Clearance for Driving in Genetic Generalized Epilepsy.

A key aspect of management of genetic generalized epilepsy involves assessing seizure control and deciding suitability for driving motor vehicles. We surveyed child neurologists and pediatric epileptologists on key questions that practitioners should ask prior to providing clearance for driving. The results showed a wide variability of practice among responders. We propose a likely appropriate process necessary to determine seizure control.

Segmentation of malignant gliomas through remote collaboration and statistical fusion.

PURPOSE: Malignant gliomas represent an aggressive class of central nervous system neoplasms. Correlation of interventional outcomes with tumor morphometry data necessitates 3D segmentation of tumors (typically based on magnetic resonance imaging). Expert delineation is the long-held gold standard for tumor segmentation, but is exceptionally resource intensive and subject to intrarater and inter-rater variability. Automated tumor segmentation algorithms have been demonstrated for a variety of imaging modalities and tumor phenotypes, but translation of these methods across clinical study designs is problematic given variation in image acquisition, tumor characteristics, segmentation objectives, and validation criteria. Herein, the authors demonstrate an alternative approach for high-throughput tumor segmentation using Internet-based, collaborative labeling. METHODS: In a study of 85 human raters and 98 tumor patients, raters were recruited from a general university campus population (i.e., no specific medical knowledge), given minimal training, and provided web-based tools to label MRI images based on 2D cross sections. The labeling goal was characterized as to extract the enhanced tumor cores on T1-weighted MRI and the bright abnormality on T2-weighted MRI. An experienced rater manually constructed the ground truth volumes of a randomly sampled subcohort of 48 tumor subjects (for both T1w and T2w). Raters' taskwise individual observations, as well as the volume wise truth estimates via statistical fusion method, were evaluated over the subjects having the ground truth. RESULTS: Individual raters were able to reliably characterize (with >0.8 dice similarity coefficient, DSC) the gadolinium-enhancing cores and extent of the edematous areas only slightly more than half of the time. Yet, human raters were efficient in terms of providing these highly variable segmentations (less than 20 s per slice). When statistical fusion was used to combine the results of seven raters per slice for all slices in the datasets, the 3D agreement of the fused results with expertly delineated segmentations was on par with the inter-rater reliability observed between experienced raters using traditional 3D tools (approximately 0.85 DSC). The cumulative time spent per tumor patient with the collaborative approach was equivalent to that with an experienced rater, but the collaborative approach could be achieved with less training time, fewer resources, and efficient parallelization. CONCLUSIONS: Hence, collaborative labeling is a promising technique with potentially wide applicability to cost-effective manual labeling of medical images.

Nimodipine improves cortical efficiency during working memory in healthy subjects.

The L-type calcium channel gene, CACNA1C, is a validated risk gene for schizophrenia and the target of calcium channel blockers. Carriers of the risk-associated genotype (rs1006737 A allele) have increased frontal cortical activity during working memory and higher CACNA1C mRNA expression in the prefrontal cortex. The aim of this study was to determine how the brain-penetrant calcium channel blocker, nimodipine, changes brain activity during working memory and other cognitive and emotional processes. We conducted a double-blind randomized cross-over pharmacoMRI study of a single 60 mg dose of oral nimodipine solution and matching placebo in healthy men, prospectively genotyped for rs1006737. With performance unchanged, nimodipine significantly decreased frontal cortical activity by 39.1% and parietal cortical activity by 42.8% during the N-back task (2-back > 0-back contrast; PFWE < 0.05; n = 28). Higher peripheral nimodipine concentrations were correlated with a greater decrease in activation in the frontal cortex. Carriers of the risk-associated allele, A (n = 14), had a greater decrease in frontal cortical activation during working memory compared to non-risk allele carriers. No differences in brain activation were found between nimodipine and placebo for other tasks. Future studies should be conducted to test if the decreased cortical brain activity after nimodipine is associated with improved working memory performance in patients with schizophrenia, particularly those who carry the risk-associated genotype. Furthermore, changes in cortical activity during working memory may be a useful biomarker in future trials of L-type calcium channel blockers.

COLLABORATIVE LABELING OF MALIGNANT GLIOMA.

Malignant gliomas represent an aggressive class of central nervous system neoplasms which are often treated by maximal surgical resection. Herein, we seek to improve the methods available to quantify the extent of tumors as seen on magnetic resonance imaging using Internet-based, collaborative labeling. In a study of clinically acquired images, we demonstrate that teams of minimally trained human raters are able to reliably characterize the gadolinium-enhancing core and edema tumor regions (Dice ≈ 0.9). The collaborative approach is highly parallel and efficient in terms of time (the total time spent by the collective is equivalent to that of a single expert) and resources (only minimal training and no hardware is provided to the participants). Hence, collaborative labeling is a very promising new technique with potentially wide applicability to facilitate cost-effective manual labeling of medical imaging data.

Collaborative Labeling of Malignant Glioma with WebMILL: A First Look.

Malignant gliomas are the most common form of primary neoplasm in the central nervous system, and one of the most rapidly fatal of all human malignancies. They are treated by maximal surgical resection followed by radiation and chemotherapy. Herein, we seek to improve the methods available to quantify the extent of tumors using newly presented, collaborative labeling techniques on magnetic resonance imaging. Traditionally, labeling medical images has entailed that expert raters operate on one image at a time, which is resource intensive and not practical for very large datasets. Using many, minimally trained raters to label images has the possibility of minimizing laboratory requirements and allowing high degrees of parallelism. A successful effort also has the possibility of reducing overall cost. This potentially transformative technology presents a new set of problems, because one must pose the labeling challenge in a manner accessible to people with little or no background in labeling medical images and raters cannot be expected to read detailed instructions. Hence, a different training method has to be employed. The training must appeal to all types of learners and have the same concepts presented in multiple ways to ensure that all the subjects understand the basics of labeling. Our overall objective is to demonstrate the feasibility of studying malignant glioma morphometry through statistical analysis of the collaborative efforts of many, minimally-trained raters. This study presents preliminary results on optimization of the WebMILL framework for neoplasm labeling and investigates the initial contributions of 78 raters labeling 98 whole-brain datasets.