scholarly journals Different Features of Metabolic Connectivity Map and Granger Causality Method in Revealing Directed Dopamine Pathways: A Pilot Study Based on Integrated PET/MR

2021 ◽  
Author(s):  
Lei Wang ◽  
Longxiao Wei ◽  
Long Jin ◽  
Yunbo Li ◽  
Yixin Wei ◽  
...  
Keyword(s):  
Granger Causality ◽  
Long Range ◽  
Effective Connectivity ◽  
Orbital Frontal Cortex ◽  
Connectivity Map ◽  
Significance Level ◽  
Common Basis ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Reward Circuit

Abstract Granger causality (GC) analysis and metabolic connectivity map (MCM) are two effective connectivity (EC) methods commonly used in functional brain researches. Although they have a common basis in central neurophysiology, their differences in depicting EC are not clear because of absenting data acquired simultaneously and exactly aligned. Integrated positron emission tomography and magnetic resonance image (PET/MR) technology makes this available. Using the “Monash rs-PET/MR” dataset obtained from the OpenNeuro database, we first conducted GC and MCM analysis of the brain dopamine reward circuit, a well-known system mainly consisting of the bilateral Orbital Frontal Cortex (OFC), Caudate (CAU), Nucleus Accumbens (NAc), Thalamus (THA) and Substantia Nigra (SN). Then, we validated their ability of describing EC to priori knowledge. The significance of each directed pathways within group were tested through one-sample t-test (for MCM) or Wilcoxcon test (for GC), the significance level was set at p<0.05 after FDR correction. Three types of connections were found: the cortico-nucleus (long-range), the nucleus-nucleus (neighborhood) and the symmetrical connections. GC revealed long-range connections including OFC-CAU and OFC-NAc; MCM revealed neighborhood connections including NAc-CAU, SN-THA, and THA-CAU, the symmetrical connections including the bilateral NAc, CAU, THA, as well as OFC-CAU. Thus, different patterns in directional networks of dopamine reward circuit revealed by GC and MCM. GC predominated at aspects of cortico-nucleus bidirected connections, while MCM of directed connections among close regions and symmetrical regions. This study implicates that research involving in effective connections should choose an appropriate analysis method according to the study purpose.

Tax revenue and economic growth in developing country: an autoregressive distribution lags approach

2020 ◽  
Vol 7 (54) ◽  
pp. 205-217
Author(s):  
Mnaku Honest Maganya
Keyword(s):  
Economic Growth ◽  
Granger Causality ◽  
Testing Procedure ◽  
Gdp Growth ◽  
Bounds Testing ◽  
Causality Test ◽  
Significance Level ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
The Government

AbstractTanzania, like most other developing countries, faces numerous economic challenges in striving to achieve sustainable economic growth and development through taxation. In the literature, the debate on how effective taxes are as a tool for promoting economic growth and economic development remains inconclusive, as various research have reported mixed effects of tax on economic growth. This article investigates the effect of taxation on economic growth in Tanzania using the recently developed technique of autoregressive distributed lag model (ARDL) bounds testing procedure for the period from 1996 to 2019. Various preliminary tests were conducted including stationary tests as well as the pair-wise Granger causality test. According to the results obtained, domestic goods and services (TGS) taxes are positively related to GDP growth and are statistically significant at 1% level. Income taxes, on the other hand, were found to be negatively related to GDP growth and to be statistically significant at 5% level. The pair-wise Granger causality results indicated that there is bidirectional Granger causality between TGS and GDP growth at 1 % significance level. The government should aim at growing, nurturing and sustaining tax base to positively drive economic growth even further.

scholarly journals Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tun-Wei Hsu ◽  
Jong-Ling Fuh ◽  
Da-Wei Wang ◽  
Li-Fen Chen ◽  
Chia-Jung Chang ◽  
...  
Keyword(s):  
Algebraic Topology ◽  
Fdg Pet ◽  
Persistent Homology ◽  
Network Connectivity ◽  
Brain Regions ◽  
Imaging Data ◽  
Cholinergic Pathways ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Neurochemical Changes

AbstractDementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping

NeuroImage ◽  
2009 ◽  
Vol 47 (4) ◽  
pp. 1844-1853 ◽  
Author(s):  
Huafu Chen ◽  
Qin Yang ◽  
Wei Liao ◽  
Qiyong Gong ◽  
Shan Shen
Keyword(s):  
Granger Causality ◽  
Motor Imagery ◽  
Effective Connectivity ◽  
Motor Areas

scholarly journals Functional Networks in Disorders of Consciousness

2017 ◽  
Vol 37 (05) ◽  
pp. 485-502 ◽  
Author(s):  
Camille Chatelle ◽  
Brian Edlow ◽  
Yelena Bodien
Keyword(s):  
Effective Connectivity ◽  
Clinical Care ◽  
Network Connectivity ◽  
Disorders Of Consciousness ◽  
Functional Network ◽  
Functional Networks ◽  
Positron Emission ◽  
Essential Components ◽  
Network Mapping

AbstractSevere brain injury may cause disruption of neural networks that sustain arousal and awareness, the two essential components of consciousness. Despite the potentially devastating immediate and long-term consequences, disorders of consciousness (DoC) are poorly understood in terms of their underlying neurobiology, the relationship between pathophysiology and recovery, and the predictors of treatment efficacy. Recent advances in neuroimaging techniques have enabled the study of network connectivity, providing great potential to improve the clinical care of patients with DoC. Initial discoveries in this field were made using positron emission tomography (PET). More recently, functional magnetic resonance (fMRI) techniques have added to our understanding of functional network dynamics in this population. Both methods have shown that whether at rest or performing a goal-oriented task, functional networks essential for processing intrinsic thoughts and extrinsic stimuli are disrupted in patients with DoC compared with healthy subjects. Atypical connectivity has been well established in the default mode network as well as in other cortical and subcortical networks that may be required for consciousness. Moreover, the degree of altered connectivity may be related to the severity of impaired consciousness, and recovery of consciousness has been shown to be associated with restoration of connectivity. In this review, we discuss PET and fMRI studies of functional and effective connectivity in patients with DoC and suggest how this field can move toward clinical application of functional network mapping in the future.

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