Effective connectivity decreases in specific brain networks with postparalysis facial synkinesis: a dynamic causal modeling study

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Ye-Chen Lu ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Si-Si Li ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Brain Networks ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Facial Synkinesis ◽  
Modeling Study

Effective connectivity in target stimulus processing: A dynamic causal modeling study of visual oddball task

NeuroImage ◽  
2007 ◽  
Vol 35 (2) ◽  
pp. 827-835 ◽  
Author(s):  
Milan Brázdil ◽  
Michal Mikl ◽  
Radek Mareček ◽  
Petr Krupa ◽  
Ivan Rektor
Keyword(s):  
Target Stimulus ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Stimulus Processing ◽  
Oddball Task ◽  
Visual Oddball ◽  
Modeling Study

P.4.006 Disrupted effective connectivity of emotional circuitry in schizophrenia: a dynamic causal modeling study

2014 ◽  
Vol 24 ◽  
pp. S85-S86
Author(s):  
B. Vai ◽  
G. Sferrazza Papa ◽  
S. Poletti ◽  
D. Radaelli ◽  
E. Smeraldi ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Modeling Study

S.07.08 Disrupted effective connectivity of emotional circuitry in schizophrenia: a dynamic causal modeling study

2014 ◽  
Vol 24 ◽  
pp. S120
Author(s):  
B. Vai ◽  
G. Sferrazza Papa ◽  
S. Poletti ◽  
D. Radaelli ◽  
I. Bollettini ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Modeling Study

P.3.b.013 Disrupted effective connectivity of emotional circuitry in schizophrenia: a dynamic causal modeling study

2014 ◽  
Vol 24 ◽  
pp. S498-S499
Author(s):  
B. Vai ◽  
G. Sferrazza Papa ◽  
S. Poletti ◽  
D. Radaelli ◽  
I. Bollettini ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Modeling Study

scholarly journals Regression dynamic causal modeling for resting-state fMRI

2020 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Computationally Efficient ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Wide Range

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

Sign in / Sign up

Export Citation Format

Share Document