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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