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Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes. Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands. Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain. •Design of a framework for time–frequency analysis of coherence in rest fMRI data•We study time–frequency coherence in form of functional network connectivity (FNC).•Enables us to jointly study temporal dynamics spectral power and phase profiles of FNCs•Identification of clusters formed by such FNCs in the time–frequency domain•Reveals significant gender differences based on occupancy measures of each cluster

With the intensification of global warming, spatiotemporal variations in the climate and their mechanisms have received increasing attention. Currently, the relationship between regional precipitation regime, large-scale circulation, and topography, particularly in high-altitude areas such as the Qinghai-Tibet Plateau, are not well understood. Herein, the spatial and temporal variability in the annual and intra-annual (wet and dry periods) precipitation at 33 stations in the Three-River Headwaters (TRH) region from 1967 to 2016 are analysed. Moreover, the empirical orthogonal function and wavelet transform coherence methods are used to analyse the relationships between the different modes of precipitation change and 14 atmospheric circulation indices. The following results were obtained. (1) The mean annual precipitation and mean dry period precipitation significantly increased over the studied period. Annual and intra-annual precipitation showed a spatial southeast-to-northwest decreasing trend. (2) Two main patterns of precipitation were observed during the studied period: a dominant pattern with high- and low-value centres located in southeast and northwest TRH, respectively, and a dipole pattern with more precipitation over southwest TRH and less precipitation over northeast TRH. (3) Precipitation had a negative correlation with latitude, positive correlation with longitude, and nonlinear relationship with elevation. (4) Precipitation changes over various parts of the studied domain were determined based on changes in the weather systems affecting the area, with different indices being correlated with different components during different times of the year.

This study presents a wavelet-based framework for mapping inter-well connectivity (IWC) between multiple injectors and producers to support waterflood optimization. The method applies Cross-Wavelet Transform Coherence (CrWTC) with a complex Morlet wavelet to injection and production rate data, enabling the time-localized and frequency-dependent identification of dynamic injector–producer communication. The novelty of this work lies in continuous coherence mapping, the use of the complex Morlet wavelet for improved sensitivity to nonstationary responses, continuous updating as new data become available, and benchmarking on both the Volve and COSTA datasets. Validation using reservoir simulation and field data showed strong qualitative agreement with expected connectivity behavior and demonstrated clearer tracking of connectivity evolution and waterfront movement than the Capacitance Resistance Method (CRM). The proposed approach improves the reliability and interpretability of IWC assessment and offers a practical tool for reservoir surveillance and waterflood management.

Hyperscanning has allowed neuroscience to expand investigations into neuronal activation during social interactions. Rather than analyzing how a single brain responds, we can compare interactions and even synchronization between multiple actors in varying situations. This technique is commonly employed using functional near‐infrared spectroscopy (fNIRS). Specifically, social cooperation and competition have been thoroughly investigated using this approach. While functional magnetic resonance imaging (fMRI)‐based hyperscanning is becoming more prevalent, a link to this fNIRS‐based foundation is missing. We here use a dual‐fMRI‐hyperscanning setup and an established task to investigate neuronal coherence during social cooperative and competitive tasks. Wavelet transform coherence (WTC) allows us to explore task‐specific frequency bands of interest of nonstationary neuronal activation signals of paired participants (n = 60). We show that cooperation, compared to a control task, increases interbrain neuronal coherence in regions associated with social interaction and the theory of mind (ToM) network. Verbal communication prior to the task expands this coherence to different regions of this network, including middle and superior temporal gyrus. This spatial shift suggests additional implementations of the ToM network depending on the cooperation approach taken by the participants. Our findings both support and expand on results by previous fNIRS‐based studies and show that WTC is an effective way to model fMRI‐based neuronal synchronization, thereby closing the gap between two popular hyperscanning methodologies. This fMRI hyperscanning study uses wavelet transform coherence to support and expand on findings of near infrared spectroscopy (fNIRS) studies showing increased inter‐brain synchronization during interactive tasks. By translating established fNIRS hyperscanning techniques to fMRI, we aim to unify the methodologies, allowing them to benefit from each other's advantages.

Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting‐state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain–body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time–frequency analysis across resting‐state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain–body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal. We characterized the dynamic relationship between cardiorespiratory activity and the blood oxygen level dependent (BOLD) signal in spatial, temporal, and spectral dimensions using a wavelet analysis to inform our understanding of autonomic states and improve our interpretation of the BOLD signal. We identified unique frequency profiles for different phase offsets in instances of coherence between the BOLD signal and systemic physiological dynamics.

One of the challenging issues in statistical downscaling of climate models is to select dominant large-scale climate variables (predictors). Correlation-based methods have been revealed to be efficacious to select the predictors; however, traditional correlation analysis has shown limited ability due to the nonstationary and nonlinear nature of climatic time series. Hence, in this study, Wavelet Coherence Transform (WTC) was employed to assess the high common powers and the multi-scale correlation between two time series (i.e., predictand and predictor) as a function of time and frequency. To this end, a coefficient correlation (CC) and a wavelet-based method were used for predictor screening and the results were compared in statistical downscaling. To apply the wavelet-based method, Continuous Wavelet Transform (CWT) was utilized to identify the potent periodicity in the time series of predictands. WTC was applied to determine the coherence between predictors and predictands in the potent periodicities, and Scale Average (SA) wavelet coherency was applied to rank them. In order to implement statistical downscaling, the ANN model was developed. In this study, three climate models including BNU-ESM Can-ESM5, and INM-CM5 have been used. The projection of the future climate based on the ANN downscaling revealed that precipitation will undergo a 7.1–28.92% downward trend, while the temperature will experience a 2.25–4.21 °C increase.

Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference 40K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGRG–M and spectrometry-based 40K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from 40K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGRG–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGRG–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGRG–M than on 40K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGRG–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks.

The accumulation of nutrients in rivers is a major cause of eutrophication, and the change in nutrient content is affected by a variety of factors. Taking the River Yi as an example, this study used wavelet analysis tools to examine the periodic changes in nutrients and environmental factors, as well as the relationship between nutrients and environmental factors. The results revealed that total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH4+–N) exhibit multiscale oscillation features, with the dominating periods of 16–17, 26, and 57–60 months. The continuous wavelet transform revealed periodic fluctuation laws on multiple scales between nutrients and several environmental factors. Wavelet transform coherence (WTC) was performed on nutrients and environmental factors, and the results showed that temperature and dissolved oxygen (DO) have a strong influence on nutrient concentration fluctuation. The WTC revealed a weak correlation between pH and TP. On a longer period, however, pH was positively correlated with TN. The flow was found to be positively correct with N and P, while N and P were found to be negatively correct with DO and electrical conductance (EC) at different scales. In most cases, TP was negatively correlated with 5-day biochemical oxygen demand (BOD5) and permanganate index (CODMn). The correlation between TN and CODMn and BOD5 was limited, and no clear dominant phase emerged. In a nutshell, wavelet analysis revealed that water temperature, pH, DO, flow, EC, CODMn, and BOD5 had a pronounced influence on nutrient concentration in the River Yi at different time scales. In the case of the combination of environmental factors, pH and DO play the largest role in determining nutrient concentration.

The present study aimed to step into two-person neuroscience by investigating the hemodynamic correlates of between-brain connectivity involved in imitation and its dependency on pacing stimuli. To test this approach, we used wireless functional near-infrared spectroscopy (fNIRS) to record simultaneously during imitation performance of a paced finger-tapping task (PFT) in two subjects over premotor cortices (PMC). During the imitation (IM) condition, a model and an imitator were recorded while tapping in synchrony with auditory stimuli separated by a constant interval (stimulus-paced mode, St-P), followed by tapping without the pacing stimulus (self-paced mode, Se-P). During the control (CO) condition, each subject (single 1 and 2) performed the PFT task with the same pacing mode pattern, but alone without reference to each other. Using wavelet transform coherence (WTC) analysis evaluating functional connectivity between brains, we found (1) that IM revealed a larger coherence increase between the model and the imitator as compared to the CO condition. (2) Within the IM condition, a larger coherence increase was found during Se-P as compared to St-P mode. Using Granger-causality (G-causality) analysis evaluating effective connectivity between brains, we found (3) that IM revealed larger G-causality as compared to the CO condition and (4) that within the IM condition, the signal of the model G-caused that of the imitator to a greater extent as compared to vice versa. Our findings designate fNIRS as suitable tool for monitoring between-brain connectivity during dynamic interactions between two subjects and that those measurements might thereby provide insight into activation patterns not detectable using typical single-person experiments. Overall, the results of the present study demonstrate the potential of simultaneously assessing brain hemodynamics in interacting subjects in several research areas where social interactions are involved. ► Imitation task vs control task was measured simultaneously in two subjects by fNIRS. ► fNIRS reflects increased between-brain connectivity during imitation versus control. ► fNIRS reflects increased Granger-causality during imitation versus control. ► fNIRS can be used for two-person neuroscience approaches.

An understanding of the trend and relationship between rainfall patterns and water quality dynamics can provide valuable guidelines for the effective management of water resources. The aim of this study was to reveal the synchronous trends in rainfall and water quality and to explore the potential connection between seasonal variation in rainfall volume and the water quality index. This study scrutinised the seasonal temporal trends of rainfall and water quality parameters of three water supply reservoirs in the Toowoomba region of Australia by applying the modified Mann–Kendall (MMK) test and innovative trend analysis (ITA) methods from data collected over 22 years (2002–2022). The models showed a significant increasing trend of rainfall in two rainfall stations during autumn season. The water quality parameters, such as PO43−, exhibited a significant decreasing trend in all seasons in three reservoirs. On the other hand, the water quality index (WQI) showed a decreasing trend in the Cooby and Cressbrook reservoirs, excepting the Perseverance reservoir, which exhibited an increasing trend. In addition to the detection of trends, this study investigated the potential correlation between seasonal variation of rainfall volume and the water quality index using the wavelet transform coherence (WTC) method. The data of twelve rainfall stations were brought into this analysis. The WTC analysis displayed an apparent correlation between the water quality index and rainfall pattern for 70% of the rainfall stations across 8–16 periods. The highest coherency was noticed in 8–16 periods from 2002–2022, as observed at both the Cooby Creek rainfall station and in the WQI of the Cooby reservoir. This evaluation revealed the intertwined dynamics of rainfall patterns and water quality, providing a deeper understanding of their interdependence and implications, which might be useful for environmental and hydrological management practices.