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Surface-based cortical registration is an important topic in medical imageanalysis and facilitates many downstream applications. Current approaches forcortical registration are mainly driven by geometric features, such as sulcaldepth and curvature, and often assume that registration of folding patternsleads to alignment of brain function. However, functional variability ofanatomically corresponding areas across subjects has been widely reported,particularly in higher-order cognitive areas. In this work, we present JOSA, anovel cortical registration framework that jointly models the mismatch betweengeometry and function while simultaneously learning an unbiasedpopulation-specific atlas. Using a semi-supervised training strategy, JOSAachieves superior registration performance in both geometry and function to thestate-of-the-art methods but without requiring functional data at inference.This learning framework can be extended to any auxiliary data to guidespherical registration that is available during training but is difficult orimpossible to obtain during inference, such as parcellations, architectonicidentity, transcriptomic information, and molecular profiles. By recognizingthe mismatch between geometry and function, JOSA provides new insights into thefuture development of registration methods using joint analysis of the brainstructure and function.

JOSA: Joint surface-based registration and atlas construction of brain geometry and function