Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders.

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Elevated accuracy in recognition of subliminal happy facial expressions in patients with panic disorder after psychotherapy.

Background: Individuals with anxiety disorders (ADs) often display hypervigilance to threat information, although this response may be less pronounced following psychotherapy. This study aims to investigate the unconscious recognition performance of facial expressions in patients with panic disorder (PD) post-treatment, shedding light on alterations in their emotional processing biases. Methods: Patients with PD (n=34) after (exposure-based) cognitive behavior therapy and healthy controls (n=43) performed a subliminal affective recognition task. Emotional facial expressions (fearful, happy, or mirrored) were displayed for 33 ms and backwardly masked by a neutral face. Participants completed a forced choice task to discriminate the briefly presented facial stimulus and an uncovered condition where only the neutral mask was shown. We conducted a secondary analysis to compare groups based on their four possible response types under the four stimulus conditions and examined the correlation of the false alarm rate for fear responses to non-fearful (happy, mirrored, and uncovered) stimuli with clinical anxiety symptoms. Results: The patient group showed a unique selection pattern in response to happy expressions, with significantly more correct "happy" responses compared to controls. Additionally, lower severity of anxiety symptoms after psychotherapy was associated with a decreased false fear response rate with non-threat presentations. Conclusion: These data suggest that patients with PD exhibited a "happy-face recognition advantage" after psychotherapy. Less symptoms after treatment were related to a reduced fear bias. Thus, a differential facial emotion detection task could be a suitable tool to monitor response patterns and biases in individuals with ADs in the context of psychotherapy.

Medida de troponina en circulación mediante un ensayo sensible para la evaluación del riesgo cardiovascular en prevención primaria / Circulating troponin as measured by a sensitive assay for cardiovascular risk assessment in primary prevention / Medição da troponina circulante por um teste sensível para a avaliação de risco cardiovascular na prevenção primária

Background: Measuring circulating cardiac troponin using novel sensitive assays has revealed that even minute elevations are associated with increased mortality in patients with coronary artery disease or even in the general population. Less well defined, however, is the incremental value of measuring circulating cardiac troponin I (cTnI) by a sensitive assay for risk assessment in primary prevention. Methods: We measured circulating concentrations of cTnI, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity C-reactive protein (hsCRP) in 5388 individuals free of known cardiovascular disease recruited into the DETECT study, a prospective longitudinal population-based cohort study. We determined the prognostic implications for incident major adverse cardio­vascular events (MACE) during 5 years of follow-up. Results: Circulating cTnI was detectable in 19% of the subjects. Increased cTnI concentrations were associated with established risk factors for atherosclerosis and demonstrated a graded relationship with all-cause mortality and incident MACE during 5-year follow-up. A single measurement of cTnI significantly improved risk prediction over established risk factors, and also added prognostic information, when adjusted for serum concentrations of NT-proBNP and hsCRP. Conclusions: Minute increases in cTnI are associated with increased mortality and incident MACE in a large primary prevention cohort and, thus, identify contributors to cardiovascular risk not fully captured by traditional risk factor assessment.

Antecedentes: La medición de troponina cardíaca en circulación mediante nuevos ensayos sensibles ha revelado que incluso mínimas elevaciones se asocian con mayor mortalidad en pacientes con enfermedad arterial coronaria, o incluso en la población general. Sin embargo, menos conocido es el valor incremental o agregado de la medición de la troponina I cardiaca (cTnI) circulante mediante un ensayo sensible para la evaluación del riesgo en prevención primaria. Métodos: Se midieron las concentraciones circulantes de cTnI, de pro- péptido natriurético tipo B N-terminal (NT-proBNP), y de proteína C reactiva de alta sensibilidad (PCRus), en 5388 personas sin enfermedad cardiovascular conocida reclutadas en el estudio DETECT, un estudio prospectivo longitudinal de cohorte de base poblacional. Se determinaron las implicancias pronósticas en la incidencia de eventos adversos cardiovasculares mayores (MACE) durante 5 años de seguimiento. Resultados: La cTnI se detectó en el 19% de los sujetos. El aumento de las concentraciones de cTnI se asoció con factores de riesgo establecidos para la aterosclerosis y demostró una relación gradual con la mortalidad por todas las causas y la incidencia de MACE durante los 5 años de seguimiento. Una sola medición de cTnI mejoró significativamente la predicción del riesgo por encima de los factores de riesgo establecidos, y también agregó información pronóstica cuando se ajustó por la concentración sérica de NT-proBNP y PCRus. Conclusiones: Mínimos incrementos de cTnI se asociaron con mayor mortalidad e incidencia de MACE en una gran cohorte de prevención primaria y, por tanto, la identificación de sujetos con riesgo cardiovascular no siempre son detectados completamente por la evaluación de factores de riesgo tradicionales.