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Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that is used for treating various neurological disorders such as major depressive disorder. TMS has been gaining popularity in the field of neurostimulation of the cerebellum, since the cerebellum is a complex structure connected with almost the entire central nervous system and TMS has promise for non-invasively probing cerebellar function. Recent studies have discovered that the cerebellum plays an important role not only in motor planning and behavior but also in the cognitive domain. However, few studies have explored how different coil designs and anatomical variations affect the effectiveness of cerebellar TMS. Therefore, in this paper, we investigated the effects of cerebellar TMS with different coil designs positioning on several locations. Finite-element modeling was conducted with coil and D-B80 coil. Each coil was positioned in the center, 1 and 3 cm to the left of center of the cerebellum, and all the locations were tangential to the scalp at a distance of 5 mm. Furthermore, 50 MRI derived head models were used in the computer modeling to examine how anatomical variations affect the distribution and intensity of electric field in cerebellar TMS. [ABSTRACT FROM AUTHOR]

The central nervous system (CNS) is the most difficult target for drug delivery therapies. Despite datasets available describing physiological, biochemical, cellular, and metabolic properties of the CNS, the development of infusion therapies still faces major delivery challenges. There is a need for the integration of data obtained from different experimental modalities to design molecular therapies. In this paper, we propose a novel mathematical method for the integration of datasets to generate useful dosing criteria for infusion therapies. A case study is used to demonstrate the design of gene silencing therapies to down regulate NMDA receptors in the spinal cord for chronic pain management. Based on experimentally derived kinetics for short interfering RNA (siRNA) and magnetic resonance images, the biodistribution and pharmacokinetics of siRNAs were predicted for different infusion modes. This adaptable, multiscale computational platform enables the prediction of dose–response on an organ-wide level. The quantitative integration of valuable datasets with engineering precision is expected to accelerate the clinical implementation of novel therapeutics. [ABSTRACT FROM PUBLISHER]

It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected. [ABSTRACT FROM AUTHOR]

An important problem of the analysis of functional magnetic resonance imaging (fMRI) experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, recently the propagation-separation (PS) approach has been proposed. PS is a structure adaptive smoothing method that adapts to different shapes of activation areas. In this paper, we demonstrate how this method results in a more accurate localization of brain activity. First, it is shown in numerical simulations that PS is superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and results in less false detections. Second, in a study of 37 presurgical planning cases we found that PS and Gaussian smoothing often yield different results, and we present examples showing aspects of the superiority of PS as applied to presurgical planning. [ABSTRACT FROM AUTHOR]

Objective: It is commonly believed that in intrathecal (IT) drug delivery, agent distribution is confined to a narrow region close to the injection site, thereby undermining the efficacy of the method. Methods: To test the claim, multimodal in vivo imaging was used to experimentally observe the effects of IT infusion in cynomolgus monkey, looking at cerebrospinal fluid flow, anatomy, and dispersion of a radiolabeled tracer. Results: At high infusion rates, the tracer reached the cervical region after only 2 h, demonstrating rapid and wide distribution. The same in vivo nonhuman primate imaging data also provided evidence in support of a computational fluid dynamic model for the prediction of drug distribution following IT injection. Tracer dispersion was predicted in two specimens matching the distribution acquired with positron emission tomography (PET). For the third specimen, tracer dispersion simulations were conducted as a blind study: predictions were made before in vivo biodistribution data was known. In all cases, the computational fluid dynamics (CFD) predictions of drug dispersion after IT administration showed close spatio-temporal agreement with tracer biodistribution in vivo. Conclusion: Validation by in vivo nonhuman primate data confirms our ability to predict the biodistribution of intrathecally administered agents in subject-specific models of the central nervous system from first principles. Significance: The experiments reinstate IT delivery as a viable administration method when targeting molecules to the whole spine or the brain. The proposed computational methodology enables rational design of novel therapies for neurological diseases that require reliable, efficient, and safe delivery of therapeutic agents to specific target sites in the central nervous system. [ABSTRACT FROM AUTHOR]

An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas. [ABSTRACT FROM AUTHOR]