Abstract
The estimation of functional connectivity from the observed Blood Oxygen Level-Dependent (BOLD) signal may not be accurate because it is an indirect measure of neuronal activity or the existing deconvolution approaches assume that hemodynamic response function (HRF), which modulates the neuronal activities, is uniform across the brain regions or across the time course. We propose a novel approach using empirical mode decomposition (EMD), to reduce the effect of HRF from estimated neuronal activity signal (NAS) obtained after blind deconvolution for a BOLD time course. The first two intrinsic mode functions (IMFs), obtained during EMD of the neuronal activity signal represent its highest oscillating modes and hence have characteristic of impulses. The sum of the first two IMFs is computed as a refined representation of neuronal activity signal to estimate resting state connectome using the framework of dictionary learning. Usefulness of the proposed method has been demonstrated using two resting state datasets (healthy control and attention deficit hyperactivity disorder) taken from ‘1000 Functional Connectomes’. For quantitative analysis, Jaccard distances are computed between spatial maps obtained using BOLD signals and refined activity signals. Results show that maps obtained using NAS are a subset of that obtained using BOLD signal and hence avoid false acceptance of active voxels, which illustrates the importance of refined NAS.
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Notes
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http://fcon_1000.projects.nitrc.orgfcpClassicFcpTable.html.
- 2.
http://fcon_1000.projects.nitrc.org/indi/adhd200/.
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Das, S., Sao, A.K., Biswal, B. (2020). Precise Estimation of Resting State Functional Connectivity Using Empirical Mode Decomposition. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_7
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