FuNP (Fusion of Neuroimaging Preprocessing) Pipelines: A Fully Automated Preprocessing Software for Functional Magnetic Resonance Imaging

Abstract

The preprocessing of functional magnetic resonance imaging (fMRI) data is necessary to remove unwanted artifacts and transform the data into a standard format. There are several neuroimaging data processing tools that are widely used, such as SPM, AFNI, FSL, FreeSurfer, Workbench, and fMRIPrep. Different data preprocessing pipelines yield differing results, which might reduce the reproducibility of neuroimaging studies. Here, we developed a preprocessing pipeline for T1-weighted structural MRI and fMRI data by combining components of well-known software packages to fully incorporate recent developments in MRI preprocessing into a single coherent software package. The developed software, called FuNP (Fusion of Neuroimaging Preprocessing) pipelines, is fully automatic and provides both volume- and surface-based preprocessing pipelines with a user-friendly graphical interface. The reliability of the software was assessed by comparing resting-state networks (RSNs) obtained using FuNP with pre-defined RSNs using open research data (n = 90). The obtained RSNs were well-matched with the pre-defined RSNs, suggesting that the pipelines in FuNP are reliable. In addition, image quality metrics (IQMs) were calculated from the results of three different software packages (i.e., FuNP, FSL, and fMRIPrep) to compare the quality of the preprocessed data. We found that our FuNP outperformed other software in terms of temporal characteristics and artifacts removal. We validated our pipeline with independent local data (n = 28) in terms of IQMs. The IQMs of our local data were similar to those obtained from the open research data. The codes for FuNP are available online to help researchers.

Keywords: data preprocessing; fully automated software; functional magnetic resonance imaging; fusion of existing software; volume- and surface-based preprocessing.

FIGURE 1

Diagram of the preprocessing steps for volume-based (A) T1-weighted structural MRI and (B) fMRI data.

FIGURE 2

Preprocessing steps for volume-based T1-weighted structural MRI data. (A) De-oblique step. Example images of (left) tilted and (right) non-tilted data are shown. (B) Matched data with different orientations to the same orientation. (C) Magnetic field inhomogeneity correction. (D) Non-brain tissue removal. (E) Registration onto the standard space. (F) Segmentation of brain tissues into gray matter (GM; red), white matter (WM; yellow), and cerebrospinal fluid (CSF) (blue).

FIGURE 3

Preprocessing steps for volume-based fMRI data. (A) Removal of the first few volumes. (B) Slice timing correction. (C) Head motion correction (left) and volume scrubbing (right). (D) Intensity normalization. (E) Two-stage registration. (F) Nuisance variable removal via ICA-FIX. (G) Temporal filtering. (H) Spatial smoothing.

FIGURE 4

Diagram of the preprocessing steps for surface-based (A) T1-weighted structural MRI and (B) fMRI data.

FIGURE 5

Screenshot of the developed software, called FuNP.

FIGURE 6

Generated VICs using the HCP data (labeled in a white font) along with pre-defined RSNs (labeled in a yellow font) (Smith et al., 2009). The cross-correlation values of the spatial maps between the generated VICs and RSNs are presented.

FIGURE 7

Generated SICs using the HCP data matched with known RSNs.

FIGURE 8

The IQMs of the volume-based preprocessed rs-fMRI data using different software packages. The values were plotted using violin plots. The white circle denotes the median value. The AOR and AQI were very small but the results of some software packages had high variability.