Probabilistic Voxel-to-Connectome Mapping (PVCM): A Framework Linking White Matter to Connectome
Poster No:
1292
Submission Type:
Abstract Submission
Authors:
Yanlin Yu1, Chengyi Yuan1, Qihao Guo2, Chu-Chung Huang1
Institutions:
1East China Normal University, Shanghai, Asia, 2Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, Asia
First Author:
Co-Author(s):
Qihao Guo
Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Shanghai, Asia
Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Shanghai, Asia
Introduction:
Understanding the relationship between white matter (WM) fiber and inter-cortical connection is crucial for unraveling the brain network organization and the dysfunctions caused by WM damage. Although individual-level tractography method has been widely applied, there is currently a lack of normative model for mapping the relationship between spatial location of WM and the probabilistic connectivity between brain regions. To address this gap, we propose the Probabilistic Voxel-to-Connectome Mapping Model (PVCM). PVCM links each WM voxel to the probabilistic structural connectome, enabling the construction of normative connectivity. The PVCM method shows significant potential for applications in both normative connectome modeling and pathological contexts.
Methods:
We utilized the HCP 7T dataset (N=178) to perform whole-brain tractography, generating 2 million streamlines per subject after SIFT processing (Tournier et al., 2019). A labeled white matter mask in MNI space, with each voxel assigned a unique label, was first nonlinearly registered to individual DWI spaces using nearest-neighbor interpolation. This ensured that labeled white matter voxels were spatially matched among subjects, and the endpoint coordinates of streamlines transversing each voxel were then recorded (Fig. 1a). These streamline endpoints were assigned to brain regions as defined by the selected atlas, resulting in a binary voxel-to-connectome matrix for each voxel, indicating its voxel's connectivity pattern (Fig. 1b). The probabilistic voxel-to-connectome map was then generated by averaging the binary voxel-to-connectome matrices across individuals (Fig. 1c). PVCM were generated using several widely used atlases, including Schaefer400, HCPMMP, aparc, etc.
·Figure 1. The framework and applications of PVCM.
Results:
The PVCM allows various inputs to address diverse research questions. For example: (i) By inputting a whole white matter mask, PVCM can generate normative structural connectomes (SC) (Fig. 1d). PVCM-derived normative SC demonstrated greater similarity to individual connectomes compared to Enigma-derived normative SC (Larivière et al., 2021). This was validated in young adults (dataset1, N=41, 20.3 ± 1.6 yr) and older adults (dataset2, N=209, 57.5 ± 15.0 yr) (Fig. 2a); (ii) Randomly selected ROI can be used as inputs to identify ROI-transversed streamline projections (Fig. 1e). For instance, PVCM-derived connectivity maps for ROIs in the corpus callosum showed strong correlations with individual tractography results (Fig. 2b); (iii) PVCM accommodates weighted WM maps, such as lesion probability maps, to generate lesion-weighted connectomes at the individual level (Fig. 1f). Applying white matter hyperintensity (WMH) probability maps to the PVCM allows the generation of WMH-weighted lesion connectomes. In older adults dataset (N=397, 59.5 ± 13.5 yr), we generated an averaged lesion connectomes and a WMH-affected map (left panel of Fig. 2c). Neurosynth continuous decoding of WMH-affected regions revealed the associations with action, attention, and visual attention (Fig. 2c) (www.neurosynth.org) (Yarkoni et al., 2011).
·Figure 2. The validation of PVCM-derived applications.
Conclusions:
The PVCM offers a flexible and wide range of applications for probabilistically linking white matter voxel to population-based structural connectome probabilities. Its applications demonstrate high reliability in both normative and individual-level regional connectivity. Additionally, PVCM effectively maps the WMH-related topography and the associated cognitive functions, highlighting its potential to provide novel insights into white matter lesions. This framework may also assist in deep brain stimulation interventions targeting specific white matter bundles, such as the anterior limb of the internal capsule.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis 1
Keywords:
Aging
Computational Neuroscience
Computing
Modeling
STRUCTURAL MRI
Structures
Tractography
White Matter
Workflows
1|2Indicates the priority used for review
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Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NEUROIMAGE, 202. https://doi.org/10.1016/j.neuroimage.2019.116137
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), Article 8. https://doi.org/10.1038/nmeth.1635