Investigating large-scale Granger causality analysis in presence of noise and varying sampling rate

2018 ◽  
Author(s):  
Adora M. DSouza ◽  
Anas Z. Abidin ◽  
Udaysankar Chockanathan ◽  
Axel Wismüller ◽  
Lutz Leistritz
Keyword(s):  
Granger Causality ◽  
Large Scale ◽  
Sampling Rate ◽  
Causality Analysis ◽  
Granger Causality Analysis

scholarly journals Stroke-related alterations in inter-areal communication revealed via Granger causality analysis

2021 ◽  
Author(s):  
Michele Allegra ◽  
Chiara Favaretto ◽  
Nicholas Metcalf ◽  
Maurizio Corbetta ◽  
Andrea Brovelli
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Information Transfer ◽  
Large Scale ◽  
Functional Organization ◽  
Information Flows ◽  
Causality Analysis ◽  
Stroke Patients ◽  
Granger Causality Analysis

ABSTRACTNeuroimaging and neurological studies suggest that stroke is a brain network syndrome. While causing local ischemia and cell damage at the site of injury, stroke strongly perturbs the functional organization of brain networks at large. Critically, functional connectivity abnormalities parallel both behavioral deficits and functional recovery across different cognitive domains. However, the reasons for such relations remain poorly understood. Here, we tested the hypothesis that alterations in inter-areal communication underlie stroke-related modulations in functional connectivity (FC). To this aim, we used resting-state fMRI and Granger causality analysis to quantify information transfer between brain areas and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was strongly decreased with respect to healthy controls. Second, information transfer within the affected hemisphere, and from the affected to the intact hemisphere was reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they were correlated with impaired performance in several behavioral domains. Overall, our results support the hypothesis that stroke perturbs inter-areal communication within and across hemispheres, and suggest novel therapeutic approaches aimed at restoring normal information flow.SIGNIFICANCE STATEMENTA thorough understanding of how stroke perturbs brain function is needed to improve recovery from the severe neurological syndromes affecting stroke patients. Previous resting-state neuroimaging studies suggested that interaction between hemispheres decreases after stroke, while interaction between areas of the same hemisphere increases. Here, we used Granger causality to reconstruct information flows in the brain at rest, and analyze how stroke perturbs them. We showed that stroke causes a global reduction of inter-hemispheric communication, and an imbalance between the intact and the affected hemisphere: information flows within and from the latter are impaired. Our results may inform the design of stimulation therapies to restore the functional balance lost after stroke.

scholarly journals Large-scale Granger causality analysis on resting-state functional MRI

2016 ◽  
Author(s):  
Adora M. D'Souza ◽  
Anas Zainul Abidin ◽  
Lutz Leistritz ◽  
Axel Wismüller
Keyword(s):  
Granger Causality ◽  
Functional Mri ◽  
Resting State ◽  
Large Scale ◽  
Causality Analysis ◽  
Granger Causality Analysis

scholarly journals Memory Matters: A Case for Granger Causality in Climate Variability Studies

2018 ◽  
Vol 31 (8) ◽  
pp. 3289-3300 ◽  
Author(s):  
Marie C. McGraw ◽  
Elizabeth A. Barnes
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Climate Variability ◽  
Granger Causality ◽  
Large Scale ◽  
Linear Regression Analysis ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Significant Relationships ◽  
Large Memory

Abstract In climate variability studies, lagged linear regression is frequently used to infer causality. While lagged linear regression analysis can often provide valuable information about causal relationships, lagged regression is also susceptible to overreporting significant relationships when one or more of the variables has substantial memory (autocorrelation). Granger causality analysis takes into account the memory of the data and is therefore not susceptible to this issue. A simple Monte Carlo example highlights the advantages of Granger causality, compared to traditional lagged linear regression analysis in situations with one or more highly autocorrelated variables. Differences between the two approaches are further explored in two illustrative examples applicable to large-scale climate variability studies. Given that Granger causality is straightforward to calculate, Granger causality analysis may be preferable to traditional lagged regression analysis when one or more datasets has large memory.

scholarly journals Effective connectivity of the large-scale brain networks for procrastination: A Granger causality analysis

2019 ◽  
Vol 50 (1) ◽  
pp. 54-69
Author(s):  
ZhiYi CHEN ◽  
Ti SU ◽  
Rong ZHANG ◽  
Ting XU ◽  
Fan ZHOU ◽  
...  
Keyword(s):  
Granger Causality ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Causality Analysis ◽  
Granger Causality Analysis

Fraud and Bank Performance in Nigeria – Var Granger Causality Analysis

e-Finanse ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 20-26
Author(s):  
Taiwo A. Muritala ◽  
Muftau A. Ijaiya ◽  
Olatanwa H. Afolabi ◽  
Abdulrasheed B. Yinus
Keyword(s):  
Control Systems ◽  
Granger Causality ◽  
Internal Control ◽  
Banking Sector ◽  
Bank Performance ◽  
Causality Analysis ◽  
Vector Autoregressive ◽  
Granger Causality Analysis ◽  
Long Run ◽  
Multivariate Cointegration

AbstractThis paper examines the causality between fraud and bank performance in Nigeria over the period 2000-2016 for quarterly financial data using Johansen’s Multivariate Cointegration Model and Vector Autoregressive (VAR) Granger Causality analysis. The results show a long-run relationship between the variables. Bank performance was found to be linked to Granger fraud variables and vice versa at 10% significant level. This study reveals that there was a direct causal relationship between bank performance and fraud because increase in fraudulent activities in the banking sector leads to reduction in bank performance. Hence, this study recommends that internal control systems of banks should be strengthened so as to detect and prevent fraud. In this way, bank assets would be protected.

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