Timing of repetition suppression of event-related potentials to unattended objects

Current theories of object perception emphasize the automatic nature of perceptual inference. Repetition suppression (RS), the successive decrease of brain responses to repeated stimuli, is thought to reflect the optimization of perceptual inference through neural plasticity. While functional imaging studies revealed brain regions that show suppressed responses to the repeated presentation of an object, little is known about the intra‐trial time course of repetition effects to everyday objects. Here, we used event‐related potentials (ERPs) to task‐irrelevant line‐drawn objects, while participants engaged in a distractor task. We quantified changes in ERPs over repetitions using three general linear models that modeled RS by an exponential, linear, or categorical “change detection” function in each subject. Our aim was to select the model with highest evidence and determine the within‐trial time‐course and scalp distribution of repetition effects using that model. Model comparison revealed the superiority of the exponential model indicating that repetition effects are observable for trials beyond the first repetition. Model parameter estimates revealed a sequence of RS effects in three time windows (86–140, 322–360, and 400–446 ms) and with occipital, temporoparietal, and frontotemporal distribution, respectively. An interval of repetition enhancement (RE) was also observed (320–340 ms) over occipitotemporal sensors. Our results show that automatic processing of task‐irrelevant objects involves multiple intervals of RS with distinct scalp topographies. These sequential intervals of RS and RE might reflect the short‐term plasticity required for optimization of perceptual inference and the associated changes in prediction errors and predictions, respectively, over stimulus repetitions during automatic object processing

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 (18O 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

Spatiotemporal information transfer pattern differences in motor selection

Analysis of information transfer between variables in brain images is currently a popular topic, e.g. [1]. Such work typically focuses on average information transfer (i.e. transfer entropy [2]), yet the dynamics of transfer from a source to a destination can also be quantified at individual time points using the local transfer entropy (TE) [3]. This local perspective is known to reveal dynamical structure that the average cannot. We present a method to quantify local TE values in time between source and destination regions of variables in brain-imaging data, combining: a. computation of inter-regional transfer between two regions of variables (e.g. voxels) [1], with b. the local perspective of the dynamics of such transfer in time [3]. Transfer is computed over samples from all variables – there is no training in or subset selection of variables to use. We apply this method to a set of fMRI measurements where we could expect to see differences in local information transfer between two conditions at specific time steps. The fMRI data set analyzed (from [4]) contains brain activity recorded from 7 localized regions while 12 subjects (who gave informed written consent) were asked to freely decide whether to pus

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

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