Optimized preoperative motor cortex mapping in brain tumors using advanced processing of transcranial magnetic stimulation data.

BACKGROUND AND OBJECTIVE: Transcranial magnetic stimulation (TMS) is a useful technique to help localize motor function prior to neurosurgical procedures. Adequate modelling of the effect of TMS on the brain is a prerequisite to obtain reliable data. METHODS: Twelve patients were included with perirolandic tumors to undergo TMS-based motor mapping. Several models were developed to analyze the mapping data, from a projection to the nearest brain surface to motor evoked potential (MEP) amplitude informed weighted average of the induced electric fields over a multilayer detailed individual head model. The probability maps were compared with direct cortical stimulation (DCS) data in all patients for the hand and in three for the foot. The gold standard was defined as the results of the DCS sampling (with on average 8 DCS-points per surgery) extrapolated over the exposed cortex (of the tailored craniotomy), and the outcome parameters were based on the similarity of the probability maps with this gold standard. RESULTS: All models accurately gauge the location of the motor cortex, with point-cloud based mapping algorithms having an accuracy of 83-86%, with similarly high specificity. To delineate the whole area of the motor cortex representation, the model based on the weighted average of the induced electric fields calculated with a realistic head model performs best. The optimal single threshold to visualize the field based maps is 40% of the maximal value for the anisotropic model and 50% for the isotropic model, but dynamic thresholding adds information for clinical practice. CONCLUSIONS: The method with which TMS mapping data are analyzed clearly affects the predicted area of the primary motor cortex representation. Realistic electric field based modelling is feasible in clinical practice and improves delineation of the motor cortex representation compared to more simple point-cloud based methods.

Publisher Correction: Using high-amplitude and focused transcranial alternating current stimulation to entrain physiological tremor.

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Using high-amplitude and focused transcranial alternating current stimulation to entrain physiological tremor.

Transcranial alternating current stimulation (tACS) is a noninvasive neuromodulation method that can entrain physiological tremor in healthy volunteers. We conducted two experiments to investigate the effectiveness of high-amplitude and focused tACS montages at entraining physiological tremor. Experiment 1 used saline-soaked sponge electrodes with an extra-cephalic return electrode and compared the effects of a motor (MC) and prefrontal cortex (PFC) electrode location. Average peak-amplitude was 1.925 mA. Experiment 2 used gel-filled cup-electrodes in a 4 × 1 focused montage and compared the effects of MC and occipital cortex (OC) tACS. Average peak-amplitude was 4.45 mA. Experiment 1 showed that unfocused MC and PFC tACS both produced phosphenes and significant phase entrainment. Experiment 2 showed that focused MC and OC tACS produced no phosphenes but only focused MC tACS caused significant phase entrainment. At the group level, tACS did not have a significant effect on tremor amplitude. However, with focused tACS there was a significant correlation between phase entrainment and tremor amplitude modulation: subjects with higher phase entrainment showed more tremor amplitude modulation. We conclude that: (1) focused montages allow for high-amplitude tACS without phosphenes and (2) high amplitude focused tACS can entrain physiological tremor.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).