An open-source automated platform for three-dimensional visualization of subdural electrodes using CT-MRI coregistration.

OBJECTIVE: Visualizing implanted subdural electrodes in three-dimensional (3D) space can greatly aid in planning, executing, and validating resection in epilepsy surgery. Coregistration software is available, but cost, complexity, insufficient accuracy, or validation limit adoption. We present a fully automated open-source application, based on a novel method using postimplant computerized tomography (CT) and postimplant magnetic resonance (MR) images, for accurately visualizing intracranial electrodes in 3D space. METHODS: CT-MR rigid brain coregistration, MR nonrigid registration, and prior-based segmentation were carried out on seven patients. Postimplant CT, postimplant MR, and an external labeled atlas were then aligned in the same space. The coregistration algorithm was validated by manually marking identical anatomic landmarks on the postimplant CT and postimplant MR images. Following coregistration, distances between the center of the landmark masks on the postimplant MR and the coregistered CT images were calculated for all subjects. Algorithms were implemented in open-source software and translated into a "drag and drop" desktop application for Apple Mac OS X. RESULTS: Despite postoperative brain deformation, the method was able to automatically align intrasubject multimodal images and segment cortical subregions, so that all electrodes could be visualized on the parcellated brain. Manual marking of anatomic landmarks validated the coregistration algorithm with a mean misalignment distance of 2.87 mm (standard deviation 0.58 mm)between the landmarks. Software was easily used by operators without prior image processing experience. SIGNIFICANCE: We demonstrate an easy to use, novel platform for accurately visualizing subdural electrodes in 3D space on a parcellated brain. We rigorously validated this method using quantitative measures. The method is unique because it involves no preprocessing, is fully automated, and freely available worldwide. A desktop application, as well as the source code, are both available for download on the International Epilepsy Electrophysiology Portal (https://www.ieeg.org) for use and interactive refinement.

Temporal changes of neocortical high-frequency oscillations in epilepsy.

High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

The genetic structure of Drosophila ananassae populations from Asia, Australia and Samoa.

Information about genetic structure and historical demography of natural populations is central to understanding how natural selection changes genomes. Drosophila ananassae is a widespread species occurring in geographically isolated or partially isolated populations and provides a unique opportunity to investigate population structure and molecular variation. We assayed microsatellite repeat-length variation among 13 populations of D. ananassae to assess the level of structure among the populations and to make inferences about their ancestry and historic biogeography. High levels of genetic structure are apparent among all populations, particularly in Australasia and the South Pacific, and patterns are consistent with the hypothesis that the ancestral populations are from Southeast Asia. Analysis of population structure and use of F-statistics and Bayesian analysis suggest that the range expansion of the species into the Pacific is complex, with multiple colonization events evident in some populations represented by lineages that show no evidence of recent admixture. The demographic patterns show isolation by distance among populations and population expansion within all populations. A morphologically distinct sister species, D. pallidosa, collected in Malololelei, Samoa, appears to be more closely related to some of the D. ananassae populations than many of the D. ananassae populations are to one another. The patterns of genotypic diversity suggest that many of the individuals that we sampled may be morphologically indistinguishable nascent species.