Corticospinal Tract Lesion Load Originating From Both Ventral Premotor and Primary Motor Cortices Are Associated With Post-stroke Motor Severity

Lesion load of the corticospinal tract (CST-LL), a measure of overlap between a stroke lesion and the CST, is one of the strongest predictors of motor outcomes following stroke. CST-LL is typically calculated by using a probabilistic map of the CST originating from the primary motor cortex (M1). However, higher order motor areas also have projections that contribute to the CST and motor control. In this retrospective study, we examined whether evaluating CST-LL from additional motor origins is more strongly associated with post-stroke motor severity than using CST-LL originating from M1 only. We found that lesion load to both the ventral premotor (PMv) cortex and M1 were more strongly related to stroke motor severity indexed by Fugl-Meyer Assessment cut-off scores than CST-LL of M1 alone, suggesting that higher order motor regions add clinical relevance to motor impairment.

Mapping efficacious deep brain stimulation for pediatric dystonia

OBJECTIVEThe objective of this study was to report the authors’ experience with deep brain stimulation (DBS) of the internal globus pallidus (GPi) as a treatment for pediatric dystonia, and to elucidate substrates underlying clinical outcome using state-of-the-art neuroimaging techniques.METHODSA retrospective analysis was conducted in 11 pediatric patients (6 girls and 5 boys, mean age 12 ± 4 years) with medically refractory dystonia who underwent GPi-DBS implantation between June 2009 and September 2017. Using pre- and postoperative MRI, volumes of tissue activated were modeled and weighted by clinical outcome to identify brain regions associated with clinical outcome. Functional and structural networks associated with clinical benefits were also determined using large-scale normative data sets.RESULTSA total of 21 implanted leads were analyzed in 11 patients. The average follow-up duration was 19 ± 20 months (median 5 months). Using a 7-point clinical rating scale, 10 patients showed response to treatment, as defined by scores < 3. The mean improvement in the Burke-Fahn-Marsden Dystonia Rating Scale motor score was 40% ± 23%. The probabilistic map of efficacy showed that the voxel cluster most associated with clinical improvement was located at the posterior aspect of the GPi, comparatively posterior and superior to the coordinates of the classic GPi target. Strong functional and structural connectivity was evident between the probabilistic map and areas such as the precentral and postcentral gyri, parietooccipital cortex, and brainstem.CONCLUSIONSThis study reported on a series of pediatric patients with dystonia in whom GPi-DBS resulted in variable clinical benefit and described a clinically favorable stimulation site for this cohort, as well as its structural and functional connectivity. This information could be valuable for improving surgical planning, simplifying programming, and further informing disease pathophysiology.

Set-Min sketch: a probabilistic map for power-law distributions with application to k-mer annotation

Motivation: In many bioinformatics pipelines, k-mer counting is often a required step, with existing methods focusing on optimizing time or memory usage. These methods usually produce very large count tables explicitly representing k-mers themselves. Solutions avoiding explicit representation of k-mers include Minimal Perfect Hash Functions (MPHFs) or Count-Min sketches. The former is only applicable to static maps not subject to updates, while the latter suffers from potentially very large point-query errors, making it unsuitable when counters are required to be highly accurate. Results: We introduce Set-Min sketch, a sketching technique inspired by Count-Min sketch, for representing associative maps, more specifically, k-mer count tables. We show that Set-Min sketch provides a very low error rate, both in terms of the probability and the size of errors, much lower than a Count-Min sketch of similar dimensions. On the other hand, Set-Min sketches are shown to take up to an order of magnitude less space than MPHF-based solutions, especially for large values of k. Space-efficiency of Set-min takes advantage of the power-law distribution of k-mer counts in genomic datasets.

BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.