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). Warming caused a decrease in the content of MNC due to a decline in microbial biomass carbon and reduced microbial carbon use efficiency. This reduction was primarily caused by warming-induced limitations in available soil phosphorus, which, in turn, constrained the production of microbial biomass. Conversely, warming increased the activity of soil extracellular enzymes, specifically N-acetylglucosaminidase and leucine aminopeptidase, which accelerated the decomposition of MNC. These findings collectively demonstrate that decoupling of MNC formation and decomposition underlie the observed MNC loss under climate warming, which could affect SOC content in temperate forest ecosystems more widespread.

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). Warming increased the concentration of total amino acids and of total sugars by 15% and 21%, respectively, but not organic acids. We found that soil warming and sampling date, along with their interaction, directly influenced the primary metabolite profiles. Specifically, in warming plots, concentrations of arginine, glycine, lysine, threonine, tryptophan, mannose, ribose, fructose, glucose and oxaloacetic acid increased by 51.4%, 19.9%, 21.5%, 19.3%, 22.1%, 23.0%, 38.0%, 40.7%, 19.8% and 16.7%, respectively. Rather than being driven by single compounds, changes in metabolite profiles reflected a general up- or downregulation of most metabolic pathway network. This emphasises the importance of metabolomics approaches in investigating root metabolic pathways and understanding the effects of climate change on tree root metabolism.

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. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.

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. To maintain fixation, subjects had to respond to a stimulus at the center of the visual field. During the entire experiment, in which subjects performed 8 runs, each consisting of 100 trials, brain activity was measured with functional magnetic resonance imaging (MRI). Using a 3-T Siemens Trio MRI scanner, 220 echo-planar images were acquired in each run, with a repetition time of 1.5 s and voxel size of 3 x 3 x 3 mm. The dataset is publicly available from OpenNeuro and additionally includes region of interest maps for visual areas V1 to V4, left and right, obtained from another retinotopic mapping experiment. As such, the dataset allows for accurate mapping of receptive fields and their properties across several stages of human visual cortex.

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. However, functional magnetic resonance imaging (fMRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N = 906) of task-free ("resting state") fMRI data from the UK Biobank (UKB). Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three-year period, 50% of selected participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p < 0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.

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). Long-term warming decreased soil total P pools, likely due to higher outputs of P from soils by increasing net plant P uptake and downward transportation of colloidal and particulate P. Warming increased the sorption strength to more recalcitrant soil P fractions (absorbed to iron oxyhydroxides and clays), thereby further reducing bioavailable P in soil solution. As a response, soil microbes enhanced the production of acid phosphatase, though this was not sufficient to avoid decreases of soil bioavailable P and microbial biomass P (and biotic phosphate immobilization). This study therefore highlights how long-term soil warming triggers changes in biotic and abiotic soil P pools and processes, which can potentially aggravate the P constraints of the trees and soil microbes and thereby negatively affect the C sequestration potential of these forests.

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 (18 O incorporation into DNA) and respiration after C, N and P amendments. Soil samples were taken from two soil depths (0-10, 10-20 cm) in control and warmed (>14 years warming, +4°C) plots in the Achenkirch soil warming experiment. Soils were amended with combinations of glucose-C, inorganic/organic N and inorganic/organic P in a full factorial design, followed by incubation at their respective mean field temperatures for 24 h. Soil microbes were generally C-limited, exhibiting 1.8-fold to 8.8-fold increases in microbial growth upon C addition. Warming consistently caused soil microorganisms to shift from being predominately C limited to become C-P co-limited. This P limitation possibly was due to increased abiotic P immobilization in warmed soils. Microbes further showed stronger growth stimulation under combined glucose and inorganic nutrient amendments compared to organic nutrient additions. This may be related to a prolonged lag phase in organic N (glucosamine) mineralization and utilization compared to glucose. Soil respiration strongly positively responded to all kinds of glucose-C amendments, while responses of microbial growth were less pronounced in many of these treatments. This highlights that respiration-though easy and cheap to measure-is not a good substitute of growth when assessing microbial element limitation. Overall, we demonstrate a significant shift in microbial element limitation in warmed soils, from C to C-P co-limitation, with strong repercussions on the linkage between soil C, N and P cycles under long-term warming.

