Emotional Creativity: A Meta-analysis and Integrative Review

Emotional creativity (EC) is a pattern of cognitive abilities and personality traits related to originality and appropriateness in emotional experience. EC has been found to be related to various constructs across different fields of psychology during the past 30 years, but a comprehensive examination of previous research is still lacking. The goal of this review is to explore the reliability of use of the Emotional Creativity Inventory (ECI) across studies, to test gender differences and to compare levels of EC in different countries. Thirty-five empirical studies focused on EC were retrieved and the coefficients required for the meta-analysis extracted. The meta-analysis revealed that women showed significantly higher EC than men (total N = 3,555). The same gender differences were also found when testing scores from three ECI subscales, i.e. emotional novelty, emotional preparedness and emotional effectiveness/authenticity. When comparing EC in 10 different countries (total N = 4,375), several cross-cultural differences were revealed. The Chinese sample showed a significantly lower average ECI total score than all the other countries. Based on the integration of results, the avenues for future research on EC and the breadth of influence of the concept of EC across different fields of psychology are discussed. Keywords: Emotional Creativity, Review, Meta-Analyses, Meta-Analysis, Definition, Emotional Creativity Inventory, ECI, Reliability, Gender Differences, Cross-cultural, Cross-culture, Personality Traits, NEO Personality Inventory, Big Five, Extraversion, Agreeableness, Openness to Experience, Introversion, Neuroticism, Emotions, Creativity, Cognition, Cognitive Abilities, Affect, Fantasy, Coping, Alexithymia, Anhedonia, Self-understanding, Motivation, Creativeness, Innovative Performance, Creative Ability, Artistic Creativity, Creative Thinking. MeSH Headings: Emotions, Creativity, Affect, Affective Symptoms, Gender, Sex, Gender Identity, Cross-Cultural Comparison, Transcultural Studies, Temperament, Extraversion, Neuroticism, Anhedonia, Creativeness, Cognition, Cognitive Function, Artistic Creativity, Creative Ability, Creative Thinkin

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease

The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

MRI-based brain volumetry and retinal optical coherence tomography as the biomarkers of outcome in acute methanol poisoning

Background: Basal ganglia lesions are typical findings on magnetic resonance imaging (MRI) of the brain in survivors of acute methanol poisoning. However, no data are available on the association between the magnitude of damaged brain regions, serum concentrations of markers of acute methanol toxicity, oxidative stress, neuroinflammation, and the rate of retinal nerve ganglion cell loss.Objectives: To investigate the association between MRI-based volumetry of the basal ganglia, retinal nerve fibre layer (RNFL) thickness and prognostic laboratory markers of outcomes in acute methanol poisoning.Methods: MRI-based volumetry of putamen, nucleus caudatus and globus pallidus was performed and compared with laboratory parameters of severity of poisoning and acute serum markers of oxidative damage of lipids (8-isoprostan, MDA, HHE, HNE), nucleic acids (8-OHdG, 8 - OHG, 5 - OHMU), proteins (o-Thyr, NO-Thyr, Cl-Thyr) and leukotrienes (LTC4, LTD4, LTE4, LTB4), as well as with the results of RNFL measurements by optic coherence tomography (OCT) in 16 patients with acute methanol poisoning (Group I) and in 28 survivors of poisoning two years after discharge with the same markers measured within the follow-up examination (Group II). The control group consisted of 28 healthy subjects without methanol poisoning.Results: The survivors of acute methanol poisoning had significantly lower volumes of basal ganglia than the controls. The patients with MRI signs of methanol-induced toxic brain damage had significantly lower volumes of basal ganglia than those without these signs. A positive correlation was found between the volume of putamen and arterial blood pH on admission (r = 0.45; p = 0.02 and r = 0.44; p = 0.02 for left and right putamen, correspondingly). A negative correlation was present between the volumes of putamen and acute serum lactate (r = -0.63; p < 0.001 and r = -0.59; p = 0.01), creatinine (r = -0.53; p = 0.01 and r = -0.47; p = 0.01) and glucose (r = -0.55; p < 0.001 and r = -0.50; p = 0.01) concentrations.The volume of basal ganglia positively correlated with acute concentrations of markers of lipoperoxidation (8-isoprostan: r = 0.61; p < 0.05 and r = 0.59; p < 0.05 for left and right putamen, correspondingly) and inflammation (leukotriene LTB4: r = 0.61; p < 0.05 and r = 0.61; p < 0.05 for left and right putamen, correspondingly). The higher the volume of the basal ganglia, the higher the thickness of the RNFL, with the strongest positive association between global RNFL and the volume of putamen bilaterally (all p < 0.01). In the follow-up markers of oxidative stress and inflammation, only o-Thyr concentration negatively correlated with the volume of putamen bilaterally (r = -0.39; p < 0.05 and r = -0.37; p < 0.05 for left and right putamen, correspondingly).Conclusion: In survivors of acute methanol poisoning with signs of toxic brain damage, the magnitude of affected areas correlated with acute parameters of severity of poisoning, markers of oxidative stress and neuroinflammation. There was a positive association between the basal ganglia volume and the thickness of RNFL, making OCT an important screening test and MRI-based volumetry the confirmative diagnostic method for the detection of CNS sequelae of methanol poisoning

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease

The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer

ForestTemp – Sub-canopy microclimate temperatures of European forests

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1 degrees C (standard deviation +/- 1.6 degrees C) lower in summer and 2.0 degrees C higher (+/- 0.7 degrees C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.ISSN:1354-1013ISSN:1365-248

ForestClim—Bioclimatic variables for microclimate temperatures of European forests

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.ISSN:1354-1013ISSN:1365-248