TY - JOUR
T1 - A Multivariate Empirical Mode Decomposition-Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI
AU - Wang, Kuo Wei
AU - Chen, Chia Yuen
AU - Chang, Hsiao Huang
AU - Hsu, Chuan Chih
AU - Lan, Gong Yau
AU - Hsu, Hao Teng
AU - Shyu, Kuo Kai
AU - Chan, Wing P.
AU - Lee, Po Lei
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Olfactory dysfunction is related to several clinical neurodegenerative diseases, such as Alzheimer's disease, multiple sclerosis, degenerative ataxias, Parkinson's disease, and so on. Owing to the individual difference in the sensory adaption of human smell function, the olfactory responses usually exhibit large inter-individual difference and change over time after repeated stimulations. The traditional analysis tools, such as statistical parametric mapping (SPM) in functional magnetic resonance imaging technique (fMRI) analysis, utilize the paradigm-based linear correlation and statistical techniques to discriminate the activation areas from background activities. However, these traditional approaches extract olfactory-induced responses using the stereotypic template or paradigm generated model. The olfactory-induced hemodynamic responses affected by internal/external events, such as changes of smell fatigue and attention, are not considered and therefore could result in misleading results. In this paper, owing to the stochastic characteristic of olfactory-induced responses, we adopted multivariate empirical mode decomposition (MEMD) to extract olfactory-induced hemodynamic responses in MRI blood-oxygen-level dependent (BOLD) signals. The MEMD is an efficient data-driven approach to extract nonlinear and non-stationary signals, which is an improved method to expand traditional empirical mode decomposition (EMD) from one channel to multi-channel processing. We applied MEMD to decompose time series of BOLD signals from each slice into multivariate intrinsic mode functions (IMF). The MEMD enables common features of different scales in an image slice to be arranged in distinct IMFs. Each IMF is an analytic, self-constructed, well-defined, and data-driven function with time-varying frequencies. Each IMF was examined by checking its correlation with the paradigm-generated template. The task-related IMFs were chosen to reconstruct olfactory-induced hemodynamic responses. The group analysis of MEMD-processed data showed olfactory-induced activations in the anterior cingulate cortex and middle frontal gyrus.
AB - Olfactory dysfunction is related to several clinical neurodegenerative diseases, such as Alzheimer's disease, multiple sclerosis, degenerative ataxias, Parkinson's disease, and so on. Owing to the individual difference in the sensory adaption of human smell function, the olfactory responses usually exhibit large inter-individual difference and change over time after repeated stimulations. The traditional analysis tools, such as statistical parametric mapping (SPM) in functional magnetic resonance imaging technique (fMRI) analysis, utilize the paradigm-based linear correlation and statistical techniques to discriminate the activation areas from background activities. However, these traditional approaches extract olfactory-induced responses using the stereotypic template or paradigm generated model. The olfactory-induced hemodynamic responses affected by internal/external events, such as changes of smell fatigue and attention, are not considered and therefore could result in misleading results. In this paper, owing to the stochastic characteristic of olfactory-induced responses, we adopted multivariate empirical mode decomposition (MEMD) to extract olfactory-induced hemodynamic responses in MRI blood-oxygen-level dependent (BOLD) signals. The MEMD is an efficient data-driven approach to extract nonlinear and non-stationary signals, which is an improved method to expand traditional empirical mode decomposition (EMD) from one channel to multi-channel processing. We applied MEMD to decompose time series of BOLD signals from each slice into multivariate intrinsic mode functions (IMF). The MEMD enables common features of different scales in an image slice to be arranged in distinct IMFs. Each IMF is an analytic, self-constructed, well-defined, and data-driven function with time-varying frequencies. Each IMF was examined by checking its correlation with the paradigm-generated template. The task-related IMFs were chosen to reconstruct olfactory-induced hemodynamic responses. The group analysis of MEMD-processed data showed olfactory-induced activations in the anterior cingulate cortex and middle frontal gyrus.
KW - functional magnetic resonance imaging
KW - Multivariate empirical mode decomposition
KW - olfactory
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U2 - 10.1109/ACCESS.2019.2893923
DO - 10.1109/ACCESS.2019.2893923
M3 - Article
AN - SCOPUS:85061827411
SN - 2169-3536
VL - 7
SP - 15375
EP - 15388
JO - IEEE Access
JF - IEEE Access
M1 - 8620252
ER -