Failed cooperative, but not competitive, interaction between large-scale brain networks impairs working memory in schizophrenia

2016 ◽  
Vol 46 (6) ◽  
pp. 1211-1224 ◽  
Author(s):  
W. Pu ◽  
Q. Luo ◽  
L. Palaniyappan ◽  
Z. Xue ◽  
S. Yao ◽  
...  
Keyword(s):  
Working Memory ◽  
Granger Causality ◽  
Large Scale ◽  
Dynamic Interaction ◽  
Healthy Controls ◽  
Causality Analysis ◽  
Imaging Data ◽  
Planar Imaging ◽  
Attentional Deficit ◽  
Attention Disorder

BackgroundA large-scale network named the default mode network (DMN) dynamically cooperates and competes with an external attention system (EAS) to facilitate various cognitive functioning that is prominently impaired in schizophrenia. However, it is unclear whether the cognitive deficit in schizophrenia is related to the disrupted competition and/or cooperation between these two networks.MethodA total of 35 schizophrenia patients and 30 healthy controls were scanned using gradient-echo echo-planar imaging during n-back working memory (WM) processing. Brain activities of the DMN and EAS were measured using general linear modelling of the functional magnetic resonance imaging data. Dynamic interaction between the DMN and EAS was decomposed into two directions using Granger causality analysis.ResultsWe observed a significant failure of DMN suppression in patients with schizophrenia, which was significantly related to WM/attentional deficit. Granger causality modelling showed that in healthy controls, while the EAS inhibitorily influenced the DMN, the DMN exerted an ‘excitatory’ or cooperative influence back on the EAS, especially in those with lower WM accuracy. In schizophrenia, this ‘excitatory’ DMN→EAS influence within the reciprocal EAS–DMN loop was significantly reduced, especially in patients with WM/attentional deficit.ConclusionsThe dynamic interaction between the DMN and EAS is likely to be comprised of both competitive and cooperative influences. In healthy controls, both the ‘inhibitory’ EAS→DMN interaction and ‘excitatory’ DMN→EAS interaction are correlated with WM performance. In schizophrenia, reduced ‘cooperative’ influence from the DMN to dorsal nodes of the EAS occurs in the context of non-suppression of the DMN and may form a possible pathophysiological substrate of WM deficit and attention disorder.

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 Working memory networks and activation patterns in schizophrenia and bipolar disorder: comparison with healthy controls

2014 ◽  
Vol 204 (4) ◽  
pp. 290-298 ◽  
Author(s):  
Christine Lycke Brandt ◽  
Tom Eichele ◽  
Ingrid Melle ◽  
Kjetil Sundet ◽  
Andrés Server ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Working Memory ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Networks ◽  
Memory Task ◽  
Control Group ◽  
Healthy Controls ◽  
Activation Patterns ◽  
Patient Groups

BackgroundSchizophrenia and bipolar disorder are severe mental disorders with overlapping genetic and clinical characteristics, including cognitive impairments. An important question is whether these disorders also have overlapping neuronal deficits.AimsTo determine whether large-scale brain networks associated with working memory, as measured with functional magnetic resonance imaging (fMRI), are the same in both schizophrenia and bipolar disorder, and how they differ from those in healthy individuals.MethodPatients with schizophrenia (n = 100) and bipolar disorder (n = 100) and a healthy control group (n = 100) performed a 2-back working memory task while fMRI data were acquired. The imaging data were analysed using independent component analysis to extract large-scale networks of task-related activations.ResultsSimilar working memory networks were activated in all groups. However, in three out of nine networks related to the experimental task there was a graded response difference in fMRI signal amplitudes, where patients with schizophrenia showed greater activation than those with bipolar disorder, who in turn showed more activation than healthy controls. Secondary analysis of the patient groups showed that these activation patterns were associated with history of psychosis and current elevated mood in bipolar disorder.ConclusionsThe same brain networks were related to working memory in schizophrenia, bipolar disorder and controls. However, some key networks showed a graded hyperactivation in the two patient groups, in line with a continuum of neuronal abnormalities across psychotic disorders.

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 Statistical Analysis of Single-Trial Granger Causality Spectra

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Andrea Brovelli
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Granger Causality ◽  
Large Scale ◽  
Linear Models ◽  
Causality Analysis ◽  
Single Trial ◽  
Experimental Conditions ◽  
Unidirectional Coupling ◽  
Visuomotor Task

Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based ont-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

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