Conditional power of antidepressant network meta-analysis

Background Conditional power of network meta-analysis (NMA) can support the planning of randomized controlled trials (RCTs) assessing medical interventions. Conditional power is the probability that updating existing inconclusive evidence in NMA with additional trial(s) will result in conclusive evidence, given assumptions regarding trial design, anticipated effect sizes, or event probabilities. Methods The present work aimed to estimate conditional power for potential future trials on antidepressant treatments. Existing evidence was based on a published network of 502 RCTs conducted between 1979-2018 assessing acute antidepressant treatment in major depressive disorder (MDD). Primary outcomes were efficacy in terms of the symptom change on the Hamilton Depression Scale (HAMD) and tolerability in terms of the dropout rate due to adverse events. The network compares 21 antidepressants consisting of 231 relative treatment comparisons, 164 (efficacy) and 127 (tolerability) of which are currently assumed to have inconclusive evidence. Results Required sample sizes to achieve new conclusive evidence with at least 80% conditional power were estimated to range between N = 894 - 4190 (efficacy) and N = 521 - 1246 (tolerability). Otherwise, sample sizes ranging between N = 49 - 485 (efficacy) and N = 40 - 320 (tolerability) may require stopping for futility based on a boundary at 20% conditional power. Optimizing trial designs by considering multiple trials that contribute both direct and indirect evidence, anticipating alternative effect sizes or alternative event probabilities, may increase conditional power but required sample sizes remain high. Antidepressants having the greatest conditional power associated with smallest required sample sizes were identified as those on which current evidence is low, i.e., clomipramine, levomilnacipran, milnacipran, nefazodone, and vilazodone, with respect to both outcomes. Conclusions The present results suggest that conditional power to achieve new conclusive evidence in ongoing or future trials on antidepressant treatments is low. Limiting the use of the presented conditional power analysis are primarily due to the estimated large sample sizes which would be required in future trials as well as due to the well-known small effect sizes in antidepressant treatments. These findings may inform researchers and decision-makers regarding the clinical relevance and justification of research in ongoing or future antidepressant RCTs in MDD

Optimal doses of antidepressants in dependence on age: Combined covariate actions in Bayesian network meta-analysis

Background: The meta-analysis by Furukawa et al. (The Lancet Psychiatry 2019, 6(7)) reported optimal doses for antidepressants in adult major depressive disorder (MDD). The present reanalysis aimed to adjust optimal doses in dependence on age. Methods: Analysis was based on the same dataset by Cipriani et al. (The Lancet 2018, 391(10128)) comparing 21 antidepressants in MDD. Random-effects Bayesian network meta-analysis was implemented to estimate the combined covariate action using restricted cubic splines (RCS). Balanced treatment recommendations were derived for the outcomes efficacy (response), acceptability (dropouts for any reason), and tolerability (dropouts due to adverse events). Findings: The combined covariate action of dose and age suggested agomelatine and escitalopram as the best-balanced antidepressants in terms of efficacy and tolerability that may be escalated until 40 and 60 mg/day fluoxetine equivalents (mg/dayFE), respectively, for ages 30–65 years. Desvenlafaxine, duloxetine, fluoxetine, milnacipran, and vortioxetine may be escalated until 20–40 mg/dayFE, whereas bupropion, citalopram, mirtazapine, paroxetine, and venlafaxine may not be given in doses > 20 mg/dayFE. Amitriptyline, clomipramine, fluvoxamine, levomilnacipran, reboxetine, sertraline, and trazodone revealed no relevant balanced benefits and may therefore not be recommended for antidepressant treatment. None of the antidepressants was observed to provide balanced benefits in patients >70 years because of adverse events exceeding efficacy. Interpretation: Findings suggest that the combined covariate action of dose and age provides a better basis for judging antidepressant clinical benefits than considering dose or age separately, and may thus inform decision makers to accurately guide antidepressant dosing recommendations in MDD

Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

Background: For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. Methods: 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). Results: The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Conclusions: Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation

A multimodel meta-analysis assessing moderators of sexual recidivism as an indicator of treatment effectiveness in persons with sexual offense histories

Objectives This meta-analysis tested whether multimodel inference provides more conclusive evidence than traditional single-hypothesis testing regarding predictors that moderate sexual recidivism as an indicator of treatment effectiveness in persons with sexual offense histories. Methods A dataset including 35 studies equivalent to the meta-analysis by Holper et al. (Sex Abuse 2023; 0: 1–37) was used. Multimodel inference based on information theory tested 15 publication-, study-, treatment-, and individual-specific moderators. Results Only risk level was related to sexual recidivism. A greater posttreatment reduction in sexual recidivism was apparent in high- and medium- compared to low-risk individuals. This moderator explained 77% of the residual heterogeneity. Conclusions Compared to previous reports, the multimodel approach provided clearer evidence on which factors moderate sexual recidivism. Results corroborated the relevance of risk level, which relates to the risk-need-responsivity model. The findings may support treatment recommendations in persons with sexual offense histories in the criminal justice system

Moderators of Sexual Recidivism as Indicator of Treatment Effectiveness in Persons With Sexual Offense Histories: An Updated Meta-analysis

The present meta-analysis is an update of the meta-analysis by Schmucker and Lösel [Campbell Syst. Rev. 2017; 13: 1–75], which synthesized evidence on sexual recidivism as an indicator of treatment effectiveness in persons with sexual offense histories. The updated meta-analysis includes 37 samples comprising a total of 30,394 individuals with sexual offense histories, which is nearly three times the sample size reported by Schmucker and Lösel (2017: 28 samples, N = 9781). In line with Schmucker and Lösel (2017), the mean treatment effect was small with an odds ratio of 1.54 [95% CI 1.22, 1.95] ( p < .001). A moderator analysis suggested three predictors of importance, i.e., risk level, treatment specialization, and author confounding. Greater treatment effectiveness was suggested in high- and medium-compared to low-risk individuals and in specialized compared to non-specialized treatments. Authors affiliated with treatment programs reported larger effectiveness than independent authors. These findings were overall in line with Schmucker and Lösel (2017), though the effects of risk level and treatment specialization were stronger in the current meta-analysis. The findings of the updated meta-analysis reinforce the evidence for the first and second principle of the Risk-Need-Responsivity model. The results may support researchers and decision-makers in interpreting the current evidence on sexual recidivism as an indicator of treatment effectiveness, and, based on that, implement and carry out informative, methodologically sound evaluations of ongoing treatment programs in persons with sexual offense histories

Hemodynamic and affective correlates assessed during performance on the Columbia Card Task (CCT)

The study aimed to test the potential of functional near-infrared spectroscopy (fNIRS) in combination with electrodermal activity (EDA) in a decision paradigm by means of the Columbia Card Task (CCT). The CCT is a dynamic decision task characterized by assessing subjects' risk-taking via eliciting voluntary stopping points in a series of incrementally increasingly risky choices. Using the combined fNIRS-EDA approach, we aim to examine the hemodynamic and affective correlates of both decision and outcome responses during performance on the CCT. Twenty healthy subjects completed the Cold and Hot CCT version while fNIRS over prefrontal cortex and EDA were recorded. Results showed that (1) in the decision phase fNIRS revealed larger total hemoglobin concentration changes [tHb] in the Cold as compared to the Hot CCT, whereas EDA revealed an opposite pattern with larger skin conductance responses (SCRs) to the Hot as compared to the Cold CCT. (2) No significant [tHb] signals or SCRs were found in the outcome phase. (3) Coherence calculations between fNIRS and EDA in the heart rate frequency showed a significant increase during the Hot as compared to the Cold CCT. Our findings designate fNIRS as suitable tool for monitoring decision-making processes. The combination of fNIRS and EDA demonstrates the potential of simultaneously assessing the interaction between hemodynamic and affective responses which can provide additional information concerning the relationship between these two physiological systems for various research areas

