Moho depth across the Trans-European Suture Zone from P-and S-receiver functions

The Mohorovicic discontinuity, Moho for short, which marks the boundary between crust and mantle, is the main first-order structure within the lithosphere. Geodynamics and tectonic evolution determine its depth level and properties. Here, we present a map of the Moho in central Europe across the Teisseyre-Tornquist Zone, a region for which a number of previous studies are available. Our results are based on homogeneous and consistent processing of P- and S-receiver functions for the largest passive seismological data set in this region yet, consisting of more than 40 000 receiver functions from almost 500 station. Besides, we also provide new results for the crustal Vp/Vs ratio for the whole area. Our results are in good agreement with previous, more localized receiver function studies, as well as with the interpretation of seismic profiles, while at the same time resolving a higher level of detail than previous maps covering the area, for example regarding the Eifel Plume region, Rhine Graben and northern Alps. The close correspondence with the seismic data regarding crustal structure also increases confidence in use of the data in crustal corrections and the imaging of deeper structure, for which no independent seismic information is available. In addition to the pronounced, stepwise transition from crustal thicknesses of 30km in Phanerozoic Europe to more than 45 beneath the East European Craton, we can distinguish other terrane boundaries based on Moho depth as well as average crustal Vp/Vsratio and Moho phase amplitudes. The terranes with distinct crustal properties span a wide range of ages, from Palaeoproterozoic in Lithuania to Cenozoic in the Alps, reflecting the complex tectonic history of Europe. Crustal thickness and properties in the study area are also markedly influenced by tectonic overprinting, for example the formation of the Central European Basin System, and the European Cenozoic Rift System. In the areas affected by Cenozoic rifting and volcanism, thinning of the crust corresponds to lithospheric updoming reported in recent surface wave and S-receiver function studies, as expected for thermally induced deformation. The same correlation applies for crustal thickening, not only across the Trans-European Suture Zone, but also within the southern part of the Bohemian Massif. A high Poisson’s ratio of 0.27 is obtained for the craton, which is consistent with a thick mafic lower crust. In contrast, we typically find Poisson’s ratios around 0.25 for Phanerozoic Europe outside of deep sedimentary basins. Mapping of the thickness of the shallowest crustal layer, that is low-velocity sediments or weathered rock, indicates values in excess of 6km for the most pronounced basins in the study area, while thicknesses of less than 4km are found within the craton, central Germany and most of the Czech Republic.Peer reviewe

Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning

Background Predicting treatment outcome in major depressive disorder (MDD) remains an essential challenge for precision psychiatry. Clinical prediction models (CPMs) based on supervised machine learning have been a promising approach for this endeavor. However, only few CPMs have focused on model sparsity even though sparser models might facilitate the translation into clinical practice and lower the expenses of their application. Methods In this study, we developed a predictive modeling pipeline that combines hyperparameter tuning and recursive feature elimination in a nested cross-validation framework. We applied this pipeline to a real-world clinical data set on MDD treatment response and to a second simulated data set using three different classification algorithms. Performance was evaluated by permutation testing and comparison to a reference pipeline without nested feature selection. Results Across all models, the proposed pipeline led to sparser CPMs compared to the reference pipeline. Except for one comparison, the proposed pipeline resulted in equally or more accurate predictions. For MDD treatment response, balanced accuracy scores ranged between 61 and 71% when models were applied to hold-out validation data. Conclusions The resulting models might be particularly interesting for clinical applications as they could reduce expenses for clinical institutions and stress for patients

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming