Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.

ABSTRACT:

Significance

Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data.

Aim

We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination.

Approach

Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the perf

SUBMITTER: Iester C 

PROVIDER: S-EPMC11448702 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature