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 propose 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 demonstrate that VAEs significantly outperform a linear baseline and a semi-supervised counterpart. For both sFNC and dFNC interpolation, the generated results effectively capture representative and generalizable properties in the original data. The interpolated continua from controls to patients in both disorders reveal 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 weaken between the subcortical domain and sensory domains, and between the cerebellar domain and sensory regions. Finally, we show examples of how to generate continuous FNC data following pathological 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.