Development of Advanced Computational Tools for In Vivo Analysis of Dorsal Root Ganglia and Spinal Cord Integrity in Sensory Neuronopathy

• Grant number: 25/00186-4
• Support Opportunities: Research Grants - Young Investigators Grants
• Start date: August 01, 2025
• End date: July 31, 2030
• Field of knowledge: Health Sciences - Medicine - Medical Radiology
• Principal Investigator: Thiago Junqueira Ribeiro de Rezende
• Grantee: Thiago Junqueira Ribeiro de Rezende
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• City of the host institution: Ribeirão Preto
• Associated researchers: Alberto Rolim Muro Martinez ; André Monteiro Paschoal ; Antonio Carlos dos Santos ; Fernando Cendes ; José Luiz Pedroso ; Marcondes Cavalcante Franca Junior ; Orlando Graziani Povoas Barsottini ; Pedro José Tomaselli ; Wilson Marques Junior
• Associated scholarship(s): 25/24347-7 - Manual segmentation of dorsal root ganglia, BP.IC; 25/24312-9 - Manual segmentation of the dentate nucleus of the cerebellum, BP.IC; 25/24311-2 - Manual Segmentation of Dorsal Root Nerves in Magnetic Resonance Imaging, BP.IC; 25/15509-3 - Development of Advanced Computational Tools for In Vivo Analysis of Dorsal Root Ganglia and Spinal Cord Integrity in Sensory Neuronopathy, BP.JP

Abstract

This project aims to develop advanced computational tools for the in vivo analysis of dorsal root ganglia (DRG) and spinal cord integrity, focusing on sensory neuronopathy. Dorsal root ganglia are essential for transmitting sensory information from the periphery to the central nervous system; however, their morphological and functional characteristics remain underexplored, particularly in humans. Despite significant advances in understanding neuronal mechanisms, existing diagnostic methods, primarily invasive histological analyses, are inadequate for comprehensive evaluations. Recent developments in high-field magnetic resonance imaging (MRI) offer promising non-invasive alternatives yet lack standardized protocols for automatic segmentation of DRG and peripheral projections. This research proposes creating deep learning algorithms for the automated identification and quantification of DRG structures in imaging data, addressing limitations in current methodologies. We will generate a harmonized imaging protocol and develop pipelines for consistent data processing across different MRI systems. The expected outcomes include improved diagnostic sensitivity for sensory neuronopathies and enhanced understanding of their underlying pathophysiology. Ultimately, this work aims to facilitate the integration of imaging biomarkers into clinical practice for neurological disorders, providing a framework for personalized therapeutic approaches. (AU)