Multivariate Granger Causality Research Articles

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography (ECoG) and subthalamic local field potential (LFP) recordings were performed OFF therapy (N = 22), ON dopaminergic medication (N = 18) and ON subthalamic deep brain stimulation (N = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography (EMG). In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. The complex brain network is also hierarchically constructed, which is particularly suited to facilitate segregated functions and the global integration of the segregated functions. Therefore, it is of great interest to explore new approach to model the hierarchical architecture of the directed network. In the present study, we proposed a new approach, namely, stepwise multivariate Granger causality (SMGC), considering both the directed and hierarchical features of brain functional network to explore the stepwise causal relationship in the network. The simulation study demonstrated that the diverse and complex hierarchical organization could be embedded in the apparently simple directed network. The proposed SMGC method could capture the multiple hierarchy of the directed network. When applying to the real functional magnetic resonance imaging (fMRI) datasets, the core triple resting-state networks in human brain showed within-network directed connections in the first-level directed network and rich and diverse between-network pathways in the second-level hierarchical network. The default mode network (DMN) had a prominent role in the resting-state acting as both the causal source and the important relay station. Further exploratory research on the adaption of directed hierarchical network in athletes suggested the enhanced bidirectional communication between the DMN and the central executive network (CEN) and the enhanced directed connections from the salience network (SN) to the CEN in the athlete group. The SMGC approach is capable of capturing the hierarchical architecture of the brain directed functional network, which refreshes the new stepwise causal relationship in the directed network. This might shed light on the potential application for exploring the altered hierarchical organization of brain directed network in neuropsychiatric disorders.

The Impact of Macroeconomic Factors on the Banking Sector’s Profitability in Pakistan

This research aims to study and analyze the impact of macroeconomic factor on the banking sector’s profitability in Pakistan. Banking sector of Pakistan is the backbone of its economy. The study explains the importance of banking industry of Pakistan and its achievements and evolvement throughout the years. The objective of the study is to examine the macroeconomic factors’ influence on the banking sector in Pakistan and to investigate that whether the profitability is only determined by the bank-specific internal factors or if it is also influenced by macroeconomic shocks and factors of the economy. The study is quantitative and secondary data is employed in order to conduct this paper. The sample size is financial data of scheduled banks in Pakistan from the year 2000 to2022, which is converted in quarter data and 84 observations have been incorporated in the study. “Returns on Assets” (ROA) is selected as dependent variable of this study since it measures profitability of banks. Whereas “Foreign Direct Investment” (FDI), “Exchange rate” (LER), “Inflation” (INF), “GDP growth” (GROWTH), “Liquidity” (LLIQ), “Total Deposits” (LT_DEP) and “Credit’s ratio” (CREDIT) of the banks are selected as independent variables. The data for bank specific variables is collected from the websites of “State Bank of Pakistan” (SBP) and “Federal Reserves of Economic Data” (FRED) while the data for macroeconomic variables is derived from the database of “World Bank” and “Macrotrends”. For empirical analysis, we have applied “Vector Autoregression” (VAR) model, followed by “Multivariate Granger Causality test”, “Impulse Response Function” (IRF) and “Forecast Error Variance Decomposition” (FEVD). The main findings of study reveals that the profitability of Banking sector in Pakistan is more influenced by its own internal factors rather than the external / macroeconomic factors. Amongst macroeconomic variables, “Foreign Direct Investment” (FDI) and “Inflation” (INF) are found to have the significant impact, while “Exchange rate” (LER) and “GDP growth rate” (GROWTH) are found to have insignificant impact on banks’ “Return on Assets” (ROA). All the internal factors of our study i.e. “Liquidity” (LLIQ), “Deposits” (LT_DEP) and “Credit ratio” (CREDIT) are found to have significant impact on profitability, which is measured through “Return on Assets” (ROA). In short, the profitability of banking sector in Pakistan is mainly influenced by bank-specific internal factors of the industry. The macroeconomic factors are only 50 percent % affective on the banks’ performance.

Altered dynamic effective connectivity of the default mode network in type 2 diabetes.

Altered functional connectivity of resting-state functional magnetic resonance imaging (rs-fMRI) within default mode network (DMN) regions has been verified to be closely associated with cognitive decline in patients with Type 2 diabetes mellitus (T2DM), but most studies neglected the fluctuations of brain activities-the dynamic effective connectivity (DEC) within DMN of T2DM is still unknown. For the current investigation, 40 healthy controls (HC) and 36 T2DM patients have been recruited as participants. To examine the variation of DEC between T2DM and HC, we utilized the methodologies of independent components analysis (ICA) and multivariate granger causality analysis (mGCA). We found altered DEC within DMN only show decrease in state 1. In addition, the causal information flow of diabetic patients major affected areas which are closely associated with food craving and metabolic regulation, and T2DM patients stayed longer in low activity level and exhibited decreased transition rate between states. Moreover, these changes related negatively with the MoCA scores and positively with HbA1C level. Our study may offer a fresh perspective on brain dynamic activities to understand the mechanisms underlying T2DM-related cognitive deficits.

Nexus Between Pension Funds and Stock Market Sustainability Development in Nigeria

Objective: It has long been recognized that pension systems across the emerging markets including Nigeria have undergone dramatic reforms over the last twenty-eight years. This process has mainly been fueled by the demographic problem faced by modern financial systems. Hence, this study examines the short-run and long-run dynamic of pension funds on capital market development in Nigeria using ex-post facto research design. Method: Data were collected from the Central Bank of Nigeria (CBN) statistical bulletin and annual report of the pension fund commission from 1995 to 2022. Data collected were analyzed using descriptive statistics, unit root test and auto regressive lag model (ARDL) and Toda Yamamoto multivariate granger causality approach. The unit root test reveal that all the variables used in the study are non-stationary at first difference except inflation which is stationary at level. Results: The findings from the ARDL analysis reveal a short-run dynamic impact between pension funds and capital market development due to the coefficient of error correction mechanism (speed of adjustment) which is negative and significant at 5% level of significant. The result suggests that over 22% of the short run disequilibrium is corrected in the long run equilibrium. However, the results from the long-run dynamic impact reveal a positively significant impact between the proxies of pension funds (pension contributory fund, pension investment) and control variable - inflation to be positively significant with capital market development proxied by market capitalization as a ratio to GDP at 5% level of significant. The results of the Toda Yamamoto granger causality results suggest a bidirectional relationship between pension funds (pension contributory fund, pension investment) and market capitalization as a ratio of Gross Domestic Product (GDP) in Nigeria at aggregate level. This suggest that the relationship between pension funds (pension contributory fund, pension investment) and market capitalization as a ratio of Gross Domestic Product (GDP) in Nigeria was two-ways. Conclusion: The study concludes that pension fund administrators in Nigeria should understand that the rate of inflation is dynamic in Nigeria and the value of money is being lost as money is not worth it values in the next five years.

THE NEXUS OF EXTERNAL DEBT, PRIVATE INVESTMENT AND FINANCIAL DEVELOPMENT: EVIDENCE FROM SELECTED SACU COUNTRIES

This paper examines the dynamic causal relationship between financial development, external debt and investment in Lesotho, Namibia and Eswatini from 1980–2020 using a multivariate Granger–causality model. The study considers trade, savings and economic growth as intermittent variables in the analysis. Adopting the autoregressive distributed lag (ARDL) bounds testing approach, the study results show that the causal relationship between financial development, external debt and investment in Lesotho, Namibia and Eswatini from 1980–2020 is country-dependent. For Eswatini, both investment and financial development Granger cause external debt, both in the short run and in the long run. However, for Lesotho and Namibia, it is the contrary, with external debt Granger causing financial development both in the short run and in the long run. The exception is Lesotho, where external debt is also found to Granger cause investment, also both in the short run and the long run. The study, therefore, concludes that for Eswatini, immense efforts in promoting investment and financial development chiefly stimulate external debt and the real sector in both the short run and the long run. For Namibia and Lesotho, external debt drives financial development. In addition, for Lesotho, external debt also drives investment.

Utility of L-band and X-band vegetation optical depth to examine vegetation response to soil moisture droughts in South Asia

