Predictive modelling of clinically significant depressive symptoms after coronary artery bypass graft surgery: protocol for a multicentre observational study in two Swiss hospitals (the PsyCor study).

Introduction: Coronary artery bypass grafting (CABG) remains one of the most commonly performed cardiac surgeries worldwide. Despite surgical advancements, a significant proportion of patients experience psychological distress following surgery, with depression being particularly common. Current evidence regarding the effectiveness of preoperative psychological interventions in improving postoperative mental health outcomes remains inconclusive.

Bayesian Workflow for Generative Modeling in Computational Psychiatry.

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far.

Refining the Allostatic Self-Efficacy Theory of Fatigue and Depression Using Causal Inference.

Allostatic self-efficacy (ASE) represents a computational theory of fatigue and depression. In brief, it postulates that (i) fatigue is a feeling state triggered by a metacognitive diagnosis of loss of control over bodily states (persistently elevated interoceptive surprise); and that (ii) generalization of low self-efficacy beliefs beyond bodily control induces depression. Here, we converted ASE theory into a structural causal model (SCM).

Long-term soil warming decreases soil microbial necromass carbon by adversely affecting its production and decomposition.

Microbial necromass carbon (MNC) accounts for a large fraction of soil organic carbon (SOC) in terrestrial ecosystems. Yet our understanding of the fate of this large carbon pool under long-term warming is uncertain. Here, we show that 14 years of soil warming (+4°C) in a temperate forest resulted in a reduction in MNC by 11% (0-10 cm) and 33% (10-20 cm).

Long-term soil warming changes the profile of primary metabolites in fine roots of Norway spruce in a temperate montane forest.

Climate warming poses major threats to temperate forests, but the response of tree root metabolism has largely remained unclear. We examined the impact of long-term soil warming (>14 years, +4°C) on the fine root metabolome across three seasons for 2 years in an old spruce forest, using a liquid chromatography-mass spectrometry platform for primary metabolite analysis. A total of 44 primary metabolites were identified in roots (19 amino acids, 12 organic acids and 13 sugars).

A computationally informed distinction of interoception and exteroception.

While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states.

Predicting future depressive episodes from resting-state fMRI with generative embedding.

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression.

A tightly controlled fMRI dataset for receptive field mapping in human visual cortex.

Four right-handed, healthy subjects participated in a visual stimulation experiment. Subjects were viewing a dartboard-shaped flickering checkerboard stimulus, divided into 4 rings and 12 segments, defining 48 sectors in the visual field. Local contrast in each sector was continuously varying across four levels and updated every 3 s.

Long-term soil warming decreases microbial phosphorus utilization by increasing abiotic phosphorus sorption and phosphorus losses.

Phosphorus (P) is an essential and often limiting element that could play a crucial role in terrestrial ecosystem responses to climate warming. However, it has yet remained unclear how different P cycling processes are affected by warming. Here we investigate the response of soil P pools and P cycling processes in a mountain forest after 14 years of soil warming (+4 °C).

Does long-term soil warming affect microbial element limitation? A test by short-term assays of microbial growth responses to labile C, N and P additions.

Increasing global temperatures have been reported to accelerate soil carbon (C) cycling, but also to promote nitrogen (N) and phosphorus (P) dynamics in terrestrial ecosystems. However, warming can differentially affect ecosystem C, N and P dynamics, potentially intensifying elemental imbalances between soil resources, plants and soil microorganisms. Here, we investigated the effect of long-term soil warming on microbial resource limitation, based on measurements of microbial growth ( O incorporation into DNA) and respiration after C, N and P amendments.

Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil.

Fine root litter represents an important carbon input to soils, but the effect of global warming on fine root turnover (FRT) is hardly explored in forest ecosystems. Understanding tree fine roots' response to warming is crucial for predicting soil carbon dynamics and the functioning of forests as a sink for atmospheric carbon dioxide (CO). We studied fine root production (FRP) with ingrowth cores and used radiocarbon signatures of first-order, second- to third-order, and bulk fine roots to estimate fine root turnover times after 8 and 14 years of soil warming (+4 °C) in a temperate forest.

Long-term soil warming alters fine root dynamics and morphology, and their ectomycorrhizal fungal community in a temperate forest soil.

Climate warming is predicted to affect temperate forests severely, but the response of fine roots, key to plant nutrition, water uptake, soil carbon, and nutrient cycling is unclear. Understanding how fine roots will respond to increasing temperature is a prerequisite for predicting the functioning of forests in a warmer climate. We studied the response of fine roots and their ectomycorrhizal (EcM) fungal and root-associated bacterial communities to soil warming by 4°C in a mixed spruce-beech forest in the Austrian Limestone Alps after 8 and 14 years of soil warming, respectively.

An introduction to thermodynamic integration and application to dynamic causal models.

Unlabelled: In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes.

Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities.

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease.

Technical note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data.

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output).

Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses.

Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.

Regression dynamic causal modeling for resting-state fMRI.

"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI-regression dynamic causal modeling (rDCM)-extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks.

A Hilbert-based method for processing respiratory timeseries.

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators.

Hemodynamic modeling of long-term aspirin effects on blood oxygenated level dependent responses at 7 Tesla in patients at cardiovascular risk.

Aspirin is considered a potential confound for functional magnetic resonance imaging (fMRI) studies. This is because aspirin affects the synthesis of prostaglandin, a vasoactive mediator centrally involved in neurovascular coupling, a process underlying blood oxygenated level dependent (BOLD) responses. Aspirin-induced changes in BOLD signal are a potential confound for fMRI studies of at-risk individuals or patients (e.

Computational Dissociation of Dopaminergic and Cholinergic Effects on Action Selection and Inhibitory Control.

Background: Patients with schizophrenia make more errors than healthy subjects in the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation.

Switch costs in inhibitory control and voluntary behaviour: A computational study of the antisaccade task.

An integral aspect of human cognition is the ability to inhibit stimulus-driven, habitual responses, in favour of complex, voluntary actions. In addition, humans can also alternate between different tasks. This comes at the cost of degraded performance when compared to repeating the same task, a phenomenon called the "task-switch cost.