Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

Lin, Shang Hua N.; Lin, Geng Hong; Tsai, Pei Jung; Hsu, Ai Ling; Lo, Men Tzung; Yang, Albert C.; Lin, Ching Po; Wu, Changwei W.

doi:10.1016/j.jneumeth.2015.10.009

Cited by 9 publications

(11 citation statements)

References 51 publications

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“…In contrast to previous fMRI literatures using traditional EMD-based approaches [30], [31], [54], EMD was applied to each voxel or predefined region-of-interests (ROI). Nevertheless, inter-voxel or inter-ROI variations in BOLD signals might cause discrepancies in IMFs extracted from neighboring voxels or ROIs, which results in difficulties in finding common features of induced hemodynamic responses.…”

Section: Discussionmentioning

confidence: 99%

“…In those studies using traditional EMD, feature filtering or ensemble averaging is usually required to extract common features from IMFs obtained from different voxels or different ROIs. For example, Lin et al retained only IMF 2∼6 for SPM processing [31] and left other IMFs as fMRI unrelated artifacts. McGonigle et al [30] determined common frequency features by finding the most powerful median frequency across IMFs obtained from all voxels.…”

Section: Discussionmentioning

confidence: 99%

“…It has been demonstrated as a powerful data-driven tool for extracting [30] applied EMD to each voxel and rearranged IMFs to extract temporal activities at similar scales. Lin et al [31] performed ensemble empirical mode decomposition (EEMD) [32] to fix mode mixing problem of traditional EMD and applied EEMD on each voxel to enhance sensitivity of BOLD signal in restingstate MRI. Nevertheless, the EMD and EEMD methods are data-driven approaches which act as self-organized dyadic filters to arrange a single signal channel into several subbands.…”

Section: Introductionmentioning

confidence: 99%

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A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI

et al. 2019

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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-oxygenlevel 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. INDEX TERMS Multivariate empirical mode decomposition, olfactory, functional magnetic resonance imaging.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI

et al. 2019

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“…It is indicated that, among different noise-removal methods (such as band-pass filtering and Independent component analysis), EMD based methods facilitate the noise removal from fMRI data. In EMD-based methods, IMFs with specific frequency bands are identified and removed from fMRI data to enhance the functional sensitivity of the data (Typically the first and second IMFs which have the highest frequency bands among all IMFs are considered as a noise) (Lin et al, 2016). However, removing the whole high-frequency data from fMRI time series is controversial, as smoothing the signals via low-pass filtering decreases the signal to noise ratio by smoothing the peaks and amplifying the noise (Brooks et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that applying EMD-based methods on fMRI data separate inherent brain oscillations and fundamental modes embedded in BOLD signal. Each of these oscillations occupies a unique frequency band and can be used to investigate the frequency characteristics in resting-state brain networks (McGonigle et al, 2010; Zheng et al, 2010; Niazy et al, 2011; Song et al, 2014, 2015; Qian et al, 2015; Lin et al, 2016; Cordes et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression

2019

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Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.

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Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI

Kung,

Li,

Hsu

et al. 2024

NeuroImage

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Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

Cited by 9 publications

References 51 publications

A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI

A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI

Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression

Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI

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