D Rangaprakash1,2, Guo-Rong Wu3,4, Daniele Marinazzo3, Xiaoping Hu5, Gopikrishna Deshpande1,6,7,8. 1. Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama. 2. Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California. 3. Department of Data Analysis, University of Ghent, Ghent, Belgium. 4. Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China. 5. Department of Bioengineering, University of California Riverside, Riverside, California. 6. Department of Psychology, Auburn University, Auburn, Alabama. 7. Center for Health Ecology and Equity Research, Auburn University, Auburn, Alabama. 8. Alabama Advanced Imaging Consortium, Auburn University, University of South Alabama and University of Alabama at Tuscaloosa and Birmingham, Alabama.
Abstract
PURPOSE: fMRI is the convolution of the hemodynamic response function (HRF) and unmeasured neural activity. HRF variability (HRFv) across the brain could, in principle, alter functional connectivity (FC) estimates from resting-state fMRI (rs-fMRI). Given that HRFv is driven by both neural and non-neural factors, it is problematic when it confounds FC. However, this aspect has remained largely unexplored even though FC studies have grown exponentially. We hypothesized that HRFv confounds FC estimates in the brain's default-mode-network. METHODS: We tested this hypothesis using both simulations (where the ground truth is known and modulated) as well as rs-fMRI data obtained in a 7T MRI scanner (N = 47, healthy). FC was obtained using 2 pipelines: data with hemodynamic deconvolution (DC) to estimate the HRF and minimize HRFv, and data with no deconvolution (NDC, HRFv-ignored). DC and NDC FC networks were compared, along with regional HRF differences, revealing potential false connectivities that resulted from HRFv. RESULTS: We found evidence supporting our hypothesis using both simulations and experimental data. With simulations, we found that HRFv could cause a change of up to 50% in FC. With rs-fMRI, several potential false connectivities attributable to HRFv, with majority connections being between different lobes, were identified. We found a double exponential relationship between the magnitude of HRFv and its impact on FC, with a mean/median error of 30.5/11.5% caused in FC by HRF confounds. CONCLUSION: HRFv, if ignored, could cause identification of false FC. FC findings from HRFv-ignored data should be interpreted cautiously. We suggest deconvolution to minimize HRFv.
PURPOSE: fMRI is the convolution of the hemodynamic response function (HRF) and unmeasured neural activity. HRF variability (HRFv) across the brain could, in principle, alter functional connectivity (FC) estimates from resting-state fMRI (rs-fMRI). Given that HRFv is driven by both neural and non-neural factors, it is problematic when it confounds FC. However, this aspect has remained largely unexplored even though FC studies have grown exponentially. We hypothesized that HRFv confounds FC estimates in the brain's default-mode-network. METHODS: We tested this hypothesis using both simulations (where the ground truth is known and modulated) as well as rs-fMRI data obtained in a 7T MRI scanner (N = 47, healthy). FC was obtained using 2 pipelines: data with hemodynamic deconvolution (DC) to estimate the HRF and minimize HRFv, and data with no deconvolution (NDC, HRFv-ignored). DC and NDC FC networks were compared, along with regional HRF differences, revealing potential false connectivities that resulted from HRFv. RESULTS: We found evidence supporting our hypothesis using both simulations and experimental data. With simulations, we found that HRFv could cause a change of up to 50% in FC. With rs-fMRI, several potential false connectivities attributable to HRFv, with majority connections being between different lobes, were identified. We found a double exponential relationship between the magnitude of HRFv and its impact on FC, with a mean/median error of 30.5/11.5% caused in FC by HRF confounds. CONCLUSION: HRFv, if ignored, could cause identification of false FC. FC findings from HRFv-ignored data should be interpreted cautiously. We suggest deconvolution to minimize HRFv.
Authors: Brian D Berman; Christopher L Groth; Erica Shelton; Stefan H Sillau; Brianne Sutton; Kristina T Legget; Jason R Tregellas Journal: J Neurosci Res Date: 2019-11-06 Impact factor: 4.164
Authors: Jingyuan E Chen; Laura D Lewis; Catie Chang; Qiyuan Tian; Nina E Fultz; Ned A Ohringer; Bruce R Rosen; Jonathan R Polimeni Journal: Neuroimage Date: 2020-03-05 Impact factor: 6.556
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