Biology subjects

Manzano-Patron, J. P.

Publications and source records attributed to Manzano-Patron, J. P..

3 recordsLinked to original sources

bioRxiv2025

Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction

Cortical surface reconstruction has changed how we study brain morphology and geometry. However, extending these methods to non-human species has been limited by the lack of standardized pipelines, anatomical templates, and variability in imaging protocols. To address these challenges, we present Precon_all, an open-source, species-agnostic pipeline that automates cortical surface reconstruction for non-human neuroimaging. It runs reliably across a wide range of anatomical structures and imaging conditions and has been successfully applied to datasets from primates, carnivores, and artiodactyls. Its modular framework mirrors human neuroimaging workflows, supports manual quality control, and produces outputs compatible with FreeSurfer and Connectome Workbench. In doing so, it substantially reduces technical barriers to non-human neuroimaging. As data-sharing initiatives continue to expand access to non-human imaging datasets, Precon_all provides a scalable and standardized solution that supports the broader adoption of surface-based methods for studying cortical evolution through comparative neuroscience.

neuroscience↗

bioRxiv2024

Uncertainty mapping and probabilistic tractography using Simulation-Based Inference in diffusion MRI: A comparison with classical Bayes

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI.

neuroscience↗

bioRxiv2020

A White Matter Atlas and Common Connectivity Space Facilitate the Pig as a Translational Model in Neuroscience

Neuroimagings capability to quickly and rapidly phenotype the cortical organization of the whole brain brings with it the possibility to extend our understanding of cortical organization across the mammalian lineage. However, neuroimaging has thus far generally limited itself to a small number of species, with most animal studies being performed in either rodents or Non-Human Primates. Here we perform a first pass characterization of an animal which has recently seen its stock rise in the neuroscience community with the development of new models of neurological disease; the domestic pig. Characterizing the structural connectome of the pig, we create a white matter atlas, and an anatomical template which we use to build a horizontal translation between the pig and human based on a connectivity blueprint approach. We find that conserved trends of structural connectivity across species enabled spatial prediction of regions of interest between the pig and human, showing the potential horizontal translations have as a tool to assess the translational validity of porcine models of neurological disease. Releasing the anatomical template, white matter atlas, and connectivity blueprints, we hope to ease and promote the acceptance of the pig as an alternative large-animal model by the neuroimaging community.

neuroscience↗