Mert R. Sabuncu

TL;DR: An overview of various unsupervised and supervised machine learning applications to resting-state functional Magnetic Resonance Imaging (rs-fMRI) is presented and a methodical taxonomy of machine learning methods in resting- state fMRI is offered.

TL;DR: An ensemble learning strategy to combine the predictions from models trained on connectivity data extracted using different parcellations is proposed, which overcomes the limitations of traditional machine learning models for connectomes that often rely on region-based summary statistics and/or linear models.

TL;DR: This study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs, therefore, kernel regression might serve as a useful baseline algorithm for future studies.

TL;DR: A probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates is presented.

TL;DR: This article demonstrates that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods, and presents empirical results obtained with a large-scale, publicly available knee MRI dataset, where LouPE offered superior reconstruction quality across different conditions.