for neurologists, radiologists, and other specialists who treat patients with multiple sclerosis, as well as researchers in neuroimaging methods . – 16 – as the Mini-Mental State Examination (MMSE)103 or the Multiple Sclerosis Neuropsychological Questionnaire (MSNQ),104 has been questioned because of their low sensitivity in the detection of MS-specific cognitive impairment. However, detailed psychometric assessment of cognitive impairment requires considerable time and resources. The implementation of screening batteries of intermediate length, such as the Minimal Assessment of Cognitive Function (MACFIMS),93 may also be limited because of their time-consuming nature and the need for administration by experienced neuropsychologists . All of the above considerations emphasize the need for a short, validated, and accepted instrument that can capture cognitive impairment in patients with MS and can also be administered by staff without neuropsychological training . Hence, the Brief International Cognitive Assessment for MS (BICAMS)105, 106 or Single Symbol Digit Modalities Test (SDMT)107 has been suggested as suitable for use in routine clinical practice . 1.5 Paraclinical measures Magnetic resonance imaging Among the different paraclinical measures, brain magnetic resonance imaging (MRI) is one of the most accepted and sensitive tools used to monitor subclinical disease activity and diagnose MS .19, 20 Moreover, MRI measures have become common radiological endpoints in clinical research . Conventional MRI measures include the number and location of T1-hypointense, T2-hyperintense, and T1 contrast-enhancing lesions, whereas sophisticated software can also assess the T1 and T2 of contrast-enhancing lesion volumes . Lesions are typically distributed in the spinal, infratentorial, peri-ventricular, and juxta-cortical locations .85 However, the majority of cortical lesions are not seen on standard MRI scanners .108 Because of this, MS was originally considered to be a disease that predominantly affects the white matter .5, 6 Currently, pathological changes in the gray matter are increasingly recognized as an early10, 109–119 and an important determinant of disease activity in MS patients .109, 120–122 Although brain lesions in MS represent a histo-pathologically heterogeneous and dynamic group of focal brain pathology, ranging from edema and inflammation to demyelination and axonal loss, their neuro-inflammatory origin is well accepted.46 Not only accumulation of lesions but also global and regional brain atrophy are important aspects of disease progression associated with – 17 – physical1, 4, 7–9, 13, 21 and cognitive disability .16, 17, 22, 23 In MS, loss of brain volume is driven by several mechanisms including tissue loss (i .e ., loss of myelin, glial cells, neurons, and axons due to inflammatory demyelination and neurodegeneration), as well as changes in non-tissue components (i.e., fluid shift due to inflammation, hydration, endocrine influences, or environmental factors) .7–9, 24, 123, 124 Currently, there are a number of manual, semi-automated, and automated techniques7–9 used for the assessment of global and regional brain volumes such as Structural Image Evaluation using Normalization of Atrophy Cross-sectional (SIENAX),125 FreeSurfer,126, 127 NeuroQuant, MS metrix,128 or model-based segmentation/registration tool – FMRIB Integrated Registration and Segmentation Tool (FIRST).128, 129 Longitudinal methods are also available, such as Structural Image Evaluation using Normalisation of Atrophy (SIENA), which are employed to directly measure relative volume changes over time .125, 130, 131 Unfortunately, high intra-individual variability of longitudinal MRI measures due to a number of biological and technical biases does not allow for the confident evaluation of brain atrophy in clinical practice. The major limitation of traditional lesion and volumetric MRI measures lies in the fact that focal MRI lesions and regional or global brain volume changes are only partially reflective of the disseminated pathology in MS.17, 132, 133 For example, specific topography rather than lesion or brain volume may play a role in the pathogenesis of disability in MS . Hence, more advanced MRI techniques, such as magnetization transfer ratio, diffusion tensor imaging, proton MRI spectroscopy, and functional MRI measuring various aspects of MS pathology17 are likely to further improve our understanding of the associations between MRI and disability progression at different MS stages . Unfortunately, there is also a remarkably high intra-individual variability associated with these advanced MRI methods, which is a major limitation for their application in clinical practice . Finally, the spinal cord is heavily affected in patients with MS and contributes substantially to the disease progression . In MS, the spinal cord is usually characterized by focal and diffuse lesions as well as global atrophy .12, 134 However, spinal cord MRI is performed in clinical practice and studies much less frequently than brain MRI. This is mostly due to technical challenges such as an inhomogeneous magnetic field in this region, the small physical dimensions of the spinal cord, and motion artifacts within the spinal canal, together with the flow of cerebrospinal fluid and periodic motion due to respiratory and cardiac cycles .135 Moreover, spinal cord MRI – 18 – is usually not sensitive to changes in spinal cord pathology over short-term follow-ups, and there are no established cut-offs for spinal cord atrophy . Hence, quantitative assessment of the spinal cord is usually performed only for research purposes and is not monitored regularly in most patients with MS . Biochemical Cytological (plasmatic cells, lymphocytic pleocytosis) and biochemical (normal protein, normal albumin quotient, increased IgG index, and IgG quotient) studies of cerebrospinal fluid play an important role in the differential diagnosis of MS. Particularly, the identification of cerebrospinal fluid-restricted oligoclonal bands typical for MS and is widely used for diagnosis confirmation.136, 137 Furthermore, anti-aquaporin-4138 and anti-myelin oligodendrocyte glycoprotein (MOG)139 antibodies are helpful in distinguishing between MS and neuromyelitis optica and MOG antibody disease . Serum neurofilament light chain level is an exceedingly promising predictor and marker of disease activity . Several studies are currently underway to investigate its potential use in clinical practice .140–145 Optical coherent tomography Optical coherent tomography (OCT) measures the thickness of the retinal nerve fiber layer (RNFL), which contains only non-myelinated axons. The RNFL thickness is associated with disability, relapse, and brain atrophy . Importantly, OCT is helpful in distinguishing between MS and neuromyelitis optica .146 However, further studies are needed to determine whether OCT is suitable for monitoring or predicting disease progression .147 1.6 Diagnosis There is no single diagnostic test for MS. Diagnostic processes include medical history, neurological examination, and paraclinical tests, such as MRI, cerebrospinal fluid analysis, and eventual evoked potentials or OCT. For diagnosis of MS, to the following criteria must be met:85 • Neurological symptoms arising from involvement of brain, optic nerve, or spinal cord . • Confirmed dissemination of disease in space (new relapse implicating different CNS site, ≥ 1 lesions in ≥ 2 MS-typical regions of the CNS – 19 – including: spinal cord, infratentorial, periventricular, and juxtacortical/ cortical region) . • Confirmed disease dissemination over time (new relapse, new lesion, simultaneously contrast-enhancing lesion together with non-enhancing lesion on brain MRI; oligoclonal bands in the cerebrospinal fluid can be used instead of dissemination in time) . • All different diagnoses have been ruled out . 1.7 Management Currently, there is no causative cure for MS . Current therapies focus mainly on the prevention or reduction of neuroinflammation. A wide range of immune therapies with specific mechanisms of action and immune targets have been approved for MS . New immunomodulatory (disease-modifying) treatments are currently the most effective drugs for MS . A major issue is that most therapies are effective only during early disease stages, with minimal effects in late progressive phases . Most therapies for MS significantly alter the survival and trafficking of immune cells. For example, the pharmacological effects of fingolimod result in sequestration of lymphocytes in lymph nodes . Natalizumab, a monoclonal antibody, which binds to the α4 integrin sub-unit present in antigen-4 on leukocytes, inhibits the adhesion interactions of leukocytes with the vascular cell adhesion molecule present on the activated vascular endothelium of the blood-brain barrier . Rituximab and ocrelizumab, both monoclonal antibodies that target the CD20 antigen (a membrane-embedded surface molecule) present in most B cells (except terminally differentiated plasma B cells), cause B cell death . Alemtuzumab targets the CD52 antigen and depletes the T, B, and NK cell populations. Monocytes, NK, and B cells repopulate the immune system more rapidly than T cells after this treatment .148 Dimethyl fumarate treatment causes lymphopenia that reduces CD3 T cell counts with preferential depletion of CD8 cells.149 Interferon beta, which has anti-proliferative activities, also causes shortand longterm changes in diverse cell populations, particularly activated NK cells .150, 151 Glatiramer acetate has many immunological effects, including its ability to alter T cell differentiation, leading to a shift from a Th1 (pro-inflammatory) to a Th2 (anti-inflammatory) immune profile, which may dampen inflammation within CNS.152 Teriflunomide selectively and reversibly inhibits – 20 – dihydro-orotate dehydrogenase, a key mitochondrial enzyme in the de novo pyrimidine synthesis pathway, leading to a reduction in proliferation of activated T and B lymphocytes without causing cell death.153 Additionally, cytostatic therapies – such as cyclophosphamide, mitoxantrone, or azathioprine – are rarely used as off-label immunosuppressive treatments, usually for patients not indicated for disease-modifying treatment . High-dose steroids and plasma exchange are typically used to manage relapses . Furthermore, a wide range of symptomatic drugs including analgetics, spasmolytics, anti-spastics, antidepressants, or anxiolytics are used for relief of various symptoms associated with CNS dysfunction . Finally, psychotherapy, aerobic and anaerobic exercises, rehabilitation, physiotherapy, and ergotherapy are important elements of the successful and comprehensive care of MS patients .154, 155 1.8 Prediction of disease activity MS is characterized by a broad spectrum of phenotypes. Therefore, treatment efficacy may be improved by identifying MS subpopulations at a high risk of disability progression or lack of treatment response . Given that irreversible acute axonal damage is most extensive in the early disease stages,156 it is extremely important to identify individuals who do not respond to treatment as early as possible, even if the mechanisms by which this occurs are not completely understood yet .2 Therefore, an important, yet currently unmet, need for modern MS treatment is to determine applicable predictors of subsequent disease activity . In clinical practice, traditional clinical markers such as early disability progression or high relapse activity are used to predict disease activity . However, clinical predictors are neither sensitive nor specific enough for use as reliable surrogate markers of disease activity over time . Contrastingly, recent studies have shown that the characteristics of MRI pathology are very informative for future disease activity in short-,11, 157–159 mid-,158, 160, 161 and long-term studies .158, 162–165 Particularly, the most important predictors of disability progression in relapsing-remitting MS have been suggested to be the occurrence of new T2 lesions,158–162, 166 accumulation of T2 lesion volume,167, 168 T1 lesion volume,169, 170 whole brain,168, 171 and central atrophy168, 169 as well as gray matter172 and thalamic volume changes .173, 174 – 21 – In addition to MRI, serum neurofilament light chain levels show relatively good predictive value for concurrent and future disease activity . Importantly, neurofilament levels are easy to measure in the serum and act as predictors in statistical models independent of MRI markers .144 Therefore, serum neurofilament light chain levels are a promising new predictor of disease course . Interestingly, measures of blood-brain barrier function175 before interferon treatment initiation and early serum lipid profile changes during interferon therapy,176 seem to predict clinical and radiological disease activity over long-term follow-up . However, further research is required to confirm any added value of these new laboratory markers in clinical practice . 1.9 Disease monitoring It is well accepted that clinical monitoring (EDSS, relapses) of new disease activity is insufficient for reliable assessment of disease progression. However, more detailed clinical monitoring using quantitative assessments of vision, hand function, walking ability, or cognitive performance is not standardized in clinical practice and is time-consuming . It is well known that most new active lesions on brain MRI are clinically asymptomatic but are clinically relevant from a long-term perspective . Therefore, among different paraclinical measures, brain MRI is one of the most accepted and sensitive tools suitable for monitoring MS progression .19, 20 Specifically, the occurrence of new, enlarging, or contrast-enhancing lesions on MRI is a widely used surrogate marker of radiological disease activity .20 Additionally, global and regional brain atrophy are also an important part of disease progression that is associated with the development of physical1, 2, 4, 21 and cognitive16, 17, 86, 177 disabilities . Recently, efforts have been made to incorporate brain atrophy measurements into clinical practice for decision-making in patients .9, 24 Unfortunately, the high variability of longitudinal MRI measures in individual patients over time, resulting from a wide range of biological and technical biases, does not allow for a confident evaluation of brain atrophy in clinical practice . – 22 – 2. Main aims of the current work The high intra-individual variability of cerebral atrophy measures makes their applicability in individual patients with MS questionable .7–9, 25 Therefore, current brain atrophy measures are not prepared for application in clinical practice . In this study, we investigated possible methods to overcome this high variability, thereby enabling atrophy measures to become important decision-making tools in clinical practice. The main aims of this study were: 1. To investigate the agreement between MRI volumetric measures obtained using various software techniques for the assessment of lesions and brain volumes, and their changes over time . 2. To describe the dynamics of brain volume loss at different stages of MS, the association between brain volume loss and clinical measures, and investigate the effects of treatment escalation on the rate of brain volume loss . 3. To establish cutoff values for global and regional brain volume loss that can discriminate between healthy people and patients with MS . 4. To quantify the prevalence and factors associated with brain volume increase . 5. To investigate the occurrence of linear and non-linear trajectories of brain volume loss in patients with MS during follow-up . 6. To describe the proportion of patients with dissociation between brain atrophy and lesion burden using a new definition. 7. To quantify the degree to which the precision of brain volume loss assessment can be improved with high-frequency brain MRI monitoring over a short-term follow-up . 8. To investigate the predictive role of early changes in MRI outcomes with respect to the relapse and progression of disability in patients with early MS . – 23 – 9. To assess the accuracy of a wide range of early MRI markers in predicting disease activity in a homogeneous sample of relapsing-remitting MS patients using interferons . Furthermore, we investigated whether a combination of volumetric MRI markers with clinical predictors could facilitate the identification of patients with poor long-term disability outcomes . 10. To develop an MRI-based algorithm that allows the identification of patients in need of neuropsychological evaluation and those at the highest risk of cognitive decline . 11. To investigate whether the strength of the association between MRI metrics and cognitive outcomes differed among the various MS subpopulations . 12. To describe the effects of pregnancy on the lesion activity and brain volume in women with MS . – 24 – 3. Methods 3.1 Brain MRI MRI acquisition All MRI scans were performed on the same scanner (1.5-Tesla Gyroscan; Philips Medical Systems, Best, Netherlands) using the same protocol. The MRI protocol included fluid-attenuated inversion recovery (FLAIR) and T1-weighted 3-dimensional fast field echo (T1-WI/FFE 3D) sequences (Figure 1) . Volumetric assessments were performed at the Department of Radiodiagnostics, First Faculty of Medicine, and General University Hospital in Prague or at the Buffalo Neuroimaging Analysis Center, NY, USA . Figure 1: MRI protocol – 25 – MRI analysis in BNAC Image analysis was performed at the Buffalo Neuroimaging Analysis Center. The volume of T2 lesions was measured using semi-automated edge-detection contouring-thresholding technique in Jim software (http:// www .xinapse .com) .178, 179 T2 lesion analysis was performed using the aid of a “subtraction image.” Whole brain, gray matter, white matter, and lateral ventricle volume were calculated using SIENAX, which normalizes measurements for head-size. Lesion filling was performed before segmentation using an in-house developed method. The SIENA technique was applied to assess longitudinal whole-brain volume changes .125 MRI analysis in Prague Image analysis was performed at the General University Hospital in Prague with ScanView software . ScanView is a semi-automated software developed by Jan Krasensky.180 This software was used for measurement of the volume of T1 and T2 lesions, the parenchymal fraction, the whole brain, and the corpus callosum volume using a segmentation-based approach .1, 11, 22, 157, 175, 181 T2 lesion volume was measured from FLAIR and the volume of the whole brain from the T1-WI/FFE 3D. Normalized whole brain volumes needed to be normalized regarding the total intracranial volume (ICV). The normalized partial volume of the corpus callosum was measured using ScanView software and estimated in seven (4th slice being in the central position) sagittal reconstructions of T1-WI/3D/GE slices.11, 157, 175, 181 More details of ScanView software were provided elsewhere .12 3.2 Clinical assessment Neuropsychological assessment The patients were evaluated with the Czech-validated version of the BICAMS .105, 182 Cognitive processing speed was assessed with the Symbol Digit Modalities Test (SDMT)107 and the oral response form was recorded using the Paced Auditory Serial Addition Test-3 seconds (PASAT-3). Memory was tested using the Brief Visuospatial Memory Test Revised (BVMTR)183 in the visual modality and