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BACKGROUND AND PURPOSE Leptomeningeal metastases (LMs) carry a poor prognosis. Existing LM scoring systems show limited reproducibility. We assessed the contribution of education level on the reproducibility of LM scoring using structured planning and implementation of new experiments (SPINE), a novel web-based platform. METHODS Stringent radiological definitions of LM and a customized interactive scoring system were implemented in SPINE. Five patients with brain LM and 3 patients with spine, but no brain LM, were selected. Each patient's baseline post-contrast T1-weighted brain MRI was analyzed by three attending neuroradiologists, two neuroradiology fellows, and two radiology residents. Raters identified and characterized all LMs based on: (1) location (cerebrum, cerebellum, brainstem, ventricle, and/or cranial nerves); (2) shape (nodular and/or linear/curvilinear); (3) size (≥ or

Objective Multiple sclerosis (MS) is an autoimmune demyelinating disorder, which is characterized by relapses and remissions. Serum neurofilament light chain (sNfL) is an emerging biomarker of disease activity but its clinical use is still limited. In this study, we aim to characterize the temporal association between sNfL and new clinical relapses and new gadolinium‐enhancing (Gd+) lesions. Methods Annual sNfL levels were measured with a single‐molecule array (SIMOA) assay in 94 patients with MS enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women’s Hospital (CLIMB) study. We used a multivariable linear mixed‐effects model to test the temporal association of sNfL with clinical relapses and/or new Gd+ lesions. We adjusted this model for age, disease duration, sex, and disease‐modifying therapies (DMTs) use. Results In the 3 months after a Gd+ lesion, we observed an average 35% elevation in sNfL (P

Background: Magnetic resonance imaging (MRI) studies of multiple sclerosis–related fatigue had limited reproducibility. Temporal fatigue fluctuations have not been considered. Objective: To investigate whether a novel group allocation that reflects temporal dynamics of fatigue improves our ability to detect fatigue-associated structural brain abnormalities. Methods: Patient stratification based on biennial fatigue assessments: sustained fatigue (SF, n = 29, fatigued at the latest ⩾2 assessments), one time-point fatigue (1F, n = 15, fatigued at the latest, but non-fatigued at the penultimate assessment), reversible fatigue (RF, n = 31, non-fatigued at the latest assessment, but reported fatigue previously), and never fatigued (NF, n = 54). Brain parenchymal fraction (BPF) and T2 lesion volume (T2LV) were compared between these groups and were derived using a conventional, single time-point fatigued versus non-fatigued stratification. Results: The SF versus NF stratification yielded improved power. SF ( p = 0.005) and RF ( p = 0.043) showed significantly higher T2LV than NF. T2LV showed no significant differences in SF versus 1F, SF versus RF, or 1F versus RF. Fatigued versus non-fatigued patients showed significantly higher T2LV ( p = 0.030). We found no significant differences in BPF between the groups. Conclusion: Taking into account temporal fatigue dynamics increases the statistical power with respect to T2LV and may improve characterization of brain pathological correlates of MS-related fatigue.

Background: Subcortical microvascular disease represented by brain white matter hyperintensity on magnetic resonance imaging is associated with functional decline in older people with hypertension. The effects of 2 levels of 24-hour average systolic blood pressure (BP) on mobility, white matter disease progression, and cognitive function over 3 years were studied. Methods: This trial was a prospective, randomized, blinded end-points study in patients ≥75 years of age with systolic hypertension and magnetic resonance imaging evidence of white matter hyperintensity lesions. Patients were randomized to a 24-hour mean systolic BP of ≤130 mm Hg (intensive treatment) versus ≤145 mm Hg (standard treatment) with antihypertensive therapies. Primary study outcomes were changes in mobility (gait speed) and accrual of white matter hyperintensity volume after 3 years. Changes in cognitive function (executive processing) and adverse events were also evaluated. Results: In 199 randomized patients, the mean age of the cohort was 80.5 years, and 54% were women; the average 24-hour systolic BP was 149 mm Hg. Goal BPs were achieved after a median treatment period of 3 to 4 months; at that time, the mean 24-hour systolic BP was 127.7 mm Hg in the intensive treatment group and 144.0 mm Hg in the standard treatment group for an average difference of 16.3 mm Hg. Changes in gait speed were not different between treatment groups (0.40±2.0 versus 0.42±2.7 s in the intensive treatment and standard treatment groups, respectively; P =0.91), whereas changes from baseline in white matter hyperintensity volumes were smaller (0.29%) in the intensive treatment group compared with the standard treatment group (0.48%; P =0.03). Cognitive outcomes also were not different between the treatment groups. Major adverse cardiovascular events were higher in the standard treatment group compared with the intensive treatment group (17 versus 4 patients; P =0.01). Falls, with or without injury, and syncope were comparable in the treatment groups. Conclusions: Intensive lowering of ambulatory BP reduction in older patients with hypertension did not result in differences in mobility outcomes but was associated with a reduction in accrual of subcortical white matter disease. Over periods >3 years, a reduction in the accumulation of white matter disease may be a factor in conserving function. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01650402.

Background Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. Methods FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests. Results Automated analysis methods failed in 0–19% of cases in FFR-processed images versus 0–2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p Conclusions All three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants’ privacy. Key Points • Protecting participants’ privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods.

Background and ObjectivesNeurodegeneration and astrocytic activation are pathologic hallmarks of progressive multiple sclerosis (MS) and can be quantified by serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP). We investigated sNfL and sGFAP as tools for stratifying patients with progressive MS based on progression and disease activity status.MethodsWe leveraged our Comprehensive Longitudinal Investigation of MS at the Brigham and Women's Hospital (CLIMB) natural history study, which includes clinical, MRI data and serum samples collected over more than 20 years. We included patients with MS with a confirmed Expanded Disability Status Scale (EDSS) score ≥3 that corresponds with our classifier for patients at high risk of underlying progressive pathology. We analyzed sNfL and sGFAP within 6 months from the confirmed EDSS score ≥3 corresponding with our baseline visit. Patients who further developed 6-month confirmed disability progression (6mCDP) were classified as progressors. We further stratified our patients into active/nonactive based on new brain/spinal cord lesions or relapses in the 2 years before baseline or during follow-up. Statistical analysis on log-transformed sGFAP/sNfL assessed the baseline association with demographic, clinical, and MRI features and associations with future disability.ResultsWe included 257 patients with MS who had an average EDSS score of 4.0 and a median follow-up after baseline of 7.6 years. sNfL was higher in patients with disease activity in the 2 years before baseline (adjusted β = 1.21; 95% CI 1.04–1.42;p= 0.016), during the first 2 years of follow-up (adjusted β = 1.17; 95% CI = 1.01–1.36;p= 0.042). sGFAP was not increased in the presence of disease activity. Higher sGFAP levels, but not sNfL levels, were associated with higher risk of 6mCDP (adjusted hazard ratio [HR] = 1.71; 95% CI = 1.19–2.45;p= 0.004). The association was stronger in patients with low sNfL (adjusted HR = 2.44; 95% CI 1.32–4.52;p= 0.005) and patients who were nonactive in the 2 years prior or after the sample.DiscussionHigher levels of sGFAP correlated with subsequent progression, particularly in nonactive patients, whereas sNfL reflected acute disease activity in patients with MS at high risk of underlying progressive pathology. Thus, sGFAP and sNfL levels may be used to stratify patients with progressive MS for clinical research studies and clinical trials and may inform clinical care.

The original version of this Article contained an error in Fig. 2, in which the plot shown in panel c was inadvertently duplicated in panel d. The correct version of Fig. 2 is: (Figure presented.) which replaces the previous incorrect version: (Figure presented.).

International audience; Background and purposeTo determine the precise incidence of lesions at sites of high Aquaporin-4 expression (hAQP4) and their possible association with known neuromyelitis optica spectrum disease (NMOSD) lesions patterns.Materials and methodsA retrospective analysis of brain and, when available, spinal cord MRI scans of 54 NMOSD patients recruited among the French NMOSD cohort was performed. Brain lesions were annotated as MS-like, non-specific, or evocative of NMOSD. The topography of hAQP4 was reassessed by human brain atlas. The incidence of lesions in hAQP4 and their association with lesions evocative of NMOSD was estimated.ResultsAmong those included (41/54 female, mean age: 45 years) 47/54 (87%) presented brain lesions. Twenty-six/47 (55%) had lesions in hAQP4. Thirty-two/54 patients (60%) had lesions considered evocative of NMOSD. The majority of them also presented lesions in hAQP4 (65%, 21/32). Patients with lesions in hAQP4 and lesions evocative of NMOSD demonstrated more extensive myelitis compared to the other patients (7 [6–10] versus 4 [3–5] vertebral segments, P = 0.009).ConclusionThe coexistence of lesions evocative of NMOSD and in hAQP4 is associated with significantly more extensive myelitis, and might have pathophysiological and clinical significance.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials., Multiple sclerosis is a heterogeneous progressive disease. Here, the authors use an unsupervised machine learning algorithm to determine multiple sclerosis subtypes, progression, and response to potential therapeutic treatments based on neuroimaging data.

BackgroundAlthough fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS), its pathogenesis is not well understood. Neurogenic, inflammatory, endocrine, and metabolic mechanisms have been proposed. Taking into account the temporal dynamics and comorbid mood symptoms of fatigue may help differentiate fatigue phenotypes. These phenotypes may reflect different pathogeneses and may respond to different mechanism-specific treatments. Although several tools have been developed to assess various symptoms (including fatigue), monitor clinical status, or improve the perceived level of fatigue in patients with MS, options for a detailed, real-time assessment of MS-related fatigue and relevant comorbidities are still limited.ObjectiveThis study aims to present a novel mobile app specifically designed to differentiate fatigue phenotypes using circadian symptom monitoring and state-of-the-art characterization of MS-related fatigue and its related symptoms. We also aim to report the first findings regarding patient compliance and the relationship between compliance and patient characteristics, including MS disease severity.MethodsAfter developing the app, we used it in a prospective study designed to investigate the brain magnetic resonance imaging correlates of MS-related fatigue. In total, 64 patients with MS were recruited into this study and asked to use the app over a 2-week period. The app features the following modules: Visual Analogue Scales (VASs) to assess circadian changes in fatigue, depression, anxiety, and pain; daily sleep diaries (SLDs) to assess sleep habits and quality; and 10 one-time questionnaires to assess fatigue, depression, anxiety, sleepiness, physical activity, and motivation, as well as several other one-time questionnaires that were created to assess those relevant aspects of fatigue that were not captured by existing fatigue questionnaires. The app prompts subjects to assess their symptoms multiple times a day and enables real-time symptom monitoring through a web-accessible portal.ResultsOf 64 patients, 56 (88%) used the app, of which 51 (91%) completed all one-time questionnaires and 47 (84%) completed all one-time questionnaires, VASs, and SLDs. Patients reported no issues with the usage of the app, and there were no technical issues with our web-based data collection system. The relapsing-remitting MS to secondary-progressive MS ratio was significantly higher in patients who completed all one-time questionnaires, VASs, and SLDs than in those who completed all one-time questionnaires but not all VASs and SLDs (P=.01). No other significant differences in demographics, fatigue, or disease severity were observed between the degrees of compliance.ConclusionsThe app can be used with reasonable compliance across patients with relapsing-remitting and secondary-progressive MS irrespective of demographics, fatigue, or disease severity.

Editor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is NewBackgroundPostoperative delirium and postoperative cognitive dysfunction share risk factors and may co-occur, but their relationship is not well established. The primary goals of this study were to describe the prevalence of postoperative cognitive dysfunction and to investigate its association with in-hospital delirium. The authors hypothesized that delirium would be a significant risk factor for postoperative cognitive dysfunction during follow-up.MethodsThis study used data from an observational study of cognitive outcomes after major noncardiac surgery, the Successful Aging after Elective Surgery study. Postoperative delirium was evaluated each hospital day with confusion assessment method–based interviews supplemented by chart reviews. Postoperative cognitive dysfunction was determined using methods adapted from the International Study of Postoperative Cognitive Dysfunction. Associations between delirium and postoperative cognitive dysfunction were examined at 1, 2, and 6 months.ResultsOne hundred thirty-four of 560 participants (24%) developed delirium during hospitalization. Slightly fewer than half (47%, 256 of 548) met the International Study of Postoperative Cognitive Dysfunction-defined threshold for postoperative cognitive dysfunction at 1 month, but this proportion decreased at 2 months (23%, 123 of 536) and 6 months (16%, 85 of 528). At each follow-up, the level of agreement between delirium and postoperative cognitive dysfunction was poor (kappa less than .08) and correlations were small (r less than .16). The relative risk of postoperative cognitive dysfunction was significantly elevated for patients with a history of postoperative delirium at 1 month (relative risk = 1.34; 95% CI, 1.07–1.67), but not 2 months (relative risk = 1.08; 95% CI, 0.72–1.64), or 6 months (relative risk = 1.21; 95% CI, 0.71–2.09).ConclusionsDelirium significantly increased the risk of postoperative cognitive dysfunction in the first postoperative month; this relationship did not hold in longer-term follow-up. At each evaluation, postoperative cognitive dysfunction was more common among patients without delirium. Postoperative delirium and postoperative cognitive dysfunction may be distinct manifestations of perioperative neurocognitive deficits.

Background: Fatigue in multiple sclerosis (MS) has been inconsistently associated with disruption of specific brain circuitries. Temporal fluctuations of fatigue have not been considered. Objective: The aim of this study was to investigate the association of fatigue with brain diffusion abnormalities, using robust criteria for patient stratification based on longitudinal patterns of fatigue. Methods: Patient stratification: (1) sustained fatigue (SF, n = 26): latest two Modified Fatigue Impact Scale (MFIS) ⩾ 38; (2) reversible fatigue (RF, n = 25): latest MFIS < 38 and minimum one previous MFIS ⩾ 38; and (3) never fatigued (NF, n = 42): MFIS always < 38 (five assessments minimum). 3T brain magnetic resonance imaging (MRI) was used to perform voxel-wise comparison of fractional anisotropy (FA) between the groups controlling for age, sex, disease duration, physical disability, white matter lesion load (T2LV), and depression. Results: SF and, to a lesser extent, RF patients showed lower FA in multiple brain regions compared to NF patients, independent of age, sex, disease duration, and physical disability. In cingulo-postcommissural-striato-thalamic regions, the differences in FA between SF and NF (but not between RF and NF or SF) patients were independent of T2LV, and in ventromedial prefronto-precommissuro-striatal and temporo-insular areas, independent of T2LV and depression. Conclusion: Damage to ventromedial prefronto-precommissuro-striatal and temporo-insular pathways appears to be a specific substrate of SF in MS.

Background Subcortical small vessel disease, represented as white matter hyperintensity (WMH) on magnetic resonance images (MRI) is associated with functional decline in older people with hypertension. We evaluated the relationships of clinic and out-of-office blood pressures (BP) with WMH and functional status in older persons. Methods Using cross-sectional data from 199 older study participants enrolled in the INFINITY trial, we analyzed the clinic, 24-hour ambulatory, and home BPs and their relationships with WMH burden and mobility and cognitive outcomes. Results Volume of WMH was associated with clinic and 24-hour ambulatory systolic BP but not home systolic BP. The mobility measure, supine-to-sit time, had a significant association with 24-hour systolic BP and pulse pressure but not with diastolic BP or values obtained by home BP. Cognitive measures of processing speed (Trails Making Test Part A and the Stroop Word Test) were significantly associated with 24-hour systolic BP, but not clinic and home BPs. Conclusion These data demonstrate that ambulatory BP measurements in older people are more strongly associated with WMH and certain measures of functional status compared to home BP measurements. Hence, home BP may not be a useful substitute for ambulatory BP for assessing subcortical small vessel disease and its consequences. Further longitudinal analyses comparing clinic and various types of out-of-office BP measures with small vessel brain disease are needed. Clinicaltrials.gov identifier: NCT01650402.

Background Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. Objectives This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). Methods A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. Results All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. Conclusions The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.

BACKGROUND AND PURPOSE Lower reward responsiveness has been associated with fatigue in multiple sclerosis (MS). However, association of MS-related fatigue with damage to the mesocorticolimbic reward pathway (superolateral medial forebrain bundle [slMFB]) has not been assessed. We investigated the association of fatigue and depression with slMFB damage in MS patients stratified based on longitudinal fatigue patterns. METHODS Patient stratification: 1. Sustained Fatigue (SF): latest two Modified Fatigue Impact Scale (MFIS) ≥ 38 (n = 26); 2. Reversible Fatigue (RF): latest MFIS < 38, and at least one previous MFIS ≥ 38 (n = 25); 3. Never Fatigued (NF): ≥ 5 consecutive MFIS < 38 (n = 42); 4. Healthy Controls (n = 6). Diffusion MRI-derived measures of fractional anisotropy (FA), axial (AD), mean (MD), and radial diffusivity (RD) of the slMFB were compared between the groups. Depression was assessed using the Center for Epidemiologic Studies-Depression Scale (CES-D). RESULTS Depressed (CES-D ≥ 16) SF patients showed significantly higher MD and RD than nondepressed SF and RF, and depressed RF patients, and significantly lower FA than nondepressed SF and depressed RF patients in their left slMFB. Depressed SF patients showed significantly higher left slMFB MD and AD than healthy controls. CONCLUSION Microstructural changes to the left slMFB may play a role in the comorbid development of fatigue and depression in MS.

