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.
Mental disorders are a leading cause of disability, morbidity, and mortality among civilian and military populations. Most available treatments have limited efficacy, particularly in disorders where symptoms vary over relatively short time scales. Targeted modulation of neural circuits, particularly through open-loop deep brain stimulation (DBS), showed initial promise but has failed in blinded clinical trials. We propose a new approach, based on targeting neural circuits linked to functional domains that cut across diagnoses. Through that framework, which includes measurement of patients using six psychophysical tasks, we seek to develop a closed-loop DBS system that corrects dysfunctional activity in brain circuits underlying those domains. We present convergent preliminary evidence from functional neuroimaging, invasive human electrophysiology, and human brain stimulation experiments suggesting that this approach is feasible. Using the Emotional Conflict Resolution (ECR) task as an example, we show that emotion-related networks can be identified and modulated in individual patients. Invasive and non-invasive methodologies both identify a network between prefrontal cortex, cingulate cortex, insula, and amygdala. Further, stimulation in cingulate and amygdala changes patients' performance in ways that are linked to the task's emotional content. We present preliminary statistical models that predict this change and allow us to track it at a single-trial level. As these diagnostic and modeling strategies are refined and embodied in an implantable device, they offer the prospect of a new approach to psychiatric treatment and its accompanying neuroscience.
With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.
Deep brain stimulation (DBS) is a circuit-oriented treatment for mental disorders. Unfortunately, even well-conducted psychiatric DBS clinical trials have yielded inconsistent symptom relief, in part because DBS’ mechanism(s) of action are unclear. One clue to those mechanisms may lie in the efficacy of ventral internal capsule/ventral striatum (VCVS) DBS in both major depression (MDD) and obsessive-compulsive disorder (OCD). MDD and OCD both involve deficits in cognitive control. Cognitive control depends on prefrontal cortex (PFC) regions that project into the VCVS. Here, we show that VCVS DBS’ effect is explained in part by enhancement of PFC-driven cognitive control. DBS improves human subjects’ performance on a cognitive control task and increases theta (5–8Hz) oscillations in both medial and lateral PFC. The theta increase predicts subjects’ clinical outcomes. Our results suggest a possible mechanistic approach to DBS therapy, based on tuning stimulation to optimize these neurophysiologic phenomena.
Major depressive episodes are the largest cause of psychiatric disability, and can often resist treatment with medication and psychotherapy. Advances in the understanding of the neural circuit basis of depression, combined with the success of deep brain stimulation (DBS) in movement disorders, spurred several groups to test DBS for treatment-resistant depression. Multiple brain sites have now been stimulated in open-label and blinded studies. Initial open-label results were dramatic, but follow-on controlled/blinded clinical trials produced inconsistent results, with both successes and failures to meet endpoints. Data from follow-on studies suggest that this is because DBS in these trials was not targeted to achieve physiologic responses. We review these results within a technology-lifecycle framework, in which these early trial “failures” are a natural consequence of over-enthusiasm for an immature technology. That framework predicts that from this “valley of disillusionment,” DBS may be nearing a “slope of enlightenment.” Specifically, by combining recent mechanistic insights and the maturing technology of brain-computer interfaces (BCI), the next generation of trials will be better able to target pathophysiology. Key to that will be the development of closed-loop systems that semi-autonomously alter stimulation strategies based on a patient's individual phenotype. Such next-generation DBS approaches hold great promise for improving psychiatric care.
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