Abstract #0062

Nuisance Regression of High-frequency FMRI Data: De-noising Can Be Noisy

Jingyuan E. Chen^1,2, Hesamoddin Jahanian², and Gary H. Glover^1,2

¹Electrical Engineering, Stanford University, Stanford, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States

A growing number of studies using fast sampling have demonstrated the persistence of functional connectivity (FC) in resting state (RS) networks beyond the conventional 0.1 Hz. However, some RS studies have reported frequencies (e.g., up to 5 Hz) not easily supported by canonical hemodynamic response functions. Here, we investigated the influence of a common preprocessing step – whole-band (the entire frequency band resolved by a short TR) linear nuisance regression (LNR) – on RSFC. We demonstrated via both simulation and real data that LNR can introduce network structures in HF bands, which may largely account for the observations of HF-RSFC.

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