Altered grey matter integrity and network vulnerability relate to epilepsy occurrence in patients with multiple sclerosis.

BACKGROUND AND PURPOSE: The aim of this study was to investigate the relevance of compartmentalized grey matter (GM) pathology and network reorganization in multiple sclerosis (MS) patients with concomitant epilepsy. METHODS: From 3-T magnetic resonance imaging scans of 30 MS patients with epilepsy (MSE group; age 41 ± 15 years, 21 females, disease duration 8 ± 6 years, median Expanded Disability Status Scale [EDSS] score 3), 60 MS patients without epilepsy (MS group; age 41 ± 12 years, 35 females, disease duration 6 ± 4 years, EDSS score 2), and 60 healthy subjects (HS group; age 40 ± 13 years, 27 females) the regional volumes of GM lesions and of cortical, subcortical and hippocampal structures were quantified. Network topology and vulnerability were modelled within the graph theoretical framework. Receiver-operating characteristic (ROC) curve analysis was applied to assess the accuracy of GM pathology measures to discriminate between MSE and MS patients. RESULTS: Higher lesion volumes within the hippocampus, mesiotemporal cortex and amygdala were detected in the MSE compared to the MS group (all p < 0.05). The MSE group had lower cortical volumes mainly in temporal and parietal areas compared to the MS and HS groups (all p < 0.05). Lower hippocampal tail and presubiculum volumes were identified in both the MSE and MS groups compared to the HS group (all p < 0.05). Network topology in the MSE group was characterized by higher transitivity and assortativity, and higher vulnerability compared to the MS and HS groups (all p < 0.05). Hippocampal lesion volume yielded the highest accuracy (area under the ROC curve 0.80 [0.67-0.91]) in discriminating between MSE and MS patients. CONCLUSIONS: High lesion load, altered integrity of mesiotemporal GM structures, and network reorganization are associated with a greater propensity for epilepsy occurrence in people with MS.

The predictive value of lesion and disconnectome loads for upper limb motor impairment after stroke.

OBJECTIVE: The putative effect of lesion-induced brain damage on post-stroke upper limb motor impairment can be estimated by overlaying a patient's lesion or its surrogate with key motor areas. We assessed the predictive value of imaging-based brain damage measures for cross-sectional upper limb motor impairment and subsequent upper limb motor outcome after stroke. METHODS: In 47 stroke patients, upper limb motor impairment was evaluated with the Upper-Extremity Fugl-Meyer Assessment (UE-FMA) at 2 weeks (2W) and 3 months (3M) post-stroke. Given each patient's lesion identified at 2W, we considered the disconnectome, estimated as an ensemble of structural and functional connections passing through the lesion, as a surrogate of the lesion. The lesion load and the disconnectome load were measured by overlaying the lesion and disconnectome with the corticospinal tract (CST) and motor cortex (MC), and their association with the UE-FMA score at 2W and 3M was assessed. RESULTS: Whereas the disconnectome loads on the CST and MC were better in predicting the UE-FMA score at 2W, the lesion load on the CST was better in predicting the UE-FMA score at 3M. Furthermore, when the CST lesion load was combined with the UE-FMA score at 2W, the UE-FMA score at 3M was better predicted, with smaller generalization error, than by using either measure alone. CONCLUSIONS: The combination of the CST lesion load and baseline upper limb motor impairment would provide a tailored fusion of imaging and clinical measures for more accurate motor outcome prediction.

Quantitative assessment of lesion load and efficacy of 3 cycles of albendazole in disseminated cysticercosis: a prospective evaluation.

BACKGROUND: The management of disseminated cysticercosis is unclear and largely considered hazardous. The role of albendazole remains controversial in such patients. METHODS: A tertiary care, University hospital-based prospective intervention study was conducted from December 2015 to December 2017. Patients with disseminated cysticercosis, defined as the presence of multiple viable neurocysticerci (≥ 3) in the brain along with involvement of an additional extra site, were included in the study. Patients with cysticercal encephalitis were excluded. A detailed evaluation, including ophthalmoscopy, ocular B scans, ultrasound abdomen, and X-rays were done. Albendazole was administered at a dose of 15 mg/kg/day in 3 cycles of 28 days each. All patients were also given adjuvant corticosteroids and anti-epileptic drugs. Clinical and radiological follow up was carried out at a difference of 3 months between each treatment cycle. For radiological quantification, lesions were counted at 10 pre-specified levels. Statistical analysis was done to estimate the difference in seizure frequency and lesion load. RESULTS: Twenty-nine patients (21 with > 20 lesions; 8 with ≤ 20 lesions) were given albendazole as per the protocol. There was a significant reduction in the occurrence of seizures (P < 0.001) and headache (P < 0.001). A significant reduction in lesion load from baseline to third follow-up was seen in the estimations done at different levels (P < 0.001). No patient developed serious side-effect warranting cessation of therapy. CONCLUSION: Cyclical use of albendazole appears efficacious in treating disseminated cysticercosis. The method of quantification described may be used in future studies for objective assessment. TRIAL REGISTRATION: ISRCTN11630542; 28th September 2019; Retrospectively registered.

Estimating effects of graded white matter damage and binary tract disconnection on post-stroke language impairment.

Despite the critical importance of close replications in strengthening and advancing scientific knowledge, there are inherent challenges to conducting replications of lesion-based studies. In the present study, we conducted a close conceptual replication of a study (i.e., Hope et al., 2016) that found that fluency and naming scores in post-stoke aphasia were more strongly associated with a binary measure of structural white matter integrity (tract disconnection) than a graded measure (lesion load). Using a different sample of stroke patients (N = 128) and four language deficit measures (aphasia severity, picture naming, and composite scores for speech production and semantic cognition), we examined tract disconnection and lesion load in three white matter tracts that have been implicated in language processing: arcuate fasciculus, uncinate fasciculus, and inferior fronto-occipital fasciculus. We did not find any consistent evidence that binary tract disconnection was more strongly associated with language impairment over and above lesion load, though individual deficit measures differed with respect to whether lesion load or tract disconnection was the stronger predictor. Given the mixed findings, we suggest caution when using such indirect estimates of structural white matter integrity, and direct individual measurements (for example, using diffusion weighted imaging) should be preferred when they are available. We end by highlighting the complex nature of replication in lesion-based studies and offer some potential solutions.

The impact of vascular risk factors on brain volume and lesion load in patients with early multiple sclerosis.

BACKGROUND: Vascular risk factors (VRF) in multiple sclerosis (MS) patients have been associated with lower brain volumes. It is currently unknown if this association already exists in early MS and how it develops over time. METHODS: We identified 82 patients with clinically isolated syndrome (CIS) ( n = 61) or with early relapsing-remitting MS ( n = 21) and assessed their VRF including arterial hypertension, hyperlipidaemia, diabetes mellitus and smoking. We analysed T2-lesion load, normalized brain volume (NBV), cortical grey (cGMV) and white matter volumes (WMV), thalamic and basal ganglia volumes at baseline and follow-up magnetic resonance imaging (MRI) and assessed the percentage of brain volume change (PBVC) using SIENA. RESULTS: Patient mean age was 32.4 (±8.7) years and 54 (65%) were women. Median follow-up period was 42 (29-54) months. In total, 26 patients (31.7%) had one or more VRF (VRF+). At baseline, VRF+ patients had a lower NBV (1530.9 cm3 vs 1591.2 cm3, p = 0.001), a lower cGMV (628.5 cm3 vs 668.6 cm3, p = 0.002) and WMV (752.2 cm3 vs 783.9 cm3, p = 0.009) than VRF-negative patients. Similar results were obtained at follow-up. PBVC was comparable between patients with and without VRF. CONCLUSION: VRF are associated with lower brain volume already in early MS but do not lead to increased brain volume loss during 3.5 years of follow-up.

Changes to the septo-fornical area might play a role in the pathogenesis of anxiety in multiple sclerosis.

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.

Amplitude-Integrated Electroencephalography Improves the Identification of Infants with Encephalopathy for Therapeutic Hypothermia and Predicts Neurodevelopmental Outcomes at 2 Years of Age.

