NHP-DWIProc: a diffusion MRI processing suite towards non-human primate datasets
Poster No:
1850
Submission Type:
Abstract Submission
Authors:
Jason Kai1, Florian Rupprecht1, Samuel Alldritt1, Kyoungseob Byeon1, Shaun Warrington2, Stamatios Sotiropoulos2, Michael Milham1, Gregory Kiar1, Ting Xu1
Institutions:
1Child Mind Institute, New York, NY, 2University of Nottingham, Nottingham, United Kingdom
First Author:
Co-Author(s):
Introduction:
Non-human primates (NHPs) are essential animal models for understanding human brain organization due to their close evolutionary and neuroanatomical similarities to humans. Their unique role in neuroscience has enabled critical advancements in studying brain connectivity, bridging the gap between invasive animal studies and non-invasive human imaging [1-3]. However, preprocessing NHP diffusion MRI (dMRI) data presents unique challenges compared to human data, including variability in acquisition protocols, lower signal-to-noise ratio (SNR), and species-specific anatomical differences [4]. Existing pipelines, largely developed for human imaging, fail to address these challenges effectively, highlighting the need for preprocessing tools for NHP dMRI [5]. To address these issues, we introduce NHP-DWIProc, a customizable processing suite designed to accommodate diverse acquisition protocols and inter-species anatomical variability. We demonstrate its utility using publicly available PRIME-DE datasets and validate its results with a gold-standard tracer-injection study and a high-resolution ex-vivo macaque dataset.
Methods:
NHP-DWIProc was developed in Python, leveraging Niwrap [6] to interface with common neuroimaging command-line tools. The workflow is modular, splitting processing into three key stages (Fig. 1): (i) preprocessing, (ii) reconstruction, and (iii) post-analysis. Each stage comprises configurable steps accessible via a command-line interface or a configuration file, ensuring flexibility and integration with external datasets at each stage of the workflow.
To demonstrate the utility, we applied NHP-DWIProc to process dMRI data from the UCDavis 2009 cohort (n=38), available via PRIME-DE. Previously processed anatomical data [2] was integrated at various stages, including tissue type map generations for whole-brain anatomically constrained tractography (25M streamlines) seeded from subcortical structures and the cortical grey-white matter interface. We aligned tractography to the template space and applied the Markov91 parcellation [7], enabling the construction of the structural connectivity matrix (91 parcels x 91 parcels x 2 hemispheres). Finally, we compared the group averaged connectivity matrix from in vivo dMRI data with those constructed from a high-resolution ex-vivo dataset [8,9], as well as from a tracer-injection dataset (29 injection sites x 91 parcels) [7,10].
To demonstrate the utility, we applied NHP-DWIProc to process dMRI data from the UCDavis 2009 cohort (n=38), available via PRIME-DE. Previously processed anatomical data [2] was integrated at various stages, including tissue type map generations for whole-brain anatomically constrained tractography (25M streamlines) seeded from subcortical structures and the cortical grey-white matter interface. We aligned tractography to the template space and applied the Markov91 parcellation [7], enabling the construction of the structural connectivity matrix (91 parcels x 91 parcels x 2 hemispheres). Finally, we compared the group averaged connectivity matrix from in vivo dMRI data with those constructed from a high-resolution ex-vivo dataset [8,9], as well as from a tracer-injection dataset (29 injection sites x 91 parcels) [7,10].
Results:
Fig. 1A illustrates the workflow of NHP-DWIProc and demonstrates the large-scale quality control figures generated at various stages of processing PRIME-DE data. A manual quality control process was conducted for identification of discrepancies from processed data. As a result, two runs for a single participant were excluded due to incomplete data.
Fig. 2A and 2B present structural connectivity matrices derived from our pipeline using in vivo dMRI data. These results were validated against the connectivity matrix built from an independent pipeline with high-resolution ex-vivo data and a gold-standard tracer-based connectivity matrix. The structural connectome exhibited high similarity across different datasets and methodologies.
Fig. 2A and 2B present structural connectivity matrices derived from our pipeline using in vivo dMRI data. These results were validated against the connectivity matrix built from an independent pipeline with high-resolution ex-vivo data and a gold-standard tracer-based connectivity matrix. The structural connectome exhibited high similarity across different datasets and methodologies.
Conclusions:
In this work, we demonstrated the feasibility and reproducibility of NHP-DWIProc, a configurable NHP dMRI processing suite, applying it to a PRIME-DE dataset. This tool, specifically designed for NHP dMRI data with parallels to human-focused processing tools, supports optimal species-specific analyses and enhances cross-species comparison. Future work will integrate advanced modelling and analysis, as well as optimization of processing parameters to both in-vivo and ex-vivo NHP datasets. Ultimately, NHP-DWIProc aims to advance translational research toward cross-species discoveries.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Neuroinformatics and Data Sharing:
Workflows 1
Keywords:
Open-Source Code
Open-Source Software
Tractography
Workflows
Other - Non-human primates; diffusion MRI; structural connectivity; connectome; chemical tracer
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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[2] Haber, S. N., Tang, W., Choi, E. Y., Yendiki, A., Liu, H., Jbabdi, S., ... & Phillips, M. (2020). Circuits, networks, and neuropsychiatric disease: transitioning from anatomy to imaging. Biological Psychiatry, 87(4), 318-327.
[3] Kai, J., Khan, A. R., Haast, R. A., & Lau, J. C. (2022). Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability. NeuroImage, 262, 119553.
[4] Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., ... & Schroeder, C. E. (2018). An open resource for non-human primate imaging. Neuron, 100(1), 61-74.
[5] Valcourt Caron, A., Shmuel, A., Hao, Z., & Descoteaux, M. (2023). versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database. Frontiers in Neuroinformatics, 17, 1191200.
[6] Florian Rupprecht, Jason Kai, birajstha, Elizabeth Kenneally, Connor Lane, Greg Kiar, Mathieu Dugré, John Vito, mina94az, Tristan Glatard, & Steve Giavasis. (2024). childmindresearch/niwrap. Zenodo. https://doi.org/10.5281/zenodo.14509680
[7] Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., ... & Kennedy, H. (2014). A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral cortex, 24(1), 17-36.
[8] Warrington, S., Thompson, E., Bastiani, M., Dubois, J., Baxter, L., Slater, R., ... & Sotiropoulos, S. N. (2022). Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications. Science Advances, 8(42), eabq2022.
[9] Howell, A. M., Warrington, S., Fonteneau, C., Cho, Y. T., Sotiropoulos, S. N., Murray, J. D., & Anticevic, A. (2024). The spatial extent of anatomical connections within the thalamus varies across the cortical hierarchy in humans and macaques. eLife, 13.
[10] Donahue, C. J., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T. E., Dyrby, T. B., ... & Glasser, M. F. (2016). Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. Journal of Neuroscience, 36(25), 6758-6770.