Henk-Jan Westeneng

Brain morphologic changes in asymptomatic C9orf72 repeat expansion carriers

Objective: To investigate possible effects of the C9orf72 repeat expansion before disease onset, we assessed brain morphology in asymptomatic carriers. Methods: Aiming to diminish the effects of genetic variation between subjects, apart from the C9orf72 repeat expansion, 16 carriers of the repeat expansion were compared with 23 noncarriers from the same large family with a history of amyotrophic lateral sclerosis (ALS). Cortical thickness, subcortical volumes, and white matter connectivity, as assessed from high-resolution T1-weighted and diffusion-weighted MRIs, were evaluated. For comparison, we included 14 C9orf72 carriers with ALS and 28 healthy, unrelated controls. Results: We found temporal, parietal, and occipital regions to be thinner (p < 0.05) and the left caudate and putamen to be smaller (p < 0.05) in asymptomatic carriers compared with noncarriers. Cortical thinning of the primary motor cortex and decreased connectivity of white matter pathways (global, corticospinal tract, and corpus callosum) were observed in patients with C9orf72-associated ALS, but not in asymptomatic carriers. Conclusions: Asymptomatic C9orf72 carriers show cortical and subcortical differences compared with noncarriers from the same family, possibly effects of the C9orf72 repeat expansion on the brain. Of note, changes in the primary motor regions and motor-related tracts were found exclusively in patients with ALS, indicating that such motor changes may be a disease phenomenon.

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

Widespread structural brain involvement in ALS is not limited to the C9orf72 repeat expansion

Background In patients with a C9orf72 repeat expansion (C9+), a neuroimaging phenotype with widespread structural cerebral changes has been found. We aimed to investigate the specificity of this neuroimaging phenotype in patients with amyotrophic lateral sclerosis (ALS). Methods 156 C9− and 14 C9+ patients with ALS underwent high-resolution T1-weighted MRI; a subset (n=126) underwent diffusion-weighted imaging. Cortical thickness, subcortical volumes and white matter integrity were compared between C9+ and C9− patients. Using elastic net logistic regression, a model defining the neuroimaging phenotype of C9+ was determined and applied to C9− patients with ALS. Results C9+ patients showed cortical thinning outside the precentral gyrus, extending to the bilateral pars opercularis, fusiform, lingual, isthmus-cingulate and superior parietal cortex, and smaller volumes of the right hippocampus and bilateral thalamus, and reduced white matter integrity of the inferior and superior longitudinal fasciculus compared with C9− patients (p<0.05). Among 128 C9− patients, we detected a subgroup of 27 (21%) with a neuroimaging phenotype congruent to C9+ patients, while 101 (79%) C9− patients showed cortical thinning restricted to the primary motor cortex. C9− patients with a ‘C9+’ neuroimaging phenotype had lower performance on the frontal assessment battery, compared with other C9− patients with ALS (p=0.004). Conclusions This study shows that widespread structural brain involvement is not limited to C9+ patients, but also presents in a subgroup of C9− patients with ALS and relates to cognitive deficits. Our neuroimaging findings reveal an intermediate phenotype that may provide insight into the complex relationship between genetic factors and clinical characteristics.

Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor neuron disease with a variable natural history. There are no accurate models that predict the disease course and outcomes, which complicates risk assessment and counselling for individual patients, stratification of patients for trials, and timing of interventions. We therefore aimed to develop and validate a model for predicting a composite survival endpoint for individual patients with ALS. METHODS We obtained data for patients from 14 specialised ALS centres (each one designated as a cohort) in Belgium, France, the Netherlands, Germany, Ireland, Italy, Portugal, Switzerland, and the UK. All patients were diagnosed in the centres after excluding other diagnoses and classified according to revised El Escorial criteria. We assessed 16 patient characteristics as potential predictors of a composite survival outcome (time between onset of symptoms and non-invasive ventilation for more than 23 h per day, tracheostomy, or death) and applied backward elimination with bootstrapping in the largest population-based dataset for predictor selection. Data were gathered on the day of diagnosis or as soon as possible thereafter. Predictors that were selected in more than 70% of the bootstrap resamples were used to develop a multivariable Royston-Parmar model for predicting the composite survival outcome in individual patients. We assessed the generalisability of the model by estimating heterogeneity of predictive accuracy across external populations (ie, populations not used to develop the model) using internal-external cross-validation, and quantified the discrimination using the concordance (c) statistic (area under the receiver operator characteristic curve) and calibration using a calibration slope. FINDINGS Data were collected between Jan 1, 1992, and Sept 22, 2016 (the largest data-set included data from 1936 patients). The median follow-up time was 97·5 months (IQR 52·9-168·5). Eight candidate predictors entered the prediction model: bulbar versus non-bulbar onset (univariable hazard ratio [HR] 1·71, 95% CI 1·63-1·79), age at onset (1·03, 1·03-1·03), definite versus probable or possible ALS (1·47, 1·39-1·55), diagnostic delay (0·52, 0·51-0·53), forced vital capacity (HR 0·99, 0·99-0·99), progression rate (6·33, 5·92-6·76), frontotemporal dementia (1·34, 1·20-1·50), and presence of a C9orf72 repeat expansion (1·45, 1·31-1·61), all p<0·0001. The c statistic for external predictive accuracy of the model was 0·78 (95% CI 0·77-0·80; 95% prediction interval [PI] 0·74-0·82) and the calibration slope was 1·01 (95% CI 0·95-1·07; 95% PI 0·83-1·18). The model was used to define five groups with distinct median predicted (SE) and observed (SE) times in months from symptom onset to the composite survival outcome: very short 17·7 (0·20), 16·5 (0·23); short 25·3 (0·06), 25·2 (0·35); intermediate 32·2 (0·09), 32·8 (0·46); long 43·7 (0·21), 44·6 (0·74); and very long 91·0 (1·84), 85·6 (1·96). INTERPRETATION We have developed an externally validated model to predict survival without tracheostomy and non-invasive ventilation for more than 23 h per day in European patients with ALS. This model could be applied to individualised patient management, counselling, and future trial design, but to maximise the benefit and prevent harm it is intended to be used by medical doctors only. FUNDING Netherlands ALS Foundation.

