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Background: Disease progression is observed across the spectrum of people with multiple sclerosis (MS) and identification of effective treatment strategies to halt progression remains one of the greatest unmet clinical needs. Objectives: The Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo) was designed to evaluate a wide range of factors associated with the onset and rate of clinical disease progression in MS and to describe the interplay between these factors. Design: A prospective cohort study. Methods: CanProCo is a national, prospective, observational cohort study that has recruited 944 individuals from 5 large academic MS centers in Canada. Participants include people with radiologically isolated syndrome (RIS), early relapsing-remitting and primary progressive MS (RRMS, PPMS), and healthy controls (HCs). Annually, participants complete self-reported questionnaires, undergo clinical evaluation and, if clinically indicated, magnetic resonance images (MRIs) of the brain and cervical spinal cord; in a subset of participants (n = 399), blood, and research MRIs of the brain and cervical spinal cord are collected. Linkages to health administrative databases are available at three sites. Results: Overall, 944 participants were recruited (53 HCs, 63 RIS, 751 RRMS, 77 PPMS). RIS and MS participants had a mean age of 39.0 years and 70.5% female. The mean time since diagnosis was 2.7 years. There were differences observed in the Expanded Disability Status Scale score and components of the MS performance test (walking speed test, manual dexterity test, processing speed test, and low-contrast visual acuity) between RIS and MS subtypes. Questionnaires revealed more symptoms of depression and anxiety and impaired physical and mental quality of life in people with RIS/MS versus HCs and differences across RIS/MS subtypes. Conclusion: Physical and mental neurological disability is prevalent even in the earliest stages of MS. Transdisciplinary approaches such as those used in CanProCo are needed to better characterize clinical progression in MS. Additional CanProCo results, including MRI, biological, and pharmaco-economic data will be forthcoming. Going forward, CanProCo's data sharing and collaborative vision will facilitate numerous global collaborations, which will inform the development and implementation of effective interventions for people with MS around the world. [ABSTRACT FROM AUTHOR]

Lymphangioleiomyomatosis (LAM) is a rare disease involving cystic lung destruction by invasive LAM cells. These cells harbor loss‐of‐function mutations in TSC2, conferring hyperactive mTORC1 signaling. Here, tissue engineering tools are employed to model LAM and identify new therapeutic candidates. Biomimetic hydrogel culture of LAM cells is found to recapitulate the molecular and phenotypic characteristics of human disease more faithfully than culture on plastic. A 3D drug screen is conducted, identifying histone deacetylase (HDAC) inhibitors as anti‐invasive agents that are also selectively cytotoxic toward TSC2−/− cells. The anti‐invasive effects of HDAC inhibitors are independent of genotype, while selective cell death is mTORC1‐dependent and mediated by apoptosis. Genotype‐selective cytotoxicity is seen exclusively in hydrogel culture due to potentiated differential mTORC1 signaling, a feature that is abrogated in cell culture on plastic. Importantly, HDAC inhibitors block invasion and selectively eradicate LAM cells in vivo in zebrafish xenografts. These findings demonstrate that tissue‐engineered disease modeling exposes a physiologically relevant therapeutic vulnerability that would be otherwise missed by conventional culture on plastic. This work substantiates HDAC inhibitors as possible therapeutic candidates for the treatment of patients with LAM and requires further study. [ABSTRACT FROM AUTHOR]

