Berto, G. | Search bioRxiv

bioRxiv2025

DCHS1 Modulates Forebrain Proportions in Modern Humans via a Glycosylation Change

Comparative anatomical studies of primates and extinct hominins, including Neanderthals, show that the modern human brain is characterised by a disproportionately enlarged neocortex relative to the striatum. To explore the molecular basis of this difference, we screened for missense mutations that are unique to modern humans and occur at high frequency and that alter post-translational sites. One such mutation was identified in DCHS1, a protocadherin family gene, and it was found to disrupt an N-glycosylation site in modern humans. Using CRISPR/Cas9-editing we introduced into human-induced pluripotent stem cells (hiPSCs) this ancestral DCHS1 variant present in Neanderthals and other primates, representing the ancestral state before the modern human-specific substitution. Leveraging hiPSCs-derived neural organoids, we observed an expansion of striatal progenitors at the expense of the neocortex, mirroring the anatomical distribution seen in non-human primates. We further identify the ephrin receptor EPHA4 as a binding partner of DCHS1 and show that modern human-specific alterations in DCHS1 modulate EPHA4-ephrin signalling, contributing to a gradual shift in the neocortex-to-striatum ratio - a hallmark of brain organisation in our species.

neuroscience↗

bioRxiv2020

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

neuroscience↗