Controls and relationships of soil organic carbon abundance and persistence vary across pedo-climatic regions.

One of the largest uncertainties in the terrestrial carbon cycle is the timing and magnitude of soil organic carbon (SOC) response to climate and vegetation change. This uncertainty prevents models from adequately capturing SOC dynamics and challenges the assessment of management and climate change effects on soils. Reducing these uncertainties requires simultaneous investigation of factors controlling the amount (SOC abundance) and duration (SOC persistence) of stored C. We present a global synthesis of SOC and radiocarbon profiles (nProfile = 597) to assess the timescales of SOC storage. We use a combination of statistical and depth-resolved compartment models to explore key factors controlling the relationships between SOC abundance and persistence across pedo-climatic regions and with soil depth. This allows us to better understand (i) how SOC abundance and persistence covary across pedo-climatic regions and (ii) how the depth dependence of SOC dynamics relates to climatic and mineralogical controls on SOC abundance and persistence. We show that SOC abundance and persistence are differently related; the controls on these relationships differ substantially between major pedo-climatic regions and soil depth. For example, large amounts of persistent SOC can reflect climatic constraints on soils (e.g., in tundra/polar regions) or mineral absorption, reflected in slower decomposition and vertical transport rates. In contrast, lower SOC abundance can be found with lower SOC persistence (e.g., in highly weathered tropical soils) or higher SOC persistence (e.g., in drier and less productive regions). We relate variable patterns of SOC abundance and persistence to differences in the processes constraining plant C input, microbial decomposition, vertical C transport and mineral SOC stabilization potential. This process-oriented grouping of SOC abundance and persistence provides a valuable benchmark for global C models, highlighting that pedo-climatic boundary conditions are crucial for predicting the effects of climate change and soil management on future C abundance and persistence.

Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia.

Introduction: We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling. Methods: The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data. Results: We found decreased large-scale functional connectivity across sensory and association areas and for all functional subnetworks for the ScZ group. Additionally global synchrony was reduced in patients while metastability was unaltered. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system. Discussion: The current study suggests that large-scale alterations in ScZ are more likely the result of global rather than local network changes.

Controls on timescales of soil organic carbon persistence across sub-Saharan Africa.

Given the importance of soil for the global carbon cycle, it is essential to understand not only how much carbon soil stores but also how long this carbon persists. Previous studies have shown that the amount and age of soil carbon are strongly affected by the interaction of climate, vegetation, and mineralogy. However, these findings are primarily based on studies from temperate regions and from fine-scale studies, leaving large knowledge gaps for soils from understudied regions such as sub-Saharan Africa. In addition, there is a lack of data to validate modeled soil C dynamics at broad scales. Here, we present insights into organic carbon cycling, based on a new broad-scale radiocarbon and mineral dataset for sub-Saharan Africa. We found that in moderately weathered soils in seasonal climate zones with poorly crystalline and reactive clay minerals, organic carbon persists longer on average (topsoil: 201 ± 130 years; subsoil: 645 ± 385 years) than in highly weathered soils in humid regions (topsoil: 140 ± 46 years; subsoil: 454 ± 247 years) with less reactive minerals. Soils in arid climate zones (topsoil: 396 ± 339 years; subsoil: 963 ± 669 years) store organic carbon for periods more similar to those in seasonal climate zones, likely reflecting climatic constraints on weathering, carbon inputs and microbial decomposition. These insights into the timescales of organic carbon persistence in soils of sub-Saharan Africa suggest that a process-oriented grouping of soils based on pedo-climatic conditions may be useful to improve predictions of soil responses to climate change at broader scales.

Carbon sequestration in the subsoil and the time required to stabilize carbon for climate change mitigation.

Soils store large quantities of carbon in the subsoil (below 0.2 m depth) that is generally old and believed to be stabilized over centuries to millennia, which suggests that subsoil carbon sequestration (CS) can be used as a strategy for climate change mitigation. In this article, we review the main biophysical processes that contribute to carbon storage in subsoil and the main mathematical models used to represent these processes. Our guiding objective is to review whether a process understanding of soil carbon movement in the vertical profile can help us to assess carbon storage and persistence at timescales relevant for climate change mitigation. Bioturbation, liquid phase transport, belowground carbon inputs, mineral association, and microbial activity are the main processes contributing to the formation of soil carbon profiles, and these processes are represented in models using the diffusion-advection-reaction paradigm. Based on simulation examples and measurements from carbon and radiocarbon profiles across biomes, we found that advective and diffusive transport may only play a secondary role in the formation of soil carbon profiles. The difference between vertical root inputs and decomposition seems to play a primary role in determining the shape of carbon change with depth. Using the transit time of carbon to assess the timescales of carbon storage of new inputs, we show that only small quantities of new carbon inputs travel through the profile and can be stabilized for time horizons longer than 50 years, implying that activities that promote CS in the subsoil must take into consideration the very small quantities that can be stabilized in the long term.

Computational mechanisms of belief updating in relation to psychotic-like experiences.

Introduction: Psychotic-like experiences (PLEs) may occur due to changes in weighting prior beliefs and new evidence in the belief updating process. It is still unclear whether the acquisition or integration of stable beliefs is altered, and whether such alteration depends on the level of environmental and belief precision, which reflects the associated uncertainty. This motivated us to investigate uncertainty-related dynamics of belief updating in relation to PLEs using an online study design. Methods: We selected a sample (n = 300) of participants who performed a belief updating task with sudden change points and provided self-report questionnaires for PLEs. The task required participants to observe bags dropping from a hidden helicopter, infer its position, and dynamically update their belief about the helicopter's position. Participants could optimize performance by adjusting learning rates according to inferred belief uncertainty (inverse prior precision) and the probability of environmental change points. We used a normative learning model to examine the relationship between adherence to specific model parameters and PLEs. Results: PLEs were linked to lower accuracy in tracking the outcome (helicopter location) (ß = 0.26 ± 0.11, p = 0.018) and to a smaller increase of belief precision across observations after a change point (ß = -0.003 ± 0.0007, p < 0.001). PLEs were related to slower belief updating when participants encountered large prediction errors (ß = -0.03 ± 0.009, p = 0.001). Computational modeling suggested that PLEs were associated with reduced overall belief updating in response to prediction errors (ßPE = -1.00 ± 0.45, p = 0.028) and reduced modulation of updating at inferred environmental change points (ßCPP = -0.84 ± 0.38, p = 0.023). Discussion: We conclude that PLEs are associated with altered dynamics of belief updating. These findings support the idea that the process of balancing prior belief and new evidence, as a function of environmental uncertainty, is altered in PLEs, which may contribute to the development of delusions. Specifically, slower learning after large prediction errors in people with high PLEs may result in rigid beliefs. Disregarding environmental change points may limit the flexibility to establish new beliefs in the face of contradictory evidence. The present study fosters a deeper understanding of inferential belief updating mechanisms underlying PLEs.

Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches.

To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.

Beyond bulk: Density fractions explain heterogeneity in global soil carbon abundance and persistence.

Understanding the controls on the amount and persistence of soil organic carbon (C) is essential for predicting its sensitivity to global change. The response may depend on whether C is unprotected, isolated within aggregates, or protected from decomposition by mineral associations. Here, we present a global synthesis of the relative influence of environmental factors on soil organic C partitioning among pools, abundance in each pool (mg C g-1  soil), and persistence (as approximated by radiocarbon abundance) in relatively unprotected particulate and protected mineral-bound pools. We show that C within particulate and mineral-associated pools consistently differed from one another in degree of persistence and relationship to environmental factors. Soil depth was the best predictor of C abundance and persistence, though it accounted for more variance in persistence. Persistence of all C pools decreased with increasing mean annual temperature (MAT) throughout the soil profile, whereas persistence increased with increasing wetness index (MAP/PET) in subsurface soils (30-176 cm). The relationship of C abundance (mg C g-1  soil) to climate varied among pools and with depth. Mineral-associated C in surface soils (<30 cm) increased more strongly with increasing wetness index than the free particulate C, but both pools showed attenuated responses to the wetness index at depth. Overall, these relationships suggest a strong influence of climate on soil C properties, and a potential loss of soil C from protected pools in areas with decreasing wetness. Relative persistence and abundance of C pools varied significantly among land cover types and soil parent material lithologies. This variability in each pool's relationship to environmental factors suggests that not all soil organic C is equally vulnerable to global change. Therefore, projections of future soil organic C based on patterns and responses of bulk soil organic C may be misleading.

