Objective: Reducing unsuccessful treatment trials could improve depression treatment. Quantitative analysis of the electro-encephalogram (QEEG) might predict treatment response, and is being commercially marketed for this purpose. The authors sought to (1) quantify the reliability of QEEG for response prediction in depressive illness and (2) identify methodological limitations of the available evidence. Method: The authors performed a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio (DOR). Raters also judged each article on indicators of good research practice. Results: In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed sensitivity 0.72 (0.67–0.76), specificity 0.68 (0.63–0.73), log(DOR) 1.89 (1.56–2.21), and area under the receiver-operator curve 0.76 (0.71–0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias (arcsine asymmetry test, t=6.33, p=2.64e-8). Most studies did not use ideal practices. Conclusions: QEEG does not appear clinically reliable for predicting depression treatment response due to under-reporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of prior findings. Until these limitations are remedied, QEEG is not recommended for guiding psychiatric treatment selection.

Psychiatric disorders are increasingly understood as dysfunctions of hyper-or hypoconnectivity in distributed brain circuits. A prototypical example is obsessive compulsive disorder (OCD), which has been repeatedly linked to hyper-connectivity of cortico-striatal-thalamo-cortical (CSTC) loops. Deep brain stimulation (DBS) and lesions of CSTC structures have shown promise for treating both OCD and related disorders involving over-expression of automatic/habitual behaviors. Physiologically, we propose that this CSTC hyper-connectivity may be reflected in high synchrony of neural firing between loop structures, which could be measured as coherent oscillations in the local field potential (LFP). Here we report the results from the pilot patient in an Early Feasibility study (https://clinicaltrials.gov/ct2/show/NCT03184454) in which we use the Medtronic Activa PC+ S device to simultaneously record and stimulate in the supplementary motor area (SMA) and ventral capsule/ventral striatum (VC/VS). We hypothesized that frequency-mismatched stimulation should disrupt coherence and reduce compulsive symptoms. The patient reported subjective improvement in OCD symptoms and showed evidence of improved cognitive control with the addition of cortical stimulation, but these changes were not reflected in primary rating scales specific to OCD and depression, or during blinded cortical stimulation. This subjective improvement was

Deep brain stimulation has preliminary evidence of clinical efficacy, but has been difficult to develop into a robust therapy, in part because its mechanisms are incompletely understood. We review evidence from movement and psychiatric disorder studies, with an emphasis on how deep brain stimulation changes brain networks. From this, we argue for a network-oriented approach to future deep brain stimulation studies. That network approach requires methods for identifying patients with specific circuit/network deficits. We describe how dimensional approaches to diagnoses may aid that identification. We discuss the use of network/circuit biomarkers to develop self-adjusting "closed loop" systems.