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

• Published in: Molecular psychiatry 2021-09, Vol.26 (9), p.4944-4957
• Main Authors: Cai, Weidong, Warren, Stacie L., Duberg, Katherine, Pennington, Bruce, Hinshaw, Stephen P., Menon, Vinod
• Format: Article
• Language: English
• Subjects: 59/36; 631/378; 692/699/476/1311; Attention; Attention Deficit Disorder with Hyperactivity; Attention deficit hyperactivity disorder; Bayes Theorem; Bayesian analysis; Behavioral Sciences; Biological Psychology; Brain - diagnostic imaging; Brain Mapping; Child; Children; Cognition; Computational neuroscience; Decision making; Development and progression; Diffusion models; Functional magnetic resonance imaging; Health aspects; Humans; Magnetic Resonance Imaging; Mathematical models; Medicine; Medicine & Public Health; Nerve Net; Nervous system; Neural circuitry; Neural networks; Neural Pathways; Neurosciences; Pediatric research; Pharmacotherapy; Physiological aspects; Psychiatry; Psychological research; Variability
• ISSN: 1359-4184; 1476-5578; 1476-5578
• DOI: 10.1038/s41380-021-01022-3

Children with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.