M. Muge Karaman1,
Andrew S. Nencka2, Daniel B. Rowe1, 2
1Department
of Mathematics, Statistics, and Computer Science, Marquette University,
Milwaukee, WI, United States; 2Department of Biophysics, Medical
College of Wisconsin, Milwaukee, WI, United States
Temporal processing is a common practice in fMRI and functional connectivity MRI studies as a way to improve the resulting images. However, such processing alters the signal and noise properties of the data and could have severe effect on the statistical maps, including functional activations, computed from the processed images. We develop a mathematical framework that allows one to analytically analyze the effects of time series preprocessing, and thus contributes to produce more accurate functional activations. This exact method considers linear operators to perform spatial processing, reconstruction and Fourier anomalies correction, and temporal processing on the acquired signal measurements.