Literature DB >> 27448367

Experimental Validation of Dynamic Granger Causality for Inferring Stimulus-Evoked Sub-100 ms Timing Differences from fMRI.

Yunzhi Wang, Santosh Katwal, Baxter Rogers, John Gore, Gopikrishna Deshpande.   

Abstract

Decoding the sequential flow of events in the human brain non-invasively is critical for gaining a mechanistic understanding of brain function. In this study, we propose a method based on dynamic Granger causality analysis to measure timing differences in brain responses from fMRI. We experimentally validate this method by detecting sub-100 ms timing differences in fMRI responses obtained from bilateral visual cortex using fast sampling, ultra-high field and an event-related visual hemifield paradigm with known timing difference between the hemifields. Classical Granger causality was previously shown to be able to detect sub-100 ms timing differences in the visual cortex. Since classical Granger causality does not differentiate between spontaneous and stimulus-evoked responses, dynamic Granger causality has been proposed as an alternative, thereby necessitating its experimental validation. In addition to detecting timing differences as low as 28 ms using dynamic Granger causality, the significance of the inference from our method increased with increasing delay both in simulations and experimental data. Therefore, it provides a methodology for understanding mental chronometry from fMRI in a data-driven way.

Entities:  

Mesh:

Year:  2016        PMID: 27448367      PMCID: PMC5570592          DOI: 10.1109/TNSRE.2016.2593655

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  38 in total

1.  Survey: interpolation methods in medical image processing.

Authors:  T M Lehmann; C Gönner; K Spitzer
Journal:  IEEE Trans Med Imaging       Date:  1999-11       Impact factor: 10.048

2.  Interpolation revisited.

Authors:  P Thévenaz; T Blu; M Unser
Journal:  IEEE Trans Med Imaging       Date:  2000-07       Impact factor: 10.048

3.  Estimating Granger causality from fourier and wavelet transforms of time series data.

Authors:  Mukeshwar Dhamala; Govindan Rangarajan; Mingzhou Ding
Journal:  Phys Rev Lett       Date:  2008-01-10       Impact factor: 9.161

Review 4.  The free-energy principle: a unified brain theory?

Authors:  Karl Friston
Journal:  Nat Rev Neurosci       Date:  2010-01-13       Impact factor: 34.870

5.  The variability of human, BOLD hemodynamic responses.

Authors:  G K Aguirre; E Zarahn; M D'esposito
Journal:  Neuroimage       Date:  1998-11       Impact factor: 6.556

6.  Adaptive AR modeling of nonstationary time series by means of Kalman filtering.

Authors:  M Arnold; W H Miltner; H Witte; R Bauer; C Braun
Journal:  IEEE Trans Biomed Eng       Date:  1998-05       Impact factor: 4.538

7.  Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.

Authors:  Martin Havlicek; Karl J Friston; Jiri Jan; Milan Brazdil; Vince D Calhoun
Journal:  Neuroimage       Date:  2011-03-09       Impact factor: 6.556

8.  Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data.

Authors:  Martin Havlicek; Jiri Jan; Milan Brazdil; Vince D Calhoun
Journal:  Neuroimage       Date:  2010-06-01       Impact factor: 6.556

9.  Identifying neural drivers with functional MRI: an electrophysiological validation.

Authors:  Olivier David; Isabelle Guillemain; Sandrine Saillet; Sebastien Reyt; Colin Deransart; Christoph Segebarth; Antoine Depaulis
Journal:  PLoS Biol       Date:  2008-12-23       Impact factor: 8.029

10.  Is Granger causality a viable technique for analyzing fMRI data?

Authors:  Xiaotong Wen; Govindan Rangarajan; Mingzhou Ding
Journal:  PLoS One       Date:  2013-07-04       Impact factor: 3.240

View more
  6 in total

1.  Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets.

Authors:  Pradyumna Lanka; D Rangaprakash; Michael N Dretsch; Jeffrey S Katz; Thomas S Denney; Gopikrishna Deshpande
Journal:  Brain Imaging Behav       Date:  2020-12       Impact factor: 3.978

2.  Advancing functional connectivity research from association to causation.

Authors:  Andrew T Reid; Drew B Headley; Ravi D Mill; Ruben Sanchez-Romero; Lucina Q Uddin; Daniele Marinazzo; Daniel J Lurie; Pedro A Valdés-Sosa; Stephen José Hanson; Bharat B Biswal; Vince Calhoun; Russell A Poldrack; Michael W Cole
Journal:  Nat Neurosci       Date:  2019-10-14       Impact factor: 24.884

3.  FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response.

Authors:  D Rangaprakash; Reza Tadayonnejad; Gopikrishna Deshpande; Joseph O'Neill; Jamie D Feusner
Journal:  Brain Imaging Behav       Date:  2021-06       Impact factor: 3.224

4.  Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain.

Authors:  Gopikrishna Deshpande; Yun Wang; Jennifer Robinson
Journal:  Brain Inform       Date:  2022-01-17

5.  Zinc Nanoparticles Enhance Brain Connectivity in the Canine Olfactory Network: Evidence From an fMRI Study in Unrestrained Awake Dogs.

Authors:  Bhavitha Ramaihgari; Oleg M Pustovyy; Paul Waggoner; Ronald J Beyers; Chester Wildey; Edward Morrison; Nouha Salibi; Jeffrey S Katz; Thomas S Denney; Vitaly J Vodyanoy; Gopikrishna Deshpande
Journal:  Front Vet Sci       Date:  2018-07-02

6.  Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma.

Authors:  D Rangaprakash; Michael N Dretsch; Jeffrey S Katz; Thomas S Denney; Gopikrishna Deshpande
Journal:  Front Neurosci       Date:  2019-08-23       Impact factor: 4.677

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.