  The technology of functional Magnetic Resonance Imaging (fMRI) based on Blood
Oxygen Level Dependent (BOLD) signal has been widely used in clinical
treatments and brain function researches. The BOLD signal has to be
preprocessed before being analyzed using either functional connectivity
measurements or statistical methods. Current researches show that data
preprocessing steps may influence the results of analysis, yet there is no
consensus on preprocessing method. In this paper, an evaluation method is
proposed for analyzing the preprocessing pipeline of resting state BOLD fMRI
(rs-BOLD fMRI) data under putative task experiment designs to cast some lights
on the preprocessing stage, covering both first and second level analysis. The
choices of preprocessing parameters and steps are altered to investigate
preprocessing pipelines while observing statistical analysis results, trying to
reduce false positives as reported by Eklund et al. in their 2016 PNAS paper.
All of the experiment data are separated into 7 datasets, consisting of 220
healthy control samples and 136 patient data that are from 38 incomplete Spinal
Cord Injury (SCI) patients and 16 Cerebral Stroke (CS) patients, including
multiple scans of some patients at different time. These data were acquired
from two different MRI scanners, which may cause difference in analysis
results. The evaluation result shows that it has little effect to change
parameters in each steps of the classical preprocessing pipeline, which
consists head motion correction, normalization and smoothing. Removing time
points and the following detrend step can reduce false positives. However,
covariates regression and filtering has complicated effects on the data. Note
that for single subject analysis, false positives declined consistently after
filtering. The result of patient data and healthy controls data which are
scanned under the same machine with ...