Neuromelanin-Sensitive Magnetic Resonance Imaging in Schizophrenia: A Meta-Analysis of Case-Control Studies.

Background: Psychiatry is in urgent need of reliable biomarkers. Novel neuromelanin-sensitive magnetic resonance imaging (NM-MRI) sequences provide a time-efficient and non-invasive way to investigate the human brain in-vivo. This gives insight into the metabolites of dopaminergic signaling and may provide further evidence for potential dopaminergic alterations in patients with schizophrenia (SCZ). The present systematic review provides a meta-analysis of case-control studies using neuromelanin-sensitive sequences in SCZ vs. healthy controls (HC). Methods: According to predefined search terms and inclusion criteria studies were extracted on PubMed. Meta-analyses with a fixed and random-effects model with inverse variance method, DerSimonian-Laird estimator for τ2, and Cohen's d were calculated. Bias was assessed using funnel plots. The primary study outcome was contrast-to-noise ratio (CNR) in the substantia nigra compared between HC and SCZ. Results: The total sample of k = 6 studies included n = 183 cases and n = 162 controls. Across all studies we found a significant elevation of CNR in the substantia nigra (d = 0.42 [0.187; 0.655], z = 3.521, p < 0.001) in cases compared to controls. We found no significant difference in the control region of locus coeruleus (d = -0.07 [-0.446; 0.302], z = -0.192, p = 0.847), with CNR for the latter only reported in k = 3 studies. Conclusion: CNR in the substantia nigra were significantly elevated in cases compared to controls. Our results support neuromelanin as a candidate biomarker for dopaminergic dysfunction in schizophrenia. Further studies need to assess this candidate marker in large, longitudinal cohorts and address potential effects of disease state, medication and correlations with symptoms.

Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective.

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are "easily" reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

Fat and Carbohydrate Interact to Potentiate Food Reward in Healthy Weight but Not in Overweight or Obesity.

Prior work suggests that actual, but not estimated, energy density drives the reinforcing value of food and that energy from fat and carbohydrate can interact to potentiate reward. Here we sought to replicate these findings in an American sample and to determine if the effects are influenced by body mass index (BMI). Thirty participants with healthy weight (HW; BMI 21.92 ± 1.77; M ± SD) and 30 participants with overweight/obesity (OW/OB; BMI 29.42 ± 4.44) rated pictures of common American snacks in 120-kcal portions for liking, familiarity, frequency of consumption, expected satiety, healthiness, energy content, energy density, and price. Participants then completed an auction task where they bid for the opportunity to consume each food. Snacks contained either primarily carbohydrate, primarily fat, or roughly equal portions of fat and carbohydrate (combo). Replicating prior work, we found that participants with HW bid the most for combo foods in linear mixed model analyses. This effect was not observed among individuals with OW/OB. Additionally, in contrast with previous reports, our linear regression analyses revealed a negative relationship between the actual energy density of the snacks and bid amount that was mediated by food price. Our findings support altered macronutrient reinforcement in obesity and highlight potential influences of the food environment on the regulation of food reward.

Development of MacroPics: A novel food picture set to dissociate the effects of carbohydrate and fat on eating behaviors.

