Personalized disordered circuit modeling reveals diverse neurobiological routes to transdiagnostic symptoms

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Abstract
Precision psychiatry research has strived to characterize the neural circuitry underlying transdiagnostic symptoms with the goal of devising biologically informed diagnostic and treatment strategies. However, few clinically actionable findings have emerged from these initiatives. One potential complication in reaching this goal is that similar symptoms may arise from distinct patterns of neurobiological dysfunction. Therefore, models that can provide insight into where an individual’s disordered circuits are located are important. Here, we developed a data-driven approach to identify individualized patterns of disordered connectivity using a large ( n =317), transdiagnostic population with extensive fMRI data. Connectome-based predictive modeling identified functional connectivity networks associated with a broad array of psychiatric symptoms. Symptom networks were aggregated, and connections contributing most strongly to each individual’s symptoms were identified to construct personalized “disordered circuit models”. Within these models, many features were associated with specific diagnoses and patterns of comorbidity, despite the symptoms exhibiting no diagnostic specificity. This suggests that similar symptoms can emerge through diverse neurobiological mechanisms depending on an individual’s diagnostic profile. By characterizing patient-specific neural dysfunction, disordered circuit modeling offers a promising framework for advancing precision psychiatry by accounting for diverse mechanisms from which symptoms may arise.

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