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Kung YC, Li CW, Hsu AL, Liu CY, Wu CW, Chang WC, Lin CP. Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI. Neuroimage 2024; 289:120535. [PMID: 38342188 DOI: 10.1016/j.neuroimage.2024.120535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/23/2024] [Accepted: 02/08/2024] [Indexed: 02/13/2024] Open
Abstract
Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15-0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.
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Affiliation(s)
- Yi-Chia Kung
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Ai-Ling Hsu
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Chi-Yun Liu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan; Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
| | - Wei-Chou Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
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Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors. SENSORS 2022; 22:s22145249. [PMID: 35890928 PMCID: PMC9321517 DOI: 10.3390/s22145249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 01/25/2023]
Abstract
Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.
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Hsu AL, Li CW, Qin P, Lo MT, Wu CW. Localizing Spectral Interactions in the Resting State Network Using the Hilbert-Huang Transform. Brain Sci 2022; 12:140. [PMID: 35203903 PMCID: PMC8870154 DOI: 10.3390/brainsci12020140] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 11/16/2022] Open
Abstract
Brain synchronizations are orchestrated from neuronal oscillations through frequency interactions, such as the alpha rhythm during relaxation. Nevertheless, how the intrinsic interaction forges functional integrity across brain segregations remains elusive, thereby motivating recent studies to localize frequency interactions of resting-state fMRI (rs-fMRI). To this point, we aim to unveil the fMRI-based spectral interactions using the time-frequency (TF) analysis; however, Fourier-based TF analyses impose restrictions on revealing frequency interactions given the limited time points in fMRI signals. Instead of using the Fourier-based wavelet analysis to identify the fMRI frequency of interests, we employed the Hilbert-Huang transform (HHT) for probing the specific frequency contribution to the functional integration, called ensemble spectral interaction (ESI). By simulating data with time-variant frequency changes, we demonstrated the Hilbert TF maps with high spectro-temporal resolution and full accessibility in comparison with the wavelet TF maps. By detecting amplitude-to-amplitude frequency couplings (AAC) across brain regions, we elucidated the ESI disparity between the eye-closed (EC) and eye-open (EO) conditions in rs-fMRI. In the visual network, the strength of the spectral interaction within 0.03-0.04 Hz was amplified in EC compared with that in EO condition, whereas a canonical connectivity analysis did not present differences between conditions. Collectively, leveraging from the instantaneous frequency of HHT, we firstly addressed the ESI technique to map the fMRI-based functional connectivity in a brand-new AAC perspective. The ESI possesses potential in elucidating the functional connectivity at specific frequency bins, thereby providing additional diagnostic merits for future clinical neuroscience.
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Affiliation(s)
- Ai-Ling Hsu
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan 33305, Taiwan;
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan;
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University, Ministry of Education), Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China;
- Pazhou Lab, Guangzhou 510335, China
- School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 32049, Taiwan;
| | - Changwei W. Wu
- Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, Taipei 11031, Taiwan
- Brain and Consciousness Research Center, Shuang Ho Hospital-Taipei Medical University, New Taipei 23561, Taiwan
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Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors. ENERGIES 2020. [DOI: 10.3390/en13133471] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In response to climate change and environmental issues, many countries have gradually optimized carbon market management and improved the carbon market trading mechanism. Carbon price prediction plays a pivotal role in promoting carbon market management when investors are guided by prediction to conduct rational carbon trading. A novel carbon price prediction methodology is constructed based on ensemble empirical mode decomposition, improved bat algorithm, and extreme learning machine (EEMD-IBA-ELM) in this study. Firstly, the carbon price is decomposed into multiple regular intrinsic mode function (IMF) components by the ensemble empirical mode decomposition, and partial autocorrelation analysis (PACF) is used to find IMF historical data affecting the current value of IMF. Secondly, the improved bat algorithm (IBA) is used to heighten extreme learning machine (ELM) while adaptive parameters are obtained. Finally, EEMD-IBA-ELM was established to predict carbon price. Simultaneously, energy price fluctuation is introduced into the carbon price prediction model. As a consequence, EEMD-IBA-ELM carbon price prediction ability is further improved. In the empirical analysis, the historical carbon price of European Climate Exchange (ECX) and Korea Exchange (KRX) markets are used to examine the effectiveness and stability of the model. Errors of carbon price prediction in ECX and KRX is 2.1982% and 1.1762%, respectively. The results show that the EEMD-IBA-ELM carbon price prediction model can accurately predict carbon price when prediction effect shows strong stability. Furthermore, carbon price prediction accurateness was significantly enhanced by using energy price fluctuation as an influencing factor of carbon price prediction.
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Moradi N, Dousty M, Sotero RC. Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression. Front Neurosci 2019; 13:736. [PMID: 31396032 PMCID: PMC6664052 DOI: 10.3389/fnins.2019.00736] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 07/02/2019] [Indexed: 12/15/2022] Open
Abstract
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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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Computational Neurophysics Lab, Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Mehdy Dousty
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,KITE, Toronto Rehab, University Health Network, Toronto, ON, Canada
| | - Roberto C Sotero
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Computational Neurophysics Lab, Department of Radiology, University of Calgary, Calgary, AB, Canada
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