Background and purpose Serum neurofilament light (sNfL) has been studied as a biomarker of disease activity in multiple sclerosis (MS). Several factors, including age, can influence its dynamics, and several studies have shown that sNfL increases with age in controls. Our objective was to explore the relationship of sNfL and age at different MS disease stages, including remission and after a gadolinium-enhancing (Gad+) lesion. Methods We included 94 patients with MS with annual sNfL measurements performed with a single-molecule array assay. We used multivariable linear mixed-effects models with random intercept to test the association between age and sNfL during remission and after a Gad+ lesion (ie, within 90 days after the Gad+ lesion). The model was adjusted for medication use and sex. Results We report a positive association between sNfL level and age during remission (adjusted estimate = 1.18% yearly increase, 95% CI = .34-2.03%, P = .008). In contrast, a negative interaction between age and Gad+ lesion status was observed (adjusted estimate = -1.73%, 95% CI = -2.85 to -.58%, P = .004). Conclusion We propose that younger patients experience a greater elevation in sNfL than older patients in response to Gad+ lesions. Our study provides potential insights into the effects of aging on neuroinflammation in MS.

Background: The amyloid cascade hypothesis characterizes the stereotyped progression of pathological changes in Alzheimer’s disease (AD) beginning with beta amyloid deposition, but does not address the reasons for amyloid deposition. Brain areas with relatively higher neuronal activity, metabolic demand, and production of reactive oxygen species in earlier life may have higher beta amyloid deposition in later life. The aim of this study was to investigate early life patterns of perfusion and late life patterns of amyloid deposition to determine the extent to which normative cerebral perfusion predisposes specific regions to future beta amyloid deposition. Materials and Methods: One hundred twenty-eight healthy, older human subjects (age: 56–87 years old; 44% women) underwent positron emission tomography (PET) imaging with [ 11 C]PiB for measures of amyloid burden. Cerebral perfusion maps derived from 47 healthy younger adults (age: 22–49; 47%) who had undergone single photon emission computed tomography (SPECT) imaging, were averaged to create a normative template, repre- sentative of young, healthy adults. Perfusion and amyloid measures were investigated in 31 cortical regions from the Hammers atlas. We examined the spatial relationship between normative perfusion patterns and amyloid pathophysiology. Results: The pattern of increasing perfusion (temporal lobe < parietal lobe < frontal lobe < insula/cingulate gyrus < occipital lobe; F(4,26) = 7.8, p = 0.0003) in young, healthy adults was not exactly identical to but approximated the pattern of increasing amyloid burden (temporal lobe < occipital lobe < frontal lobe < parietal lobe < insula/cingulate gyrus; F(4,26) = 5.0, p = 0.004) in older adults. However, investigating subregions within cortical lobes provided consistent agreement between ranked normative perfusion patterns and expected Thal staging of amyloid progression in AD (Spearman r = 0.39, p = 0.03). Conclusion: Our findings suggest that brain areas with normatively greater perfusion may be more susceptible to amyloid deposition in later life, possibly due to higher metabolic demand, and associated levels of oxidative stress and inflammation.

Multiple sclerosis (MS) is subdivided into four phenotypes on the basis of medical history and clinical symptoms. These phenotypes are defined retrospectively and lack clear pathobiological underpinning. Since Magnetic Resonance Imaging (MRI) better reflects disease pathology than clinical symptoms, we aimed to explore MRI-driven subtypes of MS based on pathological changes visible on MRI using unsupervised machine learning. In separate train and external validation sets we looked at a total of 21,170 patient-years of data from 15 randomised controlled trials and three observational cohorts to explore MRI-driven subtypes and test whether these subtypes had differential clinical outcomes. We processed MRI data to obtain measures of brain volumes, lesion volumes, and normal appearing white matter T1/T2. We identified three MRI-driven subtypes who were similar in how they accumulated MRI abnormality. Based on the earliest abnormalities suggested by our model they were called: cortex-led, normal appearing white matter-led, and lesion-led subtypes. In the external validation datasets, the lesion-led subtype showed a faster disability progression and higher disease activity than the cortex-led subtype. In all datasets, MRI-driven subtypes were associated with disability progression (βSubtype=0.04, p=0.02; βStage=-0.06, p

Background and purpose A pipeline for fully automated segmentation of 3T brain MRI scans in multiple sclerosis (MS) is presented. This 3T morphometry (3TM) pipeline provides indicators of MS disease progression from multichannel datasets with high-resolution 3-dimensional T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery (FLAIR) contrast. 3TM segments white (WM) and gray matter (GM) and cerebrospinal fluid (CSF) to assess atrophy and provides WM lesion (WML) volume. Methods To address nonuniform distribution of noise/contrast (eg, posterior fossa in 3D-FLAIR) of 3T magnetic resonance imaging, the method employs dual sensitivity (different sensitivities for lesion detection in predefined regions). We tested this approach by assigning different sensitivities to supratentorial and infratentorial regions, and validated the segmentation for accuracy against manual delineation, and for precision in scan-rescans. Results Intraclass correlation coefficients of .95, .91, and .86 were observed for WML and CSF segmentation accuracy and brain parenchymal fraction (BPF). Dual sensitivity significantly reduced infratentorial false-positive WMLs, affording increases in global sensitivity without decreasing specificity. Scan-rescan yielded coefficients of variation (COVs) of 8% and .4% for WMLs and BPF and COVs of .8%, 1%, and 2% for GM, WM, and CSF volumes. WML volume difference/precision was .49 ± .72 mL over a range of 0-24 mL. Correlation between BPF and age was r = .62 (P = .0004), and effect size for detecting brain atrophy was Cohen's d = 1.26 (standardized mean difference vs. healthy controls). Conclusions This pipeline produces probability maps for brain lesions and tissue classes, facilitating expert review/correction and may provide high throughput, efficient characterization of MS in large datasets.

Background Alcohol and in particular red wine have both immunomodulatory and neuroprotective properties, and may exert an effect on the disease course of multiple sclerosis (MS). Objective To assess the association between alcohol and red wine consumption and MS course. Methods MS patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) who completed a self-administered questionnaire about their past year drinking habits at a single time point were included in the study. Alcohol and red wine consumption were measured as servings/week. The primary outcome was the Expanded Disability Status Scale (EDSS) at the time of the questionnaire. Secondary clinical outcomes were the Multiple Sclerosis Severity Score (MSSS) and number of relapses in the year before the questionnaire. Secondary MRI outcomes included brain parenchymal fraction and T2 hyperintense lesion volume (T2LV). Appropriate regression models were used to test the association of alcohol and red wine intake on clinical and MRI outcomes. All analyses were controlled for sex, age, body mass index, disease phenotype (relapsing vs. progressive), the proportion of time on disease modifying therapy during the previous year, smoking exposure, and disease duration. In the models for the MRI outcomes, analyses were also adjusted for acquisition protocol. Results 923 patients (74% females, mean age 47 ± 11 years, mean disease duration 14 ± 9 years) were included in the analysis. Compared to abstainers, patients drinking more than 4 drinks per week had a higher likelihood of a lower EDSS score (OR, 0.41; p = 0.0001) and lower MSSS (mean difference, − 1.753; p = 0.002) at the time of the questionnaire. Similarly, patients drinking more than 3 glasses of red wine per week had greater odds of a lower EDSS (OR, 0.49; p = 0.0005) and lower MSSS (mean difference, − 0.705; p = 0.0007) compared to nondrinkers. However, a faster increase in T2LV was observed in patients consuming 1–3 glasses of red wine per week compared to nondrinkers. Conclusions Higher total alcohol and red wine intake were associated with a lower cross-sectional level of neurologic disability in MS patients but increased T2LV accumulation. Further studies should explore a potential cause-effect neuroprotective relationship, as well as the underlying biological mechanisms.

Background: Reports on the relationships between white matter lesion load (WMLL) and fatigue and anxiety in multiple sclerosis (MS) are inconsistent. Objective: To investigate the association of total and tract-specific WMLL with fatigue and anxiety. Methods: Total and regional T2 WMLL was assessed for 19 tracts in 48 MS patients (30 females). ICBM-DTI-81 Atlas-based parcellation was combined with WMLL segmentation of T2-weighted magnetic resonance imaging (MRI). Fatigue, anxiety, and depression were assessed using Fatigue Impact Scale, State Trait Anxiety Inventory, and Beck Depression Inventory, respectively. Results: Fatigue, anxiety, and depression showed significant inter-correlation. We found no association between fatigue and total or regional WMLLs, whereas anxiety was associated with total and regional WMLLs in nine tracts. After adjusting for total WMLL, age, and depression, only the column and body of the fornix (CBF) remained significantly associated with anxiety. Post hoc analyses showed no CBF lesions on T1-weighted MRI and suggested, but could not confirm, that the septum pellucidum might play a role in the pathogenesis of anxiety. Conclusion: Our results suggest that anxiety in MS patients may have a neuropathological substrate in the septo-fornical area, which requires further validation using larger sample size and ultra-high-field MRI in targeted prospective studies.

BACKGROUND AND PURPOSE Two common approaches for measuring disease severity in multiple sclerosis (MS) are the clinical exam and brain magnetic resonance imaging (MRI) scan. Although most patients show similar disease severity on both measures, some patients have clinical/MRI dissociation. METHODS Subjects from a comprehensive care MS center who had a concurrent brain MRI, spinal cord MRI, clinical examination, and patient reported outcomes were classified into three groups based on the Expanded Disability Status Scale (EDSS) and cerebral T2 hyperintense lesion volume (T2LV). The first group was the low lesion load/high disability group (LL/HD) with T2LV 6 ml and EDSS ≤ 1.5. All remaining subjects were classified as not dissociated. The three groups were compared using regression techniques for unadjusted analyses and to adjust for age, disease duration, and gender. RESULTS Twenty-two subjects were classified as LL/HD (4.1%; 95% CI: 2.6%, 6.2%), and 50 subjects were classified as HL/LD (9.4%; 95% CI: 7.0%, 12.2%). Subjects in the LL/HD group were more likely to have a progressive form of MS and had significantly lower physical quality of life in adjusted and unadjusted analysis. Subjects in HL/LD had significantly more gadolinium-enhancing lesions, and subjects in the LL/HD group had significantly more cervical spinal cord lesions. CONCLUSIONS Our results indicate that dissociation may occur between physical disability and cerebral lesion volume in either direction in patients with MS. Type of MS, brain atrophy, and spinal cord lesions may help to bridge this dissociation.

Introduction The pathogenesis of multiple sclerosis (MS)-related fatigue is multi-factorial, including neurogenic, inflammatory, endocrine, metabolic, mood, as well as sleep disorder-related mechanisms. The confounding effect of sleep disorders on the association between fatigue and neurodegenerative changes in the brain has not been investigated. Our objectives were to assess the prevalence of sleep apnea and periodic limb movements in the framework of a prospective study which investigates the neurogenic causation of treatment-resistant fatigue in MS. Methods MS patients enrolled in a National MS Society-funded prospective study (grant identifier RG-1501-03141) underwent a one-night at-home sleep test (HST) using a NOX T3 portable monitor. HST recordings were scored by a registered polysomnographic technologist. Respiratory Event (REI) and Periodic Limb Movement (PLMI) Indices were calculated for each patient. Results Out of 36 patients, 7 (20%) had mild (REI=5-14), 1 (3%) had moderate (REI=15-29), and 1 (3%) had severe sleep-disordered breathing (REI≥30). Fourteen (42%) of the patients had mild (PLMI=5-24), 4 (11%) had moderate (PLMI=25-49), and 7 (19%) had severe periodic limb movements (PLMI≥50). Overall, 81% of the patients had at least mild sleep-disordered breathing and/or periodic limb movements. Conclusion Sleep abnormalities (i.e., sleep apnea and periodic limb movements) are highly prevalent in patients with MS. We plan to compare the MRI exams of subgroups of MS patients with fatigue, to test the hypothesis that fronto-striatal circuitry is more affected by lesions in patients without sleep apnea compared to those with sleep apnea. Support This investigation was supported by a grant from the National Multiple Sclerosis Society (grant identifier RG-1501-03141). The home sleep test equipment was provided by a DURIP grant from the Office of Naval Research (grant N00014-15-1-2917).

Fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS). Despite its clinical significance, the aetiology and pathophysiology of MS-related fatigue are not well understood. Current evidence and understanding of the neuroanatomical underpinnings of MS-related fatigue are reviewed in this article. The aims of this paper are to (1) review the findings of previous structural neuroimaging studies on MS-related fatigue and summarize consistent findings regarding brain circuitry associated with fatigue in MS, (2) contextualize these findings with the neurochemistry of the relevant circuits and (3) discuss future perspectives with regard to impact on fatigue management of MS patients and methodological challenges towards improved understanding of fatigue pathogenesis. The detailed understanding of the neuroanatomical underpinnings of fatigue might contribute to the identification of novel treatment targets and factors determining treatment resistance to drugs used in current clinical practice.

Fatigue in multiple sclerosis (MS) has been associated with brain damage with low replicability. Temporal fatigue fluctuations have not been considered. We assessed whether sustained fatigue (SF) associates more strongly with grey matter (GM) changes than reversible fatigue (RF). Patients were stratified into three groups according to historical fatigue levels: SF (n = 30, i.e. patients who reported fatigue at the latest ≥2 assessments), RF (n = 31, i.e. patients not fatigued at the latest assessment, but reported fatigue previously), and never fatigued (NF, n = 37). Groups were compared for brain GM volume using cross-sectional voxel-based and volumetric analyses of 3T T1-weighted MRI. Confounding effects of depression and related medications were also investigated. SF and RF patients showed similar anatomical distribution of GM atrophy. While we robustly replicated the anatomical patterns of GM atrophy described in previous work, we also found an association between hippocampal atrophy and fatigue. Depression showed confounding effects in frontal, parietal, occipital, accumbal and thalamic regions. Assessed treatments showed confounding effects in frontal, parietal and striatal areas. Our results suggest that history of clinically-relevant fatigue in currently non-fatigued patients is associated with GM atrophy, potentially explaining inconsistent findings of previous studies that stratified patients using a single fatigue assessment.

Background: It is unclear whether fatigue is a consequence or a predictive trait of disease worsening. Objective: To investigate the predictive value of fatigue toward conversion to confirmed moderate–severe disability in patients with relapsing-remitting multiple sclerosis (RRMS). Methods: We retrospectively selected from the Comprehensive Longitudinal Investigations in MS at the Brigham and Women’s Hospital (CLIMB) study cohort RRMS patients who converted to confirmed (⩾2 years) Expanded Disability Status Scale (EDSS) score ⩾3 within a follow-up period ⩾3 years. We contrasted the Modified Fatigue Impact Scale (MFIS) score of 33 converters, obtained at least 1 year before conversion to EDSS ⩾3, with that of 33 non-converter RRMS patients matched for baseline characteristics. Results: Total MFIS score was higher in converter versus non-converter MS patients (median 37 vs 13; p Conclusion: Fatigue is a promising indicator of risk for conversion to confirmed moderate–severe disability in RRMS patients.

Despite the significant impact of postoperative delirium on surgical outcomes and the long-term prognosis of older patients, its neural basis has not yet been clarified. In this study we investigated the impact of premorbid brain microstructural integrity, as measured by diffusion tensor imaging before surgery, on postoperative delirium incidence and severity, as well as the relationship among presurgical cognitive performance, diffusion tensor imaging abnormalities and postoperative delirium. Presurgical diffusion tensor imaging scans of 136 older (≥70 years), dementia-free subjects from the prospective Successful Aging after Elective Surgery study were analysed blind to the clinical data and delirium status. Primary outcomes were postoperative delirium incidence and severity during the hospital stay, as assessed by the Confusion Assessment Method. We measured cognition before surgery using general cognitive performance, a composite score based on a battery of neuropsychological tests. We investigated the association between presurgical diffusion tensor imaging parameters of brain microstructural integrity (i.e. fractional anisotropy, axial, mean and radial diffusivity) with postoperative delirium incidence and severity. Analyses were adjusted for the following potential confounders: age, gender, vascular comorbidity status, and general cognitive performance. Postoperative delirium occurred in 29 of 136 subjects (21%) during hospitalization. Presurgical diffusion tensor imaging abnormalities of the cerebellum, cingulum, corpus callosum, internal capsule, thalamus, basal forebrain, occipital, parietal and temporal lobes, including the hippocampus, were associated with delirium incidence and severity, after controlling for age, gender and vascular comorbidities. After further controlling for general cognitive performance, diffusion tensor imaging abnormalities of the cerebellum, hippocampus, thalamus and basal forebrain still remained associated with delirium incidence and severity. This study raises the intriguing possibility that structural dysconnectivity involving interhemispheric and fronto-thalamo-cerebellar networks, as well as microstructural changes of structures involved in limbic and memory functions predispose to delirium under the stress of surgery. While the diffusion tensor imaging abnormalities observed in the corpus callosum, cingulum, and temporal lobe likely constitute the neural substrate for the association between premorbid cognition, as measured by general cognitive performance, and postoperative delirium, the microstructural changes observed in the cerebellum, hippocampus, thalamus and basal forebrain seem to constitute a separate phenomenon that predisposes to postsurgical delirium independent of presurgical cognitive status.

