Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention

Children with Attention Deficit Hyperactivity Disorder (ADHD) exhibit deficits in sustained attention, manifesting as increased intra-individual response variability (IIRV) and poor decision-making. This study uses a Research Domain Criteria approach, drift-diffusion modeling, and a Bayesian Switching Dynamic System algorithm with fMRI data to investigate latent brain state dynamics and their relation to IIRV, decision-making, and inattention. Results showed that a task-optimal latent brain state predicted decreased IIRV and increased evidence accumulation. A non-optimal state predicted inattention and differentiated children with ADHD from controls. Functional connectivity between specific brain networks predicted evidence accumulation rate and inattention. The study reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattention in ADHD.

Weidong Cai, Stacie L. Warren, Katherine Duberg, Bruce Pennington, Stephen P. Hinshaw, Vinod Menon