Introduction: Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions. Methods: We used a dataset from a completed phase-III clinical trial that evaluated the efficacy of minocycline for delaying the conversion from clinically isolated syndrome to multiple sclerosis (MS), using the McDonald 2005 diagnostic criteria. There were a total of 142 participants, and at the 2-year follow-up 81 had converted to MS, 29 remained stable, and 32 had uncertain outcomes. In a stratified 7-fold cross-validation, we trained three random forest (RF) ML models using MRI volumetric features and clinical variables to predict the conversion outcome, which represented new disease activity within 2 years of a first clinical demyelinating event. One RF was trained using subjects with the uncertain labels excluded (RFexclude), another RF was trained using the entire dataset but with assumed labels for the uncertain group (RFnaive), and a third, a probabilistic RF (PRF, a type of RF that can model label uncertainty) was trained on the entire dataset, with probabilistic labels assigned to the uncertain group. Results: Probabilistic random forest outperformed both the RF models with the highest AUC (0.76, compared to 0.69 for RFexclude and 0.71 for RFnaive) and F1-score (86.6% compared to 82.6% for RFexclude and 76.8% for RFnaive). Conclusion: Machine learning algorithms capable of modeling label uncertainty can improve predictive performance in datasets in which a substantial number of subjects have unknown outcomes. [ABSTRACT FROM AUTHOR]

Background: Multiple Sclerosis (MS) affects people in their most productive years of life. Consequently, MS can substantially affect employment and work-related outcomes. Objectives: This study characterizes productivity loss and employment status of people with multiple sclerosis (pwMS) and investigates associated factors. Methods: We used baseline data collected as part of the Canadian Prospective Cohort Study to Understand Progression in Multiple Sclerosis (CanProCo). Using the Valuation of Lost Productivity questionnaire, we measured MS-related paid work productivity loss for those employed, productivity losses incurred by those unemployed (i.e. lost employment time), and unpaid work productivity losses for all. A set of sociodemographic, disease, and performance-related factors were investigated using a two-part regression model for productivity loss and a multinomial logistic model for employment status. Results: From the cohort of 888 pwMS enrolled at baseline (mostly showing mild to moderate disability), 75% were employed, and of those unemployed, 69% attributed their unemployment to health-related issues. Total productivity loss over a 3-month period averaged 64 and 395 hours for those employed and unemployed, respectively. Some factors that affected productivity loss and employment status included use of disease-modifying therapies, fatigue, and performance indicators such as cognitive processing speed. Conclusion: Productivity loss experienced by employed and unemployed pwMS is substantial. Targeting the identified modifiable factors is likely to improve work productivity and permanence of MS patients in the workforce. [ABSTRACT FROM AUTHOR]

Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent components, such as an objective function, a data sampling strategy, and a data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation; (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically weighted Kappa; (3) utilizing eye pairs boosts the performance of DR grading and; (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves a state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42,670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available online. [ABSTRACT FROM AUTHOR]

Background and Purpose: Conventional MRI measures of multiple sclerosis (MS) disease severity, such as lesion volume and brain atrophy, do not provide information about microstructural tissue changes, which may be driving physical and cognitive progression. Myelin damage in normal‐appearing white matter (NAWM) is likely an important contributor to MS disability. Myelin water fraction (MWF) provides quantitative measurements of myelin. Mean MWF reflects average myelin content, while MWF standard deviation (SD) describes variation in myelin within regions. The myelin heterogeneity index (MHI = SD/mean MWF) is a composite metric of myelin content and myelin variability. We investigated how mean MWF, SD, and MHI compare in differentiating MS from controls and their associations with physical and cognitive disability. Methods: Myelin water imaging data were acquired from 91 MS participants and 31 healthy controls (HC). Segmented whole‐brain NAWM and corpus callosum (CC) NAWM, mean MWF, SD, and MHI were compared between groups. Associations of mean MWF, SD, and MHI with Expanded Disability Status Scale and Symbol Digit Modalities Test were assessed. Results: NAWM and CC MHI had the highest area under the curve:.78 (95% confidence interval [CI]:.69,.86) and.84 (95% CI:.76,.91), respectively, distinguishing MS from HC. Conclusions: Mean MWF, SD, and MHI provide complementary information when assessing regional and global NAWM abnormalities in MS and associations with clinical outcome measures. Examining all three metrics (mean MWF, SD, and MHI) enables a more detailed interpretation of results, depending on whether regions of interest include areas that are more heterogeneous, earlier in the demyelination process, or uniformly injured. [ABSTRACT FROM AUTHOR]

Background Myelin loss is a feature of cerebral small vessel disease (cSVD). Although physical activity levels may exert protective effects over cSVD pathology, its specific relationship with myelin content in people living with the cSVD is unknown. Thus, we investigated whether physical activity levels are associated with myelin in community-dwelling older adults with cSVD and mild cognitive impairment. Methods Cross-sectional data from 102 individuals with cSVD and mild cognitive impairment were analyzed (mean age [ SD ] = 74.7 years [5.5], 63.7% female). Myelin was measured using a magnetic resonance gradient and spin echo sequence. Physical activity was estimated using the Physical Activity Scale for the Elderly. Hierarchical regression models adjusting for total intracranial volume, age, sex, body mass index, and education were conducted to determine the associations between myelin content and physical activity. Significant models were further adjusted for white matter hyperintensity volume. Results In adjusted models, greater physical activity was linked to higher myelin content in the whole-brain white matter (R 2change =.04, p =.048). Greater physical activity was also associated with myelin content in the sagittal stratum (R 2change =.08, p =.004), anterior corona radiata (R 2change =.04, p =.049), and genu of the corpus callosum (R 2change =.05, p =.018). Adjusting for white matter hyperintensity volume did not change any of these associations. Conclusions Physical activity may be a strategy to maintain myelin in older adults with cSVD and mild cognitive impairment. Future randomized controlled trials of exercise are needed to determine whether exercise increases myelin content. [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities (WMH) are associated with impaired cognition and increased falls risk. Resistance training (RT) is a promising intervention to reduce WMH progression, improve executive functions, and reduce falls. However, the underlying neurobiological process by which RT improves executive functions and falls risk remain unclear. We hypothesized that: 1) RT reduces the level of WMH-related disruption to functional networks; and 2) reduced disruption to the sensorimotor and attention networks will be associated with improved executive function and reduced falls risk.Objective: Investigate the impact of 52 weeks of RT on WMH-related disruption to functional networks.Methods: Thirty-two older females (65-75 years) were included in this exploratory analysis of a 52-week randomized controlled trial. Participants received either twice-weekly RT or balance and tone training (control). We used lesion network mapping to assess changes in WMH-related disruption to the sensorimotor, dorsal attention, and ventral attention networks. Executive function was measured using the Stroop Colour-Word Test. Falls risk was assessed using the Physiological Profile Assessment (PPA) and the foam sway test.Results: RT significantly reduced the level of WMH-related disruption to the sensorimotor network (p = 0.012). Reduced disruption to the dorsal attention network was associated with improvements in Stroop performance (r = 0.527, p = 0.030). Reduced disruption to the ventral attention network was associated with reduced PPA score (r = 0.485, p = 0.049)Conclusion:RT may be a promising intervention to mitigate WMH-related disruption to the sensorimotor network. Additionally, reducing disruption to the dorsal and ventral attention networks may contribute to improved executive function and reduced falls risk respectively. [ABSTRACT FROM AUTHOR]

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set Segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p < 0.0001) and 10.18% (p < 0.0001), respectively. Index Terms--3-D image segmentation, brain segmentation, deformable models, geodesic active contour.

Objectives: To analyze work productivity loss and costs, including absenteeism (time missed from work), presenteeism (reduced productivity while working), and unpaid work loss, among a sample of employed people with multiple sclerosis (pwMS) in Canada, as well as its association with clinical, sociodemographic, and work-related factors. Methods: We used cross-sectional data collected as part of the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo) and information from the Valuation of Lost Productivity questionnaire. Results: Among 512 pwMS who were employed, 97% showed no or mild disability and 55% experienced productivity loss due to MS in the prior 3 months. Total productivity time loss over a 3-month period averaged 60 hours (SD = 107; 23 from presenteeism, 19 from absenteeism, and 18 from unpaid work), leading to a mean cost of lost productivity of CAD$2480 (SD = 4282) per patient, with an hourly paid productivity loss greater than the wage loss. Fatigue retained significant associations with all productivity loss outcomes. Conclusion: Unpaid work loss and productivity losses exceeding those of the employee alone (due to teamwork and associated factors) are key additional contributors of the high economic burden of MS. Workplace accommodations and treatments targeted at fatigue could lessen the economic impact of MS. [ABSTRACT FROM AUTHOR]

