No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI.

ABSTRACT: As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLM^window). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native

SUBMITTER: Kopel R 

PROVIDER: S-EPMC6503944 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature