Impact of multi-echo ICA modeling decisions on motor-task fMRI analysis.

ABSTRACT: Multi-echo independent component analysis (ME-ICA) has been demonstrated to improve sensitivity and reliability of task functional magnetic resonance imaging (fMRI) data and, in particular, motor-task data with inherent task-correlated head motion. However, previous work has shown that an overly aggressive ME-ICA denoising approach may unintentionally remove task-related signal, while a more conservative approach may not effectively mitigate noise. While the effects of varied implementations of ME-ICA on signal and noise characteristics have been tested thoroughly in breath-hold data, the effects of similar modeling decisions have not been studied in motor-task data, which present with a more localized neural response. Here, we tested and compared the impacts of three analysis methods usin

SUBMITTER: Reddy NA 

PROVIDER: S-EPMC12697738 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature