Implicit task sequence learning in patients with Parkinson's disease, frontal lesions and amnesia: The critical role of fronto–striatal loops

The purpose of this study was to investigate the role of the fronto–striatal system for implicit task sequence learning. We tested performance of patients with compromised functioning of the fronto–striatal loops, that is, patients with Parkinson's disease and patients with lesions in the ventromedial or dorsolateral prefrontal cortex. We also tested amnesic patients with lesions either to the basal forebrain/orbitofrontal cortex or to thalamic/medio-temporal regions. We used a task sequence learning paradigm involving the presentation of a sequence of categorical binary-choice decision tasks. After several blocks of training, the sequence, hidden in the order of tasks, was replaced by a pseudo-random sequence. Learning (i.e., sensitivity to the ordering) was assessed by measuring whether this change disrupted performance. Although all the patients were able to perform the decision tasks quite easily, those with lesions to the fronto–striatal loops (i.e., patients with Parkinson's disease, with lesions in the ventromedial or dorsolateral prefrontal cortex and those amnesic patients with lesions to the basal forebrain/orbitofrontal cortex) did not show any evidence of implicit task sequence learning. In contrast, those amnesic patients with lesions to thalamic/medio-temporal regions showed intact sequence learning. Together, these results indicate that the integrity of the fronto–striatal system is a prerequisite for implicit task sequence learning.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Dimethyloxalylglycine stabilizes HIF-1α in cultured human endothelial cells and increases random-pattern skin flap survival in vivo

The goal of this study was to evaluate in vitro and in vivo the effects of up-regulation of the proangiogenic hypoxia inducible factor (HIF)-1α induced by dimethyloxalylglycine on endothelial cell cultures and on skin flap survival.

"Pseudo-Beijing": evidence for convergent evolution in the direct repeat region of Mycobacterium tuberculosis

Background Mycobacterium tuberculosis has a global population structure consisting of six main phylogenetic lineages associated with specific geographic regions and human populations. One particular M. tuberculosis genotype known as “Beijing” has repeatedly been associated with drug resistance and has been emerging in some parts of the world. “Beijing” strains are traditionally defined based on a characteristic spoligotyping pattern. We used three alternative genotyping techniques to revisit the phylogenetic classification of M. tuberculosis complex (MTBC) strains exhibiting the typical “Beijing” spoligotyping pattern. Methods and Findings MTBC strains were obtained from an ongoing molecular epidemiological study in Switzerland and Nepal. MTBC genotyping was performed based on SNPs, genomic deletions, and 24-loci MIRU-VNTR. We identified three MTBC strains from patients originating from Tibet, Portugal and Nepal which exhibited a spoligotyping patterns identical to the classical Beijing signature. However, based on three alternative molecular markers, these strains were assigned to Lineage 3 (also known as Delhi/CAS) rather than to Lineage 2 (also known as East-Asian lineage). Sequencing of the RD207 in one of these strains showed that the deletion responsible for this “Pseudo-Beijing” spoligotype was about 1,000 base pairs smaller than the usual deletion of RD207 in classical “Beijing” strains, which is consistent with an evolutionarily independent deletion event in the direct repeat (DR) region of MTBC. Conclusions We provide an example of convergent evolution in the DR locus of MTBC, and highlight the limitation of using spoligotypes for strain classification. Our results indicate that a proportion of “Beijing” strains may have been misclassified in the past. Markers that are more phylogenetically robust should be used when exploring strain-specific differences in experimental or clinical phenotypes.

Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study)

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents.