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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 (

Climate warming poses major threats to temperate forests, but the response of plant root metabolism has remained unclear. Understanding and predicting the impact of climate warming on the root metabolome represents a grand challenge and a major opportunity to predict the belowground functioning of forests in a warmer climate. We here studied the impact of long-term soil warming (>14 years, ambient versus +4 °C) on the fine root metabolome across three seasons (spring, summer, and autumn) for two years in a spruce-dominated mountain forest in the Austrian Limestone Alps. Root primary metabolites were analyzed with a liquid chromatography-mass spectrometry metabolomics platform (LC-Orbitrap MS). A total of 44 primary metabolites were identified in roots (19 amino acids, 12 organic acids, and 13 sugars). Warming and season had significant effects on total primary metabolite concentration, but no interaction effect. Warming increased the amino acid and sugar concentrations but did not affect organic acids. This may be explained by increased activity of the protein amino acid (such as arginine, glycine, and lysine) biosynthesis and metabolism and/or of root carbohydrate metabolism and transport under warming. The non-metric multidimensional scaling (NMDS) showed that soil warming was not significantly affecting the primary metabolite profiles, but year and season had significant effects. Season impacted primary metabolite profiles through changing soil temperature and years by effects on the soil environment (soil temperature and soil moisture) and root morphology (root length, specific root area, specific root length, and root diameter). In addition, we found that the root metabolism activity in warmed plots was lower than in control plots at the same soil temperature. Our data indicated that root metabolism in long-term warmed soil undergoes thermal acclimation, which may help match root metabolism with the required nutrient uptake and assimilation.Keywords: Soil warming, root metabolism, primary metabolites, temperate forest.

“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., “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.

The capacity of forest soils and trees to sequester C is closely linked to soil nitrogen (N) bioavailability, a major control of microbial and plant growth and functioning. Recent meta-analyses indicated that both, soil organic C and N cycling, intensify with climate/soil warming, though little studies investigated this in long-term (decadal) warming experiments. Changes in N cycling processes have been addressed by measuring total and labile N pools, net and gross N process rates, and changes in extracellular enzyme activities. An alternative approach, integrating over longer time intervals, is to study the natural 15N abundances of different soil and plant N pools. In this study we quantified the natural 15N abundances (d15N values) of coarse and fine litter, fine roots, soil organic N, extractable organic N, microbial biomass N, ammonium and nitrate at the long-term soil warming experimental site in Achenkirch (Tyrol, Austria). This site is one of the few climate manipulation experiments in forests operating for more than 14 years and has provided unique insights into the effects of global warming on forest ecosystem processes. We analyzed ecosystem compartments across three seasons (May, August, October 2019), to investigate the consistency of warming effects on soil N cycle processes. Moreover, we developed an isotope fractionation model to decipher the isotope fractionations of the studied soil N processes and the fractions transformed by them, i.e. for depolymerization, microbial uptake, N mineralization, nitrification and soil N losses. Overall, the consistent increase in fine root δ15N in warmed soils indicated a general opening of the soil N cycle (greater N losses), which was mirrored in increased ammonium d15N values, the latter implying increased fractions of ammonium being oxidized to nitrate. Higher fractions of ammonium being nitrified makes labile N more amenable to N losses, either by leaching of nitrate or by denitrification losses. Since nitrification and denitrification exhibit strong isotope fractionation effects against 15N, the lost N is concomitantly 15N depleted, while residual substrates remaining in the ecosystem become 15N enriched, thereby explaining the 15N enrichment with increasing N cycling and N loss rates in warmed soils.

Phosphorus (P) is an essential element for all organisms, received increasing attention in global change research. Current P-related climate warming studies have mainly addressed warming effects on soil and plant P pools, but how soil P cycling processes respond to elevated temperatures has remained largely uncertain. In this study we investigated the effect of soil warming on both, soil P pools and P cycling processes (applying the 33P isotope pool dilution technique) across different soil depths (0-10 and 10-20 cm) and seasons (spring, summer, autumn) during the 15th year of soil warming (+4 °C) in a mature temperate mountain forest, in Achenkirch, Austria. Long-term warming decelerated the gross rates of phosphate (Pi) mobilization by 21%, reducing the soil Pi input. The decreased gross Pi mobilization was in part attributed to substantial losses of soil total P pools (substrates), which likely was caused by increased dissolved organic P leaching to deeper soil layers in the warming treatment. Abiotic immobilization increased in the warming treatment, due to increased sorption of Pi to iron oxyhydroxides and clay, further reducing soil Pi availability. Moreover, warming decreased biotic Pi immobilization and microbial biomass P, and as a response, microbial communities allocated more energy and nutrients into the production of acid phosphatase, indicating a strong shift in microbial carbon and nutrient allocation in response to the decreased P availability. According to linear mixed-effects models, most of the responses of the measured P pools and processes showed no interactions between warming and soil depth and/or season, indicating consistent effects of long-term soil warming on the P cycle across different soil depths and seasons. Overall, this study highlights for the first-time how long-term soil warming affects (biotic and abiotic) soil P processes and their interactions with soil P pools. Besides, it also indicates the potential of how P constraints can affect other biogeochemical cycles in response to warming.

The capacity of forest plants to sequester C is closely linked to soil nitrogen (N) availability, a major control of plant growth and ecosystem functioning. An increase of soil temperature caused by climate change affects C and N cycling in forest soils, but implications for plant available N have remained largely unclear. In recent short-term laboratory incubation studies, an increase in soil temperature has not only led to a significant increase in diffusive N fluxes, but also to a concomitant shift in bioavailable N quality for plant and microbial uptake, i.e. towards a higher proportion of inorganic N forms compared to small organic N forms such as amino acids. However, long-term effects of soil warming on diffusive soil N fluxes in situ remain largely unknown. Applying the microdialysis technique, we quantified in situ diffusive fluxes of amino acids, ammonium and nitrate at the long-term soil warming experimental site Achenkirch (Tyrol, Austria). This site is one of the few climate manipulation experiments operational for more than 15 years and has already provided a wealth of novel insights into the potential effects of global warming on forest ecosystem responses. Results from four sampling campaigns (n = 1152 microdialysis samples) during the growing season showed no effect of warming on diffusive N fluxes. Diffusive ammonium fluxes increased from spring towards autumn while nitrate fluxes followed an opposite trend. Compared to other temperate and boreal forest soils, the proportion of amino acids in the total diffusive N flux in this carbonate soil was low (13 - 30%), while the proportions of ammonium (21 – 67%) and nitrate were high (19 – 58%). In conclusion, our results suggest that in situ diffusive N fluxes, as well as the proportions of different N forms, were unaffected after 15 years of soil warming. Accordingly, warming may not be expected to increase diffusive soil N supply for root uptake in the topsoil in the long run. Diffusive N availability was mainly determined by seasonal effects and by the small-scale heterogeneity of the soil matrix.

Trees invest up to one third of the carbon (C) fixed by photosynthesis into belowground allocation, including fine root exudation into the rhizosphere. Rising soil temperatures in a warmer world could modify the allocation of labile C below ground, thereby affecting biogeochemical cycling in forest soils. Up to date our understanding of how fine root exudation of labile carbon compounds responds to warming is yet emerging and in-situ analyses under warming conditions are scarce, especially in mature forests. Using a C-free cuvette incubation method, we investigated in situ rates of root exudation from fine roots in a mature spruce forest across three seasons after 14 and 15 years of warming in the Achenkirch soil warming (+4°C) experiment. In addition, we run a complementary short-term experiment on root exudation, during which we increased soil temperatures on warmed plots stepwise up to a difference of 12°C between treatments within a few days. We found no effect of long-term soil warming on in situ root exudation rates (n = 120 roots sampled). Mean exudation rates per biomass were 16.23 ± 4.03 and 17.94 ± 2.94 µg g-1 h-1 on control and warmed plots respectively, with highest rates found in the late growing season in both treatments. Exudation rates were positively related to the specific root length and were negatively related to soil moisture, but unrelated to soil inorganic N availability and in situ soil temperature. However, the short-term temperature manipulation resulted in an exponential increase of estimated root exudation rates with soil temperature. Our results therefore indicate that fine root exudation from mature trees in the studied ecosystem is inherently controlled by soil temperature, but an interplay with other parameters such as nutrient availability, root morphology and/or soil moisture are the dominant controlling mechanisms across the seasons in the long run. Our observations further indicate that the long-term soil warming by 4°C caused only a subtle increase in root exudation per fine root surface area or per fine root biomass.

Adaptive behavior is only possible by stopping stereotypical actions to generate new plans according to internal goals. It is response inhibition —the ability to stop actions automatically triggered by exogenous cues— that allows for the flexible interplay between bottom-up, stimulus driven behaviors, and top-down strategies. In addition to response inhibition, cognitive control draws on conflict adaptation, the facilitation of top-down actions following high conflict situations. It is currently unclear whether and how response inhibition and conflict adaptation depend on GABAergic signaling, the main inhibitory neurotransmitter in the human brain. Here, we applied a recently developed computational model (SERIA) to data from two studies (N=150 & 50) of healthy volunteers performing Simon and antisaccade tasks. One of these datasets was acquired under placebo-controlled pharmacological enhancement of GABAergic transmission (lorazepam, an allosteric modulator of the GABA-A receptor). Our model-based results suggest that enhanced GABA-A signaling boosts conflict adaptation but impairs response inhibition. More generally, our computational approach establishes a unified account of response inhibition and conflict adaptation in the Simon and antisaccade tasks and provides a novel tool for quantifying specific aspects of cognitive control and their modulation by pharmacology or disease.Author SummaryOur capacity to prepare for situations that afford conflicting responses (conflict adaptation) and to stop our immediate impulses in these scenarios (response inhibition) are the hallmark of cognitive control. As these abilities require both the stopping or slowing of response tendencies, a natural question is whether they are mediated by inhibitory neurotransmission in the brain. Here, we combined computational modeling with two experiments to investigate how conflict adaptation and response inhibition interact with each other (experiment 1) and how these are modulated by lorazepam (experiment 2), a positive modulator of the GABA-A receptor, one of the main inhibitory receptors in the human brain. Using our computational model to disentangle conflict adaptation and response inhibition, our results indicate that while lorazepam impaired response inhibition, it improved conflict adaptation. Thus, our results suggests that conflict adaptation is mediated by GABA-A neurotransmission.

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 and fine root production were 17% and 128% higher in the warmed plots, respectively, after 14 years. The increase in fine root biomass (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 Cenococcum 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 towards improved nutrient foraging.

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., NeuroImage, 245, ISSN:1053-8119, ISSN:1095-9572

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

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., NeuroImage, 237, ISSN:1053-8119, ISSN:1095-9572

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)., NeuroImage, 230, ISSN:1053-8119, ISSN:1095-9572

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 theTranslationalAlgorithms forPsychiatry-AdvancingScience (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.

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., Cognitive Neurodynamics, 16 (1), ISSN:1871-4080, ISSN:1871-4099

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 will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas).HighlightsWe introduce a new estimator for respiratory volume per unit time from respiratory recordings.We demonstrate how this is able to accurately characterise atypical breathing events.This removes significantly more variance when used as a confound regressor for fMRI data.Our implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas.

“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.

The layered architecture of cortex is thought to play a fundamental role in shaping cortical computations. However, direct electrophysiological measurements of layered activity are not possible non-invasively in humans. Recent advances have shown that a distinction of two layers can be achieved using magnetoencephalography in combination with head casts and advanced spatial modeling. In this technical note, we present a dynamic causal model of a single cortical microcircuit that models event related potentials. The model captures the average dynamics of a detailed two layered circuit. It combines a temporal model of neural dynamics with a spatial model of a layer specific lead field to facilitate layer separation. In simulations we show that the spatial arrangement of the two layers can be successfully recovered using Bayesian inference. The layered model can also be distinguished from a single dipole model. We conclude that precision magnetoencephalography in combination with detailed dynamical system modeling can be used to study non-invasively the fast dynamics of layered computations.

Despite the intensified efforts to understand the impacts of climate change on forest soil C dynamics, few studies have addressed the long term effects of warming on microbially mediated soil C and nutrient processes. In the few long-term soil warming experiments the initial stimulation of soil C cycling diminished with time, due to thermal acclimation of the microbial community or due to depletion of labile soil C as the major substrate for heterotrophic soil microbes. Thermal acclimation can arise as a consequence of prolonged warming and is defined as the direct organism response to elevated temperature across annual to decadal time-scales which manifest as a physiological change of the soil microbial community. This mechanism is clearly different from apparent thermal acclimation, where the attenuated response of soil microbial processes to warming is due to the exhaustion of the labile soil C pool.The Achenkirch experiment, situated in the Northern Limestone Alps, Austria (47°34’ 50’’ N; 11°38’ 21’’ E; 910 m a.s.l.) is a long term (>15 yrs) soil warming experiment that has provided key insights into the effects of global warming on the forest soil C cycle. At the Achenkirch site, we have observed a sustained positive response of heterotrophic soil respiration and of soil CO2 efflux to warming after nine years (2013), making it an appropriate setting for testing hypotheses about continued or decreasing warming effects at decadal scales. We collected soil from six warmed and six control plots in October 2019, from 0-10 cm and 10-20 cm depth, and incubated them at three different temperatures: ambient, +4, and +10 °C. We measured potential soil enzyme activities with fluorimetric assays, gross rates of protein depolymerization, N mineralization, and nitrification with 15N isotope pool dilution approaches, and microbial growth, respiration, and C use efficiency (CUE) based on the 18O incorporation in DNA and gas analysis. Our preliminary results show that potential enzyme activities of aminopeptidase, N-acetylglucosaminidase, b-glucosidase, and acid phosphatase were stimulated by decadal soil warming by 1.7- to 3.5-fold, measured at the same i.e. ambient temperature. In contrast, the temperature sensitivity (Q10) remained unaltered between warmed and control soils for all enzyme activities (Q10=1.63-2.28), except for aminopeptidase where we observed a decrease in Q10 by 25% in warmed topsoils (0-10 cm). Aminopeptidase also had the highest temperature-sensitivity (Q10=2.39), causing a decrease of the enzymatic C: N acquisition ratio with warming. These results indicate an increasing investment in microbial N acquisition with warming. We will follow these trends based on results on gross rates of soil C and N processes, allowing to delineate decadal soil warming effects on soil microbial biogeochemistry and to understand their effect on the cross-talk between organic C and N cycling in calcareous forest soils.

Global warming may accelerate soil carbon (C) and nutrient cycling as higher temperatures accelerate soil microbial and enzymatic activities. However, this enhanced soil C cycling can diminish with time due to the depletion of labile soil C or due to thermal acclimation of soil microbes, while the increased N cycling may be dampened over time in N-rich soils. Moreover, soil climate as well as the quality and quantity of plant inputs change between seasons, which could influence the C: nitrogen (N): phosphorus (P) stoichiometry of resources available for microbes and thereby alter the warming effect on microbial activities and nutrient cycling between seasons. Such seasonal changes caused inconsistent warming effects on extracellular enzyme activities and on soil respiration in some experiments, with warming effects turning from positive to negative between seasons, yet the underlying controls of these adverse effects are far from being well understood. In this study, we therefore aimed to investigate soil warming and seasonal effects on soil C, N, and P pools and processes in a temperate calcareous mixed forest. We collected soil samples in spring, summer and fall (May, August, and October 2019) from a long-term (>15 yrs) soil warming experiment in Achenkirch, Northern Limestone Alps, Austria (47°34’ 50’’ N; 11°38’ 21’’ E; 910 m a.s.l.). The samples were incubated at the corresponding in-situ temperatures in the laboratory. Microbial growth, respiration and C use efficiency were determined by following 18O-H2O incorporation in DNA and by gas analysis. 15N pool dilution assays were applied to quantify gross rates of protein depolymerization, N mineralization, and nitrification, whilst gross rates of soil inorganic P mobilization were measured by a 33P pool dilution assay. Moreover, we measured the potential soil enzyme activities of four hydrolases and two oxidases, and determined contents of labile (extractable) and microbial biomass C, N, and P. This study will thereby provide a comprehensive insight into how soil warming influences soil microbial C, N, and P cycling in a temperate calcareous mixed forest as well as into their energetic, stoichiometric and soil microclimatic constraints. The long-term nature of this soil warming experiment will therefore allow predictions of the future biogeochemical behavior of calcareous forest soils, and deduce potential feed-backs on forest productivity, atmospheric composition and climate change.

Forests are the main contributors to the global terrestrial carbon (C) sink but several studies suggest that global warming could significantly reduce their CO2 mitigation potential. The capacity of forest plants to sequester C is closely linked to soil nitrogen (N) availability, a major control of plant growth and ecosystem functioning. An increase of soil temperature caused by global change is critically affecting soil N supply rates, both directly by increasing diffusive N fluxes in the soil solution and indirectly by accelerating soil N turn-over rates. In recent short-term laboratory incubation studies, an increase in soil temperature has not only led to a significant increase in diffusive N fluxes but also to a concomitant shift in N quality available for plant uptake towards a higher portion of inorganic N forms compared to small organic N forms such as amino acids. However, until now long-term effects of soil warming on soil N fluxes have not been studied. Here, we present first results from a study on soil N availabilities at the long-term soil warming experimental site Achenkirch (Austria) in the Limestone Alps. This site is one of the few in situ climate manipulation experiments operational for more than 10 years and has already provided a wealth of novel insights into the potential effects of global warming on forest ecosystem responses. Applying in situ microdialysis, we estimated diffusive fluxes of inorganic N and amino acids along the growing season in soils warmed by resistance heating cables since 2005 (+4 °C compared to control plots) and control soils. Fluxes of all N forms were highly variable within each subplot (2 x 2 m) and reflected the high heterogeneity of soils at this forest site. Interestingly, fluxes of amino acids were less variable than of nitrate or ammonium throughout the year, indicating comparably stable protein depolymerization rates. In summary, long-term soil warming affected diffusive N fluxes but less than other factors operating on smaller (< 1 cm) scales.

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.

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., NeuroImage, 246, ISSN:1053-8119, ISSN:1095-9572