Functional compensation of motor function in pre-symptomatic Huntington's disease

Involuntary choreiform movements are a clinical hallmark of Huntington's disease. Studies in clinically affected patients suggest a shift of motor activations to parietal cortices in response to progressive neurodegeneration. Here, we studied pre-symptomatic gene carriers to examine the compensatory mechanisms that underlie the phenomenon of retained motor function in the presence of degenerative change. Fifteen pre-symptomatic gene carriers and 12 matched controls performed button presses paced by a metronome at either 0.5 or 2 Hz with four fingers of the right hand whilst being scanned with functional magnetic resonance imaging. Subjects pressed buttons either in the order of a previously learnt 10-item finger sequence, from left to right, or kept still. Error rates ranged from 2% to 7% in the pre-symptomatic gene carriers and from 0.5% to 4% in controls, depending on the condition. No significant difference in task performance was found between groups for any of the conditions. Activations in the supplementary motor area (SMA) and superior parietal lobe differed with gene status. Compared with healthy controls, gene carriers showed greater activations of left caudal SMA with all movement conditions. Activations correlated with increasing speed of movement were greater the closer the gene carriers were to estimated clinical diagnosis, defined by the onset of unequivocal motor signs. Activations associated with increased movement complexity (i.e. with the pre-learnt 10-item sequence) decreased in the rostral SMA with nearing diagnostic onset. The left superior parietal lobe showed reduced activation with increased movement complexity in gene carriers compared with controls, and in the right superior parietal lobe showed greater activations with all but the most demanding movements. We identified a complex pattern of motor compensation in pre-symptomatic gene carriers. The results show that preclinical compensation goes beyond a simple shift of activity from premotor to parietal regions involving multiple compensatory mechanisms in executive and cognitive motor areas. Critically, the pattern of motor compensation is flexible depending on the actual task demands on motor contro

Sparse classification with MRI based markers for neuromuscular disease categorization

International audienceIn this paper, we present a novel method for disease classification between two patient populations based on features extracted from Magnetic Resonance Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the k-support SVM (ksup-SVM). The k-support regularized SVM has an inherent feature selection property, and thus it eliminates the requirement for a separate feature selection step. Our dataset consists of patients that suffer from facioscapulohumeral muscular dystrophy (FSH) and Myotonic muscular dystrophy type 1 (DM1) and our proposed method achieves a high performance. More specifically, it achieves a mean Area Under the Curve (AUC) of 0.7141 and mean accuracy 77% ± 0.013. Moreover, we provide a sparsity visualization of the features in order to indentify their discriminative value. The results suggest the potential of the combined use of MR markers to diagnose myopathies, and the general utility of the ksup-SVM. Source code is also available at https://gitorious.org/ksup-svm

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer's disease prediction and for aiding patient stratification in clinical trials.Comment: 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text overlap with arXiv:1805.0390

A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment.

OBJECTIVES: The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI). METHODS: Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects. RESULTS: Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics. CONCLUSION: Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features

The search for the primary tumor in metastasized gastroenteropancreatic neuroendocrine neoplasm.

Gastroenteropancreatic neuroendocrine tumors (NETs) often present as liver metastasis from a carcinoma of unknown primary. We recently showed that primary NETs from the pancreas, small intestine and stomach as well as their respective liver metastases differ from each other by the expression profile of the three genes CD302, PPWD1 and ABHB14B. The gene and protein expression of CD302, PPWD1, and ABHB14B was studied in abdominal NET metastases to identify the site of the respective primary tumors. Cryopreserved tissue from NET metastases collected in different institutions (group A: 29, group B: 50, group C: 132 specimens) were examined by comparative genomic hybridization (Agilent 105 K), gene expression analysis (Agilent 44 K) (groups A and B) and immunohistochemistry (group C). The data were blindly evaluated, i.e. without knowing the site of the primary. Gene expression analysis correctly revealed the primary in the ileum in 94 % of the cases of group A and in 58 % of group B. A pancreatic primary was predicted in 83 % (group A) and 20 % (group B), respectively. The combined sensitivity of group A and B was 75 % for ileal NETs and 38 % for pancreatic NETs. Immunohistochemical analysis of group C revealed an overall sensitivity of 80 %. Gene and protein expression analysis of CD302 and PPWD1 in NET metastases correctly identifies the primary in the pancreas or the ileum in 80 % of the cases, provided that the tissue is well preserved. Immunohistochemical profiling revealed CD302 as the best marker for ileal and PPWD1 for pancreatic detection

