Dillon, D. G. | Search bioRxiv

bioRxiv2026

Acute stress reorganizes pregenual cingulate and subthalamic coupling to approach-avoidance decisions in remitted depression

Major depressive disorder (MDD) is highly recurrent, and psychosocial stress is a central precipitant of relapses. Daily choices require weighing potential rewards against costs, a process disrupted in MDD that may contribute to relapse. Here, we tested whether a psychosocial stressor affects neural and computational mechanisms underlying approach-avoidance decision making in individuals with remitted MDD (rMDD, N=50) and healthy controls (N=74), prioritizing pregenual anterior cingulate cortex (pgACC) and subthalamic nucleus (STN) due to their roles in value integration and decision caution, respectively. Participants performed a dynamic approach-avoidance conflict task with fMRI before and after psychosocial stress. Hierarchical Bayesian drift-diffusion modeling evaluated whether stress modulated computational and neural mechanisms, with trial-wise pgACC and STN activity entered as neural regressors on evidence accumulation and decision threshold, respectively. Before stress, groups did not differ in any decision parameter, but pgACC activity contributed to evidence accumulation toward avoidance in controls only. After stress, rMDD showed larger increases than controls in the impact of both reward and aversiveness on accumulation toward approach and avoidance, respectively, predominantly for net-aversive offers. Furthermore, in rMDD, stress rendered both approach and avoidance decisions more sensitive to pgACC activity, and STN activity was more strongly coupled to the decision threshold than in controls. Finally, stress-induced increases in reward sensitivity in rMDD predicted one-year course of depressive symptoms. Thus, MDD remission is not characterized by a generalized blunting of decision-making, but by a latent circuit difference that acute stress reorganizes and predicts future symptoms.

neuroscience↗

bioRxiv2018

Functional connectomics of affective and psychotic pathology

Converging evidence indicates that groups of patients with nominally distinct psychiatric diagnoses are not separated by sharp or discontinuous neurobiological boundaries. In healthy populations, individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of transdiagnostic symptom profiles observed in psychiatric patients may map onto detectable patterns of network function. To examine the manner through which neurobiological variation might underlie clinical presentation we obtained functional magnetic resonance imaging (fMRI) data from over 1,000 individuals, including 210 diagnosed with a primary psychotic disorder or affective psychosis (bipolar disorder with psychosis and schizophrenia or schizoaffective disorder), 192 presenting with a primary affective disorder without psychosis (unipolar depression, bipolar disorder without psychosis), and 608 demographically and data-quality matched healthy comparison participants recruited through a large-scale study of brain imaging and genetics. Here, we examine variation in functional connectomes across psychiatric diagnoses, finding striking evidence for disease connectomic \"fingerprints\" that are commonly disrupted across distinct forms of pathology and appear to scale as a function of illness severity. Conversely, other properties of network connectivity were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. This work allows us to establish key biological and clinical features of the functional connectomes of severe mental disease.\n\nSIGNIFICANCE STATEMENTHistorically, most research on the biological origins of psychiatric illness has focused on individual diagnostic categories, studied in isolation. Mounting evidence suggests nominally distinct psychiatric diagnoses are not separated by clear neurobiological boundaries. Here, we derive functional connectomic signatures in over 1,000 individuals including patients presenting with specific categories of impairment (psychosis), clinical diagnoses, or severity of illness as reflected in treatment seeking. Our analyses revealed features of connectome functioning that are commonly disrupted across distinct forms of pathology, scaling with clinical severity. Conversely, other aspects of network connectivity were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. These data have important implications for the establishment of functional connectome fingerprints of severe mental disease.

neuroscience↗