  In recent years, the lack of reproducibility of research findings has become
an important source of concerns in many scientific fields, including functional
Magnetic Resonance Imaging (fMRI). The low statistical power often observed in
fMRI studies was identified as one of the leading causes of irreproducibility.
The development of data sharing opens up new opportunities to achieve larger
sample sizes by reusing existing data. FMRI studies proceed by first preparing
subject-level data using a given analysis pipeline and then combining those
into a group analysis. Historically the subject-level analysis pipeline was
identical for all subjects. As practices evolve towards more data reuse,
researchers might want to directly combine subject-level data thata were
processed using different pipelines. Here, we investigate the impact of
combining subject-level data processed with different pipelines in
between-group fMRI studies. We used the HCP Young-Adult dataset (N=1,080
participants) processed with 24 different pipelines. We then performed
between-group analyses comparing subject data processed with different
pipelines. We worked under the null hypothesis of no differences between
subjects and compared the estimated false-positive rates obtained with the
nominal rates. We showed that the analytical variability induced by the
parameters explored in this dataset increases the false positive rates of
studies combining data from different pipelines. We conclude that different
processed subject data cannot be combined without taking into account the
processing applied on these data.
