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Legarreta, J. H.

Publications and source records attributed to Legarreta, J. H..

2 recordsLinked to original sources

Anatomically constrained and curated cerebellar tractography (ACCURAT): an open framework and a pathway-specific neuroanatomical reference

Cerebellar pathways form extensive structural circuits linking the cerebellum with the brainstem, thalamus, and cerebrum, underlying motor, cognitive, and affective functions. Diffusion MRI tractography provides the only non-invasive method for mapping these pathways in vivo, but reconstruction of cerebellar connectivity remains challenging due to crossing fibers, peduncular bottlenecks, decussations, multi-synaptic circuits, and numerous small nuclei that define pathway origins and terminations. Here we introduce Anatomically Constrained and CURAted Tractography (ACCURAT), an open framework for reconstructing cerebellar pathways from diffusion MRI using anatomical priors and rule-based streamline queries. ACCURAT combines anatomical segmentation, densely seeded tractography, and vertex-level evaluation of anatomical constraints along streamline trajectories, enabling the isolation of pathway segments within specific nuclei while preventing their propagation across synaptic boundaries. To define these constraints, we provide a concise, pathway-by-pathway synthesis of cerebellar connectional anatomy based on experimental tract-tracing literature and organized for tractography applications. We identify pathway-specific origins, trajectories, terminations, decussation patterns, and tractography challenges, and use this information to inform tractography-ready cerebellar pathway definitions. Using ultra-high-resolution submillimeter diffusion MRI (0.76 mm gSlider acquisition) from healthy participants, we reconstruct multiple extrinsic and intrinsic cerebellar pathways, including specific components of the inferior, middle, and superior cerebellar peduncles; challenging decussating pathways such as the olivocerebellar and dentato-olivary projections; and intrinsic cerebellar pathways, including Purkinje corticonuclear projections and intracortical parallel fibers. ACCURAT generalizes across tractography algorithms, producing comparable reconstructions with both probabilistic parallel transport tractography and deterministic unscented Kalman filter tractography. Together, the ACCURAT framework and accompanying neuroanatomical reference provide an anatomically grounded, tractography-oriented resource for reconstructing cerebellar pathways in vivo and for supporting future development and evaluation of cerebellar tractography methods.

neuroscience↗

Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation

The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm2 shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI. Key pointsO_LIWe provide evidence about the performance of different dMRI contrasts for cerebellar structure segmentation using a deep learning neural network. C_LIO_LIThe diffusion MRI spherical mean provides improved and stable cerebellar structure segmentation performance. C_LIO_LIThe spherical mean is easy to compute and can be used for cerebellar structure segmentation on retrospective clinical diffusion MRI data. C_LI

neuroscience↗