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Abstract #1648

Modeling serial correlations in fMRI time series collected by faster TRs

Jingyuan Chen1 and Gary Glover2

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

Serial correlations of fMRI time series are altered at faster TRs (<1s) due to reduced signal to noise ratio per time frame. Thus, caution is advised when utilizing serial correlation models describing long TR (2~3s) conditions with fast acquisitions. Here, we show that statistical models alternative to the commonly used first order auto-regressive (AR) model – AR2 and AR1moving-average(MA)1 model —can achieve reasonable fitting for short TR data and improve the accuracy of activation estimates. Potential model bias can be further reduced by low-pass filtering.

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