Brain functional network connectivity interpolation characterizes the neuropsychiatric continuum and heterogeneity.

Psychiatric and neurodevelopmental disorders such as schizophrenia (SZ) and autism spectrum disorder (ASD) are challenging to characterize in part due to their heterogeneous presentation in individuals, with symptoms now believed to exist on a continuum. Conventional diagnostic and neuroimaging analytical approaches rely on subjective assessment or group differences, but typically ignore progression between groups or heterogeneity within a group. To estimate the neuropsychiatric continuum and heterogeneity, we proposed a functional network connectivity (FNC) interpolation framework based on a variational autoencoder (VAE) using static FNC (sFNC) and dynamic FNC (dFNC) data from controls and patients with SZ or ASD. We demonstrated that VAEs significantly outperformed a linear baseline and a semi-supervised counterpart. For both sFNC and dFNC interpolation, the generated results effectively captured representative and generalizable properties in the original data. The interpolated continua from controls to patients in both SZ and ASD revealed group-wise gradients characterized by reduced positive correlations within the auditory, sensorimotor, and visual networks, as well as between the subcortical and cerebellar domains. In contrast, anti-correlations weakened between the subcortical domain and the sensory domains, and between the cerebellar domain and the sensory domains. Finally, we showed examples of how to generate continuous FNC data following group- or state-based trajectories in the VAE latent space. The proposed framework offers added advantages over traditional methods, including data-driven discovery of hidden relationships, visualization of individual differences, imputation of missing values along a continuous spectrum, and estimation of the stage where an individual falls within the continuum.