Determining the role of sarcomeric proteins in facioscapulohumeral muscular dystrophy: a study protocol.

BackgroundAlthough muscle weakness is a hallmark of facioscapulohumeral muscular dystrophy (FSHD), the molecular mechanisms that lead to weakness in FSHD remain largely unknown. Recent studies suggest aberrant expression of genes involved in skeletal muscle development and sarcomere contractility, and activation of pathways involved in sarcomeric protein degradation. This study will investigate the contribution of sarcomeric protein dysfunction to the pathogenesis of muscle weakness in FSHD.Methods/DesignEvaluation of sarcomeric function using skinned single muscle fiber contractile studies and protein analysis in muscle biopsies (quadriceps femoris and tibialis anterior) from patients with FSHD and age- and gender-matched healthy controls. Patients with other forms of muscular dystrophy and inflammatory myopathy will be included as disease controls to assess whether results are due to changes specific for FSHD, or a consequence of muscle disease in general. A total of 56 participants will be included. Extensive clinical parameters will be measured using MRI, quantitative muscle studies and physical activity assessments.DiscussionThis study is the first to extensively investigate muscle fiber physiology in FSHD following an earlier pilot study suggesting sarcomeric dysfunction in FSHD. The results obtained in this study will increase the understanding of the pathophysiology of muscle weakness in FSHD, and possibly identify novel targets for therapeutic intervention.

Multicentre, cross-cultural, population-based, case–control study of physical activity as risk factor for amyotrophic lateral sclerosis

Objective To investigate the association between physical activity (PA) and amyotrophic lateral sclerosis (ALS) in population-based case–control studies in three European countries using a validated and harmonised questionnaire. Methods Patients with incident ALS and controls were recruited from five population-based registers in The Netherlands, Ireland and Italy. Demographic and data regarding educational level, smoking, alcohol habits and lifetime PA levels in both leisure and work time were gathered by questionnaire, and quantified using metabolic equivalent of task scores. Logistic regression models adjusting for PA-related factors were used to determine the association between PA and ALS risk, and forest plots were used to visualise heterogeneity between regions. Results 1557 patients and 2922 controls were included. We found a linear association between ALS and PA in leisure time (OR 1.07, P=0.01) and occupational activities (OR 1.06, P<0.001), and all activities combined (OR 1.06, P<0.001), with some heterogeneity between regions: the most evident association was seen in the Irish and Italian cohorts. After adjustment for other occupational exposures or exclusion of patients with a C9orf72 mutation, the ORs remained similar. Conclusion We provide new class I evidence for a positive association between PA and risk of ALS in a large multicentre study using harmonised methodology to objectively quantify PA levels, with some suggestions for population differences.

A continuous repetitive task to detect fatigability in spinal muscular atrophy

BackgroundTo determine the value of a continuous repetitive task to detect and quantify fatigability as additional dimension of impaired motor function in patients with hereditary proximal spinal muscular atrophy (SMA).ResultsIn this repeated measure case-control study 52 patients with SMA types 2–4, 17 healthy and 29 disease controls performed five consecutive rounds of the Nine-Hole Peg test to determine the presence of fatigability. We analysed differences in test performance and associations with disease characteristics. Five patients with SMA type 2 (22%) and 1 disease control (3%) could not finish five rounds due to fatigue (p = 0.01). Patients with SMA type 2 performed the test significantly more slowly than all other groups (p < 0.005) and disease controls were slower than healthy controls (p < 0.05). Patients with SMA type 2 performed round five 27% slower than round one, while healthy controls performed round five 14% faster than round one (p = 0.005). There was no difference between SMA type 3a, type 3b/4 or disease controls and healthy controls (p > 0.4). Time needed to complete each round during the five-round task increased in 15 patients with SMA type 2 (65%), 4 with type 3a (36%), 4 with type 3b/4 (22%), 9 disease controls (31%) and 1 healthy control (6%). There was no effect of age at disease onset or disease duration in SMA type 2 (p = 0.39). Test-retest reliability was high.ConclusionFatigability of remaining arm function is a feature of SMA type 2 and can be determined with continuous repetitive tasks.