Multivariate analysis of fMRI data has benefited substantially from advances in machine learning. Most recently, a range of probabilistic latent variable models applied to fMRI data have been successful in a variety of tasks, including identifying similarity patterns in neural data (Representational Similarity Analysis and its empirical Bayes variant, RSA and BRSA; Intersubject Functional Connectivity, ISFC), combining multi-subject datasets (Shared Response Mapping; SRM), and mapping between brain and behavior (Joint Modeling). Although these methods share some underpinnings, they have been developed as distinct methods, with distinct algorithms and software tools...
Authors
Michael Shvartsman
Narayanan Sundaram
Research Scientist, Parallel Computing Lab
Mikio C. Aoi
Adam Charles
Theodore C. Wilke
Jonathan D. Cohen
Related Content
Segmenting Brain Tumors with Symmetry
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task. A healthy human brain is...
A Semi-supervised Method for Multi-subject fMRI Functional Alignment
Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple...
Capturing Shared and Individual Information in fMRI Data
Cognitive neuroscience seeks to explain the organization of the brain, but typically focuses on aspects that are shared across people...
BIDS Apps: Improving Ease of Use, Accessibility, and...
In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized...