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Host pathogen interactions in relation to management of light leaf spot disease (caused by Pyrenopeziza brassicae) on Brassica species

Light leaf spot, caused by Pyrenopeziza brassicae, is currently the most damaging disease problem in oilseed rape in the UK. According to recent survey data, the severity of epidemics has increased progressively across the UK, with current yield losses of up to £160M per annum in England and more severe epidemics in Scotland. Light leaf spot is a polycyclic disease with primary inoculum consisting of air-borne ascospores produced on diseased debris from the previous cropping season. Splash-dispersed conidia produced on diseased leaves are the main component of the secondary inoculum. P. brassicae is also able to infect and cause considerable yield losses on vegetable brassicas, especially Brussels sprouts. There may be spread of light leaf spot among different brassica species. Since they have a wide host range, Pyrenopeziza brassicae populations are likely to have considerable genetic diversity and there is evidence suggesting population variations between different regions, which need further study. Available disease-management tools are not sufficient to provide adequate control of the disease. There is a need to identify new sources of resistance, which can be integrated with fungicide applications to achieve sustainable management of light leaf spot. Several major resistance genes and quantitative trait loci have been identified in previous studies, but rapid improvements in the understanding of molecular mechanisms underpinning B. napus – P. brassicae interactions can be expected through exploitation of novel genetic and genomic information for brassicas and extracellular fungal pathogens.Peer reviewe

An individual participant data analysis of prospective cohort studies on the association between subclinical thyroid dysfunction and depressive symptoms.

In subclinical hypothyroidism, the presence of depressive symptoms is often a reason for starting levothyroxine treatment. However, data are conflicting on the association between subclinical thyroid dysfunction and depressive symptoms. We aimed to examine the association between subclinical thyroid dysfunction and depressive symptoms in all prospective cohorts with relevant data available. We performed a systematic review of the literature from Medline, Embase, Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library from inception to 10th May 2019. We included prospective cohorts with data on thyroid status at baseline and depressive symptoms during follow-up. The primary outcome was depressive symptoms measured at first available follow-up, expressed on the Beck's Depression Inventory (BDI) scale (range 0-63, higher values indicate more depressive symptoms, minimal clinically important difference: 5 points). We performed a two-stage individual participant data (IPD) analysis comparing participants with subclinical hypo- or hyperthyroidism versus euthyroidism, adjusting for depressive symptoms at baseline, age, sex, education, and income (PROSPERO CRD42018091627). Six cohorts met the inclusion criteria, with IPD on 23,038 participants. Their mean age was 60 years, 65% were female, 21,025 were euthyroid, 1342 had subclinical hypothyroidism and 671 subclinical hyperthyroidism. At first available follow-up [mean 8.2 (± 4.3) years], BDI scores did not differ between participants with subclinical hypothyroidism (mean difference = 0.29, 95% confidence interval = - 0.17 to 0.76, I <sup>2</sup> = 15.6) or subclinical hyperthyroidism (- 0.10, 95% confidence interval = - 0.67 to 0.48, I <sup>2</sup> = 3.2) compared to euthyroidism. This systematic review and IPD analysis of six prospective cohort studies found no clinically relevant association between subclinical thyroid dysfunction at baseline and depressive symptoms during follow-up. The results were robust in all sensitivity and subgroup analyses. Our results are in contrast with the traditional notion that subclinical thyroid dysfunction, and subclinical hypothyroidism in particular, is associated with depressive symptoms. Consequently, our results do not support the practice of prescribing levothyroxine in patients with subclinical hypothyroidism to reduce the risk of developing depressive symptoms