Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

ABSTRACT: Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, u

SUBMITTER: Glasser MF 

PROVIDER: S-EPMC6237431 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature