Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Background: Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. Methods: We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. Results: LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52 - 0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. Conclusions: In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by better and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modelling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models can predict clinical outcomes. We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression (TRD), across multiple coils and protocols. We then compared the predictive power of those models. LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52-0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Case Report of Dual-Site Neurostimulation and Chronic Recording of Cortico-Striatal Circuitry in a Patient With Treatment Refractory Obsessive Compulsive Disorder.

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 correlated with increased SMA and VC/VS coherence in the alpha, beta, and gamma bands, signals which persisted after correcting for stimulation artifacts. We discuss the implications of this research, and propose future directions for research in network modulation in OCD and more broadly across psychiatric disorders.

Objective clinical pain analysis using serum cyclooxygenase-2 and inducible nitric oxide synthase in American patients.

BACKGROUND: Pain is a multidimensional condition of multiple origins. Determining both intensity and underlying cause are critical for effective management. Utilization of painkillers does not follow any guidelines relying on biomarkers, which effectively eliminates objective treatment. The aim of this study was to evaluate the use of serum cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) as pain biomarkers. This work could significantly advance the diagnosis and treatment of pain. METHODS: We assessed the potential utility of serum COX-2 and iNOS as objective measures of pain in a sample of American patients. Pain was scaled between level 0-5 in accordance with the level reported by the patients. Blood samples were collected from 102 patients in the emergency room. Sandwich ELISA was used to determine the COX-2 and iNOS levels in the blood serum while statistical analysis was performed using Pearson product-moment correlation coefficients, Regression and Receiver Operating Characteristics (ROC) analyses. The biomarker results were also compared with self-reports of pain by the patients using conventional pain ratings and patients were asked to report the cause of the pain. Pain levels were clustered into four groups as 0 [self-reported 0], 1 [self-reported as 1], 2 [self-reported as 2 and 3] and 3 [self-reported as 4 and 5]. Co-expression of COX-2 and iNOS could significantly alter pain development and its sensitization. Therefore, iNOS dependent COX-2 levels were employed as categorized level. RESULTS: Self-reported pain levels did not show a correlation with the serum level of COX-2 and iNOS. The lack of correlation is attributed to multiple reasons including patients' intake of painkillers prior to participation, painkiller intake habit, chronic diseases, and subjectivity of self-reported pain. Increased serum COX-2 levels were reported in relation to the subtypes of these health issues. Further, 83% of the patients who reported pain also showed the presence of COX-2 in serum, while only 53% of the patients showed the presence of iNOS in serum. Moderate relation was found between the clustered pain level and categorized COX-2 and iNOS- levels. CONCLUSIONS: The findings support the requirement of further studies to use COX-2 and iNOS as prognostic biomarkers for objective quantification of pain at the clinical level.