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The Research Domain Criteria (RDoC) initiative challenges researchers to build neurobehavioral models of psychiatric illness with the hope that such models identify better targets that will yield more effective treatment. However, a guide for building such models was not provided and symptom heterogeneity within Diagnostic Statistical Manual categories has hampered progress in identifying endophenotypes that underlie mental illness. We propose that the best chance to discover viable biomarkers and treatment targets for psychiatric illness is to investigate a triangle of relationships: severity of a specific psychiatric symptom that correlates to mental activity that correlates to a neural activity signature. We propose that this is the minimal model complexity required to advance the field of psychiatry. With an understanding of how neural activity relates to the experience of the patient, a genuine understanding for how treatment imparts its therapeutic effect is possible. After the discovery of this three-fold relationship, causal testing is required in which the neural activity pattern is directly enhanced or suppressed to provide causal, instead of just correlational, evidence for the biomarker. We suggest using non-invasive brain stimulation (NIBS) as these techniques provide tools to precisely manipulate spatial and temporal activity patterns. We detail how this approach enabled the discovery of two orthogonal electroencephalography (EEG) activity patterns associated with anhedonia and anxiosomatic symptoms in depression that can serve as future treatment targets. Altogether, we propose a systematic approach for building neurobehavioral models for dimensional psychiatry.

Autonomic dysfunction and depression are common non-motor symptoms of Parkinson’s disease (PD) that confer poorer prognosis. These PD symptoms may have overlapping pathophysiologic underpinnings. To investigate associations between autonomic and depression symptoms in early PD, and their evolution over time. We obtained data from the Parkinson’s Progression Markers Initiative, a prospective open-access database of early PD. Regression analyses were used to model effects of depression on autonomic symptoms in controls and in PD at baseline, visit 6 (24 months after baseline), and visit 12 (60 months after baseline), correcting for multiple comparisons. Data from 421 people with PD at baseline, 360 at visit 6, 300 at visit 12, and 193 controls were included. When controlling for age, depression, and anti-hypertensive medications, depression predicted autonomic symptoms in all groups. Accounting for motor symptoms did not alter these associations. When comparing groups, the influence of depression on autonomic symptoms was stronger in all PD groups compared to controls, and strongest in PD at visit 12. Depression predicted the presence of orthostatic hypotension only in the PD group at visit 12. We demonstrated the important impact of depression on autonomic symptoms in early and middle stages of PD, which are independent of motor symptoms. Though the physiologic basis of these two PD symptoms are not fully understood, our findings add to pathologic evidence of a shared mechanistic substrate, separate from that responsible for PD motor symptoms. These findings may influence clinical management and development of novel therapies.

Left frontal alpha oscillations are associated with decreased approach motivation and have been proposed as a target for non-invasive brain stimulation for the treatment of depression and anhedonia. Indeed, transcranial alternating current stimulation (tACS) at the alpha frequency reduced left frontal alpha power and was associated with a higher response rate than placebo stimulation in patients with major depressive disorder (MDD) in a recent double-blind placebo controlled clinical trial.