Jack Grinband

TL;DR: Using event-related functional MRI, it was discovered that activity in a fronto-striatal-thalamic network, consisting of the medial frontal gyrus, anterior insula, ventral striatum, and dorsomedial thalamus, was modulated by categorization uncertainty.

TL;DR: For The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas and relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human directed training.

TL;DR: It is shown that brief differences in duration are detectable, making it possible to dissociate the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.

TL;DR: This study shows that the conflict monitoring model fails to predict the relationship between error likelihood and RT, and the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task.

TL;DR: Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool in the detection and quantification of hemorrhage on NCCT.