Continuous Neural Fields for Functional Connectivity

Brain Functional connectivity (FC) is typically represented after discretizing the brain into a predefined set of regions, making the resulting representation dependent on the chosen parcellation and spatial scale. We introduce a continuous representation of resting-state functional organization in which each cortical location is associated with a low-dimensional functional embedding, and connectivity is induced by similarity between embeddings. We parameterize this representation with implicit neural representations (INRs), first learning a population functional field that captures shared organization and then adapting it to each individual through constrained subject-specific deviations. The resulting continuous fields accurately represent functional connectivity while capturing reproducible individual variation across independent acquisitions. Beyond reconstruction, the differentiable representation allows cortical regions to serve as spatial readouts of the field, whose location and geometry can be optimized for downstream analyses. Across three cohorts, optimized regions yield stronger held-out associations with age and sex than the best connections of coarse and fine parcellations in every setting, and the advantage extends to behavioral and cognitive phenotypes. Together, these results establish continuous functional fields as an alternative to parcellation-defined FC representations, supporting individualized modeling and spatially flexible analysis within a common framework.