Effective Connectivity Extracted from Resting-State fMRI Images Using Transfer Entropy

IRBM ◽  
2021 ◽  
Author(s):  
Z. Wu ◽  
X. Chen ◽  
M. Gao ◽  
M. Hong ◽  
Z. He ◽  
...  
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Transfer Entropy

scholarly journals Altered hypothalamus functional connectivity with limbic system in fibromyalgia and the modulation effect of intervention

2020 ◽  
Author(s):  
Jian Kong ◽  
Yiting Huang ◽  
Jiao Liu ◽  
Siyi Yu ◽  
Ming Cheng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Limbic System ◽  
Resting State ◽  
Tai Chi ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Trial Registration ◽  
Modulation Effect ◽  
Resting State Functional Connectivity ◽  
Before And After

Abstract Background: This study aims to investigate the resting state functional connectivity (rsFC) changes of the hypothalamus in Fibromyalgia patients and the modulation effect of effective treatments. Methods: Fibromyalgia patients and matched healthy controls (HC’s) were recruited. Resting state fMRI data were collected from fibromyalgia patients before and after a 12-week Tai Chi intervention and once from HC’s. Results: Data analysis showed that fibromyalgia patients displayed significantly decreased medial hypothalamus (MH) rsFC with the thalamus and amygdala when compared to HC’s at baseline. After the intervention, fibromyalgia patients showed increased (normalized) MH rsFC in the thalamus and amygdala. Effective connectivity analysis showed disrupted MH and thalamus interaction in fibromyalgia, which nonetheless could be partially restored by Tai Chi. Conclusions: Elucidating the role of the diencephalon and limbic system in the pathophysiology and development of fibromyalgia may facilitate the development of new treatment methods for this prevalent disorder. Trial registration: Trial registration ClinicalTrials.gov Identifier: NCT02407665. Registered 3 April 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02407665

scholarly journals The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

2019 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Measurement Noise ◽  
Resting State Networks ◽  
Cross Sectional ◽  
Global Signal ◽  
Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

scholarly journals Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

NeuroImage ◽  
2020 ◽  
Vol 208 ◽  
pp. 116367 ◽  
Author(s):  
Giulia Prando ◽  
Mattia Zorzi ◽  
Alessandra Bertoldo ◽  
Maurizio Corbetta ◽  
Marco Zorzi ◽  
...  
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Whole Brain

scholarly journals Large-scale DCMs for resting-state fMRI

2017 ◽  
Vol 1 (3) ◽  
pp. 222-241 ◽  
Author(s):  
Adeel Razi ◽  
Mohamed L. Seghier ◽  
Yuan Zhou ◽  
Peter McColgan ◽  
Peter Zeidman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Effective Connectivity ◽  
Directed Graphs ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Large Graphs ◽  
Model Inversion ◽  
Graph Theoretic

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

scholarly journals Is Resting State Functional MRI Effective Connectivity in Movement Disorders Helpful? A Focused Review Across Lifespan and Disease

2021 ◽  
Author(s):  
Bethany L. Sussman ◽  
Sarah N. Wyckoff ◽  
Jennifer Heim ◽  
Angus A. Wilfong ◽  
David Adelson ◽  
...  
Keyword(s):  
Movement Disorder ◽  
Movement Disorders ◽  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Motor Network ◽  
Adults And Children ◽  
Health And Disease ◽  
Human Motor ◽  
And Behavior

AbstractIn the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from static functional to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.Impact StatementWhile functional connectivity has elucidated much MN properties with relation to age, disease, and behavior, effective connectivity has been shown to be useful in MN-informed therapies in adults. Thus, effective connectivity may have potential to impact childhood movement disorder therapies, given the lower to no patient demand.

scholarly journals Dynamic Resting State Motor Network Connectivity of Neurotypical Children, the Groundwork for Network-Guided Therapy in Childhood Movement Disorders