the California Verbal Learning Test Second Edition (CVLT2)184 in the auditory sphere . Impairment for a single test was defined at a level of 1.5 standard deviations (z-score < 1 .5 compared to a healthy population), using the – 32 – MISSING VALUES T1 lesion volume 12.6% Thalamic volume 24.7% Corpus callosum volume 28.7% Gray and white matter volume 29.9% Lateral ventricle volume 35.8% Brain parenchymal fraction 0.9% Brain volume 0.9% T2 lesion volume 2.7% T1 lesion volume 12.6% Table 3: Number of patients with longitudinal MRI scans NUMBER OF PATIENTS Number of MRI scans per patient MRI follow-up duration (years) ≥4 ≥5 ≥6 ≥8 ≥10 ≥4 1,688 1,304 924 356 161 ≥5 1,564 1,245 904 355 161 ≥6 1,354 1,146 860 348 158 ≥8 745 701 621 320 155 ≥10 383 371 351 267 151 4.5 Healthy controls The enrollment of healthy people began in 2001 and was completed in 2014. The exclusion criteria were as follows: a history of neurological disorders affecting brain atrophy, abnormal brain MRI findings, and use of chronic anti-inflammatory or immunomodulatory medication. The original healthy control database included 133 participants (410 MRI scans) . Overall, 58 participants underwent ≥ 2 years of MRI follow-up and ≥ 3 MRI scans during the follow-up period . – 33 – 5. Summary of selected studies 5.1 A novel semiautomated pipeline to measure brain atrophy Background: Several techniques were used7–9 for the assessment of brain volume . Direct comparison of different methods is limited by methodological issues, such as the lack of a gold standard for image acquisition, short follow-up, and small sample size .191–194 Objective: We investigated the agreement between volumetric MRI measurements from two software packages, including the in-house developed ScanView, and commonly used techniques for the evaluation of T2 lesions and whole brain volume and their changes. Furthermore, we evaluated the intra-individual variability in brain volume loss progression between the two methods . Methods: The study included patients with MS from the SET2, 11, 157, 175, 195 and ASA 1, 4, 10, 186, 195, 196 studies. Together, 3340 MRI scans were included from 209 patients after the first demyelinating event suggestive of MS, 181 patients with relapsing-remitting MS, and 43 controls . Although all MRI scans were performed using the same scanner and protocol, the volumetric evaluations were performed independently at two different neuroimaging centers using different software packages . Volumetric analysis using the ScanView software was performed in Prague . Commonly used techniques, such as SIENA, SIENAX, and Jim software, have been used in Buffalo (Figure 5). Individual variability in the longitudinal MRI data was estimated using the mean squared error . Results and conclusions: The absolute volumes of the brain and lesions from both volumetric methods were significantly different but were strongly correlated. This observation underscores the fact that absolute and relative volumetric data obtained using different software cannot be adopted by other clinicians or researchers when different volumetric methods or MRI scanners are used .197, 198 – 34 – The variability of the relative whole-brain volume loss as assessed using SIENA125 was expectedly lower than that of ScanView .22, 157 The higher variability of ScanView may be explained by the segmentation-based character of the method. More specifically, we found that the mean deviation of the volume change in the whole brain was approximately 0 .30% . Considering that this residual error (± 0 .30%) was similar to the cutoff value for pathological whole brain volume loss (± 0 .40%), reliable identification of patients with accelerated brain volume loss in practice is very challenging . Figure 5: Example of lesion segmentation provided by Jim (1) and ScanView (2) software Details are provided in the publication: Uher T, Krasensky J, Vaneckova M et al . A novel semiautomated pipeline to measure brain atrophy and lesion burden in multiple sclerosis: A long-term comparative study . Journal of Neuroimaging 2017; 27(6):620-629.180 – 35 – 5.2 Evolution of brain volume loss rates in early stages of multiple sclerosis Background: A better understanding of brain volume loss trajectories throughout the course of MS is required to improve the assessment of brain atrophy in clinical practice and research . Objective: To describe the dynamics of brain volume loss at different stages of relapsing-remitting MS, the association between brain volume loss and clinical measures, and investigate the effects of treatment escalation on the rate of brain volume loss . Methods: We included 1903 patients predominantly with relapsing-remitting MS from the ASA (N = 166), SET (N = 180), and QMRI (N = 1557) cohorts with ≥ 2 MRI scans and ≥ 12 months of follow-up. Brain MRI scans (N = 7203) were performed using a single 1.5 Tesla scanner. The relationships between age or disease duration and global and tissue-specific brain volume loss rates were analyzed using mixed models . Results and conclusions: Although the rate of brain volume loss declined with longer disease duration, the effect of disease duration on brain volume loss was small . Greater brain volume loss was observed in patients with recent relapses and greater disability, although the strength of these associations was small. We found a stronger association between the loss of brain volume and the accumulation of T2 lesions. Importantly, brain volume loss was decreased by the escalation of immunomodulatory therapy . Establishing a threshold for pathological loss of brain volume in the context of normal aging is a key step toward the clinical interpretation of brain volume loss in MS . In this context, it is important to emphasize that the drivers of brain volume loss differ over time . Aging and comorbidities contribute more to overall brain atrophy in older people than in young patients with MS .199, 200 Therefore, in young patients with MS, higher rates of brain atrophy are more likely to indicate high disease activity that requires therapeutic intervention. Together, there is no clinically relevant relationship between the rate of brain volume loss and age or disease duration (Figure 6) . Accelerated loss of brain volume is weakly associated with a concurrent increase in the level of disease activity and eventually leads to more profound neurological disability . Evidence of a higher rate of brain volume loss should prompt the consideration of a change in disease-modifying