This collection comprises the abstracts presented during poster, power pitch and oral sessions at the Inaugural Conference of the International Society for Tractography (IST Conference 2025), held in Bordeaux, France, from October 13-16, 2025. The conference was designed to foster meaningful exchange and collaboration between disparate fields. The overall focus was on advancing research, innovation, and community in the common fields of interest: neuroanatomy, tractography methods and scientific/clinical applications of tractography. The included abstracts cover the latest advancements in tractography, Diffusion MRI, and related fields including new work on; neurological and psychiatric disorders, deep brain stimulation targeting, and brain development. This landmark event brought together world-leading experts to discuss critical challenges and chart the future direction of the field.
Introduction: Our study aims to develop a robust pipeline with diffusion MRI (dMRI) and tractography to automatically extract a wide range of white matter bundles in the whole brain of preterm infants at term equivalent age with anatomical particularities such as cerebral lesions, increased volumes of cerebral ventricles and extracerebral cerebrospinal fluid (CSF). As a first step, we here focused on sensory and motor tracts (cortico-spinal tract, CST) to characterize the developing microstructural properties. Methods: We collected and analyzed 3T-MRI clinical data of 105 very and extremely preterm babies (gestational age at birth: 24-32 weeks), scanned at term equivalent age (38-43 weeks of post-menstrual age-wPMA). We used the baby-XTRACT tool implemented in FSL that provides tractography protocols for mapping 42 white matter bundles (19 bilateral and 4 central tracts) defining seeding, stop and exclusion regions on the Schuh neonatal template [3]. A key point of this study was to obtain a robust registration of individual images to the template despite the brain anatomical specificities of our population. We optimized this registration by creating brain masks from a combination of iBEAT and drawEM segmentations [4][5] of super-resolved T2w images (0.8mm isotropic,obtained with NiftyMIC [6]), which enabled us to remove part of the CSF. Individual dMRI images without diffusion weighting (b=0) were then coregistered to T2weighted images, which were themselves registered to the template [7]. The registrations were conducted using Ants 2.5.3 with finely tuned parameters. Besides, following the pre-processing of dMRI data (b=1000 s/mm 2 with 42 directions), multiple fibre orientations were estimated with BEDPOSTX with a two fibres model [8]. Tractography was performed with PROBTRACKX through the baby-XTRACT framework and tract reconstructions were visually checked for the CST. Maps of diffusion tensor imaging (DTI) metrics were estimated, allowing us to compute the tract-density-weighted averages of metrics in the CST. To further evaluate the approach robustness, we assessed the correlation between metrics and PMA at scan. Results: Despite a wide range of brain anatomical features, we were able to achieve optimal registration for all infants (Figure a) as well as correct CST reconstructions (Figure b). The exploration of CST microstructure confirmed a decrease of mean, axial and radial diffusivities with PMA at scan, as well as an increase in fractional anisotropy (Figure c).Residual inter-individual variability might rely on other factors.
This study provides a proof of concept for applying the baby-XTRACT tool to clinical diffusion MRI data in very preterm infants with cerebral injury. This approach allowed us to obtain accurate extraction of the CST and will be generalized to other white matter tracts. By characterizing the tract microstructural properties, we will explore the effects of several clinical factors beyond PMA at scan (e.g., gestational age at birth, respiratory, digestive and infection complications, brain severity score proposed by Kidokoro et al [9]). This approach will also be applied to MRI data collected at 2 months of corrected age in a subgroup of 39 babies with longitudinal data to evaluate the effect of an early motor training [10]. Bimetric Invariants for Geodesic Tractometry and Machine Learning † ‡ Luc Florack, Rick Sengers, Eindhoven University of Technology, The Netherlands Introduction. Unlike streamline tractography, geodesic tractography [1, 2] must contend with the implications of the Hopf-Rinow theorem, which states that any two points in the brain can be geodesically connected. This requires additional criteria to distinguish anatomically plausible tracts from arbitrary geodesics. Specification of two side conditions will single out one (or at best a few) candidate(s), but this in itself begs the question of anatomical validity [3].
Given a candidate tract, we would therefore like to assess its anatomical plausibility. Complete systems of bimetric tractometric invariants provide data driven evidence for this purpose. By integration with pattern recognition or machine learning trained on expert feedback, geodesic redundancy enables optimization of specificity without loss of sensitivity.
Theory. A tractometric invariant of a putative tract ω is a functional ε(ω) → R independent of curve parameterization and spatial coordinates. Completeness of a system of invariants entails that all data evidence is accounted for given some symmetry constraint. The system is said to be irreducible if there are no mutual dependencies among its elements. It is notoriously hard to construct such systems in general [4], but some simple known instances are relevant for our purpose.
Application. We focus on global invariants assigned to an entire tract, and impose the constraint that only zeroth order data confined to the tract’s spatial locus are admitted. A trivial example is the complete ir
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