A multimodal processing workflow for ultrahigh-field MRI data

Motivation: Ultrahigh-field 7T MRI provides exceptional detail on brain microstructure, function, and connectivity but requires advanced processing to handle its complexity and multimodal nature. Current workflows often lack the integration needed for such rich data. Goal(s): Develop a standardized, scalable workflow for multimodal 7T MRI, enabling reliable, high-resolution, surface-based connectomes for individual and group analysis. Approach: We combined methods like deep learning-based surface generation, label-based registration, and direct sampling from native MRI spaces to create accurate surface-based connectomes and multiparameter MRI maps. Results: This workflow generated consistent, reliable connectomes across subjects, facilitating the creation of standardized and individualized multimodal brain models using high-field MRI. Impact: This standardized workflow ensures consistent, high-resolution analysis and generates sharable outputs for large-scale data processing. It promotes reproducibility and collaboration, advancing our understanding of brain structure-function relationships and enhancing scientific impact across the research community.