Emerging evidence suggests that fat and carbohydrate interact to potentiate the reward value of food (DiFeliceantonio et al., 2018). The primary goal of the current study was to develop a novel picture set to facilitate research into the effects of macronutrient composition on food choice and eating behavior. Toward this aim, we developed "MacroPics." In Experiment 1, we photographed 120-kcal portions of 60 snack foods falling into one of the three macronutrient categories: (1) mostly carbohydrate, (2) mostly fat, or (3) a combination of fat and carbohydrate. Sixty-one participants rated the images for liking, familiarity, frequency of consumption, healthiness, estimated energy content (in kcal), and expected satiation. A subset of these images consisting of 36 items was then selected in an iterative process to minimize differences in ratings between the macronutrient categories while simultaneously ensuring similar within-category variability on a number of food characteristics (e.g., energy density, portion size, retail price) and visual properties (e.g., color, complexity, visual area). In Experiment 2, an independent sample of 67 participants rated the pictures of the final 36-item MacroPics. Both experiments reveal similar participant ratings across categories for item liking, familiarity, frequency, healthiness, and estimated energy content. Protein content was higher in the fat compared to the carbohydrate and combination categories, leading to higher ratings of estimated satiety and energy density for fatty foods. Item and macronutrient category characteristics of the final MacroPics set are reported.

No evidence for an association between obesity and milkshake liking.

BACKGROUND: Prevailing models of obesity posit that hedonic signals override homeostatic mechanisms to promote overeating in today's food environment. What researchers mean by "hedonic" varies considerably, but most frequently refers to an aggregate of appetitive events including incentive salience, motivation, reinforcement, and perceived pleasantness. Here we define hedonic as orosensory pleasure experienced during eating and set out to test whether there is a relationship between adiposity and the perceived pleasure of a palatable and energy-dense milkshake. METHODS: The perceived liking, wanting, and intensity of two palatable and energy-dense milkshakes were assessed using the Labeled Hedonic Scale (1), visual analog scale (VAS), and Generalized Labeled Magnitude Scale (2) in 110 individuals ranging in body mass index (BMI) from 19.3 to 52.1 kg/m2. Waist circumference, waist-hip ratio, and percent body fat were also measured. Importantly, unlike the majority of prior studies, we attempted to standardize internal state by instructing participants to arrive to the laboratory neither hungry nor full and at least 1-h fasted. Data were analyzed with general linear and linear mixed effects models (GLMs). Hunger ratings were also examined prior to hedonic measurement and included as covariates in our analyses. RESULTS: We identified a significant association between ratings of hunger and milkshake liking and wanting. By contrast, we found no evidence for a relationship between any measure of adiposity and ratings of milkshake liking, wanting, or intensity. CONCLUSIONS: We conclude that adiposity is not associated with the pleasure experienced during consumption of our energy-dense and palatable milkshakes. Our results provide further evidence against the hypothesis that heightened hedonic signals drive weight gain.

Psychometric Evaluation of the German Version of the Dietary Fat and Free Sugar-Short Questionnaire.

BACKGROUND: The Dietary Fat and Free Sugar-Short Questionnaire (DFS) is a cost- and time-efficient self-report screening instrument to estimate dietary intake of saturated fat and free sugar. To date, only the English version has been psychometrically evaluated. We assessed the psychometric characteristics of the German version of the DFS in individuals with normal weight, overweight, and obesity. METHOD: Sixty-five adult participants completed a German translation of the DFS and a validated food frequency questionnaire (FFQ). We correlated participants' percentage of energy intake from saturated fat and free sugar from the FFQ with the DFS scores. To establish test-retest reliability, participants completed the DFS a second time. To investigate convergent validity, we correlated participants' DFS scores with self-reported eating behavior and sensitivity to reward. RESULTS: DFS scores correlated with percentage of energy from free sugar (rs = 0.443) and saturated fatty acids (rs =0.258) but not with non-target nutrients. The correlation between DFS scores and percentage energy from free sugar was not moderated by body mass index (BMI), whereas the correlation with percentage energy from saturated fat slightly decreased with BMI. Intra-class correlation as an indicator of test-retest reliability was 0.801. DFS scores correlated significantly with restraint of eating behavior (rs = -0.380) and feelings of hunger (rs = 0.275). Correlations of the DFS score with disinhibited eating and sensitivity to rewards failed to be significant. CONCLUSION: Our data indicate that the German version of the DFS might be a psychometrically sound self-report instrument to estimate saturated fat and free sugar intake of German adults.