Background and Purpose— On top of functional outcome, accurate prediction of cognitive outcome for stroke patients is an unmet need with major implications for clinical management. We investigated whether stroke location may contribute independent prognostic value to multifactorial predictive models of functional and cognitive outcomes. Methods— Four hundred twenty-eight consecutive patients with ischemic stroke were prospectively assessed with magnetic resonance imaging at 24 to 72 hours and at 3 months for functional outcome using the modified Rankin Scale and cognitive outcome using the Montreal Cognitive Assessment (MoCA). Statistical maps of functional and cognitive eloquent regions were derived from the first 215 patients (development sample) using voxel-based lesion-symptom mapping. We used multivariate logistic regression models to study the influence of stroke location (number of eloquent voxels from voxel-based lesion-symptom mapping maps), age, initial National Institutes of Health Stroke Scale and stroke volume on modified Rankin Scale and MoCA. The second part of our cohort was used as an independent replication sample. Results— In univariate analyses, stroke location, age, initial National Institutes of Health Stroke Scale, and stroke volume were all predictive of poor modified Rankin Scale and MoCA. In multivariable analyses, stroke location remained the strongest independent predictor of MoCA and significantly improved the prediction compared with using only age, initial National Institutes of Health Stroke Scale, and stroke volume (area under the curve increased from 0.697–0.771; difference=0.073; 95% confidence interval, 0.008–0.155). In contrast, stroke location did not persist as independent predictor of modified Rankin Scale that was mainly driven by initial National Institutes of Health Stroke Scale (area under the curve going from 0.840 to 0.835). Similar results were obtained in the replication sample. Conclusions— Stroke location is an independent predictor of cognitive outcome (MoCA) at 3 months post stroke.

We propose an unsupervised spatio-temporal image filtering method based on mean-shift.We adapt the spatial and feature range domains to handle temporal evolution.A constraint is added on the sample's evolution to select temporal neighbors.Our method outperforms standard mean-shift by adequately considering time information.Effectiveness is demonstrated on both synthetic and real brain MRI time-series data. A new spatio-temporal filtering scheme based on the mean-shift procedure, which computes unsupervised spatio-temporal filtering for univariate feature evolution, is proposed in this paper. Our main contributions are on one hand the modification of the spatial/range domains to appropriately integrate the temporal feature into the mean-shift iterative form and on the other hand the addition of a trajectory constraint in the feature space with the use of the infinity norm. Therefore, only the samples living the same life in the feature space will converge. Major assets of the standard mean-shift framework such as convergence and bandwidth parameters adjustment are preserved. In this paper, we study the relative importance of the bandwidth parameters and the efficiency of the proposed method is assessed on synthetic data and compared to the standard mean-shift framework for spatio-temporal data filtering. The obtained results have allowed us to undertake a first study on real data, which has led to encouraging results in identifying spatio-temporal evolution of multiple sclerosis lesions appearing on T2-weighted MR images.

Automated segmentation of brain MRI scans into tissue classes is commonly used for the assessment of multiple sclerosis (MS). However, manual correction of the resulting brain tissue label maps by an expert reader remains necessary in many cases. Since automated segmentation data awaiting manual correction are "missing", we proposed to use multiple imputation (MI) to fill-in the missing manually-corrected MRI data for measures of normalized whole brain volume (brain parenchymal fraction-BPF) and T2 hyperintense lesion volume (T2LV). Automated and manually corrected MRI measures from 1300 patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) were identified. Simulation studies were conducted to assess the performance of MI with missing data both missing completely at random and missing at random. An imputation model including the concurrent automated data as well as clinical and demographic variables explained a high proportion of the variance in the manually corrected BPF (R(2)=0.97) and T2LV (R(2)=0.89), demonstrating the potential to accurately impute the missing data. Further, our results demonstrate that MI allows for the accurate estimation of group differences with little to no bias and with similar precision compared to an analysis with no missing data. We believe that our findings provide important insights for efficient correction of automated MRI measures to obviate the need to perform manual correction on all cases.

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French “Investissements d’Avenir” program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the “Agence nationale de la recherche”, within the framework of the “Investissements d’Avenir” program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French “Investissements d’Avenir” program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images

We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.

Background and purpose The aim of this prospective study is to investigate and evaluate in clinical practice the diagnostic impact of 3DFLAIR in regards to 2DT2/PD in terms of infratentorial lesions detection in multiple sclerosis (MS). Material and methods 164 MS patients from the OFSEP database were reviewed retrospectively. MR examinations were performed on 1.5T or 3T systems from four different centers. Infratentorial lesions were counted and allocated to different regions of the posterior fossa by three raters independently (junior resident, resident with an expertise in neuroradiology, and senior neuro-radiologist) on the 3DFLAIR and 2DT2/PD. Both sequences do not have the same spatial resolution but reflect what is recommended by most of the consensus and done in clinical practice. Results With an overall number of 528 for Rater-1 and 798 for Rater-2 infratentorial lesions, 3DFLAIR had a significantly higher number of lesions detected than 2DT2/PD (303 for Rater-1 and 370 for Rater-2). The prevalence of trigeminal lesions detected by using 3DFLAIR was also significantly higher than 2DT2/PD. ROC analysis showed 3DFLAIR to be more specific and sensitive than 2DT2/PD. An overall difference between all three Raters has been observed. The more the Rater is experienced the more lesions he detects. Conclusion Along with the radiologist ability to detect lesions based on his level of experience, the OFSEP optimized 3DFLAIR can significantly improve infratentorial lesion detection in MS compared to 2DT2/PD. This is important in MS follow-up that takes into account new lesions number to adapt patients’ treatment.

