WhiFuN: A Toolbox to Map the White Matter Functional Networks of the Human Brain.
Poster No:
1394
Submission Type:
Abstract Submission
Authors:
Pratik Jain1, Andrew Micheal2, Pan Wang3, Xin Di1, Bharat Biswal1
Institutions:
1New Jersey Institute of Technology, Newark, NJ, 2Duke Institute for Brain Sciences, Durham, NC, 3The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, Sichuan
First Author:
Co-Author(s):
Introduction:
Functional connectivity (FC) derived from functional magnetic resonance imaging (fMRI) of gray matter (GM) regions has proven effective in identifying reliable markers for both healthy and clinical populations. Despite white matter (WM) constituting approximately 50% of the human brain and evidence supporting the presence of blood oxygen level-dependent (BOLD) signals in WM, most current analyses remain limited to GM regions. To address this gap, we introduce the White Matter Functional Networks (WhiFuN) Toolbox, a comprehensive solution for automated preprocessing of both WM and GM fMRI data. WhiFuN can compute WM and GM functional networks (FNs), extract average time series signals from the regions of interest (ROIs), and identify group differences between cohorts or associations with behavioral data with appropriate statistical tests. Users can also access standard WM atlases, such as the JHU atlas [3,4], or manually provide a new atlas to define the ROIs and create the corresponding FC. Designed with a user-friendly graphical interface, WhiFuN enables seamless execution from preprocessing to group-level analysis without requiring any prior programming expertise.
Methods:
WhiFuN was developed using MATLAB (version R2022b) under the Windows environment. In addition to custom-written code, several functions from SPM12 were used for preprocessing and quality control. Figure 1 shows a graphical representation of the main features of WhiFuN. To demonstrate the application of WhiFuN, we present sex differences in the WM-FC. Data from the human connectome project (HCP) 100 unrelated dataset (46 M, mean age 29.1 ± 3.7 years) was used. The rsfMRI data corresponding to rest1 (LR) scans were used to create the WM and GM-FNs, and the rest2 (LR) scan/session was used to evaluate reproducibility. The unprocessed raw images were preprocessed using WhiFuN with the default parameters for the preprocessing steps (See Figure 2A). The preprocessing was evaluated using the quality control plots saved by WhiFuN. Moreover, the toolbox created WM/GM-FNs using the K-means algorithm described in [1]. A grid search of K-values between 2 and 22 was done and an optimal K-value of 10 for WM and 9 for GM was chosen using the elbow and average dice-coefficient methods [1]. WhiFuN also segmented Corpus Callosum (CC), such that the CC sub-regions (called CC-networks) were maximally correlated to the WM-FNs [2]. Figure 2A shows the main GUI of WhiFuN. The statistics module of WhiFuN was used to find sex differences in 98 subjects (2 subjects' scans were discarded as they had excessive motion), considering age as a covariate. Additionally, WhiFuN automatically generates WM, GM, and CC maps with six views as publication-quality image files that users can use directly in manuscripts. These brain maps appear adjacent to the FC matrix (see Figure 2B) to better visualize the connections.
Results:
Figure 2B (completely generated by WhiFuN) shows the t-values for the sex differences between the subjects. A significant sex difference was observed in FC between the WM-FN that included the posterior corona radiata, the superior longitudinal fasciculus (WM9), and the left body of CC (CC6) (t = 3.67, p (FDR corrected) = 0.04). The mean FC between WM9 and the CC6 in females was 0.38, while the mean of the same connection in males was 0.15. The user can observe the mean connection values using Display FC feature of WhiFuN. It was found that connectivity between WM9 and CC6 was also significantly different between sexes when the second session data was used (t = 3.59, p(FDR corrected) = 0.026)
Conclusions:
We have developed WhiFuN with Graphical User interface that streamlines the complete workflow of analyzing WM and GM BOLD signals from preprocessing and quality control mechanisms, generating publication-ready visualizations of FNs and FC matrices, computing statistical tests on FCs, thereby accelerating WM BOLD analyses. Using WhiFuN significant sex differences in healthy controls were found in WM-CC-FC.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Machine Learning
Open-Source Software
White Matter
Other - toolbox; White-Matter Functional Connectivity; K-means
1|2Indicates the priority used for review
Abstract Information
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2. Wang P., et al. (2020), The Organization of the Human Corpus Callosum Estimated by Intrinsic Functional Connectivity with White-Matter Functional Networks, Cerebral Cortex, 30(5), 3313-3324.
3. Oishi K., et. al. (2009), Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants, NeuroImage, 46(2), 486-499.
4. Mori S., et. al., (2008), Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template, NeuroImage, 40(2), 570-582.