Computing a gradient map on a cortical surface mesh by regressing scalar values onto neighbors projected into the tangent plane. The gradient map is then segmented with a priority-flooding watershed algorithm built on a heap-based priority queue.
Using the squared Mahalanobis distance, which is chi-squared distributed under multivariate normality, to compare connectivity fingerprints between one cortical region and every other region, with Ledoit-Wolf covariance estimates and a check that the empirical distances follow the expected distribution.
Translating, rotating, and warping an MRI slice with OpenCV, as a first step toward simulating head motion.
What I took away from Neurohackademy 2018 at the UW eScience Institute. Mostly it was learning how to write software with a team, while building parcellation_fragmenter.
Using rank-one updates to fold new observations into the DMD linear operator without recomputing a full matrix inverse.
An introduction to dynamic mode decomposition, which splits spatiotemporal data into spatial modes and their time dynamics. I derive the algorithm using a truncated SVD, with resting-state MRI as the motivating use case.