Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi-automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface-based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring-based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface-based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions.

Background: Brain-and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlasfree brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. Methods: Proton-density, T 2-weighted, T 1-weighted, brain-free water, 3D FLAIR, 3D T 2-weighted, and 3D T 2 *-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV þ subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV þ subjects (cohort

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