Silent progression in disease activity-free relapsing multiple sclerosis.

ObjectiveRates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow-up. Notably, "no evidence of disease activity" at 2 years did not predict long-term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long-term disability accumulation.MethodsDisability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0-5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA-DRB1*15:01 as covariates.ResultsRelapses were associated with a transient increase in disability over 1-year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05).InterpretationLong-term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing-remitting MS. Ann Neurol 2019;85:653-666

Progression of atypical parkinsonian syndromes: PROSPECT-M-UK study implications for clinical trials

The advent of clinical trials of disease-modifying agents for neurodegenerative disease highlights the need for evidence-based endpoint selection. Here we report the longitudinal PROSPECT-M-UK study of progressive supranuclear palsy, corticobasal syndrome, multiple system atrophy and related disorders, to compare candidate clinical trial endpoints. In this multicentre United Kingdom study, participants were assessed with serial questionnaires, motor examination, neuropsychiatric and magnetic resonance imaging assessments at baseline, six and twelve-months. Participants were classified by diagnosis at baseline and study end, into Richardson syndrome, progressive supranuclear palsy-subcortical (progressive supranuclear palsy-parkinsonism and progressive gait freezing subtypes), progressive supranuclear palsy-cortical (progressive supranuclear palsy-frontal, progressive supranuclear palsy-speech-and-language, and progressive supranuclear palsy-corticobasal syndrome subtypes), multiple system atrophy-parkinsonism, multiple system atrophy-cerebellar, corticobasal syndrome with and without evidence of Alzheimer’s disease pathology and indeterminate syndromes. We calculated annual rate of change, with linear mixed modelling, and sample sizes for clinical trials of disease modifying agents, according to group and assessment type. Two hundred forty-three people were recruited (117 progressive supranuclear palsy, 68 corticobasal syndrome, 42 multiple system atrophy and 16 indeterminate; 138 [56.8%] male; age at recruitment 68.7 ± 8.61 years). One hundred fifty-nine completed six-month assessment (82 progressive supranuclear palsy, 27 corticobasal syndrome, 40 multiple system atrophy and 10 indeterminate) and 153 completed twelve-month assessment (80 progressive supranuclear palsy, 29 corticobasal syndrome, 35 multiple system atrophy and 9 indeterminate). Questionnaire, motor examination, neuropsychiatric and neuroimaging measures declined in all groups, with differences in longitudinal change between groups. Neuroimaging metrics would enable lower sample sizes to achieve equivalent power for clinical trials than cognitive and functional measures, often achieving N < 100 required for one-year two-arm trials (with 80% power to detect 50% slowing). However, optimal outcome measures were disease specific. In conclusion, phenotypic variance within progressive supranuclear palsy, corticobasal syndrome and multiple system atrophy is a major challenge to clinical trial design. Our findings provide an evidence base for selection of clinical trial endpoints, from potential functional, cognitive, clinical or neuroimaging measures of disease progression

Spinal Cord Atrophy Predicts Progressive Disease in Relapsing Multiple Sclerosis

Objective A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS). Methods From a single-center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12-year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion. Results Patients who developed SPMS showed faster cord atrophy rates (-2.19%/yr) at least 4 years before conversion compared to their RRMS matches (-0.88%/yr, p < 0.001). Spinal cord atrophy rates decelerated after conversion (-1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (-1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p < 0.0001) and 53% (p < 0.0001) shorter time to silent progression and SPMS conversion, respectively. Interpretation Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 202

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Diagnosis Across the Spectrum of Progressive Supranuclear Palsy and Corticobasal Syndrome

IMPORTANCE: Patients with atypical parkinsonian syndromes (APS), including progressive supranuclear palsy (PSP), corticobasal syndrome (CBS) and multiple system atrophy (MSA), may be difficult to distinguish in early stages and are often misdiagnosed as Parkinson’s disease (PD). The diagnostic criteria for PSP have been updated to encompass a range of clinical subtypes, but have not been prospectively studied. OBJECTIVE: To define the distinguishing features of PSP and CBS, and to assess their usefulness in facilitating early diagnosis and separation from PD. DESIGN, SETTING, PARTICIPANTS: Cohort study which recruited APS and PD patients from movement disorder clinics across the UK from September 2015 to December 2018, and will follow up patients over 5 years. APS patients were stratified into PSP-Richardson syndrome, PSP-subcortical (including PSP-parkinsonism and PSP-progressive gait freezing cases), PSP-cortical (including PSP-frontal and PSP/CBS overlap cases), MSA-parkinsonism, MSA-cerebellar, CBS-Alzheimer’s and CBS-non-Alzheimer’s groups. MAIN OUTCOME MEASURES: Baseline group comparisons were conducted using: 1) Clinical trajectory; 2) Cognitive screening scales; 3) Serum neurofilament light chain (NF-L); 4) TRIM11, ApoE and MAPT genotypes; 5) Volumetric MRI. RESULTS: 222 APS cases (101 PSP, 55 MSA, 40 CBS and 26 indeterminate) were recruited (58% male; mean age at recruitment, 68.3 years). Age-matched controls (n=76) and PD cases (n=1967) were also included. Concordance between the ante-mortem clinical diagnosis and pathological diagnosis was achieved in 12/13 (92%) of PSP and CBS cases coming to post-mortem. Applying the MDS PSP diagnostic criteria almost doubled the number of patients diagnosed with PSP. 49/101 (49%) of reclassified PSP patients did not have classical PSP-Richardson syndrome. PSP-subcortical patients had a longer diagnostic latency and a more benign clinical trajectory than PSP-Richardson syndrome and PSP-cortical (p<0.05). PSP-subcortical was distinguished from PSP-cortical and PSP-Richardson syndrome by cortical volumetric MRI measures (AUC 0.84-0.89), cognitive profile (AUC 0.80-0.83), serum NF-L (AUC 0.75-0.83) and TRIM11 rs564309 genotype. Midbrain atrophy was a common feature of all PSP subtypes. 8/17 (47%) of CBS patients with CSF analysis were identified as having CBS-Alzheimer’s. CBS-Alzheimer’s patients had a longer diagnostic latency, relatively benign clinical trajectory, greater cognitive impairment and higher APOE-ε4 allele frequency than CBS-non-Alzheimer’s (p<0.05, AUC 0.80-0.87). Serum NF-L levels distinguished PD from PSP and CBS (p<0.05, AUC 0.80). CONCLUSIONS AND RELEVANCE: Clinical, therapeutic and epidemiological studies focusing on PSP-Richardson syndrome are likely to miss a large number of patients with underlying PSP-tau pathology. CSF analysis defines a distinct CBS-Alzheimer’s subgroup. PSP and CBS subtypes have distinct characteristics that may enhance their early diagnosis

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

A Global Census of Fission Yeast Deubiquitinating Enzyme Localization and Interaction Networks Reveals Distinct Compartmentalization Profiles and Overlapping Functions in Endocytosis and Polarity

Proteomic, localization, and enzymatic activity screens in fission yeast reveal how deubiquitinating enzyme localization and function are tuned

Power estimation for non-standardized multisite studies

AbstractA concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions

Genetic Variants For Head Size Share Genes and Pathways With Cancer

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer