Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety

Abstract

Background: Despite tremendous advances in characterizing human brain circuits that govern emotional and cognitive functions that are impaired in depression and anxiety, we lack a circuitbased taxonomy that captures transdiagnostic heterogeneity and informs clinical decision making.

Methods: We develop and test a novel system for quantifying six brain circuits reproducibly and at the individual patient level. We implement standardized circuit definitions relative to a healthy reference group, and algorithms to generate circuit function and dysfunction scores for the overall circuit and its constituent regions.

Outcomes: In primary and generalizability samples of depression and anxiety (n=251) we demonstrate that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases – core characteristics that transcend diagnoses – and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits implicate more specific cognitive symptoms and valence-congruent behavioral disruptions. Circuit dysfunction scores also predict response to pharmacological and behavioral therapies in an independent sample (n=207).

Interpretation: Our findings articulate circuit dimensions that parse the heterogeneity of depression and anxiety and have direct clinical translational significance. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials and clinics to advance more precise classifications and treatment targets for psychiatry.

Funding: This work was supported by the National Institutes of Health [grant numbers R01MH101496 (LMW), UH2HL132368 (JM, LMW), F32MH108299 (ANG-P), T32MH019938 (TMB), and K23MH113708 (TMB)].

Declaration of Interests: LMW declares US Pants. App. 10/034,645 and 15/820,338: Systems and methods for detecting complex networks in MRI image data. ANG-P, TMB, ZS, KAG, SLF, ASK, BRS, and BHG declare no competing interests.

Ethics Approval Statement: All procedures were approved by the Stanford University Institutional Review Board (IRB 27937 and 41837) or Western Sydney Area Health Service Human Research Ethics Committee. All participants provided written informed consent prior to study procedures.