CANDI: Continuous Amortized Implicit Neural Representations for Neuroimaging Data

Neural activity unfolds as a continuous spatiotemporal field, yet most neuroimaging models operate on discrete sampling geometries imposed by acquisition hardware, such as voxel grids and fixed electrode montages. Implicit neural representations (INRs) provide a natural framework for modeling continuous signals, but conventional INRs are fitted independently to each instance, requiring costly test-time optimization and producing representations that do not transfer across subjects. We introduce CANDI (Continuous Amortized Neuroimaging Decoding via INR), a common continuous-field representation framework that can incorporate modality-specific observation geometry and inductive biases to amortize continuous INR inference across subjects. CANDI uses a selective state-space backbone to encode long spatiotemporal contexts in linear time and introduces spherical harmonic positional encodings for scalp-based imaging motivated by the differential operators underlying spherical spline interpolation. These encodings capture the intrinsic geometry and smoothness of continuous fields observed on two-dimensional scalp manifolds. CANDI outperforms current amortized INR baselines on 4D fMRI spatiotemporal reconstruction and outperforms classical spherical splines on EEG unseen-electrode spatial querying on hold-out sets of up to 87.5% unseen electrodes, while producing representations and reconstructions that retain strong downstream-task utility.