scholarly journals Time-of-day effects in resting-state fMRI: changes in Effective Connectivity and BOLD signal

2021 ◽  
Author(s):  
Liucija Vaisvilaite ◽  
Vetle Hushagen ◽  
Janne Grønli ◽  
Karsten Sprecht
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Time Of Day ◽  
Bold Signal

scholarly journals Time-of-day effects in resting-state fMRI: changes in Effective Connectivity and BOLD signal

2020 ◽  
Author(s):  
Liucija Vaisvilaite ◽  
Vetle Hushagen ◽  
Janne Gronli ◽  
Karsten Specht
Keyword(s):  
Resting State ◽  
Empirical Bayes ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Time Of Day ◽  
Blood Oxygenation ◽  
Bold Signal ◽  
Human Connectome Project ◽  
Using Data ◽  
Density Dynamic

The current project explored the hypothesis that time-of-day dependent metabolic variations may contribute to reduced reliability in resting-state fMRI studies. We have investigated time-of-day effects in the spontaneous fluctuations (>0.1Hz) of the blood oxygenation level dependent (BOLD) signal. Using data from the human connectome project (HCP) release S1200, cross-spectral density dynamic causal modelling (DCM) was used to analyze time-dependent effects on the hemodynamic response and effective connectivity parameters. Hierarchical group-parametric empirical Bayes (PEB) found no support for changes in effective connectivity, whereas the hemodynamic parameters exhibited a significant time-of-day dependent effect. We conclude that these findings urge the need to account for the time of data acquisition in future MRI studies.

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.

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