Regional Time Series Research Articles

Impact scenarios on groundwater availability of southern Italy by joint application of regional climate models (RCMs) and meteorological time series.

Nowadays the phenomenon of global warming is unequivocal, as confirmed by the latest reports of the IPCC and studies of the climate-change impacts on ecosystems, global economy, and populations. The effect of climate change on groundwater is a very relevant task especially for regions dependent chiefly on groundwater availability, as for the southern Italy. In such a territorial framework, to achieve a detailed hydro-climatological characterization, an Ensemble of 15 RCMs (E15) derived from the EURO-CORDEX project was analyzed considering two IPCC Representative Concentration Pathways (RCP4.5 and RCP8.5). The E15 was calibrated over the period (1950-1996) by a statistical comparison with data observed by the regional meteorological network managed by the former National Hydrological Service (SIMN), Department of Naples. The effects of climate change on air temperature (T), precipitation (P) and, consequently, on actual evapotranspiration (ETR) and effective precipitation Pe (P - ETR) were analyzed until 2100. The latter was considered as a proxy of groundwater recharge of the principal aquifer systems, represented chiefly by the karst aquifers. As a principal result, it was found that the E15 is basically able to reproduce the observed annual precipitation (OBSP) and mean annual air temperature (OBST), being characterized by a very similar frequency distribution. Accordingly, an inferential statistical approach was performed for calibrating E15 precipitation (E15P) and air temperature (E15T) based on the compensation of the difference with OBSP (+ 7%) and OBST (-16%). The E15 projects a reduction in precipitation and an increase in air temperature under both RCPs, with a divergence point between the two scenarios occurring by about 2040. As a principal result, Pe shows declining trends for both RCP scenarios, reaching a decrease of the 11-yrs moving average down to -20%, for RCP4.5, and -50%, for RCP8.5, even if characterized by relevant inter-annual fluctuations.

Modeling NPP and NDVI time series in different bioclimatic regions of Iran.

Vegetation is one of the important components of ecosystems that usually changes seasonally. An accurate parameterization of vegetation cover dynamics by developing time series models can strengthen our understanding of vegetation change. This research aims to investigate and model the temporal changes of net primary production (NPP) and normalized difference vegetation index (NDVI) across bioclimatic regions of Iran, including the Khazari, Baluchi, semi-desert, steppe, semi-steppe, and arid forests. We used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor products for NPP and NDVI time series (MOD17A2 and MOD13Q1, respectively). The SARIMA (Seasonal Autoregressive Integrated Moving Average) time series model is developed for NPP and NDVI time series. The investigation of autocorrelation functions (ACF) showed a strong seasonality in NPP and NDVI at the 12-month lag time. Comparing the lag times from 1 to 24 month for different regions shows that the NPP variable has a stronger seasonality. The evaluation of error criteria which showed NPP time series models based on RMSE, R2, MRE, and CE criteria was better, while based on the ME criteria, the models perform better for NDVI time series (for example, in Khazari region for NPP and NDVI time series, respectively, ME = 3.67, 0.05, RMSE = 0.12, 0.18, R2 = 0.87, 0.63, MRE = 0.02, 0.12, and CE = 0.84, 0.12). The selected models provided a short-term forecasting of the NPP and NDVI index for study regions at 24-month time, which is useful for the planning and management to reduce vegetation degradation and preserve ecosystem and biodiversity.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

Mapping Ratoon Rice Fields Based on SAR Time Series and Phenology Data in Cloudy Regions

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally.

An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-based Brain Decoding.

Brain decoding that classifies cognitive states using the functional fluctuations of the brain can provide insightful information for understanding the brain mechanisms of cognitive functions. Among the common procedures of decoding the brain cognitive states with functional magnetic resonance imaging (fMRI), extracting the time series of each brain region after brain parcellation traditionally averages across the voxels within a brain region. This neglects the spatial information among the voxels and the requirement of extracting information for the downstream tasks. In this study, we propose to use a fully connected neural network that is jointly trained with the brain decoder to perform an adaptively weighted average across the voxels within each brain region. We perform extensive evaluations by cognitive state decoding, manifold learning, and interpretability analysis on the Human Connectome Project (HCP) dataset. The performance comparison of the cognitive state decoding presents an accuracy increase of up to 5% and stable accuracy improvement under different time window sizes, resampling sizes, and training data sizes. The results of manifold learning show that our method presents a considerable separability among cognitive states and basically excludes subject-specific information. The interpretability analysis shows that our method can identify reasonable brain regions corresponding to each cognitive state. Our study would aid the improvement of the basic pipeline of fMRI processing.

A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

Temporal distributions of aerosols over the Horn of Africa–Ethiopia using MODIS satellite data: Part 01

There are large temporal variations in the optical properties and types of aerosols. These are due to the effects of environmental and meteorological conditions, industrial and agricultural influences, and other human influences and natural factors in each ecological functional area. Aerosol optical depth AOD and Ångström exponent AE parameters were extracted from the Moderate Resolution Imaging Spectroradiometer MODIS satellite with a 3 km resolution of MOD04_3K and MYD04_3K. The study covers sixteen selected stations in Horn Africa–Ethiopia with neighboring Eritrea, Djibouti, and South Sudan countries clustered into four regions for the periods of 2001–2022 to obtain detailed information on the temporal behaviors and classification of aerosols in each season. The results show that the temporal variability of AOD and AE values is large, with values ranging from 0.00 to 2.10 and 0.67 to 1.23, with minimum values most likely between December 16 and January 07, and maximum values between June 22 and July 24 for both Terra and Aqua in the southeast and northeast clusters, respectively. The lowest AOD and AE values were most common in the Belg (during September–November) and sometimes in Bega (during December–February), while the highest were in the Kiremt season (during June–August). The minimum values were observed at the Kebri Dahar site, and the maximum were at the Erta Ale site, which belongs to Ethiopia. Appropriate AOD and AE thresholds were used to classify the aerosol types into marine (MR), dust (DS), biomass burning (BB), desert dust (DD), urban (UR), and the residual cases were considered mixture (MX) types. The study concludes that marine aerosols were the most abundant at 47.71%, followed by urban aerosols at 28.29%, while desert dusts, enriched at 14.65%, accounted for the third place with free of dust particles. The Mann–Kendall (MK) statistical trend test was applied to the annual time series in all clustered regions and shows an almost positive increasing variability with a slight decrease in the forward series and vice–versa.

Towards efficient model recommendation: An innovative hybrid graph neural network approach integrating multisignature analysis of electrical time series

In the energy sector, it is important to meticulously choose an accurate forecasting model because making informed decisions is crucial for optimal grid operation. This article proposes a hybrid graph neural network (GNN) that successfully captures complex patterns by combining interactions based on features and time. The proposed architecture uses diverse decomposition methods, such as statistical, dynamic, and spectral, to uncover hidden patterns. The integration of attention and graph convolution layers improves the flow of information, and the cross-modal fusion layer competently combines nodes and edges. This configuration can efficaciously assimilate disparate features, providing an advantage over other approaches. The hybrid GNN with an analytic hierarchy process (AHP) performs better than the existing models when tested exhaustively on diverse regional energy demand and pricing time series. It captures complex patterns more effectively than K-nearest neighbour (KNN), random forest regressor (RFR), and GNN-based model suggestion methods. For the nonstationary price data from New South Wales and the Punjab energy demand time series, the Kendall’s Tau coefficient is 0.73 and 0.81 and the Spearman’s Rank coefficient is 0.74 and 0.91, respectively. This paper advances the field of time series forecasting by offering a novel strategy for improving model proposals by efficiently merging hybrid GNN with multiple feature modalities.

Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation

Abstract. An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Comparative analysis of power consumption time series in deprived and developed regions of Iran

This paper presents a comparative analysis of power consumption time series at 12 o'clock every day between 2020 and 2022 for one distribution network in Sistan and one in Tehran. The aim of this study is to compare the development and climate differences between these regions, as well as the impact of social, industrial, and environmental factors. By comparing a deprived area with an area in the capital, we aim to identify potential disparities in power consumption and identify potential areas for improvement. We employed the CRP tool software and toolkit for time series analysis and used various methods to compare and predict the predictability of each time series. Our findings suggest significant differences in power consumption between the two regions, which could be attributed to socio-economic and environmental factors. Overall, this study sheds light on the potential impact of regional differences on power consumption and highlights the need for further research in this area.

Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression

AbstractA large literature on the sensitivity of streamflow response to climate variations has emerged over the past several decades, but the underlying mechanisms are not fully understood. The usual approaches to this problem have been simple and do not fully address its complexity, which involves the individual and joint effects of large‐scale climate modes and smaller‐scale weather features on streamflow volumes, the presence of persistence in these time series and their effects on antecedent conditions. Ongoing improvements in observational and reanalysis datasets and online access allow for better quantification of the connections between streamflow response, climate variability modes and regional weather features. The purpose of this paper is to determine whether Bayesian networks can be used to better identify key factors and their associated pathways leading to streamflow generation. A Markov blanket approach is described and illustrated using monthly streamflow series recorded at eight gauging stations within or immediately adjacent to the coastal region of eastern mainland Australia. The method is compared and contrasted with conventional multiple linear regression with discussion around this type of approach as part of a growing interest in machine learning applications. One example is used to illustrate the application of both approaches to a streamflow series exhibiting strong non‐stationarity. Results for the other streamflow series are included in a concise synthesis. Overall, the results suggest that Bayesian networks have several desirable features in terms of transparency, interpretability and explanatory insight. The findings from this study lend support to the use of Bayesian networks for modelling connections between streamflow volume and the variability of climate and regional weather. This improved understanding of the key controls of streamflow variability is intended to help address growing needs around informing social, cultural, economic and ecological aspects of water planning and management.

Driving factors analysis of spatial-temporal evolution of vegetation ecosystem in rocky desertification restoration area of Guizhou Province, China.

The investigation of the temporal-spatial characteristics and driving factors of vegetation ecosystem (VE) alterations held significant practical implications for the evaluation of the efficacy of rocky desertification management initiatives and safeguarding the ecological environment in the rocky desertification restoration region of Guizhou. We computed the comprehensive ecological quality index (Q) of vegetation based on the normalized difference vegetation index (NDVI) and net primary productivity (NPP). Combined with temperature, precipitation, sunshine duration, rocky desertification grade, land use, and the time series of various regions being included in national ecological functional zones, we analyzed the spatial-temporal distribution characteristics of VE changes and their response to climate change (CC) and ecological engineering (EE) by using partial derivative analysis method and scenario setting method in rocky desertification restoration areas in Guizhou. Results demonstrated that (1) the average values of NDVI, NPP, and Q all showed a fluctuating upward trend since 2000. Although the VE status of rocky desertification area was obviously worse than that of no rocky desertification area, it has a higher growth rate, especially the growth rates of NDVI, NPP, and Q in severe rocky desertification area were as high as 0.0050 year-1, 9.0733 g C m-2 year-1, and 0.7829 year-1, and the area with high recovery degree accounted for 93.19%, followed by the middle rocky desertification area. (2) CC was the main driving factor for NDVI and Q recovery, and EE was the main driving factor for NPP recovery. The contribution of EE to NPP and Q recovery increased with the increase of rocky desertification, as high as 82.13% and 30.31% in severe rocky desertification area. (3) The more serious the rocky desertification was, the more dependent the vegetation restoration was on ecological engineering, and the more difficult the restoration was. It was urgent to solve the ecological environmental problems. (4) EE played a greater role in the restoration of VE in the early stage of implementation. Its role gradually decreased in the later stages of implementation, while the role of CC increased. We provide a scientific basis for the follow-up treatment of rocky desertification, ecological environment restoration, and ecological protection effectiveness evaluation in Guizhou.

Оценка опережающего индикатора ВРП методом темпорального дезагрегирования

Introduction. Тhe estimation of gross regional product high-frequency leading indicator is relevant for the reliable analysis of current trends in regional economy and early understanding of its changes in the periods of high uncertainty since gross regional product is published on an annual basis. One approach to receive this indicator is a temporal disaggregation method, which has proven to be reasonable in foreign literature for disaggregating gross domestic product. At the same time, temporal disaggregation of regional economic time series has been understudied. Purpose. The purpose of the study is to refer to a temporal disaggregation method to estimate an unobservable indicator with high accuracy approximation for annual GRPs. Materials and Methods. The study analyzed Rosstat data for various periods, which characterize the economic growth in the Republic of Bashkortostan, as well as Bank of Russia data of enteprises’ monitoring. X-13ARIMA-SEATS methods for seasonal adjustment, temporal disaggregation methods (Chou–Lin, Fernandez and Litterman) and ARIMA for short-term forecast were used. Results. The article presents the results of temporal disaggregation of the gross regional product of the Republic of Bashkortostan. The best specification was estimated by the Chow–Lin method and includes indicators that characterize industrial production, retail trade, as well as enterprises’ survey data about the fluctuations in the ruble exchange rate. The ARIMA model gave a short-term forecast for a gross regional product leading monthly indicator. Unlike a random walk model with a forecast of up to 2-year lead time, a combination of temporal disaggregation method and ARIMA gave a better out-of-sample annual GRP forecast. Conclusion. The study successfully tested a temporal disaggregation method for the gross regional product of the Republic of Bashkortostan. In practice, this method provides reliable forecast estimates of the gross regional product for the current economic analysis with regard to available high-frequency data. It is shown that the use of survey data can improve the quality of gross regional product forecast.

Forest tent caterpillar (Lepidoptera: Lasiocampidae) across Canada, 1938–2001: II. Emergent periodicity from asynchronous eruptive anomalies

Abstract Using aerial sketch map data spanning more than 1 000 000 km2 across Canada, I use cluster analysis to show that outbreaks of forest tent caterpillar, Malacosoma disstria Hübner (Lepidoptera: Lasiocampidae), through the 20th century have occurred regularly every decade or so; however, there are two distinct aspects to the patterning of outbreaks. The dominant mode of variability is a nonrecurring pattern of singular spike anomalies, lasting just a few years, that are regional in extent but are not synchronised across the country. The regional time series derived from cluster analysis that are dominated by these singular spike eruptions exhibit extreme skewness and kurtosis, are not stationary in mean or variance, and are not amenable to classical time-series analysis. Although these regional-scale eruptive anomalies tend to occur periodically in aggregate, their central location always varies in an unpredictable manner, resulting in aperiodic local behaviour. Range-wide periodicity is thus an emergent property from asynchronous, aperiodic eruptions aggregated across regions. The second mode of variability is a low-amplitude fluctuation of weak periodicity that is weakly synchronised across the country. These observations support a hybrid cyclic–eruptive theory of outbreak occurrence that is not consistent with the simpler idea of spatially synchronised cycling.

Changes in cloudiness contribute to changing seasonality in the Baltic Sea region

In the Baltic Sea region, a significant increase in solar radiation has been detected during the past half-century. Changes in shortwave irradiance are associated with atmospheric transparency and cloudiness parameters like cloud fraction and albedo. One of the most important reasons for day-to-day changes in cloudiness is the synoptic-scale atmospheric circulation; thus, we look for reasons for solar radiation trends due to changes in atmospheric circulation. We analysed regional time series and trends from satellite-based cloud climate data record CLARA-A2 for the Baltic Sea region in 1982–2018. As the rise in solar radiation depends on the seasonally averaged values of total fractional cloud cover (CFC), surface incoming shortwave radiation (SIS) and occurrences of circulation types were analysed. We show that the shift in seasonality connected to the earlier accumulated sums of SIS is at least partly explained by the changes in synoptic-scale atmospheric circulation.

A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders.

Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-consuming cognitive tests and potential human bias in clinics. To address this challenge, we propose a novel adversarial self-supervised graph neural network (GNN) based on graph contrastive learning, named A-GCL, for diagnosing neurodevelopmental disorders using functional magnetic resonance imaging (fMRI) data. Taking advantage of the success of GNNs in psychiatric disease diagnosis using fMRI, our proposed A-GCL model is expected to improve the performance of diagnosis and provide more robust results. A-GCL takes graphs constructed from the fMRI images as input and uses contrastive learning to extract features for classification. The graphs are constructed with 3 bands of the amplitude of low-frequency fluctuation (ALFF) as node features and Pearson's correlation coefficients (PCC) of the average fMRI time series in different brain regions as edge weights. The contrastive learning creates an edge-dropped graph from a trainable Bernoulli mask to extract features that are invariant to small variations of the graph. Experiment results on three datasets - Autism Brain Imaging Data Exchange (ABIDE) I, ABIDE II, and attention deficit hyperactivity disorder (ADHD) - with 3 atlases - AAL1, AAL3, Shen268 - demonstrate the superiority and generalizability of A-GCL compared to the other GNN-based models. Extensive ablation studies verify the robustness of the proposed approach to atlas selection and model variation. Explanatory results reveal key functional connections and brain regions associated with neurodevelopmental disorders.

Establishing SMMS approach to accurately mine the characteristics of regional precipitation trends

Under the joint influence of climate change and human activities, the temporal and spatial variability and trend of precipitation have changed. The trend of precipitation investigated using traditional methods is quite different, which may be opposite of that at adjacent stations. This contradiction may lead to erroneous decision making in regional water resources planning and management, and may cause safety hazard. In order to evaluate the variation in temporal and spatial characteristics of regional precipitation and avoid different trends of regional precipitation time series, this study developed System clustering - Maximum entropy - Multi window sliding trend - Same frequency trend (SMMS) approach, which leads to the method for analyzing regional precipitation changes, trends, and laws. The local microclimate zones (LMZs) are redefined, so that the general law of precipitation variation will not be significantly different from the actual situation and the SMMS method is more accurate and comprehensive in identifying regional precipitation trends compared to traditional methods. Making use of the SMMS approach, the trend analysis of regional representative stations method can not only reasonably divide the study area and reduce computational complexity, but also analyze it according to the typical representative station with the maximum regional information. Furthermore, the impact caused by the length of data can be avoided, and the trend of precipitation intensity can be identified. It is necessary to fully understand the new characteristics of temporal and spatial changes of precipitation and make early preparations to mitigate the potential impact of climate or weather disasters.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Regional pollen-based Holocene temperature and precipitation patterns depart from the Northern Hemisphere mean trends

Abstract. A mismatch between model- and proxy-based Holocene climate change, known as the “Holocene conundrum”, may partially originate from the poor spatial coverage of climate reconstructions in, for example, Asia, limiting the number of grid cells for model–data comparisons. Here we investigate hemispheric, latitudinal, and regional mean time series and time-slice anomaly maps of pollen-based reconstructions of mean annual temperature, mean July temperature, and annual precipitation from 1908 records in the Northern Hemisphere extratropics. Temperature trends show strong latitudinal patterns and differ between (sub-)continents. While the circum-Atlantic regions in Europe and eastern North America show a pronounced Middle Holocene temperature maximum, western North America shows only weak changes, and Asia mostly shows a continuous Holocene temperature increase. Likewise, precipitation trends show certain regional peculiarities such as the pronounced Middle Holocene precipitation maximum between 40 and 50∘ N in Asia and Holocene increasing trends in Europe and western North America, which can all be linked with Holocene changes in the regional circulation pattern responding to temperature change. Given a background of strong regional heterogeneity, we conclude that the calculation of global or hemispheric means, which initiated the Holocene conundrum debate, should focus more on understanding the spatiotemporal patterns and their regional drivers.

Planning ahead: Predictable switching recruits task-active and resting-state networks.

Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.