treatment to prevent neurological disability . – 36 – Details are provided in the publication: Uher T, Krasensky J, Malpas C, et al . Evolution of brain volume loss rates in early stages of multiple sclerosis . Neurology, Neuroimmunology, & Neuroinflammation 2021; 8(3):e979 .13 5.3 Pathological cut-offs of brain volume loss Background: Thresholds for regional and global pathological loss of brain volume have not yet been defined. Monitoring brain atrophy rates can improve the identification of patients with progressive diseases. Objective: We aimed to define cut-off values of brain volume loss capable of discriminating between controls and MS patients by establishing cut-off values for the whole brain7–9, 124 gray matter, thalamus,109, 201 and corpus callosum volume loss rates .157, 180, 202–204 Methods: This study included the following cohorts:386 patients after the first clinical event suggestive of MS from the SET study2, 11, 157, 205 or the QMRI program; 964 relapsing-remitting MS patients from the ASA study,1, 186, 195, 196, 205, 206 or the QMRI program; 63 secondary progressive MS patients from the QMRI program; and 58 age-matched controls. In total, 11,438 MRI scans were evaluated . Figure 6: Relationship between whole-brain % volume loss and age (left) or disease duration (right) – 37 – Results and conclusions: Cut-off values for brain volume loss rates were identified as possible discriminators between controls and patients with MS. We found similar brain, gray matter, thalamic, and corpus callosum volume loss pathological cut-offs. The corpus callosum volume loss cut-offs showed a slightly higher sensitivity to discriminate between controls and patients with MS, as compared with other regional or global brain structures . Owing to the relatively low accuracy of the identified cutoffs, any increase in their sensitivity led to a decrease in their specificity. Therefore, highly specific cutoffs may reliably identify pathological brain atrophy in only a proportion of individuals with the highest rates of brain volume loss (Figure 7). We hypothesize that the accuracy of the identified cutoff values might be even lower in clinical practice due to greater intra-individual fluctuations of brain volume measures Figure 7: Distribution of annualized relative changes of whole brain volume loss with the cut-offs discriminating controls from MS patients – 38 – in real-world practice as a result of various biases .7, 8, 124, 180 We suggest that defined cutoffs are not yet prepared for use in clinical practice. Details are provided in the publication: Uher T, Vaneckova M, Krasensky J, et al. Pathological cut-offs of global and regional brain volume loss in multiple sclerosis . Multiple Sclerosis Journal 2019; 25(4):541-553.180 5.4 Interpretation of brain volume increase in multiple sclerosis Background: MS is typically associated with accelerated brain atrophy,8, 9, 25 but brain volume increase (BVI) can also occur, .207 This can complicate the interpretation of changes in brain volume . However, the clinical relevance of the BVI in patients with MS has not been investigated . Objective: To quantify the prevalence and factors associated with BVI in MS patients . Methods: We examined 366 patients with MS (2,317 scans) and 44 controls (132 scans). MRI was performed on the same 1.5-Tesla scanner using an identical scanning protocol . Volumetric analysis of brain volume changes was performed using SIENA and ScanView software. BVI was defined as a percentage of brain volume change > 0%. We compared the clinical and MRI outcomes between patients with and without BVI . Results and conclusions: We found a high prevalence (15.9%) of MRI scans with BVI (Figure 8). This is in contrast to the well-known accelerated brain volume loss in MS .8, 9, 25 We do not have direct evidence, but considering the measurement error of volumetric analysis180, 208 and average rate of brain volume loss in MS (from –0 .4 to –1 .0%),8, 9, 24, 209 the majority of cases of BVI seemed to be a result of measurement errors. This assumption was also supported by the observation that consecutive BVI were not associated with disease stabilization. The frequency of BVI identified by ScanView and SIENA software was between 31 .7% and 44 .5%, indicating that even small differences in methods180 have a relatively strong effect on the classification of MRI scans. Nevertheless, we hypothesize that in some cases, BVI is associated with a real increase in brain volume, especially when considering the effect of biological factors such as fluid shift due to inflammation,210 hydration status,124 daytime,205 endocrine influences211 or environmental and cardiovascular factors .212, 213 The neuroprotective effects – 39 – of highly effective treatments leading to BVI might be another relevant option warranting further investigation .207 Taken together, clinicians should be aware of the frequent occurrence of BVI, interpret it with great caution, and use precise and accurate MRI volumetric techniques . Details are provided in the publication: Uher T, Bergsland N, Krasensky J, et al. Interpretation of brain volume increase in multiple sclerosis. Journal of Neuroimaging 2021; 31(2):401-407.26 Figure 8: Examples of increasing normalized brain volume trajectories . 5.5 Occurrence of non-linear brain volume loss in patients with MS Background: In recent MS studies with repeated MRI measurements, linear regression slopes have been used to estimate individual rates of brain volume loss . Although a linear course of brain volume loss has been assumed, the potential occurrence of non-linear brain volume loss trajectories has not been investigated . – 40 – Objective: To investigate the frequency of the non-linear course of brain volume loss in MS . Methods: We included 1,546 MS patients from the QMRI program with ≥ 5 MRI scans (mean = 9.3, median 7.0 scans) and ≥ 4 years (mean = 7 .0, median 6 .3 years) follow-up . Most patients were treated with disease-modifying agents . Brain volume loss was measured using the ScanView software. We calculated the coefficients of determination for the individual linear regression models (lin-R2) and quadratic regression models . A Non-linear trajectory was assumed if the quadratic model fit the trajectory of brain volume loss better than the linear model (quad-R2 > 5% or > 10% higher than lin-R2; P < 0.01). The characteristics of patients with linear and non-linear brain volume loss were compared using the Mann-Whitney U test and adjusted logistic regression . Results and conclusions: A total of 98 (6 .3%) patients showed non-linear brain volume loss (quad-R2 > 5% higher than lin-R2) (Figure 9). The prevalence of non-linear brain volume loss decreased to 63 (4 .0%) when a stricter definition (> 10% higher) was applied. Non-linear brain volume loss showed deceleration in 44 (2 .8 %) (Figure 10) and acceleration in 19 (1 .2 %) patients (Figure 11) . Occurrence of non-linear brain volume loss was 27 .3% (> 5% higher) or 11 .3% (> 10% higher) in patients with ≥10 years follow-up. The incidence of non-linear brain volume loss was 29.3% (> 5% higher) and 12.6% (> 10% higher) in patients with ≥ 15 MRI scans, respectively (Figure 12) . Patients with non-linear deceleration of brain volume loss (> 5% higher) had a higher brain parenchymal fraction at baseline (p = 0 .003), a higher rate of brain volume loss (p < 0 .0001), increased volume of T2 lesions (p < 0.001), greater progression of disability (p = 0 .001), younger age (p = 0 .002), and shorter disease duration (p = 0 .017) than patients with linear brain volume loss . Patients with non-linear brain volume loss acceleration (> 5%) were similar to those with linear brain volume loss (Figure 13) . In summary, most patients with MS had a linear trajectory of brain volume loss over a short-term follow-up period . However, a considerable proportion of non-linear brain volume loss trajectories was found in patients with a longer follow-up and a higher number of MRI scans. Therefore, the assumption of linearity in brain volume loss needs to be verified, particularly in long-term MRI studies . Factors associated with non-linear brain volume loss need to be investigated . – 41 – Figure 9: Brain volume loss trajectories with deceleration and acceleration over follow-up Figure 10: Individual brain volume loss trajectories with deceleration over follow-up – 48 – 5.9 Early MRI predictors of clinical progression after 48 months in CIS patients treated with intramuscular interferon beta-1a Background: An important yet unmet need for modern MS treatment is to determine MRI predictors of subsequent activity in the early stages of MS . MRI pathology predicts new relapsing activity .2, 109, 157, 165, 215–217 Considering the occurrence of irreversible CNS damage in the early phases of the disease,156 it is important to identify patients with the highest risk of disease progression and lack of treatment response .2 Objective: We investigated the ability of baseline and 6-month MRI outcomes to predict relapse activity and development of confirmed disability progression in patients treated with weekly intramuscular interferon beta-1a after MS onset over 48 months . Methods: A prospective observational SET study was conducted in 210 patients after their first clinical attack. Adjusted Cox proportional hazards models were used for statistical analyses. The investigated MRI predictors included the number of active T2 lesions, number and volume of gadolinium-enhancing lesions, volume changes of the cortical and deep gray matter, thalamus, hippocampus, and lateral ventricle volume . Results and conclusions: Several MRI predictors of disease activity were identified. Approximately half of the patients who had relapsing Figure 16: Kaplan–Meier curves of SDP based on individual composite prediction scores – 49 – activity during follow-up had already experienced a new relapse activity during the first 6 months of the study. This observation supports previous findings of the early occurrence of new relapse activity with a subsequent decline in its incidence .18, 218–221 Presence of gadolinium-enhancing lesions, high T2 lesion burden, accelerated corpus callosum volume loss, and accelerated lateral ventricle volume enlargement over the 6 months after treatment initiation with interferons helped to identify patients with the highest risk for disease activity. In summary, our results support the findings of previous studies .2, 217, 218, 221 Hence, further controlled or comparative studies are needed to confirm the clinical relevance and reliability of MRI surrogate markers of treatment failure, and to investigate the effectiveness of new immunomodulatory drugs in high-risk patients in the early stages of MS . Details are provided in publication: Uher T, Horakova D, Kalincik T, et al . Early magnetic resonance imaging predictors of clinical progression after 48 months in clinically isolated syndrome patients treated with intramuscular interferon β-1a. European Journal of Neurology 2015; 22(7):1113-23.11 5.10 Combining clinical and MRI markers enhances prediction of 12-year disability Background: Abnormal MRI measures are the best predictors of disease activity in short-,11, 157–159 mid-,158, 160, 161 and long-term follow-up studies .158, 162–165 Based on previous research, the most discussed predictors of future disease activity in relapsing-remitting MS were the following imaging markers: active T2 lesions,158–162, 166 increase in the volume of the T2 lesion volume,167, 168 and the volume of the T1 lesion volume,169, 170 whole brain168, 171 and central atrophy,168, 169 gray matter,172 and changes in thalamic volume changes .173, 174 Importantly, many of the previous studies investigated only a limited number of MRI measures,166, 168, 169, 173 investigated patient in heterogeneous anti-inflammatory treatments or in different disease stages .168, 169, 173 Objective: We evaluated the predictive accuracy of a wide range of MRI markers in patients with relapsing-remitting MS treated with intramuscular interferon beta-1a over long-term follow-up. We hypothesized that the combination of different MRI and clinical markers,162, 222 may improve the long-term identification of patients with SDP. – 50 – Methods: We included 177 patients from the observational ASA study who were treated with intramuscular interferon beta-1a alone or in combination with oral steroids or azatioprine .186 Adjusted Cox proportional hazards models were used for statistical analyses . Results and conclusions: The best predictors of SDP included: T2 lesion number and volume; T1 lesion volume; corpus callosum and thalamus volumes at 12 months; EDSS score and EDSS change; number of new or newly enlarging T2 lesions; and relative change in corpus callosum volume (Figure 17 and 18) . Importantly, the accuracy of single MRI predictors is relatively low, ranging from 52% to 68% . However, the combination of single MRI findings and clinical predictors in the composite score increased the predictive accuracy at the individual patient level . For example, the Figure 17: Proportions of patients fulfilling individual risk criteria for prediction of sustained expanded disability status scale (EDSS) progression by 1 step (CC = corpus callosum; HR = hazard ratio; LVV = lateral ventricle volume; T1 or T2-LN = T1 or T2 lesion number; T1 or T2-LV = T1 or T2 lesion volume) – 51 – progression of the risk of disability in 12 years was five-fold higher in patients with three positive predictors than in patients without any positive predictors. Therefore, a combination of clinical and conventional imaging predictors with regional volumetric markers (such as corpus callosum and thalamic volumes) may improve the identification of patients at the highest risk of disability progression who may benefit from the early initiation of effective immunomodulatory treatment . Details are provided in the publication: Uher T, Vaneckova M, Sobisek L, et al . Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis . Multiple Sclerosis Journal 2017; 23(1):51-61.1 Figure 18: Proportions of patients fulfilling individual risk criteria for prediction of sustained expanded disability status scale (EDSS) progression by 2 steps (CC = corpus callosum; HR = hazard ratio; LVV = lateral ventricle volume; T1 or T2-LN = T1 or T2 lesion number; T1 or T2-LV = T1 or T2 lesion volume) – 52 – 5.11 Identification of MS patients at highest risk of cognitive impairment using integrated brain MRI assessment approach Background: Cognitive impairment is an important determinant of employment status and associated societal costs,89, 90 and adversely affects social functioning, coping, quality of life, and treatment adherence among MS patients .91, 92 Although abbreviated neuropsychological batteries such as BICAMS,106 have been suggested for use in routine practice, they are still not accessible to most patients with MS . Although there is a correlation between brain MRI measures and worse cognitive functioning,16, 17, 223 single MRI markers reflect only a small part of the neuropathology, resulting in cognitive impairment in MS patients with .17 Hence, it remains to be investigated whether the integration of MRI measures reflecting inflammatory and neurodegenerative processes may improve our identification of patients with either cognitive dysfunction or at the highest risk of cognitive decline in the future . Objective: We examined whether assessing the burden of lesions together with atrophy of the whole brain on MRI improves our ability to identify MS patients with cognitively impairment . Methods: Of 1,253 patients enrolled in the study, 1,052 patients with all cognitive and volumetric MRI and clinical data available were included in the analysis . Brain MRI and neuropsychological assessments were performed using BICAMS. MRI volumetric measures, such as T1 and T2 lesion volumes and normalized whole brain volume measured by brain parenchymal fraction, were examined in this study because of their relatively good availability in practice .3, 224–227 Multivariable logistic regression and individual prediction analysis were used to investigate the associations between MRI markers and cognitive impairment. The results of the primary analysis were validated at two subsequent time points (12 and 24 months) . Results and conclusions: Lesion and normalized brain volumes were independently correlated with cognitive function in patients with MS . High T2 lesion volume (> 3.5 ml) resulted in a three-fold greater prevalence of cognitive impairment in patients with high brain volume but a six-fold greater prevalence of cognitive impairment in patients with low normalized brain volume (brain parenchymal fraction < 0 .85) (Figure 19) . Considering the negative and positive predictive values of the combined MRI markers, we – 53 – suggest that the MRI algorithm may improve identification, particularly in patients with a low probability of cognitive impairment . MRI markers were also associated with a higher risk of cognitive decline in the following year . The risk of confirmed cognitive decline at follow-up was greater in patients with a high volume of T2 lesions (odds ratio [OR] = 2.1; 95% CI 1.1–3.8) and a low brain parenchymal fraction (OR = 2.6; 95% CI 1.4–4.7). In summary, a combination of the assessment of lesion burden and normalized brain volume improves the identification of patients with MS and cognitive impairment and can help clinicians select patients suitable for the assessment of cognitive functions . Figure 19: Proportions of cognitively impaired patients in subgroups of patients defined by dichotomized T2 lesion volume and brain parenchymal fraction at baseline of the study Details are provided in the publication: Uher T, Vaneckova M, Sormani MP, et al. Identification of multiple sclerosis patients at highest risk of cognitive impairment using an integrated brain magnetic resonance imaging assessment approach . European Journal of Neurology 2017; 24(2):292-301 .22 – 54 – 5.12 Cognitive clinico-radiological paradox in early stages of multiple sclerosis Background: Brain MRI measures, such as the volume of T1 and T2 lesions,96, 228 pathologies of normal-appearing white matter,132, 133 cortical lesions,229, 230 gray matter,201, 231 or thalamic atrophy100, 133 correlate with cognitive functioning in patients with MS . However, several previous studies have not found an association between MRI pathology and cognitive performance .86, 232–241 Furthermore, the magnitude of previously published correlations varies considerably between studies .16, 17, 242 We hypothesized that the strength of the correlation between imaging and cognitive measures is influenced by disease stage, disease burden, and patient characteristics. Objective: We investigated whether the strength of the association between MRI metrics and cognitive outcomes differed among various subpopulations of patients with MS . Methods: This study included a large sample of 1,052 patients with predominantly relapsing-remitting MS . All patients underwent brain MRI using a single scanner with volumetric assessment of T1 and T2 lesion volumes and brain parenchymal fractions . All patients were evaluated using the BICAMS battery and PASAT. Figure 20: The strength of associations between brain MRI (brain normalized volumes and T1 and T2 lesion volume) and cognitive measures (Symbol Digit Modalities Test) in MS subpopulations. Subgroups of patients stratified by disease duration (CCF = corpus callosum fraction, GMF = gray matter fraction; ThalF = thalamic fraction; WMF = white matter fraction) – 55 – Figure 21: The strength of associations between normalized regional brain volumes and cognitive measures in MS subpopulations. Subgroups of patients stratified by age, disease duration, EDSS, T2 lesion volume, or brain parenchymal fraction were used for graphical purposes – 56 – Results and conclusions: We found that the strength of the correlation between brain MRI and cognitive measures increased with advanced disease. The correlations between imaging and cognitive measures were low in patients with a low disease burden but significantly stronger in patients with a high disease burden with a long duration of the disease, older age, greater disability, greater lesion, and lower brain volume (Figure 20 and 21) . Taken together, our results suggest that greater brain damage is associated with greater cognitive dysfunction, especially in patients with a greater cumulative burden of preexisting diseases. The results of this study have several implications . First, the characteristics of a patient’s disease should be considered when interpreting the results of cognitive research . In this context, there is also a need for balanced recruitment of patients into clinical trials . Finally, asymptomatic radiological disease progression cannot be considered benign, because the clinical consequences of subclinical brain damage may be delayed. We believe that the results of this study could help explain the reasons for the lack of associations found in the literature between imaging and cognitive measures in several cross-sectional233, 238–241 and longitudinal studies,86, 97 performed in patients with a low disease burden . Details are provided in the publication: Uher T, Krasensky J, Sobisek L, et al . Cognitive clinico-radiological paradox in early stages of multiple sclerosis . Annals of Clinical & Translational Neurology 2017; 5(1):81-91.189 5.13 Pregnancy-induced brain MRI changes in women with multiple sclerosis Background: The effects of pregnancy on the CNS in women with MS are not well understood . Objective: To describe the effects of pregnancy on the lesion activity and brain volume in women with MS . Methods: We included 62 women with relapsing-remitting MS and 221 women with available MRI time points . All women underwent clinical visits at baseline (< 24 and > 6 months before pregnancy), pre-pregnancy (< 6 months before pregnancy), postpartum (< 3 months after delivery), and during the follow-up period (> 12 and < 24 months after delivery) (Figure 22) . – 57 – Results and conclusions: Women in the postpartum period showed a higher volume of T2 lesions, lower normalized brain volume, and greater acceleration of brain volume loss than those in the pre-pregnancy period . Furthermore, at 12-24 months after delivery, 41 women had a higher volume of T2 lesions and lower normalized brain volume than before pregnancy (Figure 23 and 24). Taken together, pregnancy considerably affects Figure 22: Study design Figure 23: The rate of annualized brain volume at prepregnancy period (2), at early postpartum period (3), and at follow-up (e .g ., late postpartum period) (4) – 64 – a more accurate, pathologically representative, and objective tool for the description of disease patterns compared to clinical assessment confused by several biases . MRI was hypothesized to identify MS phenotypes that are directly influenced by pathophysiological mechanisms.244 Furthermore, a combination of MRI and clinical results was suggested to improve the identification of MS phenotypes. To the best of our knowledge, few attempts have been made to systematically investigate specific MRI phenotypes in MS. Moreover, previous studies were cross-sectional, employed small sample sizes, and investigated only conventional or global MRI volumetric measures .226, 245, 246 For example, recent cross-sectional studies described four MRI phenotypes based on brain imaging measures of inflammation (assessed by T2 lesion volume or the occurrence of contrast-enhancing lesions) and brain imaging measures of axonal/tissue loss (assessed by brain parenchymal fraction). Although a higher T2 lesion burden was associated with greater brain atrophy in most patients, a considerable proportion of patients had either a high T2 lesion load or high brain atrophy . Different pathophysiological mechanisms, individual regeneration and repair capacity, neuronal and axonal integrity, or dominant gray matter pathology may explain the appearance of different MRI patterns . Particularly, the correlation between disability and MRI outcomes was different in the MRI subgroups, indicating the potential prognostic role of MRI phenotypes .226 It is also worth noting that MRI phenotypes were not related to the current clinical classification of MS. This agrees with other studies showing MRI variability between individuals with established clinical MS subtypes . In summary, the findings of previous research emphasize that the current clinical classification does not closely overlap with MRI phenotypes and that further research is needed to identify specific disease patterns in more detail. 6.6 Summary of the main findings • In most patients, there is high intra-individual variability in longitudinal brain MRI volumetric measures . • Established cut-offs for pathological brain atrophy have a relatively low accuracy . • The measurement error of brain atrophy estimates is comparable to the suggested cut-offs of the pathological brain atrophy rate . – 65 – • A considerable proportion of MRI scans present with brain volume increase . • Assessment of MRI trajectories (based on multiple MRI scans over long-term follow-up) and complex evaluation of a spectrum of global and regional brain atrophy and lesional volumetric markers may allow for the use of volumetric measures in individual patients in the future . • Some regional brain atrophy measures (corpus callosum and lateral ventricle) may be more suitable for disease monitoring because of their greater disease specificity and higher inter-individual variability but similar intra-individual variability compared to whole-brain atrophy measures . • Cross-sectional volumetric measures (e .g ., lesion volume or brain parenchymal fraction) have higher interindividual variability and lower intra-individual variability than longitudinal volumetric measures. 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