A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI
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 response...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8620252/ |
| _version_ | 1828977223493746688 |
|---|---|
| author | Kuo-Wei Wang Chia-Yuen Chen Hsiao-Huang Chang Chuan-Chih Hsu Gong-Yau Lan Hao-Teng Hsu Kuo-Kai Shyu Wing P. Chan Po-Lei Lee |
| author_facet | Kuo-Wei Wang Chia-Yuen Chen Hsiao-Huang Chang Chuan-Chih Hsu Gong-Yau Lan Hao-Teng Hsu Kuo-Kai Shyu Wing P. Chan Po-Lei Lee |
| author_sort | Kuo-Wei Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T14:53:33Z |
| format | Article |
| id | doaj.art-bfadab8eb5b9477c8695565bee26ef82 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:53:33Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bfadab8eb5b9477c8695565bee26ef822022-12-21T22:57:04ZengIEEEIEEE Access2169-35362019-01-017153751538810.1109/ACCESS.2019.28939238620252A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRIKuo-Wei Wang0Chia-Yuen Chen1Hsiao-Huang Chang2Chuan-Chih Hsu3Gong-Yau Lan4Hao-Teng Hsu5Kuo-Kai Shyu6Wing P. Chan7Po-Lei Lee8https://orcid.org/0000-0002-3590-4507Department of Medical Imaging, Landseed Hospital, Taoyuan, TaiwanDepartment of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanDivision of Cardiovascular Surgery, Taipei Veterans General Hospital, Taipei, TaiwanDepartment of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanDepartment of Medical Imaging, Taipei Medical University Hospital, Taipei, TaiwanDepartment of Electrical Engineering, National Central University, Taoyuan, TaiwanDepartment of Electrical Engineering, National Central University, Taoyuan, TaiwanDepartment of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanDepartment of Electrical Engineering, National Central University, Taoyuan, TaiwanOlfactory 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.https://ieeexplore.ieee.org/document/8620252/Multivariate empirical mode decompositionolfactoryfunctional magnetic resonance imaging |
| spellingShingle | Kuo-Wei Wang Chia-Yuen Chen Hsiao-Huang Chang Chuan-Chih Hsu Gong-Yau Lan Hao-Teng Hsu Kuo-Kai Shyu Wing P. Chan Po-Lei Lee A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI IEEE Access Multivariate empirical mode decomposition olfactory functional magnetic resonance imaging |
| title | A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI |
| title_full | A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI |
| title_fullStr | A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI |
| title_full_unstemmed | A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI |
| title_short | A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI |
| title_sort | multivariate empirical mode decomposition x2013 based data driven approach for extracting task dependent hemodynamic responses in olfactory induced fmri |
| topic | Multivariate empirical mode decomposition olfactory functional magnetic resonance imaging |
| url | https://ieeexplore.ieee.org/document/8620252/ |
| work_keys_str_mv | AT kuoweiwang amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT chiayuenchen amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT hsiaohuangchang amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT chuanchihhsu amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT gongyaulan amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT haotenghsu amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT kuokaishyu amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT wingpchan amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT poleilee amultivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT kuoweiwang multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT chiayuenchen multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT hsiaohuangchang multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT chuanchihhsu multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT gongyaulan multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT haotenghsu multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT kuokaishyu multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT wingpchan multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri AT poleilee multivariateempiricalmodedecompositionx2013baseddatadrivenapproachforextractingtaskdependenthemodynamicresponsesinolfactoryinducedfmri |