Systematically dismantling the technical barriers of individualized brain parcellation through empirical benchmarking, generative modeling, and scalable software ecosystems.
Group-level analyses obscure critical individual variations in functional network topology. While Precision Functional Mapping (PFM) addresses this by capturing individualized neural endophenotypes, its application is currently restricted by technical barriers: the degradation of spatial reliability in sparse clinical datasets and a lack of unified standards for aligning structural-functional correspondence across subjects.
Addressing these challenges demands more than isolated algorithmic novelty; it requires a domain-informed meta-scientific approach. The Individual Brain Project pursues two synergistic goals:
Establishing a comprehensive "Knowledge Space" to systematically benchmark PFM failures, providing the physiological constraints required to develop generative diffusion frameworks and unified correspondence algorithms.
Integrating novel PFM algorithms into the Individual Brain Toolbox, a standardized software ecosystem designed to deploy personalized functional parcellation across healthy and clinical cohorts.
Clinical MRI datasets routinely fall short of deep-scanning standards, resulting in a quantifiable loss of network topology and test-retest reliability.
Current PFM algorithms exhibit systematic inconsistencies in aligning structural and functional constraints across individuals, compromising predictive validity.
Algorithms are developed in silos. The field lacks a dynamic registry to catalogue comparative performance and methodological trade-offs.
The technical expertise required to execute complex PFM methods limits their deployment in applied clinical settings.