Amygdala Functional Hyperconnectivity: A Multiple Symptom Subdomain Marker of the Bipolar Risk to Disorder Spectrum

Identifying reproducible neural markers of bipolar disorder(BD) risk is critical for early detection and differentiation from unipolar/major depression. We previously demonstrated that inter-amygdala functional connectivity(FC) and bilateral-ventrolateral-right-dorsolateral-prefrontal-cortex(vlPFC-dlPFC)-FC were positively associated with both mania/hypomania and depression risk and mania/hypomania risk, respectively, as measured by the Mood Spectrum Self-Report(MOODS-SR) 'mood' subdomains, replicated in three independent samples. We tested whether these neural markers also generalized to the MOODS-SR 'cognition' and 'energy' subdomains and whether they are elevated in individuals with BD versus those at-risk.

Three independent young adult samples without BD(n=299/ages 18-30) completed an fMRI approach emotion-processing task(Discovery n=114/age=21.60±1.91; Test sample-1 n=103/21.57±2.09; Test sample-2 n=82/23.43±2.86), and a fourth sample with BD(n=32/25.11±3.73) completed the same protocol. Poisson loglinear models tested whether previously identified neural markers of MOODS-SR mood subdomains were also associated with manic and depressive cognition and energy subdomains. One-way ANOVAs compared neural variables showing significant relationships with subdomain scores between low-risk, high-risk, and BD groups.

Across all risk samples, inter-amygdala-FC was positively associated with manic and depressive mood and cognition subdomains(qFDRs<0.001-0.01); vlPFC-dlPFC-FC was positively associated with manic mood and cognition subdomains(qFDRs<0.001-0.048). Inter-amygdala-FC differed significantly across groups(F_2,328=3.56, P=0.03) and was higher in BD versus low-risk and in high-risk versus low-risk groups(Ps=0.033).

Inter-amygdala-FC emerged as a robust, cross-dimensional correlate of BD risk, linking subsyndromal risk to syndromal BD, whereas vlPFC-dlPFC-FC was specific to mania risk. Findings support a multidimensional, circuit-based model of BD risk supporting early identification and prevention.