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 (CO2). 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. Fine root turnover times of the individual root fractions were estimated with a one-pool model. Soil warming strongly increased fine root production by up to 128 % within one year, but after two years, the production was less pronounced (+35 %). The first-year production was likely very high due to the rapid exploitation of the root-free ingrowth cores. The radiocarbon signatures of fine roots were overall variable among treatments and plots. Soil warming tended to decrease fine root turnover times of all the measured root fractions after 8 and 14 years of warming, and there was a tendency for trees to use older carbon reserves for fine root production in warmed plots. Furthermore, soil warming increased fine root turnover from 50 to 106 g C m-2 yr-1 (based on two different approaches). Our findings suggest that future climate warming may increase carbon input into soils by enhancing fine root turnover. If this increase may partly offset carbon losses by increased mineralization of soil organic matter in temperate forest soils is still unclear and should guide future research.

Advances in spiral fMRI: A high-resolution dataset.

We present data collected for the research article "Advances in Spiral fMRI: A High-resolution Study with Single-shot Acquisition" (Kasper et al. 2022). All data was acquired on a 7T ultra-high field MR system (Philips Achieva), equipped with a concurrent magnetic field monitoring setup based on 16 NMR probes. For task-based fMRI, a visual quarterfield stimulation paradigm was employed, alongside physiological monitoring via peripheral recordings. This data collection contains different datasets pertaining to different purposes: (1) Measured magnetic field dynamics (k0, spiral k-space trajectories, 2nd order spherical harmonics, concomitant fields) during ultra-high field fMRI sessions from six subjects, as well as concurrent temperature curves of the gradient coil, to explore MR system and subject-induced variability of field fluctuations and assess the impact of potential correction methods. (2) MR Raw Data, i.e., coil and concurrent encoding magnetic field (trajectory) data, of a single subject, as well as nominal spiral gradient waveforms, precomputed B0 and coil sensitivity maps, to enable testing of alternative image reconstruction approaches for spiral fMRI data. (3) Reconstructed image time series of the same subject alongside behavioral and physiological logfiles, to reproduce the fMRI preprocessing and analysis, as well as figures presented in the research article related to this article, using the published analysis code repository. All data is provided in standardized formats for the respective research area. In particular, ISMRMRD (HDF5) is used to store raw coil data and spiral trajectories, as well as measured field dynamics. Likewise, the NIfTI format is used for all imaging data (anatomical MRI and spiral fMRI, B0 and coil sensitivity maps).

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. Fine root biomass (FRB) and fine root production were 17% and 128% higher in the warmed plots, respectively, after 14 years. The increase in FRB (13%) was not significant after 8 years of treatment, whereas specific root length, specific root area, and root tip density were significantly higher in warmed plots at both sampling occasions. Soil warming did not affect EcM exploration types and diversity, but changed their community composition, with an increase in the relative abundance of Cenoccocum at 0-10 cm soil depth, a drought-stress-tolerant genus, and an increase in short- and long-distance exploration types like Sebacina and Boletus at 10-20 cm soil depth. Warming increased the root-associated bacterial diversity but did not affect their community composition. Soil warming did not affect nutrient concentrations of fine roots, though we found indications of limited soil phosphorus (P) and potassium (K) availability. Our findings suggest that, in the studied ecosystem, global warming could persistently increase soil carbon inputs due to accelerated fine root growth and turnover, and could simultaneously alter fine root morphology and EcM fungal community composition toward improved nutrient foraging.

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

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. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09696-9.

Advances in spiral fMRI: A high-resolution study with single-shot acquisition.

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination.

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. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM.

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. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasize speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice). In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.

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). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

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.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. SIGNIFICANCE STATEMENT: In psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.

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. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

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. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

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.g. with cardiovascular conditions or stroke) who receive low-dose aspirin prophylactically and are compared to healthy controls without aspirin. To examine the severity of this potential confound, we combined high field (7 Tesla) MRI during a simple hand movement task with a biophysically informed hemodynamic model. We compared elderly individuals receiving aspirin for primary or secondary prophylactic purposes versus age-matched volunteers without aspirin medication, testing for putative differences in BOLD responses. Specifically, we fitted hemodynamic models to BOLD responses from 14 regions activated by the task and examined whether model parameter estimates were significantly altered by aspirin. While our analyses indicate that hemodynamics differed across regions, consistent with the known regional variability of BOLD responses, we neither found a significant main effect of aspirin (i.e., an average effect across brain regions) nor an expected drug × region interaction. While our sample size is not sufficiently large to rule out small-to-medium global effects of aspirin, we had adequate statistical power for detecting the expected interaction. Altogether, our analysis suggests that patients with cardiovascular risk receiving low-dose aspirin for primary or secondary prophylactic purposes do not show strongly altered BOLD signals when compared to healthy controls without aspirin.