Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

ABSTRACT:

Significance

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results.

Aim

We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses.

Approach

We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kal

SUBMITTER: Guan S 

PROVIDER: S-EPMC11498316 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature