Mert R. Sabuncu

TL;DR: In this paper , the authors investigated the brain's regional activation selectivity and inter-individual differences in human brain responses to various sets of natural and synthetic (generated) images via two functional MRI (fMRI) studies and found that individual-specific synthetic (and not natural) images derived using a personalized encoding model elicited significantly higher responses compared to synthetic images derived from the group level or other subjects' encoding models.

TL;DR: This work considers the novel problem of detecting cannabis intoxication based on resting-state fNIRS data and suggests that a recurrent neural network model trained on dynamic functional connectivity matrices, computed on sliding windows, coupled with the proposed data augmentation strategy yields the best accuracy for this application.

TL;DR: In this paper , the authors introduce patchwork learning (PL), a novel paradigm that integrates information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites.

TL;DR: In this paper , the authors propose a non-learnable and nonparametric Nadaraya-Watson (NW) prediction head that can be used with any neural network architecture, where the weights are computed from distances between the query feature and support features.

TL;DR: The approach implemented here may enable clinicians to more accurately predict a chronic stroke patient’s individual response to intervention and improve the accuracy of prognosis in chronic stroke patients.