Paul Sajda

TL;DR: It is concluded that most microstructural and mechanical properties of the distal tibia can be derived efficiently from µMR images and can provide additional information regarding bone quality.

TL;DR: Analysis of human electroencephalography data is used to show that population responses on the scalp can capture choice-predictive activity that builds up gradually over time with a rate proportional to the amount of sensory evidence, consistent with the properties of a drift-diffusion-like process as characterized by computational modeling.

TL;DR: Single-trial analysis of electroencephalography is used to ascertain the cortical origins of response time variability in a rapid serial visual presentation (RSVP) task and finds that the majority of the latency is introduced by component activity which begins far-frontally 200 ms prior to the response and proceeds to become parietally distributed near the time of response.

TL;DR: A probability model over image spaces that employs a pyramid representation to factor images across scale and a tree-structured set of hidden variables to capture long-range spatial dependencies and is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree.

TL;DR: The results show that trial-to-trial variability in EEG components, acquired simultaneously with fMRI, can yield task-relevant BOLD activations that are otherwise unobservable using traditional fMRI analysis.