Bivariate Granger Causality Research Articles

Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

Uncertainty in Central and Eastern European markets. Evidence from Twitter-based uncertainty measures

ABSTRACT Purpose Increasing uncertainty has frequently been associated with alterations in investor behaviour in scientific discourse. Central and Eastern Europe (CEE) despite generating 10% of the EU’s GDP, has largely been overlooked in investigations into the relationship between socialmedia uncertainty and its impact on the stock markets. The purpose of this article is to examine the dependence between the largest stock indices of the CEE markets and Twitter uncertainty measures. Design/methodology/approach The Vector Autoregressive (VAR) methodology, specifically bivariate Granger causality is applied to investigate the relationship between social media messages and market behaviour reflected in returns of the main stock indices for Croatian CROBEX, Czech PX, Hungarian BUX, Polish WIG and Romanian BET indices. Findings The author discovers substantial evidence indicating that the CEE region exhibits heterogeneity concerning its relationship with uncertainty in stock markets, with measures related to recession, political events, and natural disasters being of utmost relevance. Originality/value­ While the VAR model and Granger causality have been studied in relation to many markets, there is a lack of studies on the effect of Twitter-based wider spectrum of uncertainty measures on the CEE region. Twitter-based uncertainty measures on the CEE region represent a significant knowledge gap, that when addressed, could offer valuable insight into the region’s financial dynamics, thus potentially affecting hedging strategies, enhancing investor’s awareness and informing policy-making decisions.

Distinct brain dynamics and networks for processing short and long auditory time intervals

Psychophysical studies suggest that time intervals above and below 1.2 s are processed differently in the human brain. However, the neural underpinnings of this dissociation remain unclear. Here, we investigate whether distinct or common brain networks and dynamics support the passive perception of short (below 1.2 s) and long (above 1.2 s) empty time intervals. Twenty participants underwent an EEG recording during an auditory oddball paradigm with .8- and 1.6-s standard time intervals and deviant intervals either shorter (early) or longer (delayed) than the standard interval. We computed the auditory ERPs for each condition at the sensor and source levels. We then performed whole brain cluster-based permutation statistics for the CNV, N1 and P2, components, testing deviants against standards. A CNV was found only for above 1.2 s intervals (delayed deviants), with generators in temporo-parietal, SMA, and motor regions. Deviance detection of above 1.2 s intervals occurred during the N1 period over fronto-central sensors for delayed deviants only, with generators in parietal and motor regions. Deviance detection of below 1.2 s intervals occurred during the P2 period over fronto-central sensors for delayed deviants only, with generators in primary auditory cortex, SMA, IFG, cingulate and parietal cortex. We then identified deviance related changes in directed connectivity using bivariate Granger causality to highlight the networks dynamics associated with interval processing above and below 1.2. These results suggest that distinct brain dynamics and networks support the perception of time intervals above and below 1.2 s.

Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment

This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.

Network metrics to localize the Seizure Onset Zone: A Directed Functional Connectivity Study Using MEG Data (P2-1.004)

