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Test-retest reliability of regression dynamic causal modeling


Frässle, Stefan; Stephan, Klaas E (2022). Test-retest reliability of regression dynamic causal modeling. Network Neuroscience, 6(1):135-160.

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 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 intraclass correlation coefficient 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 with 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.

Keywords: Connectomics; Effective connectivity; Generative model; Regression dynamic causal modeling; Test-retest reliability; rDCM

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 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 intraclass correlation coefficient 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 with 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.

Keywords: Connectomics; Effective connectivity; Generative model; Regression dynamic causal modeling; Test-retest reliability; rDCM

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Life Sciences > General Neuroscience
Physical Sciences > Computer Science Applications
Physical Sciences > Artificial Intelligence
Physical Sciences > Applied Mathematics
Uncontrolled Keywords:Applied Mathematics, Artificial Intelligence, Computer Science Applications, General Neuroscience
Language:English
Date:1 February 2022
Deposited On:09 Nov 2022 12:41
Last Modified:27 Apr 2024 01:38
Publisher:MIT Press
ISSN:2472-1751
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1162/netn_a_00215
PubMed ID:35356192
Project Information:
  • : FunderRené and Susanne Braginsky Foundation
  • : Grant ID
  • : Project Title
  • : FunderSNSF
  • : Grant ID320030_179377
  • : Project TitleComputational neuroimaging of predictive coding in interoception
  • : FunderUniversity of Zurich
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)