Biology subjects

Sarubbo, S.

Publications and source records attributed to Sarubbo, S..

8 recordsLinked to original sources

bioRxiv2026

RADIAL AND TANGENTIAL FIBER COMPONENTS IN HUMAN SUPERFICIAL WHITE MATTER AND THEIR ASSOCIATIONS WITH MACROSCALE FUNCTION

The superficial white matter (SWM), a thin layer of white matter beneath the cortex, contains a complex arrangement of short- and long-range fiber systems that support communication between cortical regions. These fiber populations include subcortical U-shaped fibers (U-fibers) that predominantly follow a tangential trajectory along the gray matter-white matter interface and long-range fibers that extend more radially through the deeper white matter to connect distant cortical regions. Here, we developed a surface-based framework to dissociate radial and tangential fiber components within the human SWM and investigated their microstructural properties and functional relevance using in-vivo high-field MRI. These components were quantified using apparent fiber density (AFD), fractional anisotropy (FA), and mean diffusivity (MD). We further contextualized SWM fiber architecture by examining depth-dependent microstructural profiles and their relationship with normative patterns of cortical organization and hierarchical specialization. The anatomical assessment of the proposed fiber orientation separation was supported by tractography-based bundle analyses, while the anatomical accuracy of tractography reconstructions was confirmed through comparison with post-mortem white matter dissection data. To assess their functional relevance, the fiber predominance map derived from radial and tangential AFD was related to spatial patterns of functional variability across the cortex. Regions with greater radially oriented fiber contribution exhibited more gradual changes in functional similarity across geodesic distance, whereas tangentially oriented fiber-enriched regions showed more rapid local functional differentiation. These findings reveal distinct structural and functional signatures of radial and tangential SWM fiber components and provide a framework for investigating their contributions to cortical organization and functional specialization. SIGNIFICANCE STATEMENTThe human cerebral cortex is organized into specialized regions that support diverse functions, but the structural principles underlying this organization remain incompletely understood. The superficial white matter (SWM), a thin layer of white matter beneath the cortex, contains intermingled short- and long-range fiber systems that support communication between cortical regions. However, these fiber populations have remained difficult to characterize because they are closely intertwined within the SWM. By developing a reproducible framework for mapping and quantifying distinct SWM fiber systems, this work provides a foundation for systematic investigation of this complex compartment. Integration of multimodal datasets, together with open sharing of analytical tools and standardized features, promotes transparency, reproducibility, enabling future studies of how superficial connectivity supports brain organization.

neuroscience↗

bioRxiv2025

Unveiling Oligodendrocyte-Lineage Differentiation in the Glioblastoma Infiltrative Zone Through Spatial Transcriptomics

IDH-wildtype glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by limited therapeutic options and inevitable recurrence. This recurrence is driven by the highly infiltrative behavior of GBM cells, which prevents complete resection. Here, we performed spatially resolved transcriptomic profiling of four surgically defined GBM regions to characterize the infiltrative tumor compartment. Across multiple datasets and profiling approaches, the infiltrative zone showed reduced cellular heterogeneity compared with the tumor core. By comparison with an integrated reference of single-cell RNA sequencing data, we revealed that this reduction reflects a preferential differentiation of infiltrative GBM cells toward an oligodendrocyte-like lineage, accompanied by attenuated inflammatory signaling. In contrast, necrotic and proliferative regions display pronounced astrocyte-like and mesenchymal-like programs. Combined immunohistochemistry and fluorescence in situ hybridization confirmed the enrichment of oligodendrocyte-like tumor cells in the infiltrative area. Together, these findings identify the oligodendrocyte-like state as a defining feature and potential therapeutic vulnerability of infiltrative GBM driving brain invasion and relapse.

cancer biology↗

bioRxiv2025

Supervised white matter bundle segmentation in glioma patients with transfer learning

In clinical settings, the virtual dissection of white matter tracts represents an informative source of information for monitoring neurological conditions or to support the planning of a treatment. The automation of this task using data-driven approaches, and in particular deep learning models, provides promising evidence of good accuracy when carried out on healthy individuals. However, the lack of large clinical datasets and the profound differences between healthy and clinical populations hinder the translation of these results to patients. Here, we investigated for the first time the effectiveness of transfer learning in adapting a deep learning architecture trained on a healthy population to glioma patients. Importantly, we provided the first thorough characterization of domain shift and its complexity, distinguishing systematic (i.e. measurement and pre-processing related) from tumor specific components. Our results suggest that (i) models trained on a large normative healthy population have a significant performance drop when the inference is carried out on patients; (ii) transfer learning can be an effective strategy to overcome the shortage of clinical data and to manage the systematic shift; (iii) fine tuning of the learning model cannot accommodate large white matter deformations induced by the tumor. The results were coherent across the five white matter bundles and three different input modalities tested, highlighting their robustness and generalizability. Our work provides valuable insights for advancing automated white matter segmentation in clinical populations and enhancing clinical transfer learning applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/659452v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1c2bbbcorg.highwire.dtl.DTLVardef@1b3555eorg.highwire.dtl.DTLVardef@a9bb4dorg.highwire.dtl.DTLVardef@147dc5e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWhite matter bundle segmentation is essential for planning glioma tumor resection. C_LIO_LIWe trained a deep learning model on a healthy dataset to segment white matter bundles. C_LIO_LIWe observed a sub-optimal performance on glioma patients due to domain shift. C_LIO_LITransfer learning mitigated domain shift between healthy and clinical populations. C_LIO_LIWe highlighted domain shift domains solved/not solved by transfer learning. C_LI

neuroscience↗

bioRxiv2023

TR(acking) individuals down: exploring the effect of temporal resolution in resting-state functional MRI fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisitions temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s. We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make substantial contributions to subject identifiability, whereas sensory-motor regions become influential only when integrating data from different TRs. We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signals sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

neuroscience↗

bioRxiv2023

Supervised White Matter Bundle Segmentation in Glioma Patients with Transfer Learning

In clinical neuroscience, the segmentation of the main white matter bundles is propaedeutic for many tasks such as preoperative neurosurgical planning and monitoring of neuro-related diseases. Automating bundle segmentation with data-driven approaches and deep learning models has shown promising accuracy in the context of healthy individuals. The lack of large clinical datasets is preventing the translation of these results to patients. Inference on patients data with models trained on healthy population is not effective because of domain shift. This study aims to carry out an empirical analysis to investigate how transfer learning might be beneficial to overcome these limitations. For our analysis, we consider a public dataset with hundreds of individuals and a clinical dataset of glioma patients. We focus our preliminary investigation on the corticospinal tract. The results show that transfer learning might be effective in partially overcoming the domain shift.

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↗

bioRxiv2019

Tractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tract

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called \"virtual dissection\". Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. The contribution of this study is to provide the first large-scale, international, multi-center variability assessment of the \"virtual dissection\" of the pyramidal tract (PyT). Eleven (11) experts and thirteen (13) non-experts in neuroanatomy and \"virtual dissection\" were asked to perform 30 PyT segmentation and their results were compared using various voxel-wise and streamline-wise measures. Overall the voxel representation is always more reproducible than streamlines ({approx}70% and {approx}35% overlap respectively) and distances between segmentations are also lower for voxel-wise than streamline-wise measures ([3/4]3mm and [3/4]umm respectively). This needs to be seriously considered before using tract-based measures (e.g. bundle volume versus streamline count) for an analysis. We show and argue that future bundle segmentation protocols need to be designed to be more robust to human subjectivity. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction techniques in this era of open and collaborative science.

neuroscience↗

bioRxiv2018

Superoanterior Fasciculus (SAF): Novel fiber tract revealed by diffusion MRI fiber tractography

Substantial progress in acquisition, processing, and analysis boosted the reliability of diffusion-weighted MRI and increased the accuracy of mapping white matter pathways with fiber tractography. Since the introduction of region of interest (ROI) based virtual dissection by Conturo et al. in 1999, researchers have used tractography to identify white matter pathways, which faithfully represented previously known structures revealed by dyeing studies or post-mortem descriptions. The reconstructed streamlines are subjects of bundle-specific in vivo investigations to show differences between groups (e.g., comparing fractional anisotropy (FA) between patients and healthy controls) or to describe the relation between diffusion scalars and metrics of interest (e.g.: normal aging or changes due to learning). By applying a reverse strategy in using diffusion-weighted MRI tractography first, then supporting the findings with other techniques, we have identified a bilateral tract in the frontal cortex - the superoanterior fasciculus (SAF). The tract resembles the anterior shape of the cingulum bundle, but is located more frontally. To erase the chance that our findings are confounded by acquisition, processing or modeling artifacts, we analyzed a total of 421 subjects from four cohorts with different acquisition schemes and diverse processing pipelines. The findings were also completed with other non-MRI techniques, such as polarized light microscopy and dissection. Tractography results demonstrate a long pathway and are consistent among cohorts, while dissection indicates a series of U-shaped fibers connecting adjacent gyri. In conclusion, we hypothesize that these consecutive U-shaped fibers emerge to form a pathway, thereby resulting in a multicomponent bundle.

neuroscience↗