<h3>Objective:</h3> We aimed to analyze ictal networks via magnetoencephalography-based (MEG) directed functional connectivity (FC), to better guide intracranial EEG (iEEG) studies. <h3>Background:</h3> Epilepsy is a network disorder. Thus, the importance of integrating connectivity-based approaches during epilepsy presurgical evaluation. Studies applying MEG non-directed FC evidenced a good correlation with post-surgical outcomes. Directed FC, such as Spectral Bivariate Granger Causality (SBGC), has the advantage of differentiating probable sinks from hubs in networks. <h3>Design/Methods:</h3> Ictal MEG and MRI data from eight drug-resistant epilepsy patients (four pediatric, six females) were included. Source space SBGC was performed and grouped in frequency bands. Nodes with higher influence in outgoing signals (hubs) and receiving signals (authority) were identified. The top 2 hub and authority nodes were selected and compared to clinical data. <h3>Results:</h3> Seven patients had iEEG, three of which had subsequent epilepsy surgery. Another patient underwent direct surgery. Two patients continued presenting seizures after resection. All others were seizure-free after 1-month and 18-month follow-ups. For all patients, the irritative zone (80%) or nearby regions (20%) were captured by one of the top two hub nodes in the highest power frequency band and the delta band. For the patients with seizure freedom, at least one of the top two hub nodes included the Seizure Onset Zone (SOZ). In the patient with an 18-month Engel 1A classification, both top hub nodes were included in the resection area. For an adult patient with multifocal SOZs, the highest authority node represented another less active SOZ. <h3>Conclusions:</h3> The regions identified by our connectivity metrics demonstrated overlap with regions identified with iEEG in the majority of the patients, and those patients in which resection included a hub node had favorable post-surgical outcomes. We contend that network metrics using SBGC and MEG data can be useful to better understand epilepsy dynamics and enhance epilepsy management in clinical practice. <b>Disclosure:</b> Dr. Cardoso Da Fonseca has nothing to disclose. Ms. Askari has nothing to disclose. Dr. Proskovec has nothing to disclose. Dr. Alick-Lindstrom has nothing to disclose. Dr. Lowden has nothing to disclose. Dr. Talai has nothing to disclose. The institution of Joseph Maldjian has received research support from NIH. Dr. Davenport has nothing to disclose.

A sex-dependent functional-effective connectivity model for diagnostic classification of Autism Spectrum Disorder using resting-state fMRI

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with deficits in social interactions. Currently, its diagnosis is done by behavioral tests. Previous studies have shown that connections between brain regions can be a suitable biomarker for the diagnosis of ASD from normal control (NC). Also, differences in the connections of the brain regions of ASD individuals have been shown between male and female groups. This study, proposed a sex-dependent functional-effective connectivity model for the diagnostic classification of ASD using resting-state fMRI (rs-fMRI).After preprocessing, dual regression independent component analysis (ICA) was applied to obtain the time course of each resting state network (RSN) for each subject. To investigate the role of sex, two male and female groups were considered. Functional connectivity (FC) between time series of RSNs were extracted using full and partial correlation. Also, effective connectivity (EC) was extracted using bivariate granger causality. Two feature selection approacheswere proposedbased on different modes of combining FC and EC features. Finally, the classification accuracy was obtained using thirteen different classifiers for each sex group.The highest classification accuracy of 96.6% in the male group was achieved by using the combination of partial correlation (FC) and granger causality (EC) features, while in the female group, the highest classification accuracy of 93.3% was obtained by all the combinations that used full correlation (FC) features.The results have shown the combination of features obtained from FC and EC can lead to improved classification accuracy in male and female groups.

The relationship between geopolitical risk and crude oil prices: evidence from nonlinear and frequency domain causality tests

ABSTRACT This paper aims to investigate the possible explanatory effect of geopolitical risks on the oil price changes from February 1986 to December 2019 by employing the nonlinear bivariate Granger causality test and frequency domain Granger causality test based on the geopolitical risk index. The results suggest that there exists a nonlinear causality from geopolitical risks to crude oil prices. Moreover, geopolitical risks have a short-term impact on oil prices, less than 12 months. Actual geopolitical events have a smaller and less lasting impact on oil prices than pure geopolitical risks.

Dynamics of Inflation, Interest and Exchange Rates with the Effects of Money Supply and GDP in Nigeria: Evidence from BVARX Models

Economic theory revealed evidence of interrelationships among inflation, interest and exchange rates. Such theory further states possible effects of money supply and GDP on inflation, interest and exchange rates. This study focuses on the dynamic interrelationships among inflation, interest and exchange rates with the effects of money supply and GDP in Nigeria using Bayesian Vector Autoregression with Exogenous variables (BVARX) models. To achieve this, data was sourced from the Central bank of Nigeria (CBN) website spanning the period of 2010Q1-2020Q4. The study proposed six (6) versions of BVARX models with lag 2 using Normal-Wishart, Normal Flat and Flat-Flat priors. The results revealed superior BVARX models with Flat-Flat prior using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as means of model selection. Lastly, evidence from Bivariate Granger causality testing reveals that the past lag values of inflation rate help in predicting exchange rate in Nigeria.

A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI

Objective. Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for the diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer-aided diagnosis system (CADS) for ASD which considers the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging. The proposed CADS classifies ASD males from normal males, and ASD females from normal females. Approach. After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity measures of full correlation and partial correlation and the effective connectivity measure of bivariate Granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group. Main results. In the female group, a classification accuracy of 93.3% was obtained using full correlation while in the male group, a classification accuracy of 86.7% was achieved using both full correlation and bivariate Granger causality. Also, in the mixed group, a classification accuracy of 83.3% was obtained using full correlation. Significance. This supports the importance of considering sex in diagnosing ASD patients from normal controls.

Winners and losers in brain computer interface competitive gaming: directional connectivity analysis

Objective. To characterize the direction within and between brain connectivity in winning and losing players in a competitive brain-computer interface game. Approach. Ten dyads (26.9 ± 4.7 yr old, eight females and 12 males) participated in the study. In a competitive game based on neurofeedback, they used their relative alpha (RA) band power from the electrode location Pz, to control a virtual seesaw. The players in each pair were separated into winners (W) and losers (L) based on their scores. Intrabrain connectivity was analyzed using multivariate Granger causality (GC) and directed transfer function, while interbrain connectivity was analyzed using bivariate GC. Main results. Linear regression analysis revealed a significant relationship (p < 0.05) between RA and individual scores. During the game, W players maintained a higher RA than L players, although it was not higher than their baseline RA. The analysis of intrabrain GC indicated that both groups engaged in general social interactions, but only the W group succeeded in controlling their brain activity at Pz. Group L applied an inappropriate metal strategy, characterized by strong activity in the left frontal cortex, indicative of collaborative gaming. Interbrain GC showed a larger flow of information from the L to the W group, suggesting a higher capability of the W group to monitor the activity of their opponent. Significance. Both innate neurological indices and gaming mental strategies contribute to game outcomes. Future studies should investigate whether there is a causal relationship between these two factors.

Exodus in the American metropolis: Predicting Black population decline in Chicago neighbourhoods

Urban population decline in the largest metropolitan regions of the United States is now explained almost exclusively by a ‘Black exodus’. In Chicago, competing ‘push’ explanations have been put forth to explain Black population loss in urban neighbourhoods, including housing instability, cost of living, unemployment and crime. However, no study to date estimates the predictive power of each of these factors. This article seeks to answer the research question: which neighbourhood characteristics predict Black exodus in Chicago? We explore relationships between Black population loss in Chicago and a comprehensive range of metrics representing economic and social conditions. A fixed-effects multivariate panel regression is specified for the years 2010 to 2018 at the census tract level and cross-checked with bivariate Granger causality tests. We find that foreclosure filings predict Black population decline, and suggest that government prioritise foreclosure relief policies to stem Black exodus.

An age-dependent Connectivity-based computer aided diagnosis system for Autism Spectrum Disorder using Resting-state fMRI

Autism spectrum disorder (ASD) is characterized by repetitive behaviors and social interactions. Due to the problems of diagnosing ASD using behavioral symptoms by experts, it seems necessary to propose accurate computer aided diagnosis systems (CADS) for ASD. Recent studies have reported brain connectivity as an important biomarker of ASD. Several studies have also suggested the role of age as an important factor in the brain connectivity disorders of individuals with ASD. In this study, we intend to present an age-dependent connectivity-based CADS for ASD using resting-state fMRI (rs-fMRI).First, the preprocessing was performed on the rs-fMRI data. Second, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs). This was followed by obtaining individualized components of RSNs for each subject using dual-regression. Then, full and partial correlation measures were used to extract functional connectivity features and bivariate granger causality was used to extract effective connectivity features between RSNs. To consider the role of age in the classification process, three age groups of children, adolescents and adults were taken into account, and feature selection was performed for each age group separately by using an embedded approach in which all classifiers of WEKA were used simultaneously. Finally, classification accuracy, sensitivity and specificity were obtained for each age group.The proposed CADS was able to operate with 95.23% classification accuracy in the children group using classification via clustering classifier. Furthermore, discriminative biomarkers of functional connectivity were obtained in this age group which might play an important role in diagnosing ASD.

GOVERNMENT SIZE AND ECONOMIC GROWTH IN KENYA: A MULTIVARIATE DYNAMIC CAUSAL LINKAGE

This study empirically examines the dynamic causal relationship between government size and economic growth in Kenya during the 1970-2017 period using the autoregressive distributed lag (ARDL) bounds testing approach. A multivariate Granger-causality model was used in order to reduce the omission-of-variable bias, which has been found to be associated with bivariate Granger-causality models. For this purpose, the study used domestic investment and trade openness as intermittent variables between government size (proxied by government final consumption expenditure) and economic growth. The results of the study are consistent with the Keynesian view as they reveal that, in Kenya, there is a distinct unidirectional causal flow from government size to economic growth - both in the short run and in the long run. This shows that, in Kenya, government expenditure Granger-causes real sector growth. Based on these results, policy makers in Kenya are recommended to pay special attention to the national government size when implementing growth-enhancing policies.

Modern Ubasute: Public Discourse and Sentiment about Older Adults and COVID19 Using Machine Learning

This study examined public discourse and sentiment on social media regarding older adults in COVID-19. Twitter data (N=82,893) related to both older adults and COVID-19 and dated from January 23rd to May 20th, 2020, were analyzed. Classification of tweets involved supervised machine learning. Latent Dirichlet Allocation was used to identify dominant themes in public discourse using, accompanied by a qualitative thematic analysis. Sentiment analysis was conducted based on the NRC Emotion Lexicon. The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). More than one in ten (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” The daily average of ageist content was 18%, with the highest of 52.8% on March 11th, 2020. We extracted 14 themes, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. The COVID-19 pandemic has exposed and intensified ageism in our society. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.

Wavelet-based analysis of guar futures in India: did we kill the golden goose?

PurposeGuar Gum (GG) is used in Shale oil exploration. Excessive price increase in the Guar futures market had a spillover impact on Guar spot prices and affected Guar export from India as Shale oil producers started exploring alternate sources. In this paper, the role of excessive speculation in the futures market, and its adverse impact on the guar-based agri-business ecosystem have been empirically explored.Design/methodology/approachVolatility spillover dynamics between WTI crude oil and Guar futures have been explored using bivariate-Granger Causality, BEKK–GARCH models with Wavelet multi-resolution analysis. The wavelet-based models capture the multi-scale features of mean and volatility spillover to identify the effect of heterogenous investment behavior in the time and frequency domain.FindingsThe results provide evidence that excessive speculation in futures markets increases spot market volatility. The results also suggest that the excess presence of short-term investors can destabilize the futures market.Research limitations/implicationsThe purpose of the commodity futures market is to support price discovery and risk management. However, speculative practices can destabilize these purposes leading to the failure of the business ecosystem.Originality/valueThe novelty of this paper is twofold. First, it explores the economic linkages between the spot and futures market and tests whether the presence of heterogeneous traders affects the economic linkages. Second, it models the impact of short-term speculative investment on the destabilization of the spot market.

Synergistic Information Transfer in the Global System of Financial Markets.

Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system.

Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning.

ObjectivesThis study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse.MethodsTwitter data (N=82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including Linguistic Inquiry and Word Count, supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics.ResultsThe most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than one in ten (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic.DiscussionAgeist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.

Granger Causality of the Electroencephalogram Reveals Abrupt Global Loss of Cortical Information Flow during Propofol-induced Loss of Responsiveness.

Background:It is a commonly held view that information flow between widely separated regions of the cerebral cortex is a necessary component in the generation of wakefulness (also termed “connected” consciousness). This study therefore hypothesized that loss of wakefulness caused by propofol anesthesia should be associated with loss of information flow, as estimated by the effective connectivity in the scalp electroencephalogram (EEG) signal.Methods:Effective connectivity during anesthesia was quantified by applying bivariate Granger to multichannel EEG data recorded from 16 adult subjects undergoing a slow induction of, and emergence from, anesthesia with intravenous propofol. During wakefulness they were conducting various auditory and motor tasks. Functional connectivity using EEG coherence was also estimated.Results:There was an abrupt, substantial, and global decrease in effective connectivity around the point of loss of responsiveness. Recovery of behavioral responsiveness was associated with a comparable recovery in information flow pattern (expressed as normalized values). The median (interquartile range) change was greatest in the delta frequency band: decreasing from 0.15 (0.21) 2 min before loss of behavioral response, to 0.06 (0.04) 2 min after loss of behavioral response (P < 0.001). Regional decreases in information flow were maximal in a posteromedial direction from lateral frontal and prefrontal regions (0.82 [0.24] 2 min before loss of responsiveness, decreasing to 0.17 [0.05] 2 min after), and least for information flow from posterior channels. The widespread decrease in bivariate Granger causality reflects loss of cortical coordination. The relationship between functional connectivity (coherence) and effective connectivity (Granger causality) was inconsistent.Conclusions:Propofol-induced unresponsiveness is marked by a global decrease in information flow, greatest from the lateral frontal and prefrontal brain regions in a posterior and medial direction. Loss of information flow may be a useful measure of connected consciousness.

Modeling the Impact of Inflation Uncertainty on Food Sector Growth

Background: One of the most fundamental objectives of the macroeconomic policies is to realize the relationship between economic growth and inflation. According to some monetary policy advisors, inflation reflects erosion in consumer’s purchasing power. Inflation as an important economic variable, affect the economic growth and its impact on economic growth has been proposed in various theories. Agriculture plays an important role in providing the food security in Iran. Methods: A Bivariate GARCH model was employed to investigate the relationship between inflation uncertainty and agricultural growth. Results: The Augmented Dickey Fuller and Phillips Perron tests indicated all variables were stationary. Estimated models were utilized to generate the conditional variances of inflation and agriculture growth as proxies of inflation and growth variability. During the entire period 1990-2012, Bivariate Granger Causality test indicated that inflation uncertainty was the cause of growth in agriculture. This finding was in line with the hypothesis presented by (Logue and Sweeney, 1981). Conclusion: Due to the causality relation of inflation uncertainty and growth in agriculture, macro policy decision-makers are recommended to consider the price policies for improving agricultural production.

Neural primacy of the dorsolateral prefrontal cortex in patients with obsessive-compulsive disorder

The dorsolateral prefrontal cortex (DLPFC), a key structure in the executive system, has consistently emerged as a crucial element in the pathophysiology of obsessive–compulsive disorder (OCD). However, the neural primacy of the DLPFC remains elusive in this disorder. We investigated the causal interaction (measured by effective connectivity) between the DLPFC and the remaining brain areas using bivariate Granger causality analysis of resting-state fMRI collected from 88 medication-free OCD patients and 88 matched healthy controls. Additionally, we conducted seed-based functional connectivity (FC) analyses to identify network-level neural functional alterations using the bilateral DLPFC as seeds. OCD patients demonstrated reduced FC between the right DLPFC and right orbitofrontal cortex (OFC), and activity in the right OFC had an inhibitory effect on the right DLPFC. Additionally, we observed alterations in both feedforward and reciprocal influences between the inferior temporal gyrus (ITG) and the DLPFC in patients. Furthermore, activity in the cerebellum had an excitatory influence on the right DLPFC in OCD patients. These findings may help to elucidate the psychopathology of OCD by detailing the directional connectivity between the DLPFC and the rest of the brain, ultimately helping to identify regions that could serve as treatment targets in OCD.