ABSTRACT

Subcortical neuronal activity is highly relevant for mediating communication in large-scale brain networks. While electroencephalographic (EEG) recordings provide appropriate temporal resolution and coverage to study whole brain dynamics, the feasibility to detect subcortical signals is a matter of debate. Here, we investigate if scalp EEG can detect and correctly localize signals recorded with intracranial electrodes placed in the centromedial thalamus, and in the nucleus accumbens. Externalization of deep brain stimulation (DBS) electrodes, placed in these regions, provides the unique opportunity to record subcortical activity simultaneously with high-density (256 channel) scalp EEG. In three patients during rest with eyes closed, we found significant correlation between alpha envelopes derived from intracranial and EEG source reconstructed signals. Highest correlation was found for source signals in close proximity to the actual recording sites, given by the DBS electrode locations. Therefore, we present direct evidence that scalp EEG indeed can sense subcortical signals.

ObjectiveTo create probabilistic stimulation maps (PSMs) of deep brain stimulation (DBS) effects on tremor suppression and stimulation-induced side-effects in patients with essential tremor (ET).MethodMonopolar reviews from 16 ET-patients which consisted of over 600 stimulation settings were used to create PSMs. A spherical model of the volume of neural activation was used to estimate the spatial extent of DBS for each setting. All data was pooled and voxel-wise statistical analysis as well as nonparametric permutation testing was used to confirm the validity of the PSMs.ResultsPSMs showed tremor suppression to be more pronounced by stimulation in the zona incerta (ZI) than in the ventral intermediate nucleus (VIM). Paresthesias and dizziness were most commonly associated with stimulation in the ZI and surrounding thalamic nuclei.DiscussionOur results support the assumption, that the ZI might be a very effective target for tremor suppression. However stimulation inside the ZI and in its close vicinity was also related to the occurrence of stimulation-induced side-effects, so it remains unclear whether the VIM or the ZI is the overall better target. The study demonstrates the use of PSMs for target selection and evaluation. While their accuracy has to be carefully discussed, they can improve the understanding of DBS effects and can be of use for other DBS targets in the therapy of neurological or psychiatric disorders as well. Furthermore they provide a priori information about expected DBS effects in a certain region and might be helpful to clinicians in programming DBS devices in the future.

Introduction: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods: A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results: A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion: Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.

The results show that both imaging modalities can be used to determine lead orientation angles with high accuracy. CT is superior to x-ray imaging, but oblique leads (polar angle > 40°) show limited precision due to the current design of the directional marker.

Background: Directional deep brain stimulation (DBS) allows steering the stimulation in an axial direction which offers greater flexibility in programming. However, accurate anatomical visualization of the lead orientation is required for interpreting the observed stimulation effects and to guide programming. Objectives: In this study we aimed to develop and test an accurate and robust algorithm for determining the orientation of segmented electrodes based on standard postoperative CT imaging used in DBS. Methods: Orientation angles of directional leads (Cartesia^TM; Boston Scientific, Marlborough, MA, USA) were determined using CT imaging. Therefore, a sequential algorithm was developed that quantitatively compares the similarity of the observed CT artifacts with calculated artifact patterns based on the lead’s orientation marker and a geometric model of the segmented electrodes. Measurements of seven ground truth phantoms and three leads with 60 different configurations of lead implantation and orientation angles were analyzed for validation. Results: The accuracy of the determined electrode orientation angles was –0.6 ± 1.5° (range: –5.4 to 4.2°). This accuracy proved to be sufficiently high to resolve even subtle differences between individual leads. Conclusions: The presented algorithm is user independent and provides highly accurate results for the orientation of the segmented electrodes for all angular constellations that typically occur in clinical cases.

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