scholarly journals Multiple-Shooting Adjoint Method for Whole-Brain Dynamic Causal Modeling

2021 ◽  
pp. 58-70
Author(s):  
Juntang Zhuang ◽  
Nicha Dvornek ◽  
Sekhar Tatikonda ◽  
Xenophon Papademetris ◽  
Pamela Ventola ◽  
...  
Keyword(s):  
Adjoint Method ◽  
Causal Modeling ◽  
Multiple Shooting ◽  
Dynamic Causal Modeling ◽  
Whole Brain

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.

scholarly journals Test-retest reliability of regression dynamic causal modeling

2021 ◽  
Author(s):  
Stefan Frässle ◽  
Klaas Enno Stephan
Keyword(s):  
Brain Connectivity ◽  
Clinical Applications ◽  
Causal Modeling ◽  
Group Level ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Test Retest Reliability

Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability – a test-theoretical property of particular importance for clinical applications – together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project (HCP) dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean ICC 0.24-0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates to measures of functional connectivity, rDCM performed favorably – particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.

scholarly journals Test-retest reliability of regression dynamic causal modeling

2021 ◽  
pp. 1-51
Author(s):  
Stefan Frässle ◽  
Klaas E. Stephan
Keyword(s):  
Brain Connectivity ◽  
Clinical Applications ◽  
Causal Modeling ◽  
Group Level ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Test Retest Reliability

Abstract Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability – a test-theoretical property of particular importance for clinical applications – together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project (HCP) dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean ICC 0.24–0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates to measures of functional connectivity, rDCM performed favorably – particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.

scholarly journals Regression dynamic causal modeling for resting‐state fMRI

2021 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Dynamic Causal Modeling

scholarly journals Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations

2017 ◽  
Vol 11 ◽  
Author(s):  
Yuan Yang ◽  
Bekir Guliyev ◽  
Alfred C. Schouten
Keyword(s):  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Cortical Responses

A rigorous approach for testing the constructionist hypotheses of brain function

2012 ◽  
Vol 35 (3) ◽  
pp. 148-149 ◽  
Author(s):  
Gopikrishna Deshpande ◽  
K. Sathian ◽  
Xiaoping Hu ◽  
Joseph A. Buckhalt
Keyword(s):  
Granger Causality ◽  
Strong Evidence ◽  
Bayesian Modeling ◽  
Brain Function ◽  
Brain Regions ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Rigorous Approach ◽  
Target Article ◽  
Co Activation

AbstractAlthough the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

Dynamic causal modeling: A generative model of slice timing in fMRI

NeuroImage ◽  
2007 ◽  
Vol 34 (4) ◽  
pp. 1487-1496 ◽  
Author(s):  
Stefan J. Kiebel ◽  
Stefan Klöppel ◽  
Nikolaus Weiskopf ◽  
Karl J. Friston
Keyword(s):  
Generative Model ◽  
Causal Modeling ◽  
Dynamic Causal Modeling
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