Vegetation Water Content (VWC) holds essential information about plant photosynthetic activity, phenological changes, ability to withstand extreme events, and soil-plant-atmosphere interactions. However, there is no consensus on the extent to which Soil Moisture (SM) and atmospheric dryness (Vapor Pressure Deficit, VPD) affect VWC during droughts. In this work, we determine the causal relationships with VWC in response to VPD and SM when plants are subjected to SM droughts during the growing season. We use satellite-derived Vegetation Optical Depth (VOD), obtained from two frequencies (L- and X- bands), to represent VWC in different parts of the plant (stem, branches, and leaves). The holistic response of VOD considering VPD and SM interactions is determined using bivariate and multivariate Granger causality tests. The analysis is carried out over South Asia.The results show a statistically significant coupling between VPD and SM at semi-weekly scales over 53.20% of the study area. In these locations, we observe a dominant influence of VPD compared to SM on VODL and VODX while subjected to soil moisture droughts during the growing season. In the areas where VPD and SM are decoupled, we find both VPD and SM to influence VODL and VODX. VODL and VODX largely exhibit contrasting causal responses. We observe a surge in VODL due to water stress while subjected to soil moisture droughts under all land cover conditions. In the case of VODX, we noticed both an increase and a decrease in leaf water content while subjected to SM droughts. The former scenario is noticed predominantly under moderate to severe drought intensity conditions during which plants attempt to maintain a stable water column (through increased stomatal regulation) for their functioning as a response to SM droughts. The latter scenario is noticed in most pixels, including forests and croplands. The observed leaf water content loss in forests is due to their mild drought intensities. We also observe a stronger causal influence of VPD and SM in low LAI areas than in high LAI areas, indicating a pressing plant water stress in sparse vegetation conditions during SM droughts.

Precious Metals and Oil Price Dynamics

The aim of this study is to examine dynamic relationship among the prices of four precious metals (gold, silver, platinum, and palladium), oil price, and the US dollar/euro exchange rate. Recent literature for precious metals (gold, silver, platinum, palladium) and oil depict a lot contradiction results of their nexus in the long and short runs. So that, there is still much to investigate and learn about their relationship between them and with the exchange rate. The monthly data span from January 1990 to October 2021 is utilized, the study applies the co-integration analysis, multivariate Granger causality test and Variance Decomposition (VDC) analysis. The result shows the evidence of a long-run equilibrium relationship but weak feedbacks in the short run. The precious metal markets respond significantly (but temporarily) to a shock in any of the prices of the other metal prices and the exchange rate. Furthermore, we discover some evidence of market overreactions in the palladium and platinum cases as well as in the oil market. The study implies whether there are overreactions and re-adjustments or not, investors may diversify at least a portion of the risk away by investing in precious metals, oil, and the euro.

Tourism and economic growth in Asia: a symmetric and asymmetric causality

ABSTRACT This study aims to investigate the causal relationship between economic growth and real international tourism revenues (RITRs) in 11 Asian areas from 1995 to 2019. The method employed in this study involves utilizing a special bootstrap multivariate Granger causality test. The results of this analysis complement the findings of the asymmetric panel causality test, as they reveal a causal relationship between positive shocks to RITRs and positive shocks to real gross domestic product (RGDP) in Singapore and Thailand, respectively. Additionally, negative shocks in Cambodia, Hong Kong and Macau demonstrate a causal link between RITRs and RGDP, suggesting that fluctuations in tourism revenues can impact the economic growth of these regions.

External Debt, Investment, and Financial Development in Botswana and South Africa: A Multivariate Causal Analysis

This paper employs a multivariate Granger-causality model to evaluate the causal relationship between external debt, financial development, and investment in Botswana and South Africa from 1980–2020. The study includes savings, trade, and economic growth as intermittent variables. Using the autoregressive distributed lag (ARDL) bounds testing approach; empirical results reveal that, for both Botswana and South Africa, there is no distinct short- and long-run Granger-causality relationship between external debt and either financial development and/or investment. However, for Botswana, there is a short-run unidirectional causal relationship from investment to financial development. While for South Africa, the opposite is true. That is, there is a short-run and long-run unidirectional causal relationship from financial development to investment. The policy implication for Botswana is to stimulate the real sector in the short run; immense efforts in promoting investment are recommended. Other results for Botswana support the concurrent promotion of investment, savings, and economic growth because these variables are found to have a short-run and long-run bidirectional causal relationship. For South Africa, it is primarily financial development that drives investment and economic growth. Therefore, policy should promote financial development to stimulate investment, both in the short and long run.

The export-economic growth nexus: The case of Saudi Arabia

Export strategies are the means by which the country can dispose of its domestic production surpluses to bridge successive deficits in national balances of payments and achieve economic growth. These strategies are of particular importance to the economy of Saudi Arabia, as it has opted in the last decade to diversify its economy and migrate gradually away from an economy heavily reliant on oil exports. Given the importance of diversifying the economy, this study aims to examine the relationship between exports and economic growth in the Saudi Arabian economy. The multivariate Granger Causality Test and cointegration, which is the most common model, was used in examining the short-term and long-term patterns of exports, non-oil exports, GDP, GDP per capita, and government spending from 1991 to 2016. The findings support a long-standing connection involving Saudi exports and the country’s rate of economic expansion. Unidirectional causality exists between exports, non-oil exports, and economic growth expansion, which means the growth rate rises as exports grow. In addition, the findings revealed the presence of bidirectional causality between the variables. Indeed, export promotion strategies are imperative to fulfill Saudi Arabia’s aspiration of robust and long-term economic growth. AcknowledgmentThe authors acknowledge the Deanship of Scientific Research at King Faisal University (KFU) in Saudi Arabia for the financial support provided under (Grant No. 186183).

Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study.

Major depressive disorder (MDD) has been associated with aberrant effective connectivity (EC) among the default mode network (DMN), salience network (SN), and central executive network (CEN)-collectively referred to as triple networks. However, prior research has predominantly concentrated on broad frequency bands (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the influence of distinct rhythms on triple network causal dynamics. In the present study, we aim to investigate EC alterations within the triple networks across various frequency bands in patients with MDD. Utilizing a data-driven frequency decomposition approach and a multivariate Granger causality analysis, we characterized frequency-specific EC patterns of triple networks in 49 MDD patients and 54 healthy controls. A support vector machine classifier was subsequently employed to assess the discriminative capacity of the frequency-specific EC features. Our findings revealed that, compared to controls, patients exhibited not only enhanced mean EC within the CEN in the conventional frequency band (0.01-0.08 Hz), but also decreased mean EC from the SN to the DMN in a higher frequency band (0.12-0.18 Hz), and increased mean EC from the CEN to the SN in a sub-frequency band (0.04-0.08 Hz); the latter was significantly correlated with disease severity. Moreover, optimal classification performance for distinguishing patients from controls was attained by combining EC features across all three frequency bands, with the area under the curve (AUC) value of 0.8831 and the corresponding accuracy, sensitivity, and specificity of 89.97%, 92.63%, and 87.32%, respectively. These insights into EC changes within the triple networks across multiple frequency bands offer valuable perspectives on the neurobiological basis of MDD and could aid in developing frequency-specific EC features as potential biomarkers for disease diagnosis.

Root cause diagnosis of plant-wide oscillations based on fuzzy kernel multivariate Granger causality

BackgroundPlant-wide oscillations are commonly observed in industrial processes and can have significant impacts on product quality and energy consumption. Accurately diagnosing the root causes of these oscillations is essential to mitigate their effects. However, when these oscillations occur, the resulting data often exhibits strong nonlinearity, making root cause diagnosis a challenging task. MethodIn this study, we propose the use of kernel multivariate Granger causality (KMGC) test as an analytical tool for root cause diagnosis. The kernel function allows for low-dimensional data to be mapped to a higher-dimensional space, making it easier to process data with strong nonlinearity. To address the computational complexity associated with complex kernel functions, we have combined the KMGC with a fuzzification function to create the fuzzy kernel multivariate Granger causality (FKMGC) analysis method. This approach improves computational efficiency, particularly in industrial scenarios where Gaussian kernel functions are commonly used. Significant findingsOur simulation and plant-wide oscillation case study demonstrate that FKMGC accurately identifies the root cause of oscillations. Furthermore, FKMGC significantly reduces analysis time compared to Gaussian kernel-based algorithms, with a 95% reduction in running time observed in our experiments. These findings suggest that FKMGC is an effective and efficient tool for diagnosing the root causes of plant-wide oscillations in industrial settings.

What is the link between temperature, carbon dioxide and methane? A multivariate Granger causality analysis based on ice core data from Dome C in Antarctica

This paper continues the work of Kang and Larsson Theoretical and Applied Climatology 116:537–548 (2014) What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data. Theoretical and Applied Climatology 116:537-548, by adding methane to the study of causality between temperature and carbon dioxide. The data used goes 800,000 years back in time which is possible due to it being extracted from ice cores located at Dome C in Antarctica. First we use linear interpolation to make the three data sets equidistant to be able to employ statistical methods. We adjust for a deterministic trend and find the best model fit to be an autoregressive model with lag two and a piecewise quadratic trend. By employing multivariate Granger causality tests we find strong evidence that temperature, carbon dioxide and methane all Granger cause each other in both directions i.e. carbon dioxide Granger causes temperature and temperature Granger causes carbon dioxide, etc. This is in accordance with the findings of Kang and Larsson, with the extension that we can add methane to establish a trivariate feedback system between temperature, carbon dioxide and methane.

Fiscal sustainability, monetary policy and economic growth in the Euro Area: In search of the ultimate causal path

To assess the ultimate causal flow between monetary policy indicators, fiscal sustainability and economic growth has been deeply studied in the literature. However, this issue is still open to discussion due to mixed results and caveats/limitations of existing studies. Importantly, previous analyses mostly focus on bivariate/trivariate systems, missing a relevant piece of economic drivers. We analyse jointly these interdependencies by applying multivariate Granger Causality and determining an ultimate ”causality path” excluding redundant relationships. Thus, we combine recent developments introduced to estimate the Granger causality procedure based on Meta-analysis in heterogeneous mixed panels and graphical models searching iteratively for the existing dependencies between a multivariate set of information. Our results provide novel empirical evidence suggesting that monetary policy variables play a leading role in the resulting complex economic system. Furthermore, we do find evidence supporting the role of Total Expenditure as a driver of fiscal policy.

Glutamate levels in the ventromedial prefrontal cortex and resting-state functional connectivity within reward circuits in alcohol-dependent patients.

Great progress has been made in understanding the neural mechanisms associated with alcohol-dependent (AD) patients. However, the interactions within the reward circuits of the patients need further exploration. Glutamatergic projections from the prefrontal cortex to some brain regions are present in the reward circuit. However, little is known about the potential implications of glutamate levels in the prefrontal cortex on abnormal interactions within reward circuits in AD patients. To determine the potential roles of reward circuits in drinking, we investigated differences in resting-state functional connectivity (RSFC) and multivariate Granger causality analysis between 20 AD patients and 20 healthy controls (HC). The neuroimaging findings were then correlated with clinical variables (alcohol use disorder identification test). The ventromedial prefrontal cortex (VmPFC) is believed to play a critical role in addiction disorders, and glutamatergic projections from the prefrontal cortex to several regions of the brain are present in reward circuits. Proton magnetic resonance spectroscopy was also performed to assess the difference in glutamate levels in VmPFC between AD patients and HC. The results showed that the strength of functional connectivity in the reward circuit was generally attenuated in AD patients, and the reciprocal enhancement of activity between the right insula, left thalamus and VmPFC was found to be significantly greater in AD patients. It is worth noting that although glutamate levels in the VmPFC did not show significant differences between the two groups, the level of glutamate in the VmPFC was significantly correlated with RSFC. We hope that the current findings will help us to develop new intervention models based on the important role of the VmPFC in AD.

Causal relationships between cryptocurrencies: the effects of sampling interval and sample size

Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.

Impact of blue economy sectors using causality, correlation and panel data models

IntroductionBlue economics research is a research field that is achieving more and more interest among international scientists. This study adds more knowledge as it aims to investigate the causal relationships between blue economic factors – living and non-living resources, ocean energy, port activities, shipbuilding and repair, maritime transport, coastal tourism, human development indicator, per capita income, fish capture, internal renewable water resources per capita, marine protected areas, greenhouse gas emissions and population density, all factors related to maritime activities – and the blue economy (BE) itself.MethodsIn order to reach these objectives, some special analytical and econometric methods are used. Among them the multivariate Granger causality theory, the correlation analysis and panel data techniques are employed. This set of methods helps us to study the complex set of relationships among the various factors related to the BE and their influence on per capita income (PCI) and economic growth. The data used to conduct this research were collected from the Eurostat, the E.U. statistical database. Several European countries and the span period 2010-2018 are used too.ResultsThe analysis showed evidence that per capita income is associated to other factors of the blue economy, that PCI (Granger) causes coastal tourism and population density, and that there are five other factors that positively influence PCI growth, namely, greenhouse gas emissions (GHG), maritime transport, nonliving resources and ports’ activities. This paper adds some new understandings to theory and calls the attention to society, in general, and the blue economics, in particular. For instance, if policymakers want to increase income per capita of these regions, they should take action on some variables such as human development indicator (HDI), greenhouse gas (GHG) emissions, living resources, maritime transport and ocean energy. The results reveal that blue economy is a good alternative to promote both economic growth and PCI.DiscussionThe concerns that make up the blue economy, such as ocean energy, port activities, ship building and repair, maritime transport, coastal tourism, HDI, maritime fish capture, marine protected areas, and GHG reductionare good factors to stimulate economic growth. Among these, the most influencing are maritime transport, living resources and the production of ocean energy. This study adds knowledge to theory, innovates in the methods and techniques used, in the results discovered, in its discussion, and in the mixture of blue economic factors, too. As far as we know, it is one of the first papers that fills this gap in the literature regarding the more and more important blue economy.