Background: Several magnetic resonance imaging (MRI) studies investigated the evolution of multiple sclerosis (MS) lesions to understand the pathophysiological mechanisms leading to blood-brain barrier breakdown and lesion formation. Only a few assessed the early natural history of MS lesions using short-interval longitudinal MRI. Objective: The purpose of this study was to characterize MS lesion occurrence and early evolution on high-resolution MRI acquired at weekly intervals. Methods: Active lesions were characterized on 3D fluid attenuation inversion recovery (FLAIR) and gadolinium-enhanced 3D T1-weighted MRI performed weekly (seven weeks) on five untreated patients with relapsing–remitting MS (RRMS). Results: Active lesions ( n=212) were detected in all patients. All showed contrast-enhancement on at least one time-point. Most new lesions (83.5%) were visible on FLAIR and post-contrast T1-weighted images at first detection; 11.2% showed activity on FLAIR images, one or more weeks before the appearance of contrast-enhancement; 12.5% enhanced before being apparent on FLAIR. Conclusion: Blood brain barrier disruption is a constant step in the natural history of active MS lesions, but does not always constitute the initial event. These findings are consistent with the existence of a subpopulation of lesions with an ‘inside-out’ genesis, where neurodegenerative processes might precede microglial activation, and a subsequent adaptive immune response.

Several potential vascular risk factors exist for the development and accumulation of subcortical white matter disease in older people. We have reported that in older people followed for up to 4 years white matter hyperintensity (WMH) lesions on magnetic resonance imaging nearly doubled in volume and were associated with alterations in mobility and cognitive function. Herein we review the genetic, metabolic, and vascular risk factors that have been evaluated in association with the development and pathogenesis of WMH in older persons. Our research efforts have focused on systemic hypertension, particularly in the out-of-office setting as 24-hour ambulatory blood pressure (BP) has proven to be a stronger indicator of the progression of WMH in older people and the associated functional decline than doctor’s office BP. Based on relations between 24-hour systolic BP levels, the accrual of WMH, and functional decline, we have designed the INFINITY trial, the first interventional study to use ambulatory BP to guide antihypertensive therapy to address this problem in the geriatric population.

Background:Intracranial volume (ICV) has been proposed as a measure of maximum lifetime brain size. Accurate ICV measures require neuroimaging which is not always feasible for epidemiologic investigations. We examined head circumference as a useful surrogate for ICV in older adults.Methods:99 older adults underwent Magnetic Resonance Imaging (MRI). ICV was measured by Statistical Parametric Mapping 8 (SPM8) software or Functional MRI of the Brain Software Library (FSL) extraction with manual editing, typically considered the gold standard. Head circumferences were determined using standardized tape measurement. We examined estimated correlation coefficients between head circumference and the two MRI-based ICV measurements.Results:Head circumference and ICV by SPM8 were moderately correlated (overall r = 0.73, men r = 0.67, women r = 0.63). Head circumference and ICV by FSL were also moderately correlated (overall r = 0.69, men r = 0.63, women r = 0.49).Conclusions:Head circumference measurement was strongly correlated with MRI-derived ICV. Our study presents a simple method to approximate ICV among older patients, which may prove useful as a surrogate for cognitive reserve in large scale epidemiologic studies of cognitive outcomes. This study also suggests the stability of head circumference correlation with ICV throughout the lifespan.

Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately., Highlights • A linear mixed-effect model is useful in modeling longitudinal neuroimaging data. • A model including baseline age and the longitudinal change with age is preferable. • Protocol specific residual variance is important in modeling brain atrophy in MS. • The inclusion of MRI protocol in modeling brain atrophy and lesion load is warranted.

The objective of this study was to test a new version of the Magnetic Resonance Disease Severity Scale (MRDSS2), incorporating cerebral gray matter (GM) and spinal cord involvement from 3 T MRI, in modeling the relationship between MRI and physical disability or cognitive status in multiple sclerosis (MS). Fifty-five MS patients and 30 normal controls underwent high-resolution 3 T MRI. The patients had an Expanded Disability Status Scale score of 1.6±1.7 (mean±SD). The cerebral normalized GM fraction (GMF), the T2 lesion volume (T2LV), and the ratio of T1 hypointense LV to T2LV (T1/T2) were derived from brain images. Upper cervical spinal cord area (UCCA) was obtained from spinal cord images. A within-subject d-score (difference of MS from normal control) for each MRI component was calculated, equally weighted, and summed to form MRDSS2. With regard to the relationship between physical disability and MRDSS2 or its individual components, MRI–Expanded Disability Status Scale correlations were significant for MRDSS2 (r=0.33, P=0.013) and UCCA (r=�0.33, P=0.015), but not for GMF (P=0.198), T2LV (P=0.707), and T1/T2 (P=0.240). The inclusion of UCCA appeared to drive this MRI–disability relationship in MRDSS2. With regard to cognition, MRDSS2 showed a larger effect size (P=0.035) than its individual components [GMF (P=0.081), T2LV (P=0. 179), T1/T2 (P=0.043), and UCCA (P=0.818)] in comparing cognitively impaired with cognitively preserved patients (defined by the Minimal Assessment of Cognitive Function in MS). Both cerebral lesions (T1/T2) and atrophy (GMF) appeared to drive this relationship. We describe a new version of the MRDSS, which has been expanded to include cerebral GM and spinal cord involvement. MRDSS2 has concurrent validity with clinical status. NeuroReport 25:1156–1161 © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. NeuroReport 2014, 25:1156–1161

Background and purpose Enlarged perivascular spaces (EPVSs) have been associated with relapses and brain atrophy in multiple sclerosis (MS). We investigated the association of EPVS with clinical and MRI features of disease worsening in a well-characterized cohort of relapsing-remitting MS patients prospectively followed for up to 10 years. Methods Baseline EPVSs were scored on 1.5T MRI in 30 converters to moderate-severe disability, and 30 nonconverters matched for baseline characteristics. Results EPVS scores were not significantly different between converters and nonconverters, nor associated with accrual of lesions or brain atrophy. Conclusions Our preliminary findings from a relatively small study sample argue against a potential use of EPVS as early indicator of risk for disease worsening in relapsing-remitting MS patients in a clinical setting. Although the small sample size and clinical 1.5T MRI may have limited our ability to detect a significant effect, we provided estimates of the association of EPVS with clinical and MRI indicators of disease worsening in a well-characterized cohort of MS patients.

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.