A Python-Based Toolkit for Advanced White Matter Tractometry and Voxel-Level Connectivity
Poster No:
1787
Submission Type:
Abstract Submission
Authors:
Vandana Shrinivasa1, Pooja Murugiah1, Radha Kumari2, Hampali Shamanth2, Rimjhim Agrawal2
Institutions:
1National Institute of Technology Karnataka, Surathkal, Karnataka, 2BrainsightAI, Bengaluru, Karnataka
First Author:
Co-Author(s):
Introduction:
White matter tractometry plays a crucial role in elucidating the brain's structural organization and its relationship to function. Although established software packages (e.g., DSI Studio; Yeh, 2020) provide standard tract statistics, researchers often require more extensible frameworks to analyze a broader spectrum of geometric properties. We have developed a new Python-based toolkit that computes an expanded set of shape descriptors, incorporates higher-order geometric derivatives, and morphological features to cortical parcellations. By providing an open, modular, and fully programmable environment, this toolkit facilitates richer interpretations of white matter architecture and improves the reproducibility and scalability of advanced tractometry analyses.
Methods:
Data and Implementation:
We tested our toolkit on publicly available diffusion MRI datasets containing multiple canonical white matter pathways (e.g., association, projection and commissural tracts). The pipeline is implemented in Python, leverages standard neuroimaging libraries, and organizes results into structured DataFrames for subsequent statistical or machine learning analyses.
Shape-Based Metrics:
Beyond conventional measures such as tract length, volume, and surface area, the toolkit extracts additional shape descriptors (e.g., curl, elongation, directional spread). These metrics are computed using custom algorithms that were cross-checked against established references for reliability (Alexander 2000; Yeh, 2020). Unlike existing solutions, which often limit users to predefined measures, our framework allows on-the-fly addition of new shape metrics, granting researchers fine-grained control over their analyses.
Higher-Order Derivative Analyses:
To capture subtle morphological nuances, we implemented higher-order derivative computations on normalized tract coordinates. Up to fourth-order derivatives quantify complex geometric traits like curvature and torsion, offering a sensitive means to detect microstructural or pathological variations. The eigen-decomposition of point-wise covariance matrices of these derivatives provides a clear, quantitative handle on geometric complexity not easily assessed by first-order metrics alone (Lazar, 2010)
.
Voxel-Level Connectivity Mapping:
In addition to shape characterization, the toolkit incorporates voxel-wise connectivity analysis. By co-registering tract endpoints to cortical or subcortical parcellations and computing voxel-level overlaps and directional metrics, researchers can identify region-specific structural connections. This approach integrates morphological data with anatomically defined targets, helping relate tract geometry to potential functional implications (Nucifora, 2007).
We tested our toolkit on publicly available diffusion MRI datasets containing multiple canonical white matter pathways (e.g., association, projection and commissural tracts). The pipeline is implemented in Python, leverages standard neuroimaging libraries, and organizes results into structured DataFrames for subsequent statistical or machine learning analyses.
Shape-Based Metrics:
Beyond conventional measures such as tract length, volume, and surface area, the toolkit extracts additional shape descriptors (e.g., curl, elongation, directional spread). These metrics are computed using custom algorithms that were cross-checked against established references for reliability (Alexander 2000; Yeh, 2020). Unlike existing solutions, which often limit users to predefined measures, our framework allows on-the-fly addition of new shape metrics, granting researchers fine-grained control over their analyses.
Higher-Order Derivative Analyses:
To capture subtle morphological nuances, we implemented higher-order derivative computations on normalized tract coordinates. Up to fourth-order derivatives quantify complex geometric traits like curvature and torsion, offering a sensitive means to detect microstructural or pathological variations. The eigen-decomposition of point-wise covariance matrices of these derivatives provides a clear, quantitative handle on geometric complexity not easily assessed by first-order metrics alone (Lazar, 2010)
.
Voxel-Level Connectivity Mapping:
In addition to shape characterization, the toolkit incorporates voxel-wise connectivity analysis. By co-registering tract endpoints to cortical or subcortical parcellations and computing voxel-level overlaps and directional metrics, researchers can identify region-specific structural connections. This approach integrates morphological data with anatomically defined targets, helping relate tract geometry to potential functional implications (Nucifora, 2007).
·Shape metrics
·Higher order derivatives of streamlines
Results:
Comparisons of fundamental shape metrics against established software confirmed consistency (differences ≤1–2%), ensuring that our newly developed metrics and methods produce stable, reproducible outcomes. The higher-order derivatives revealed distinct geometric patterns, demonstrating improved sensitivity to subtle tract variations. Voxel-level connectivity analyses consistently localized endpoints to their corresponding anatomical regions, underscoring the toolkit's precision and potential for integrative studies combining structure, connectivity, and eventually multimodal data.
Conclusions:
We present a novel, fully programmable Python toolkit that extends beyond conventional white matter tractometry to offer refined shape metrics, higher-order geometric analyses, and voxel-level connectivity assessments. Developed entirely by our team, this toolkit addresses the need for flexible, extensible, and transparent pipelines in diffusion MRI research. By providing a comprehensive framework for morphometric and connectivity analyses, our solution empowers investigators to uncover more intricate relationships between white matter structure and brain function, paving the way for deeper insights into both healthy and pathological conditions.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Neuroinformatics and Data Sharing:
Informatics Other
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
Computing
Data analysis
Data Organization
Informatics
Machine Learning
Statistical Methods
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Doyen, S., et al. (n.d.). Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex. PMC Articles.
Lazar, M. (2010). Mapping brain anatomical connectivity using white matter tractography. NeuroImage, 49(3), 2347–2363. https://doi.org/10.1016/j.neuroimage.2009.11.007
Nucifora, P. G., Verma, R., Lee, S. K., & Melhem, E. R. (2007). Diffusion-tensor MR imaging and tractography: Exploring brain microstructure and connectivity. Radiology, 245(2), 367–384. https://doi.org/10.1148/radiol.2452070192
Yeh, F.-C. (2020). Shape analysis of the human association pathways. NeuroImage, 223, 117329. https://doi.org/10.1016/j.neuroimage.2020.117329