Background: Advanced magnetic resonance imaging (MRI) methods can provide more specific information about various microstructural tissue changes in multiple sclerosis (MS) brain. Quantitative measurement of T1 and T2 relaxation, and diffusion basis spectrum imaging (DBSI) yield metrics related to the pathology of neuroinflammation and neurodegeneration that occurs across the spectrum of MS. Objective: To use relaxation and DBSI MRI metrics to describe measures of neuroinflammation, myelin and axons in different MS subtypes. Methods: 103 participants (20 clinically isolated syndrome (CIS), 33 relapsing-remitting MS (RRMS), 30 secondary progressive MS and 20 primary progressive MS) underwent quantitative T1, T2, DBSI and conventional 3T MRI. Whole brain, normal-appearing white matter, lesion and corpus callosum MRI metrics were compared across MS subtypes. Results: A gradation of MRI metric values was seen from CIS to RRMS to progressive MS. RRMS demonstrated large oedema-related differences, while progressive MS had the most extensive abnormalities in myelin and axonal measures. Conclusion: Relaxation and DBSI-derived MRI measures show differences between MS subtypes related to the severity and composition of underlying tissue damage. RRMS showed oedema, demyelination and axonal loss compared with CIS. Progressive MS had even more evidence of increased oedema, demyelination and axonal loss compared with CIS and RRMS. [ABSTRACT FROM AUTHOR]

Purpose: The decomposition of multi‐exponential decay data into a T2 spectrum poses substantial challenges for conventional fitting algorithms, including non‐negative least squares (NNLS). Based on a combination of the resolution limit constraint and machine learning neural network algorithm, a data‐driven and highly tailorable analysis method named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS) was proposed. Theory and Methods: The theory of SAME‐ECOS was derived. Then, a paradigm was presented to demonstrate the SAME‐ECOS workflow, consisting of a series of calculation, simulation, and model training operations. The performance of the trained SAME‐ECOS model was evaluated using simulations and six in vivo brain datasets. The code is available at https://github.com/hanwencat/SAME‐ECOS. Results: Using NNLS as the baseline, SAME‐ECOS achieved over 15% higher overall cosine similarity scores in producing the T2 spectrum, and more than 10% lower mean absolute error in calculating the myelin water fraction (MWF), as well as demonstrated better robustness to noise in the simulation tests. Applying to in vivo data, MWF from SAME‐ECOS and NNLS was highly correlated among all study participants. However, a distinct separation of the myelin water peak and the intra/extra‐cellular water peak was only observed in the mean T2 spectra determined using SAME‐ECOS. In terms of data processing speed, SAME‐ECOS is approximately 30 times faster than NNLS, achieving a whole‐brain analysis in 3 min. Conclusion: Compared with NNLS, the SAME‐ECOS method yields much more reliable T2 spectra in a dramatically shorter time, increasing the feasibility of multi‐component T2 decay analysis in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Spinal cord atrophy provides a clinically relevant metric for monitoring MS. However, the spinal cord is imaged far less frequently than brain due to artefacts and acquisition time, whereas MRI of the brain is routinely performed. Objective: To validate spinal cord cross-sectional area measurements from routine 3DT1 whole-brain MRI versus those from dedicated cord MRI in healthy controls and people with MS. Methods: We calculated cross-sectional area at C1 and C2/3 using T2*-weighted spinal cord images and 3DT1 brain images, for 28 healthy controls and 73 people with MS. Correlations for both groups were assessed between: (1) C1 and C2/3 using cord images; (2) C1 from brain and C1 from cord; and (3) C1 from brain and C2/3 from cord. Results and Conclusion: C1 and C2/3 from cord were strongly correlated in controls (r = 0.94, p <0.0001) and MS (r = 0.85, p <0.0001). There was strong agreement between C1 from brain and C2/3 from cord in controls (r = 0.84, p <0.0001) and MS (r = 0.81, p <0.0001). This supports the use of C1 cross-sectional area calculated from brain imaging as a surrogate for the traditional C2/3 cross-sectional area measure for spinal cord atrophy. [ABSTRACT FROM AUTHOR]

Background: Myelin water imaging (MWI) was recently optimized to provide quantitative in vivo measurement of spinal cord myelin, which is critically involved in multiple sclerosis (MS) disability. Objective: To assess cervical cord myelin measurements in relapsing-remitting multiple sclerosis (RRMS) and progressive multiple sclerosis (ProgMS) participants and evaluate the correlation between myelin measures and clinical disability. Methods: We used MWI data from 35 RRMS, 30 ProgMS, and 28 healthy control (HC) participants collected at cord level C2/C3 on a 3 T magnetic resonance imaging (MRI) scanner. Myelin heterogeneity index (MHI), a measurement of myelin variability, was calculated for whole cervical cord, global white matter, dorsal column, lateral and ventral funiculi. Correlations were assessed between MHI and Expanded Disability Status Scale (EDSS), 9-Hole Peg Test (9HPT), timed 25-foot walk, and disease duration. Results: In various regions of the cervical cord, ProgMS MHI was higher compared to HC (between 9.5% and 31%, p ⩽ 0.04) and RRMS (between 13% and 26%, p ⩽ 0.02), and ProgMS MHI was associated with EDSS (r = 0.42–0.52) and 9HPT (r = 0.45–0.52). Conclusion: Myelin abnormalities within clinically eloquent areas are related to clinical disability. MWI metrics have a potential role for monitoring subclinical disease progression and adjudicating treatment efficacy for new therapies targeting ProgMS. [ABSTRACT FROM AUTHOR]

Background: Cerebral small vessel disease is a major underlying cause of subcortical ischemic vascular cognitive impairment (SIVCI). Myelin loss is a feature of cerebral small vessel disease, which may underly cognitive and functional decline in older adults living with SIVCI. Resistance training (RT) is a form of exercise associated with slower progression of white matter lesions, a cerebral small vessel disease marker. However, it remains to be determined whether RT can counteract myelin loss in individuals living with SIVCI. Thus, we examined the impact of a 12‐month, twice‐weekly progressive RT program on myelin content in those with SIVCI. Method: This was a sub‐study of a 12‐month single‐blinded, randomized controlled trial. Participants (n = 91) were randomized to RT or an active control group (balance and tone exercises [BAT]). Study eligibility included: 1) age 55 years and older; 2) magnetic resonance imaging (MRI) evidence of cerebral small vessel disease; 3) mild cognitive impairment; and 4) the absence of dementia. Myelin was measured using a multi‐echo gradient and spin echo T2 relaxation MRI sequence, indexed as myelin water fraction (MWF). Differences between groups at 12 months were assessed via analysis of covariance adjusting for baseline MWF and estimated intracranial volume. MWF data from fifteen white matter tracts were analyzed. Post‐hoc regressions were conducted to determine if changes in MWF were associated with changes in mobility measured with the Short Physical Performance Battery (SPPB). Result: Seventy‐three participants (RT = 36) aged 74.1 (SD = 5.7), 64% females with high‐quality MRI data were included in the analyses. At 12 months, RT showed lower MWF levels compared with BAT in the genu of the corpus callosum (estimated mean difference [RT – BAT]: ‐0.610, 95% CI: ‐1.041 to ‐0.178, p = 0.006), fornix (‐0.883, 95% CI: ‐1.570 to ‐0.197, p = 0.012), and combined white matter tracts (‐0.326, 95% CI: ‐0.643 to ‐0.009, p = 0.044). Changes in MWF in the combined white matter tracs were positively associated with changes in SPPB (Unstandardized B = 0.124, SE = 0.061, p = 0.047). Conclusion: Contrary to our expectations, 12 months of balance and tone exercises, but not RT, showed positive changes in myelin. These improvements may result in better mobility in older adults with SIVCI. [ABSTRACT FROM AUTHOR]