OBJECTIVES: To examine whether using an amplitude-integrated electroencephalography (aEEG) severity pattern as an entry criterion for therapeutic hypothermia better selects infants with hypoxic-ischemic encephalopathy and to assess the time-to-normal trace for aEEG and magnetic resonance imaging (MRI) lesion load as 24-month outcome predictors. STUDY DESIGN: Forty-seven infants meeting Norwegian therapeutic hypothermia guidelines were enrolled prospectively. Eight-channel EEG/aEEG was recorded from 6 hours until after rewarming, and read after discharge. Neonatal MRI brain scans were scored for summated (range 0-11) regional lesion load. A poor outcome at 2 years was defined as death or a Bayley Scales of Infant-Toddler Development cognitive or motor composite score of <85 or severe hearing or visual loss. RESULTS: Three severity groups were defined from the initial aEEG; continuous normal voltage (CNV; n = 15), discontinuous normal voltage (DNV; n = 18), and a severe aEEG voltage pattern (SEVP; n = 14). Any seizure occurrence was 7% CNV, 50% DNV, and 100% SEVP. Infants with SEVP with poor vs good outcome had a significantly longer median (IQR) time-to-normal trace: 58 hours (9-79) vs 18 hours (12-19) and higher MRI lesion load: 10 (3-10) vs 2 (1-5). A poor outcome was noted in 3 of 15 infants with CNV, 4 of 18 infants with DNV, and 8 of 14 infants with SEVP. Using multiple stepwise linear regression analyses including only infants with abnormal aEEG (DNV and SEVP), MRI lesion load significantly predicted cognitive and motor scores. For the SEVP group alone, time-to-normal trace was a stronger outcome predictor than MRI score. No variable predicted outcome in infants with CNV. CONCLUSIONS: Selection of infants with encephalopathy for therapeutic hypothermia after perinatal asphyxia may be improved by including only infants with an early moderate or severely depressed background aEEG trace.

Detection and Predictive Value of Fractional Anisotropy Changes of the Corticospinal Tract in the Acute Phase of a Stroke.

BACKGROUND AND PURPOSE: A decrease in fractional anisotropy (FA) of the ipsilesional corticospinal tract (CST) distal to stroke lesions in the subacute (eg, 30 days) and chronic phase has been correlated with poor motor outcomes, but it is unclear whether FA values obtained within the acute stroke phase (here defined as 80 hours after onset) can predict later outcome. METHODS: Fifty-eight patients underwent an assessment of motor impairment in the acute phase and at 3 months using the upper extremity Fugl-Meyer assessment. FA values, obtained within 80 hours after stroke onset, were determined in 2 regions of interest: cerebral peduncle and a stretch of the CST caudal to each stroke lesion (nearest-5-slices). RESULTS: The FA laterality index for the cerebral peduncle-regions of interest was a poor predictor of 3-month outcome (R(2)=0.044; P=0.137), whereas the slope over the FA laterality index of the nearest-5-slices showed a relatively weak but significant prediction (R(2)=0.11; P=0.022) with the affected side having lower FA values. Initial upper extremity Fugl-Meyer (R(2)=0.69; P<0.001) and the weighted CST lesion load (R(2)=0.71; P<0.001) were strong predictors of 3-month outcome. In multivariate analyses, controlling for initial upper extremity Fugl-Meyer, weighted CST lesion load, and days-of-therapy, neither the FA laterality index of the cerebral peduncle nor the slope over the FA laterality index of the nearest-5-slices significantly contributed to the prediction of 86% of the variance in the upper extremity Fugl-Meyer at 3 months. CONCLUSIONS: FA reductions of the CST can be detected near the ischemic lesion in the acute stroke phase, but offer minimal predictive value to motor outcomes at 3 months.

Magnetic resonance imaging correlates of physical disability in relapse onset multiple sclerosis of long disease duration.

BACKGROUND: Understanding long-term disability in multiple sclerosis (MS) is a key goal of research; it is relevant to how we monitor and treat the disease. OBJECTIVES: The Magnetic Imaging in MS (MAGNIMS) collaborative group sought to determine the relationship of brain lesion load, and brain and spinal cord atrophy, with physical disability in patients with long-established MS. METHODS: Patients had a magnetic resonance imaging (MRI) scan of their brain and spinal cord, from which we determined brain grey (GMF) and white matter (WMF) fractional volumes, upper cervical spinal cord cross-sectional area (UCCA) and brain T2-lesion volume (T2LV). We assessed patient disability using the Expanded Disability Status Scale (EDSS). We analysed associations between EDSS and MRI measures, using two regression models (dividing cohort by EDSS into two and four sub-groups). RESULTS: In the binary model, UCCA (p < 0.01) and T2LV (p = 0.02) were independently associated with the requirement of a walking aid. In the four-category model UCCA (p < 0.01), T2LV (p = 0.02) and GMF (p = 0.04) were independently associated with disability. CONCLUSIONS: Long-term physical disability was independently linked with atrophy of the spinal cord and brain T2 lesion load, and less consistently, with brain grey matter atrophy. Combinations of spinal cord and brain MRI measures may be required to capture clinically-relevant information in people with MS of long disease duration.

Predictors of long-term disability in multiple sclerosis patients using routine magnetic resonance imaging data: A 15-year retrospective study.

INTRODUCTION: Early identification of patients at high risk of progression could help with a personalised treatment strategy. Magnetic resonance imaging (MRI) measures have been proposed to predict long-term disability in multiple sclerosis (MS), but a reliable predictor that can be easily implemented clinically is still needed. AIM: Assess MRI measures during the first 5 years of the MS disease course for the ability to predict progression at 10+ years. METHODS: Eighty-two MS patients (53 females), with ≥10 years of clinical follow-up and having two MRI scans, were included. Clinical data were obtained at baseline, follow-up and at ≥10 years. White matter lesion (WML) counts and volumes, and four linear brain sizes were measured on T2/FLAIR 'Fluid-Attenuated-Inversion-Recovery' and T1-weighted images. RESULTS: Baseline and follow-up inter-caudate diameter (ICD) and third ventricular width (TVW) measures correlated positively with Expanded Disability Status Scale, ≥10 or more of WMLs showed a high sensitivity in predicting progression, at ≥10 years. A steeper rate of lesion volume increase was observed in subjects converting to secondary progressive MS. The sensitivity and specificity of both ICD and TVW, to predict disability at ≥10 years were 60% and 64%, respectively. CONCLUSION: Despite advances in brain imaging and computerised volumetric analysis, ICD and TVW remain relevant as they are simple, fast and have the potential in predicting long-term disability. However, in this study, despite the statistical significance of these measures, the clinical utility is still not reliable.

The elusive metric of lesion load.

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images.

Magnetic resonance (MR) imaging (MRI) is commonly used to diagnose, assess and monitor stroke. Accurate and timely segmentation of stroke lesions provides the anatomico-structural information that can aid physicians in predicting prognosis, as well as in decision making and triaging for various rehabilitation strategies. To segment stroke lesions, MR protocols, including diffusion-weighted imaging (DWI) and T2-weighted fluid attenuated inversion recovery (FLAIR) are often utilized. These imaging sequences are usually acquired with different spatial resolutions due to time constraints. Within the same image, voxels may be anisotropic, with reduced resolution along slice direction for diffusion scans in particular. In this study, we evaluate the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions using single contrast (DWI) and dual contrast images (T2w FLAIR and DWI). The predicted segmentations correlate with post-stroke motor outcome measured by the National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer Upper Extremity (FM-UE) index based on the lesion loads overlapping the corticospinal tracts (CST), which is a neural substrate for motor movement and function. Although the four methods performed similarly, the 2D multimodal U-Net achieved the best results with a mean Dice of 0.737 (95% CI: 0.705, 0.769) and a relatively high correlation between the weighted lesion load and the NIHSS scores (both at baseline and at 90 days). A monotonically constrained quintic polynomial regression yielded R2 = 0.784 and 0.875 for weighted lesion load versus baseline and 90-Days NIHSS respectively, and better corrected Akaike information criterion (AICc) scores than those of the linear regression. In addition, using the quintic polynomial regression model to regress the weighted lesion load to the 90-Days FM-UE score results in an R2 of 0.570 with a better AICc score than that of the linear regression. Our results suggest that the multi-contrast information enhanced the accuracy of the segmentation and the prediction accuracy for upper extremity motor outcomes. Expanding the training dataset to include different types of stroke lesions and more data points will help add a temporal longitudinal aspect and increase the accuracy. Furthermore, adding patient-specific data may improve the inference about the relationship between imaging metrics and functional outcomes.

Fatigue in Multiple Sclerosis Is Associated with Reduced Expression of Interleukin-10 and Worse Prospective Disease Activity.

In multiple sclerosis (MS), fatigue is a frequent symptom that negatively affects quality of life. The pathogenesis of fatigue is multifactorial and inflammation may play a specific role. To explore the association between fatigue, central inflammation and disease course in MS in 106 relapsing-remitting (RR)-MS patients, clinical characteristics, including fatigue and mood, were explored at the time of diagnosis. NEDA (no evidence of disease activity)-3 status after one-year follow up was calculated. Cerebrospinal fluid (CSF) levels of a set of proinflammatory and anti-inflammatory molecules and peripheral blood markers of inflammation were also analyzed. MRI structural measures were explored in 35 patients. A significant negative correlation was found at diagnosis between fatigue measured with the Modified Fatigue Impact Scale (MFIS) and the CSF levels of interleukin (IL)-10. Conversely, no significant associations were found with peripheral markers of inflammation. Higher MFIS scores were associated with reduced probability to reach NEDA-3 status after 1-year follow up. Finally, T2 lesion load showed a positive correlation with MFIS scores and a negative correlation with CSF IL-10 levels at diagnosis. CSF inflammation, and particularly the reduced expression of the anti-inflammatory molecule IL-10, may exacerbate fatigue. Fatigue in MS may reflect subclinical CSF inflammation, predisposing to greater disease activity.

Spatial navigation in early multiple sclerosis: a neglected cognitive marker of the disease?

BACKGROUND: Cognitive deficits are common in early multiple sclerosis (MS), however, spatial navigation changes and their associations with brain pathology remain poorly understood. OBJECTIVE: To characterize the profile of spatial navigation changes in two main navigational strategies, egocentric (self-centred) and allocentric (world-centred), and their associations with demyelinating and neurodegenerative changes in early MS. METHODS: Participants with early MS after the first clinical event (n = 51) and age-, gender- and education-matched controls (n = 42) underwent spatial navigation testing in a real-space human analogue of the Morris water maze task, comprehensive neuropsychological assessment, and MRI brain scan with voxel-based morphometry and volumetric analyses. RESULTS: The early MS group had lower performance in the egocentric (p = 0.010), allocentric (p = 0.004) and allocentric-delayed (p = 0.038) navigation tasks controlling for age, gender and education. Based on the applied criteria, lower spatial navigation performance was present in 26-29 and 33-41% of the participants with early MS in the egocentric and the allocentric task, respectively. Larger lesion load volume in cortical, subcortical and cerebellar regions (ß ≥ 0.29; p ≤ 0.032) unlike brain atrophy was associated with less accurate allocentric navigation performance. CONCLUSION: Lower spatial navigation performance is present in up to 41% of the participants with early MS. Demyelinating lesions in early MS may disrupt neural network forming the basis of allocentric navigation.

Interaction of Age and Self-reported Physical Sports Activity on White Matter Hyperintensity Volume in Healthy Older Adults.

Cerebral white matter (WM) lesion load, as measured by white matter hyperintensity (WMH) volume with magnetic resonance imaging (MRI), has been associated with increasing age and cardiovascular risk factors, like hypertension. Physical sports activity (PSA) may play an important role in maintaining WM in the context of healthy aging. In 196 healthy older adults, we investigated whether participants reporting high levels of PSA (n = 36) had reduced total and regional WMH volumes compared to those reporting low levels of PSA (n = 160). Age group [young-old (YO) = 50-69 years; old-old (OO) = 70-89 years], PSA group, and age by PSA group interaction effects were tested, with sex, hypertension, and body mass index (BMI) as covariates. We found significant main effects for age group and age by PSA group interactions for total, frontal, temporal, and parietal WMH volumes. There were no main effects of PSA group on WMH volumes. The OO group with low PSA had greater total, frontal, temporal, and parietal WMH volumes than the YO with low PSA and OO with high PSA groups. WMH volumes for the YO and OO groups with high PSA were comparable. These findings indicate an age group difference in those with low PSA, with greater WMH volumes in older adults, which was not observed in those with high PSA. The results suggest that engaging in high levels of PSA may be an important lifestyle factor that can help to diminish WMH lesion load in old age, potentially reducing the impact of brain aging.

Cortical and Subcortical Morphometric and Iron Changes in Relapsing-Remitting Multiple Sclerosis and Their Association with White Matter T2 Lesion Load : A 3-Tesla Magnetic Resonance Imaging Study.

INTRODUCTION: This study was carried out to investigate the global and regional morphometric and iron changes in grey matter (GM) of multiple sclerosis (MS) patients and link them to the white matter (WM) lesions in a multimodal magnetic resonance imaging approach. MATERIAL AND METHODS: The study involved 30 relapsing-remitting MS (RRMS) patients along with 30 age-matched healthy controls (HC) who were scanned on a 3T Siemens Trio system. The scanning protocol included a 3D, high resolution T1, T2, and T2*-weighted sequences. The T1-w images were used in FreeSurfer for cortical reconstruction and volumetric segmentation, while T2-w images were used to extract the WM T2 lesions; however, iron and magnetic susceptibility were calculated from the phase data of the T2*-w sequence. Surface-based analyses were performed in FreeSurfer to investigate the regional cortical morphometric changes and their correlations with the expanded disability status scale (EDSS), WM T2 lesions load, cortical iron deposition and magnetic susceptibility. RESULTS: Significant differences were detected between the RRMS patients and HC for all cortical and subcortical morphometric changes. The EDSS and T2 lesion load showed weak to moderate correlation with the reduced cortical morphometric measurements, increased cortical magnetic susceptibility and iron concentration. All deep grey matter (dGM) volumes showed a significant strong positive correlation with the cortical surface area and volume in RRMS patients and HC. CONCLUSIONS: Grey matter is very much involved in the RRMS and cortical morphometric changes occur in a non-uniform pattern and are very likely to be associated with cortical iron deposition and magnetic susceptibility, dGM atrophy, WM T2 lesion load, and disability.

Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year employment status in multiple sclerosis patients.

BACKGROUND: Multiple sclerosis (MS) is frequently diagnosed in the most productive years of adulthood and is often associated with worsening employment status. However, reliable predictors of employment status change are lacking. OBJECTIVE: To identify early clinical and brain magnetic resonance imaging (MRI) markers of employment status worsening in MS patients at 12-year follow-up. METHODS: A total of 145 patients with early relapsing-remitting MS from the original Avonex-Steroids-Azathioprine (ASA) study were included in this prospective, longitudinal, observational cohort study. Cox models were conducted to identify MRI and clinical predictors (at baseline and during the first 12 months) of worsening employment status (patients either (1) working full-time or part-time with no limitations due to MS and retaining this status during the course of the study, or (2) patients working full-time or part-time with no limitations due to MS and switching to being unemployed or working part-time due to MS). RESULTS: In univariate analysis, brain parenchymal fraction, T1 and T2 lesion volume were the best MRI predictors of worsening employment status over the 12-year follow-up period. MS duration at baseline (hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.03-1.18; p = 0.040) was the only significant clinical predictor. Having one extra milliliter of T1 lesion volume was associated with a 53% greater risk of worsening employment status (HR = 1.53, 95% CI 1.16-2.02; p = 0.018). A brain parenchymal fraction decrease of 1% increased the risk of worsening employment status by 22% (HR = 0.78, 95% CI 0.65-0.95; p = 0.034). CONCLUSION: Brain atrophy and lesion load were significant predictors of worsening employment status in MS patients. Using a combination of clinical and MRI markers may improve the early prediction of an employment status change over long-term follow-up.

Pipeline for Analyzing Lesions After Stroke (PALS).

Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research specifically using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z = 3.43, p = 0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.

Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke-An Exploratory Comparison of Mismatch Patterns and Predictive Modeling.

Objectives: To investigate the relationship between imaging features derived from lesion loads and 3 month clinical assessments in ischemic stroke patients. To support clinically implementable predictive modeling with information from lesion-load features. Methods: A retrospective cohort of ischemic stroke patients was studied. The dataset was dichotomized based on revascularization treatment outcome (TICI score). Three lesion delineations were derived from magnetic resonance imaging in each group: two clinically implementable (threshold based and fully automatic prediction) and 90-day follow-up as final groundtruth. Lesion load imaging features were created through overlay of the lesion delineations on a histological brain atlas, and were correlated with the clinical assessment (NIHSS). Significance of the correlations was assessed by constructing confidence intervals using bootstrap sampling. Results: Overall, high correlations between lesion loads and clinical score were observed (up to 0.859). Delineations derived from acute imaging yielded on average somewhat lower correlations than delineations derived from 90-day follow-up imaging. Correlations suggest that both total lesion volume and corticospinal tract lesion load are associated with functional outcome, and in addition highlight other potential areas associated with poor clinical outcome, including the primary somatosensory cortex BA3a. Fully automatic prediction was comparable to ADC threshold-based delineation on the successfully treated cohort and superior to the Tmax threshold-based delineation in the unsuccessfully treated cohort. Conclusions: The confirmation of established predictors for stroke outcome (e.g., corticospinal tract integrity and total lesion volume) gives support to the proposed methodology-relating acute lesion loads to 3 month outcome assessments by way of correlation. Furthermore, the preliminary results indicate an association of further brain regions and structures with three month NIHSS outcome assessments. Hence, prediction models might observe an increased accuracy when incorporating regional (instead of global) lesion loads. Also, the results lend support to the clinical utilization of the automatically predicted volumes from FASTER, rather than the simpler DWI and PWI lesion delineations.

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.