S Tak1, J Noh2, C Cheong2, P Zeidman3, A Razi4, W D Penny5, K J Friston6. 1. Korea Basic Science Institute, Cheongju, Republic of Korea. Electronic address: stak@kbsi.re.kr. 2. Korea Basic Science Institute, Cheongju, Republic of Korea. 3. Wellcome Centre for Human Neuroimaging, University College London, London, UK. 4. Wellcome Centre for Human Neuroimaging, University College London, London, UK; Department of Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan; Monash Institute of Cognitive & Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton 3800, Australia. 5. School of Psychology, University of East Anglia, Norwich, UK. 6. Wellcome Centre for Human Neuroimaging, University College London, London, UK. Electronic address: k.friston@ucl.ac.uk.
Abstract
BACKGROUND: There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). NEW METHOD: In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). RESULTS: Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity - as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative - and furnish more efficient estimates. COMPARED WITH EXISTING METHODS: In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. CONCLUSIONS: In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.
BACKGROUND: There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). NEW METHOD: In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). RESULTS: Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity - as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative - and furnish more efficient estimates. COMPARED WITH EXISTING METHODS: In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. CONCLUSIONS: In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.
Authors: Ben J Harrison; Christopher G Davey; Hannah S Savage; Alec J Jamieson; Christine A Leonards; Bradford A Moffat; Rebecca K Glarin; Trevor Steward Journal: Cereb Cortex Date: 2022-09-19 Impact factor: 4.861
Authors: Hannah S Savage; Christopher G Davey; Tor D Wager; Sarah N Garfinkel; Bradford A Moffat; Rebecca K Glarin; Ben J Harrison Journal: Neuroimage Date: 2021-10-24 Impact factor: 7.400