ABSTRACT
Posttraumatic stress disorder (PTSD) is a devastating condition, for which there are few pharmacological agents, often with a delayed onset of action and poor efficacy. Trauma-focused psychotherapies are further limited by few trained providers and low patient engagement. This frequently results in disease chronicity as well as psychiatric and medical comorbidity, with considerable negative impact on quality of life. As such, off-label interventions are commonly used for PTSD, particularly in chronic refractory cases. Ketamine, an N-methyl-D-aspartate (NDMA) receptor antagonist, has recently been indicated for major depression, exhibiting rapid and robust antidepressant effects. It also shows transdiagnostic potential for an array of psychiatric disorders. Here, we synthesize clinical evidence on ketamine in PTSD, spanning case reports, chart reviews, open-label studies, and randomized trials. Overall, there is high heterogeneity in clinical presentation and pharmacological approach, yet encouraging signals of therapeutic safety, efficacy, and durability. Avenues for future research are discussed.
ABSTRACT
Background: Trauma and chronic stress are believed to induce and exacerbate psychopathology by disrupting glutamate synaptic strength. However, in vivo in human methods to estimate synaptic strength are limited. In this study, we established a novel putative biomarker of glutamatergic synaptic strength, termed energy-per-cycle (EPC). Then, we used EPC to investigate the role of prefrontal neurotransmission in trauma-related psychopathology. Methods: Healthy controls (n = 18) and patients with posttraumatic stress (PTSD; n = 16) completed 13C-acetate magnetic resonance spectroscopy (MRS) scans to estimate prefrontal EPC, which is the ratio of neuronal energetic needs per glutamate neurotransmission cycle (VTCA/VCycle). Results: Patients with PTSD were found to have 28% reduction in prefrontal EPC (t = 3.0; df = 32, P = .005). There was no effect of sex on EPC, but age was negatively associated with prefrontal EPC across groups (r = -0.46, n = 34, P = .006). Controlling for age did not affect the study results. Conclusion: The feasibility and utility of estimating prefrontal EPC using 13C-acetate MRS were established. Patients with PTSD were found to have reduced prefrontal glutamatergic synaptic strength. These findings suggest that reduced glutamatergic synaptic strength may contribute to the pathophysiology of PTSD and could be targeted by new treatments.
ABSTRACT
More than six decades have passed since the discovery of monoaminergic antidepressants. Yet, it remains a mystery why these drugs take weeks to months to achieve therapeutic effects, although their monoaminergic actions are present rapidly after treatment. In an attempt to solve this mystery, rather than studying the acute neurochemical effects of antidepressants, here we propose focusing on the early changes in the brain functional connectome using traditional statistics and machine learning approaches. Capitalizing on three independent datasets (n = 1,261) and recent developments in data and network science, we identified a specific connectome fingerprint that predates and predicts response to monoaminergic antidepressants. The discovered fingerprint appears to generalize to antidepressants with differing mechanism of action. We also established a consensus whole-brain hierarchical connectivity architecture and provided a set of model-based features engineering approaches suitable for identifying connectomic signatures of brain function in health and disease.
ABSTRACT
BACKGROUND: Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in providing more personalized medicine. Using a network restricted strength predictive modeling (NRS-PM) approach, the goal of the current study was to identify pretreatment functional connectome fingerprints (CFPs) that (1) predict symptom improvement regardless of treatment modality and (2) predict treatment specific improvement. METHODS: Functional magnetic resonance imaging and behavioral data from unmedicated patients with MDD (n = 200) were investigated. Participants were randomized to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000 iterations of 10 cross-validation were implemented to identify brain connectivity signatures that predict percent improvement in depression severity at week-8. RESULTS: The study identified a pretreatment CFP that significantly predicts symptom improvement independent of treatment modality but failed to identify a treatment specific CFP. Regardless of treatment modality, improved antidepressant response was predicted by high pretreatment connectivity between modules in the default mode network and the rest of the brain, but low external connectivity in the executive network. Moreover, high pretreatment internal nodal connectivity in the bilateral caudate predicted better response. CONCLUSIONS: The identified CFP may contribute to drug development and ultimately to enhanced prognostic predictions. However, the results do not assist with providing personalized medicine, as pretreatment functional connectivity failed to predict treatment specific response.