Comparative efficacy of placebos in short-term antidepressant trials for major depression : a secondary meta-analysis of placebo-controlled trials

The issue of unblinded outcome-assessors and patients has repeatedly been stressed as a flaw in allegedly double-blind antidepressant trials. Unblinding bias can for example result from a drug's marked side effects. If such unblinding bias is present for a given drug, then it might be expected that the placebos of that drug are rated significantly less effective than that of other antidepressants

Prediction of brain tissue temperature using near-infrared spectroscopy

Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of R2=0.713±0.157 (animal dataset) and R2=0.798±0.087 (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of 0.436±0.283°C (animal dataset) and 0.162±0.149°C (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications

Error detection and error memory in spatial navigation as reflected by electrodermal activity

The study investigated spatial navigation by means of electrodermal activity (EDA). Two groups of healthy subjects (group 1, age <38; group 2, age ≥38) were recorded during navigation through two 3-D virtual mazes differing in difficulty, that is, Maze Simple (MazeS) and Maze Complex (MazeC). Our results show (1) an effect of difficulty, that is, larger skin conductance responses (SCRs) and slower velocity profiles while navigating through MazeC as compared to MazeS. (2) An effect of age, that is, larger SCRs and faster velocity profiles in younger subjects (group 1) compared to older subjects (group 2). (3) An effect of maze region, that is, SCRs increased when subjects entered dead ends with group 1 (young group) decreasing in velocity, whereas group 2 (old group) increased in velocity. (4) And an error memory effect, that is, subjects who remembered an error at a given decision point (crossroads preceding dead ends in MazeC) from previous trials, and then if they did not repeat that error, elicited decreased SCRs as compared to subjects who did not remember and subsequently repeated an error. The latter aspect is the most impactful as it shows that EDA is able to reflect error detection and memory during spatial navigation. Our data designate EDA as suitable monitoring tool for identification and differentiation of the affective correlates underlying spatial navigation, which has recently attracted researchers' attention due to its increased use in 3-D virtual environment

Distribution of Response Time, Cortical, and Cardiac Correlates during Emotional Interference in Persons with Subclinical Psychotic Symptoms

A psychosis phenotype can be observed below the threshold of clinical detection. The study aimed to investigate whether subclinical psychotic symptoms are associated with deficits in controlling emotional interference, and whether cortical brain and cardiac correlates of these deficits can be detected using functional near-infrared spectroscopy (fNIRS). A data set derived from a community sample was obtained from the Zurich Program for Sustainable Development of Mental Health Services. 174 subjects (mean age 29.67 ± 6.41, 91 females) were assigned to four groups ranging from low to high levels of subclinical psychotic symptoms (derived from the Symptom Checklist-90-R). Emotional interference was assessed using the emotional Stroop task comprising neutral, positive, and negative conditions. Statistical distributional methods based on delta plots [behavioral response time (RT) data] and quantile analysis (fNIRS data) were applied to evaluate the emotional interference effects. Results showed that both interference effects and disorder-specific (i.e., group-specific) effects could be detected, based on behavioral RTs, cortical hemodynamic signals (brain correlates), and heart rate variability (cardiac correlates). Subjects with high compared to low subclinical psychotic symptoms revealed significantly reduced amplitudes in dorsolateral prefrontal cortices (interference effect, p < 0.001) and middle temporal gyrus (disorder-specific group effect, p < 0.001), supported by behavioral and heart rate results. The present findings indicate that distributional analyses methods can support the detection of emotional interference effects in the emotional Stroop. The results suggested that subjects with high subclinical psychosis exhibit enhanced emotional interference effects. Based on these observations, subclinical psychosis may therefore prove to represent a valid extension of the clinical psychosis phenotype