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Abstract #4144

Decreasing False Positives and Negatives from Spatiotemporal Processing of FMRI

M. Muge Karaman 1 , Daniel B. Rowe 1,2 , and Andrew S. Nencka 2

1 Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, WI, United States, 2 Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States

In fMRI and fcMRI, many studies have aimed to alleviate the data through spatial and temporal processing. While such processing alleviates the noise, it alters the statistical properties of the data by inducing correlations of no biological origin. We propose a linear model to precisely quantify the correlations induced by spatiotemporal processing, and expand the current complex-valued fMRI model to incorporate the effects of processing into the final analysis. The proposed model provides a true interpretation of the acquired data and in turn contributes to producing more accurate functional activation and connectivity statistics by decreasing false negative and positives.

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