ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.

摘要: Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition analysis, as particularly sensitive to structured noise resulting from hardware, software, environmental differences. Here, we investigated whether novel clean up tool was capable reducing center-related differences between healthy subjects. We analyzed three Tesla 72 subjects, half whom were scanned with eyes closed Philips Achieva system The Netherlands, open Siemens Trio the UK. After pre-statistical processing individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) used remove components data. GICA dual regression run non-parametric statistics compare spatial maps groups before after applying FIX. Large significant found all resting-state networks study sites using FIX, most which reduced non-significant between-center difference medial/primary visual network, presumably reflecting protocol, remained statistically significant. FIX helps facilitate research by diminishing In doing so, it improves combination existing different centers new settings comparison rare diseases risk genes adequate sample size remains challenge.