Can we use regional grey matter atrophy sequence to stage neurodegeneration in multiple sclerosis?

Can we use regional grey matter atrophy sequence to stage neurodegeneration in multiple sclerosis? This scientific commentary refers to ‘Progression of regional grey matter atrophy in multiple sclerosis’, by Eshagi et al. (doi:10.1093/brain/awy088). Despite therapeutic advances over the past few decades that have reduced the relapse rate in multiple sclerosis, this disease remains the main cause of non-traumatic chronic disability among young adults in the Western world, representing a major public health concern and a significant economic burden. Historically, multiple sclerosis has been described as an inflammatory autoimmune disease, where the autoimmune process induces demyelinating plaques in the CNS white matter. This results in relapses characterized by a variety of neurological symptoms depending on lesion location. The efficacy of increasingly powerful immunoactive therapies in reducing relapse frequency in pivotal trials has lent support to this concept of the disease. However, a growing body of evidence also suggests a neurodegenerative component that evolves throughout the disease course; this could explain why current therapies only mildly impact long-term disability accumulation and progression. Epidemiological studies indicate that the disability accrual characterizing progressive forms of the disease is largely independent of relapses, and may evolve as an anamnestic process, reminiscent of the course of more classical neurodegenerative diseases. Providing further support for the neurodegenerative concept, neuroimaging investigations have clearly shown tissue loss to be an early, continuous and irreversible process in multiple sclerosis, leading to the identification of grey matter atrophy as the most accurate marker of neurodegeneration, with strong prognostic value (Fisher et al., 2008; Fisniku et al., 2008). To date, the biological mechanisms underlying degeneration and grey matter atrophy in multiple sclerosis are only partly understood. They may involve at various levels acute axonal transection in inflammatory lesions, with possible subsequent tract-mediated damage in grey matter, network-mediated trans-synaptic degeneration, energy and metabolic deficits, cortical demyelination, diffuse neuroinflammation or meningeal inflammation. Determining whether grey matter atrophy follows a specific temporal sequence, as has been described for example in Alzheimer’s disease, could help elucidate the sequence of events that triggers disability worsening and shed new light on the pathophysiology of neurodegeneration in multiple sclerosis. In this issue of Brain, Eshaghi and colleagues aim to do just this by using an algorithm called an event-based model to reconstruct the sequence of regional grey matter atrophy in a large retrospective imaging dataset collected within the MAGNIMS network, covering all stages of the disease and including cross-sectional and short-term longitudinal follow up (Eshaghi et al., 2018; Fig. 1). The authors establish the most likely sequence of grey matter atrophy that is consistent across multiple sclerosis phenotypes, identifying key regions with early atrophy such as the posterior cingulate, precuneus and thalamus. After quantifying the robustness of the identified sequence with a cross-validation step, they apply the model to individual patients. This enables them to quantify individual rates of increase in event-based model stage, and to study the correlations between grey matter atrophy stage and imaging and clinical characteristics. Figure 1 View largeDownload slide Overview of event-based model used by Eshaghi et al. to determine the most likely sequence of regional atrophy in multiple sclerosis. The aim of the first step was to determine the likelihood of regional atrophy in a cohort of 3604 scans acquired in 1214 patients with multiple sclerosis and 203 controls. For each region and for each scan, the probability of atrophy was assessed using the distribution of regional volumes in the entire cohort. The second step entailed using a greedy ascent algorithm to determine the most likely sequence of regional atrophy in the multiple sclerosis cohort, assuming that the cohort as a whole reflects the entire natural history of the disease. The uncertainty of the model was checked using cross-validation methods. Finally, individual staging of each scan was based on the most likely sequence identified in the whole cohort of subjects by the event-based model. Individual staging of grey matter atrophy was further used to study association between staging and demographics, clinical and imaging characteristics. EDSS = Expanded Disability Status Scale; MS = multiple sclerosis; ROI = region of interest; RRMS = relapsing-remitting multiple sclerosis; WM = white matter. Figure 1 View largeDownload slide Overview of event-based model used by Eshaghi et al. to determine the most likely sequence of regional atrophy in multiple sclerosis. The aim of the first step was to determine the likelihood of regional atrophy in a cohort of 3604 scans acquired in 1214 patients with multiple sclerosis and 203 controls. For each region and for each scan, the probability of atrophy was assessed using the distribution of regional volumes in the entire cohort. The second step entailed using a greedy ascent algorithm to determine the most likely sequence of regional atrophy in the multiple sclerosis cohort, assuming that the cohort as a whole reflects the entire natural history of the disease. The uncertainty of the model was checked using cross-validation methods. Finally, individual staging of each scan was based on the most likely sequence identified in the whole cohort of subjects by the event-based model. Individual staging of grey matter atrophy was further used to study association between staging and demographics, clinical and imaging characteristics. EDSS = Expanded Disability Status Scale; MS = multiple sclerosis; ROI = region of interest; RRMS = relapsing-remitting multiple sclerosis; WM = white matter. The event-based model is a recently described data driven method that can learn the ordering of changes in markers within large cross-sectional and short-term longitudinal datasets. This makes it possible to define a sequence of events, here the occurrence of atrophy in each region, throughout the natural history of a disease. The model can be applied to regional atrophy measures, but in the absence of an absolute cut-off to define grey matter atrophy, an initial step is needed to estimate the likelihood of the event (i.e. atrophy) based on the distribution of values for regional volumes within the dataset, without an a priori cut-off for each region (Fonteijn et al., 2012). Eshaghi and colleagues adapted the model to provide insights into the uncertainty of the reconstructed ordering, and applied the most likely sequence to individual scans in order to stage subjects. When applied to multiple sclerosis, it should be pointed out that this model assumes that the investigated population represents the entire disease trajectory, that the sequence of events follows a similar pattern in all patients, and that the event (i.e. atrophy) is irreversible. However, a high level of individual heterogeneity characterizes multiple sclerosis, both at the clinical level, with various individual trajectories described both for relapsing and progressive forms (Signori et al., 2017), and at the brain imaging level, with the identification of several non-random patterns of atrophy (Steenwijk et al., 2016). Subtle volumetric changes may not be fully irreversible, as they are influenced by physiological conditions such as circadian fluctuation or hydration. Whether this might have influenced the uncertainty of the ordering remains an open question, but it is intriguing to read that in their cross-validation step, Eshaghi et al. found a fairly low uncertainty for both early and late events, but a greater uncertainty in intermediate stages. This may suggest that early mechanisms in the multiple sclerosis disease course are relatively homogeneous, while diversity at later stages may drive disease heterogeneity. Such an interpretation would be consistent with the findings of Steenwijk et al., which were obtained in patients with long-standing multiple sclerosis (20-year disease duration) and showed heterogeneous anatomical patterns of cortical atrophy associated with clinical deficits, especially cognitive dysfunction. Of great interest are the first regions to become atrophic in patients: posterior cingulate cortex, precuneus, thalamus and brainstem were consistently identified both in relapsing and in progressive multiple sclerosis, with the addition of insula, accumbens, and caudate in the multiple sclerosis cohort as a whole. These are highly connected regions, pointing towards a specific vulnerability of more metabolically active hubs in the brain. This sequence may also provide new insights into the earliest clinical stages of the disease, such as the recently identified prodromal stage that precedes relapses or progression (Wijnands et al., 2017). While a clear clinical description of these prodromes is lacking, they have been speculated to consist mainly of subtle cognitive or psychiatric disorders. The posterior cingulate has been identified as one of the key cortical regions associated with cognitive impairment in multiple sclerosis (Louapre et al., 2014). It is associated with complex brain homeostatic functions such as maintaining the arousal state, the balance between internal and external focus of attention, and regulating the breadth of attention (Leech and Sharp, 2014). How an early dysregulation of posterior cingulate cortex connectivity could result in impaired cognitive functioning in multiple sclerosis, that may or may not be perceived by patients, warrants further research. The accumbens, insula and caudate are also highly connected with cognitive and limbic structures, and it is tempting to hypothesize a link with the depression and fatigue often observed in even the earliest stages of multiple sclerosis. The longitudinal evolution of grey matter atrophy staging as defined by the event-based model was further analysed for individual subjects. The annual rate of change in the event-based model stage was significantly different from zero for secondary progressive, primary progressive and relapsing-remitting multiple sclerosis, but not for clinically isolated syndrome, although in the latter it showed high variability. One interpretation might be that a significant proportion of interindividual heterogeneity in patients’ trajectories is determined early in the disease course, following the first clinical event, with some patients showing no progression of the neurodegenerative component whereas others already show a clear progression. Of note, there was an association between change in event-based model stage and scores on the Expanded Disability Status Scale (EDSS) only in patients with relapsing-remitting multiple sclerosis and not in those with the progressive form, and this relationship was also very weak (0.03-point increase in annual EDSS score for every unit increase in the event-based model stage). While intriguing, this small effect size does not exclude a link between neurodegeneration and disability, but rather suggests inadequacy of the EDSS score which, beyond a certain threshold, is mainly dependent on long tracts and spinal cord damage, and is less influenced by brain pathology. Elucidating the biological processes driving the sequence of grey matter atrophy described in the Eshaghi paper could have clinical implications, as it may open up the possibility of adapting therapeutic approaches depending on the staging of patients. A mild correlation was found in the cross-sectional analysis between the volume of white matter lesions, as measured on MRI by the T2 lesion load, and the event-based model stage (an ∼ 15 ml increase in lesion load corresponded to a one-unit increase in stage). However, there was no association between the rate of change of each parameter in the longitudinal follow-up. While corroborating many previous observations, this does not fully exonerate white matter lesions as a source of ongoing neurodegeneration in multiple sclerosis. The regional localization of lesions was not taken into account here, and such a global analysis could miss any tract-dependent relationship between the two processes. More importantly, there is compelling evidence demonstrating that the biological content of lesions is largely heterogeneous, which may imply a very different influence on subsequent axonal damage and neuronal retrograde degeneration in connected areas. The remyelination potential of individual patients and lesions showed large variability (Bodini et al., 2016), with a lack of repair rendering axons more vulnerable to the inflammatory milieu. While not being enhanced by gadolinium injection, and therefore evading visualization on MRI, a proportion of white matter lesions may evolve towards chronic activation and/or a smouldering state, whereas others rapidly become inactive (Frischer et al., 2015). Beyond white matter lesions, Eshaghi et al. discuss alternative mechanisms such as cortical demyelination and meningeal inflammation, but these remain speculative as they are not yet accessible to standard clinical imaging. In future studies, it would be of major value to investigate the sequence of regional grey matter atrophy as described in the Eshaghi et al. paper in combination with advanced imaging tools allowing characterization of white matter and cortical lesions for their biological content, together with CSF-derived biomarkers reflecting the level of meningeal inflammation (Magliozzi et al., 2018). Overall this article proposes a new look at the natural history of neurodegeneration in multiple sclerosis, arguing that it can be viewed as an ordered sequential process, translated into grey matter atrophy progression over time. The pathophysiology of this phenomenon remains enigmatic and warrants further research. We may wonder how such a staging tool could be applied in clinical practice or clinical research. The uncertainty regarding attribution of intermediate stages may preclude its use in staging individual patients in clinical practice. However, investigating the progression of specific nested populations through the stages relative to the whole cohort may provide exciting clues about atypical evolution: for instance, determining how and when the temporal sequence of atrophy diverges in patients with benign or cognitive forms of multiple sclerosis may shed new light on these extreme presentations. Finally, staging patients included in clinical trials or cohort studies could allow for a stratification of patients into more homogeneous groups, representing a step towards precision medicine. References Bodini B , Veronese M , Garcia-Lorenzo D , Battaglini M , Poirion E , Chardain A , et al. Dynamic imaging of individual remyelination profiles in multiple sclerosis . Ann Neurol 2016 ; 79 : 726 – 38 . Google Scholar CrossRef Search ADS Eshaghi A , Marinescu RV , Young AL , Firth NC , Prados F , et al. Progression of regional grey matter atrophy in multiple sclerosis . Brain 2018 ; 141 : 1665 – 77 . Fisher E , Lee JC , Nakamura K , Rudick RA . Gray matter atrophy in multiple sclerosis: a longitudinal study . Ann Neurol 2008 ; 64 : 255 – 65 . Google Scholar CrossRef Search ADS PubMed Fisniku LK , Chard DT , Jackson JS , Anderson VM , Altmann DR , Miszkiel KA , et al. Gray matter atrophy is related to long-term disability in multiple sclerosis . Ann Neurol 2008 ; 64 : 247 – 54 . Google Scholar CrossRef Search ADS PubMed Fonteijn HM , Modat M , Clarkson MJ , Barnes J , Lehmann M , Hobbs NZ , et al. An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease . Neuroimage 2012 ; 60 : 1880 – 9 . Google Scholar CrossRef Search ADS PubMed Frischer JM , Weigand SD , Guo Y , Kale N , Parisi JE , Pirko I , et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque . Ann Neurol 2015 ; 78 : 710 – 21 . Google Scholar CrossRef Search ADS PubMed Leech R , Sharp DJ . The role of the posterior cingulate cortex in cognition and disease . Brain 2014 ; 137 ( Pt 1 ): 12 – 32 . Google Scholar CrossRef Search ADS PubMed Louapre C , Perlbarg V , Garcia-Lorenzo D , Urbanski M , Benali H , Assouad R , et al. Brain networks disconnection in early multiple sclerosis cognitive deficits: an anatomofunctional study . Hum Brain Mapp 2014 ; 35 : 4706 – 17 . Google Scholar CrossRef Search ADS PubMed Magliozzi R , Howell OW , Nicholas R , Cruciani C , Castellaro M , Romualdi C , et al. Inflammatory intrathecal profiles and cortical damage in multiple sclerosis . Ann Neurol 2018 , in press. [Epub ahead of print]. doi: 10.1002/ana.25197 . Signori A , Izquierdo G , Lugaresi A , Hupperts R , Grand'Maison F , Sola P , et al. Long-term disability trajectories in primary progressive MS patients: a latent class growth analysis . Mult Scler 2017 , in press. [Epub ahead of print]. doi: 10.1177/1352458517703800 . Steenwijk MD , Geurts JJ , Daams M , Tijms BM , Wink AM , Balk LJ , et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant . Brain 2016 ; 139 ( Pt 1 ): 115 – 26 . Google Scholar CrossRef Search ADS PubMed Wijnands JMA , Kingwell E , Zhu F , Zhao Y , Hogg T , Stadnyk K , et al. Health-care use before a first demyelinating event suggestive of a multiple sclerosis prodrome: a matched cohort study . Lancet Neurol 2017 ; 16 : 445 – 51 . Google Scholar CrossRef Search ADS PubMed © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain Oxford University Press

Can we use regional grey matter atrophy sequence to stage neurodegeneration in multiple sclerosis?

Brain , Volume Advance Article (6) – May 25, 2018

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Abstract

This scientific commentary refers to ‘Progression of regional grey matter atrophy in multiple sclerosis’, by Eshagi et al. (doi:10.1093/brain/awy088). Despite therapeutic advances over the past few decades that have reduced the relapse rate in multiple sclerosis, this disease remains the main cause of non-traumatic chronic disability among young adults in the Western world, representing a major public health concern and a significant economic burden. Historically, multiple sclerosis has been described as an inflammatory autoimmune disease, where the autoimmune process induces demyelinating plaques in the CNS white matter. This results in relapses characterized by a variety of neurological symptoms depending on lesion location. The efficacy of increasingly powerful immunoactive therapies in reducing relapse frequency in pivotal trials has lent support to this concept of the disease. However, a growing body of evidence also suggests a neurodegenerative component that evolves throughout the disease course; this could explain why current therapies only mildly impact long-term disability accumulation and progression. Epidemiological studies indicate that the disability accrual characterizing progressive forms of the disease is largely independent of relapses, and may evolve as an anamnestic process, reminiscent of the course of more classical neurodegenerative diseases. Providing further support for the neurodegenerative concept, neuroimaging investigations have clearly shown tissue loss to be an early, continuous and irreversible process in multiple sclerosis, leading to the identification of grey matter atrophy as the most accurate marker of neurodegeneration, with strong prognostic value (Fisher et al., 2008; Fisniku et al., 2008). To date, the biological mechanisms underlying degeneration and grey matter atrophy in multiple sclerosis are only partly understood. They may involve at various levels acute axonal transection in inflammatory lesions, with possible subsequent tract-mediated damage in grey matter, network-mediated trans-synaptic degeneration, energy and metabolic deficits, cortical demyelination, diffuse neuroinflammation or meningeal inflammation. Determining whether grey matter atrophy follows a specific temporal sequence, as has been described for example in Alzheimer’s disease, could help elucidate the sequence of events that triggers disability worsening and shed new light on the pathophysiology of neurodegeneration in multiple sclerosis. In this issue of Brain, Eshaghi and colleagues aim to do just this by using an algorithm called an event-based model to reconstruct the sequence of regional grey matter atrophy in a large retrospective imaging dataset collected within the MAGNIMS network, covering all stages of the disease and including cross-sectional and short-term longitudinal follow up (Eshaghi et al., 2018; Fig. 1). The authors establish the most likely sequence of grey matter atrophy that is consistent across multiple sclerosis phenotypes, identifying key regions with early atrophy such as the posterior cingulate, precuneus and thalamus. After quantifying the robustness of the identified sequence with a cross-validation step, they apply the model to individual patients. This enables them to quantify individual rates of increase in event-based model stage, and to study the correlations between grey matter atrophy stage and imaging and clinical characteristics. Figure 1 View largeDownload slide Overview of event-based model used by Eshaghi et al. to determine the most likely sequence of regional atrophy in multiple sclerosis. The aim of the first step was to determine the likelihood of regional atrophy in a cohort of 3604 scans acquired in 1214 patients with multiple sclerosis and 203 controls. For each region and for each scan, the probability of atrophy was assessed using the distribution of regional volumes in the entire cohort. The second step entailed using a greedy ascent algorithm to determine the most likely sequence of regional atrophy in the multiple sclerosis cohort, assuming that the cohort as a whole reflects the entire natural history of the disease. The uncertainty of the model was checked using cross-validation methods. Finally, individual staging of each scan was based on the most likely sequence identified in the whole cohort of subjects by the event-based model. Individual staging of grey matter atrophy was further used to study association between staging and demographics, clinical and imaging characteristics. EDSS = Expanded Disability Status Scale; MS = multiple sclerosis; ROI = region of interest; RRMS = relapsing-remitting multiple sclerosis; WM = white matter. Figure 1 View largeDownload slide Overview of event-based model used by Eshaghi et al. to determine the most likely sequence of regional atrophy in multiple sclerosis. The aim of the first step was to determine the likelihood of regional atrophy in a cohort of 3604 scans acquired in 1214 patients with multiple sclerosis and 203 controls. For each region and for each scan, the probability of atrophy was assessed using the distribution of regional volumes in the entire cohort. The second step entailed using a greedy ascent algorithm to determine the most likely sequence of regional atrophy in the multiple sclerosis cohort, assuming that the cohort as a whole reflects the entire natural history of the disease. The uncertainty of the model was checked using cross-validation methods. Finally, individual staging of each scan was based on the most likely sequence identified in the whole cohort of subjects by the event-based model. Individual staging of grey matter atrophy was further used to study association between staging and demographics, clinical and imaging characteristics. EDSS = Expanded Disability Status Scale; MS = multiple sclerosis; ROI = region of interest; RRMS = relapsing-remitting multiple sclerosis; WM = white matter. The event-based model is a recently described data driven method that can learn the ordering of changes in markers within large cross-sectional and short-term longitudinal datasets. This makes it possible to define a sequence of events, here the occurrence of atrophy in each region, throughout the natural history of a disease. The model can be applied to regional atrophy measures, but in the absence of an absolute cut-off to define grey matter atrophy, an initial step is needed to estimate the likelihood of the event (i.e. atrophy) based on the distribution of values for regional volumes within the dataset, without an a priori cut-off for each region (Fonteijn et al., 2012). Eshaghi and colleagues adapted the model to provide insights into the uncertainty of the reconstructed ordering, and applied the most likely sequence to individual scans in order to stage subjects. When applied to multiple sclerosis, it should be pointed out that this model assumes that the investigated population represents the entire disease trajectory, that the sequence of events follows a similar pattern in all patients, and that the event (i.e. atrophy) is irreversible. However, a high level of individual heterogeneity characterizes multiple sclerosis, both at the clinical level, with various individual trajectories described both for relapsing and progressive forms (Signori et al., 2017), and at the brain imaging level, with the identification of several non-random patterns of atrophy (Steenwijk et al., 2016). Subtle volumetric changes may not be fully irreversible, as they are influenced by physiological conditions such as circadian fluctuation or hydration. Whether this might have influenced the uncertainty of the ordering remains an open question, but it is intriguing to read that in their cross-validation step, Eshaghi et al. found a fairly low uncertainty for both early and late events, but a greater uncertainty in intermediate stages. This may suggest that early mechanisms in the multiple sclerosis disease course are relatively homogeneous, while diversity at later stages may drive disease heterogeneity. Such an interpretation would be consistent with the findings of Steenwijk et al., which were obtained in patients with long-standing multiple sclerosis (20-year disease duration) and showed heterogeneous anatomical patterns of cortical atrophy associated with clinical deficits, especially cognitive dysfunction. Of great interest are the first regions to become atrophic in patients: posterior cingulate cortex, precuneus, thalamus and brainstem were consistently identified both in relapsing and in progressive multiple sclerosis, with the addition of insula, accumbens, and caudate in the multiple sclerosis cohort as a whole. These are highly connected regions, pointing towards a specific vulnerability of more metabolically active hubs in the brain. This sequence may also provide new insights into the earliest clinical stages of the disease, such as the recently identified prodromal stage that precedes relapses or progression (Wijnands et al., 2017). While a clear clinical description of these prodromes is lacking, they have been speculated to consist mainly of subtle cognitive or psychiatric disorders. The posterior cingulate has been identified as one of the key cortical regions associated with cognitive impairment in multiple sclerosis (Louapre et al., 2014). It is associated with complex brain homeostatic functions such as maintaining the arousal state, the balance between internal and external focus of attention, and regulating the breadth of attention (Leech and Sharp, 2014). How an early dysregulation of posterior cingulate cortex connectivity could result in impaired cognitive functioning in multiple sclerosis, that may or may not be perceived by patients, warrants further research. The accumbens, insula and caudate are also highly connected with cognitive and limbic structures, and it is tempting to hypothesize a link with the depression and fatigue often observed in even the earliest stages of multiple sclerosis. The longitudinal evolution of grey matter atrophy staging as defined by the event-based model was further analysed for individual subjects. The annual rate of change in the event-based model stage was significantly different from zero for secondary progressive, primary progressive and relapsing-remitting multiple sclerosis, but not for clinically isolated syndrome, although in the latter it showed high variability. One interpretation might be that a significant proportion of interindividual heterogeneity in patients’ trajectories is determined early in the disease course, following the first clinical event, with some patients showing no progression of the neurodegenerative component whereas others already show a clear progression. Of note, there was an association between change in event-based model stage and scores on the Expanded Disability Status Scale (EDSS) only in patients with relapsing-remitting multiple sclerosis and not in those with the progressive form, and this relationship was also very weak (0.03-point increase in annual EDSS score for every unit increase in the event-based model stage). While intriguing, this small effect size does not exclude a link between neurodegeneration and disability, but rather suggests inadequacy of the EDSS score which, beyond a certain threshold, is mainly dependent on long tracts and spinal cord damage, and is less influenced by brain pathology. Elucidating the biological processes driving the sequence of grey matter atrophy described in the Eshaghi paper could have clinical implications, as it may open up the possibility of adapting therapeutic approaches depending on the staging of patients. A mild correlation was found in the cross-sectional analysis between the volume of white matter lesions, as measured on MRI by the T2 lesion load, and the event-based model stage (an ∼ 15 ml increase in lesion load corresponded to a one-unit increase in stage). However, there was no association between the rate of change of each parameter in the longitudinal follow-up. While corroborating many previous observations, this does not fully exonerate white matter lesions as a source of ongoing neurodegeneration in multiple sclerosis. The regional localization of lesions was not taken into account here, and such a global analysis could miss any tract-dependent relationship between the two processes. More importantly, there is compelling evidence demonstrating that the biological content of lesions is largely heterogeneous, which may imply a very different influence on subsequent axonal damage and neuronal retrograde degeneration in connected areas. The remyelination potential of individual patients and lesions showed large variability (Bodini et al., 2016), with a lack of repair rendering axons more vulnerable to the inflammatory milieu. While not being enhanced by gadolinium injection, and therefore evading visualization on MRI, a proportion of white matter lesions may evolve towards chronic activation and/or a smouldering state, whereas others rapidly become inactive (Frischer et al., 2015). Beyond white matter lesions, Eshaghi et al. discuss alternative mechanisms such as cortical demyelination and meningeal inflammation, but these remain speculative as they are not yet accessible to standard clinical imaging. In future studies, it would be of major value to investigate the sequence of regional grey matter atrophy as described in the Eshaghi et al. paper in combination with advanced imaging tools allowing characterization of white matter and cortical lesions for their biological content, together with CSF-derived biomarkers reflecting the level of meningeal inflammation (Magliozzi et al., 2018). Overall this article proposes a new look at the natural history of neurodegeneration in multiple sclerosis, arguing that it can be viewed as an ordered sequential process, translated into grey matter atrophy progression over time. The pathophysiology of this phenomenon remains enigmatic and warrants further research. We may wonder how such a staging tool could be applied in clinical practice or clinical research. The uncertainty regarding attribution of intermediate stages may preclude its use in staging individual patients in clinical practice. However, investigating the progression of specific nested populations through the stages relative to the whole cohort may provide exciting clues about atypical evolution: for instance, determining how and when the temporal sequence of atrophy diverges in patients with benign or cognitive forms of multiple sclerosis may shed new light on these extreme presentations. Finally, staging patients included in clinical trials or cohort studies could allow for a stratification of patients into more homogeneous groups, representing a step towards precision medicine. References Bodini B , Veronese M , Garcia-Lorenzo D , Battaglini M , Poirion E , Chardain A , et al. Dynamic imaging of individual remyelination profiles in multiple sclerosis . Ann Neurol 2016 ; 79 : 726 – 38 . Google Scholar CrossRef Search ADS Eshaghi A , Marinescu RV , Young AL , Firth NC , Prados F , et al. Progression of regional grey matter atrophy in multiple sclerosis . Brain 2018 ; 141 : 1665 – 77 . Fisher E , Lee JC , Nakamura K , Rudick RA . Gray matter atrophy in multiple sclerosis: a longitudinal study . Ann Neurol 2008 ; 64 : 255 – 65 . Google Scholar CrossRef Search ADS PubMed Fisniku LK , Chard DT , Jackson JS , Anderson VM , Altmann DR , Miszkiel KA , et al. Gray matter atrophy is related to long-term disability in multiple sclerosis . Ann Neurol 2008 ; 64 : 247 – 54 . Google Scholar CrossRef Search ADS PubMed Fonteijn HM , Modat M , Clarkson MJ , Barnes J , Lehmann M , Hobbs NZ , et al. An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease . Neuroimage 2012 ; 60 : 1880 – 9 . Google Scholar CrossRef Search ADS PubMed Frischer JM , Weigand SD , Guo Y , Kale N , Parisi JE , Pirko I , et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque . Ann Neurol 2015 ; 78 : 710 – 21 . Google Scholar CrossRef Search ADS PubMed Leech R , Sharp DJ . The role of the posterior cingulate cortex in cognition and disease . Brain 2014 ; 137 ( Pt 1 ): 12 – 32 . Google Scholar CrossRef Search ADS PubMed Louapre C , Perlbarg V , Garcia-Lorenzo D , Urbanski M , Benali H , Assouad R , et al. Brain networks disconnection in early multiple sclerosis cognitive deficits: an anatomofunctional study . Hum Brain Mapp 2014 ; 35 : 4706 – 17 . Google Scholar CrossRef Search ADS PubMed Magliozzi R , Howell OW , Nicholas R , Cruciani C , Castellaro M , Romualdi C , et al. Inflammatory intrathecal profiles and cortical damage in multiple sclerosis . Ann Neurol 2018 , in press. [Epub ahead of print]. doi: 10.1002/ana.25197 . Signori A , Izquierdo G , Lugaresi A , Hupperts R , Grand'Maison F , Sola P , et al. Long-term disability trajectories in primary progressive MS patients: a latent class growth analysis . Mult Scler 2017 , in press. [Epub ahead of print]. doi: 10.1177/1352458517703800 . Steenwijk MD , Geurts JJ , Daams M , Tijms BM , Wink AM , Balk LJ , et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant . Brain 2016 ; 139 ( Pt 1 ): 115 – 26 . Google Scholar CrossRef Search ADS PubMed Wijnands JMA , Kingwell E , Zhu F , Zhao Y , Hogg T , Stadnyk K , et al. Health-care use before a first demyelinating event suggestive of a multiple sclerosis prodrome: a matched cohort study . Lancet Neurol 2017 ; 16 : 445 – 51 . Google Scholar CrossRef Search ADS PubMed © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

BrainOxford University Press

Published: May 25, 2018

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