2021 ◽  
Author(s):  
Bethany L. Sussman ◽  
Sarah N. Wyckoff ◽  
Justin M. Fine ◽  
Jennifer Heim ◽  
Angus A. Wilfong ◽  
...  
Keyword(s):  
Functional Mri ◽  
Movement Disorders ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Motor Behavior ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Group Model ◽  
Motor Network ◽  
Brain Behavior

AbstractBackgroundNormative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state network informed treatments in pediatric movement disorders.MethodCross-spectral dynamic causal modelling of resting-state fMRI was investigated in 19 neurotypically developing 5-7-year-old children. Fully connected six-node motor network models were created for each hemisphere including primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging were used to create a group model for each hemisphere; Purdue Pegboard Test (PPBT) scores for relevant hand motor behavior were also entered as a covariate at the group level to determine the brain-behavior relationship.ResultsOverall, the resting-state functional MRI effective connectivity of motor cortico-basal ganglia-cerebellar networks was similar across hemispheres, with greater connectivity in the left hemisphere. The motor network effective connectivity relationships between the nodes were consistent and robust across subjects. Additionally, the PPBT score for each hand was positively correlated with the thalamus to contralateral cerebellum connection.DiscussionThe normative effective connectivity from resting-state functional MRI in children largely reflect the direction of inter-nodal signal predicted by other prior modalities and was consistent and robust across subjects, with differences from these prior task-dependent modalities that likely reflect the motor rest-action state during acquisition. Effective connectivity of the motor network was correlated with motor behavior, indicating effective connectivity brain-behavior relationship has physiological meaning in the normally developing. Thus, it may be helpful for future studies in children with movement disorders, wherein comparison to normative effective connectivity will be critical for network-targeted intervention.Impact StatementThis is the first study to use pediatric resting-state functional MRI to create a normative effective connectivity model of the motor network and to also show correlation with behavior, which may have therapeutic implications for children with movement disorders.

scholarly journals Dissociation in basolateral and central amygdala effective connectivity predicts the stability of emotion-related impulsivity in adolescents with borderline personality symptoms: a resting-state fMRI study

2021 ◽  
Author(s):  
Nathan T Hall ◽  
Michael N Hallquist
Keyword(s):  
Prefrontal Cortex ◽  
Emerging Adults ◽  
Resting State ◽  
Basolateral Amygdala ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Borderline Personality ◽  
Central Amygdala ◽  
Healthy Group ◽  
Growth Curve Models

Background: Borderline personality disorder (BPD) is associated with altered activity in the prefrontal cortex (PFC) and amygdala, yet no studies have examined fronto-limbic circuitry in borderline adolescents. Here, we examined the contribution of fronto-limbic connectivity to the longitudinal stability of emotion-related impulsivity (ERI), a key feature of BPD, in symptomatic adolescents and young adults. Methods: We compared resting-state effective connectivity (EC) in 82 adolescents and emerging adults with and without clinically significant borderline symptoms (n BPD = 40, ages 13-30). Group-specific directed networks were estimated amongst fronto-limbic nodes including PFC, ventral striatum (VS), central amygdala (CeN), and basolateral amygdala (BLA). We calculated directed centrality metrics and examined if these values were associated with initial levels and rates of change in ERI symptoms over a one-year follow-up using latent growth curve models (LGCMs). Results: In the healthy group, ventromedial prefrontal cortex (vmPFC) and dorsal ACC had a directed influence on CeN and VS respectively. In the borderline group bilateral BLA had a directed influence on CeN, whereas in the healthy group CeN influenced BLA. LGCMs revealed that in borderline adolescents, ERI remained stable across follow-ups. Further, higher output of R CeN in controls was associated with stronger within-person decreases in ERI. Conclusions: Functional inputs from BLA and vmPFC appear to play competing roles in influencing CeN activity. In borderline adolescents BLA may predominate over CeN activity, while in controls the ability of CeN to conversely influence BLA activity is associated with more rapid reductions in ERI.

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