Low Frequency Oscillation Signal Research Articles

Identification of power grids low-frequency oscillations through a combined MEEMD-Prony method

Currently, the primary focus of power systems is to enhance precision and resilience against interference in identifying low-frequency oscillation modes. This research proposes a new method combined with ensemble empirical mode decomposition (MEEMD) to improve the sensitivity of traditional Prony to noise in parameter analysis of low-frequency oscillation signals. The method decomposes the measurement signal into intrinsic mode functions (IMF) using MEEMD, introduces permutation entropy to detect randomness, and reconstructs the remaining IMF components. The reconstructed signal was analyzed using the Prony method to extract the characteristic parameters associated with the low-frequency oscillation frequency. Simulations using numerical signals and the EPRI-36 node system confirm the method effectively suppresses modal mixing, mitigates noise interference, and accurately identifies low-frequency oscillation parameters. This approach offers advantages over traditional methods, including enhanced resistance to noise and increased accuracy.

Low frequency oscillation modal identification based on blind source separation considering uncertainty of modal parameters

In this paper, a novel method is proposed for identifying modes of low frequency oscillation (LFO), which uses the ambient signals and considers uncertainty. In this method, blind source separation (BSS) is used to obtain a number of single-mode signals from the ambient signals. Then, based on random decrement technique (RDT) and Hilbert transform (HT), the free decay signals are extracted from the single-mode signals for modal parameter estimation. Besides, taking into account the random factors in practice, the proposed method involves the bootstrap method to evaluate the uncertainty of the estimations of the modal parameters. The performance of the method is studied with simulation signals generated by transfer functions, IEEE WSCC three-machine nine-bus system and IEEE New England 39-bus system, respectively. The results indicate that the proposed method can effectively estimate LFO parameters with high accuracy and the uncertainty of estimation can be reliably calculated. • Realizes LFO modal parameters estimation by using ambient signals. • Blind source separation model for low-frequency oscillation LFO signal separation. • Considers the uncertainty of modal parameters and has a good noise robustness. • Outperforms NExT-ERA when implemented in short data window.

Modal Identification of Low-Frequency Oscillations in Power Systems Based on Improved Variational Modal Decomposition and Sparse Time-Domain Method

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated.

Application of Hilbert Spectrum based on SWT to Low-Frequency Oscillation Analysis

Low-frequency oscillation (LFO) appears in power systems with fast excitation of a unit or large-scale connections. An LFO signal exhibits typical nonstationary and time-varying characteristics. The paper proposed an LFO analyzing technique through the Hilbert spectrum of stationary wavelet transform (SWT). The SWT-based Hilbert spectrum contains time-varying and frequency information of LFO. The analytic framework is introduced, SWT is formulated to decompose an LFO signal into the sum of several coefficient components, the instantaneous parameters of each coefficient are then individually calculated, the complete time-frequency distribution is combined, and finally, the time-varying oscillations characteristics parameters are extracted. The simulation shows that the proposed method effectively reveals time-frequency features of the LFO signal.

A Novel Finding in Cerebral Blood Flow in Patients with Sickle Cell Disease Using Bold Functional MRI

Background: Sickle cell disease (SCD) is an inherited blood disorder which is associated with acute and chronic pain, as well as other vascular complications such as arterial steno-occlusion, stroke, and silent cerebral infarction. Alterations of cerebral blood flow have been found to be associated with different painful stages in SCD, thus cerebral blood flow might be correlated with the disease severity determined by varied pain conditions in SCD. Blood oxygen level dependent (BOLD) imaging is a blood-related contrast technology in functional magnetic resonance imaging (fMRI). We have previously demonstrated the feasibility of using low frequency oscillation (LFO) in BOLD fMRI to track the hemodynamic features of the blood flow with maximum cross-correlation coefficient (MCCC) and the Delay Time (i.e., blood arrival time at different brain regions) (Tong et al., JCBFM. 2017). Positive MCCC value was found between the LFO BOLD signals extracted from the global mean (GMean, averaged signal across the whole brain) and the superior sagittal sinus (SSS) in our previous findings (Tong et al., JCBFM. 2018). In this study, we aim to use LFO BOLD signals to explore the cerebral hemodynamic features with MCCCs and their associations with hematological and disease severity related outcomes in patients with SCD. Methods: Ten subjects with SCD aged 14-43 years old (5 HbSS, 3 HbSC, 1 HbSB+, 1 HbB0) and 10 age- and sex-matched controls from a publicly available dataset Human Connectome Project (HCP) (Essen et al., Neuroimage, 2013) were included for data analysis. T1-weighted and resting-state BOLD fMRI (repetition time (TR)=1.05 sec) were collected using a 3T SIEMENS (Prisma) scanner. A BOLD fMRI dataset with TR of 0.72 sec from healthy subjects was used as control. All the BOLD fMRI data were preprocessed using FSL to correct the motion artifact. The SSS region was identified in the T1-weighted image using FSL. The corresponding SSS mask was then applied in the BOLD fMRI image to extract the BOLD fMRI signals in the SSS region. The GMean BOLD signal was averaged across the whole brain. BOLD fMRI Signals were demeaned and band-pass filtered (0.01-0.1 Hz) to acquire the LFO signals. Cross-correlation between the SSS-LFO signal and GMean-LFO signal was performed to calculate the MCCC and their corresponding Delay Time. Correlations between the MCCCs of the two LFO signals (from SSS and GMean) and each hematological index were further performed. Results: Consistent with the healthy control in our previous findings, half of the patients (3 HbSC, 1 HbSB+, and 1 HbSS) presented positive MCCC values. However, interestingly, negative MCCC values were found from another half of the patients (4 HbSS and 1 HbB0, Figure 1, p < 0.05 for all subjects). Notably, all patients with HbSC presented positive MCCC values that are similar to controls whereas 4 out 5 patients with HbSS (80%) were found to have negative MCCC values. More importantly, higher percentages of HbS and lower total Hgb (HbS(%): r=-0.73, p=0.03; Hgb(GM/dL): r=0.69, p=0.03) were found to be significantly associated with the negative MCCC values. Conclusions: We found a never-before-seen negative MCCC signature between the BOLD fMRI signals in the SSS and the GMean in a subset of patients with SCD compared with healthy controls. This might indicate a significant change in cerebral blood flow in a subset of patients with SS disease compared with SC and controls. Importantly, we further observed that this never-before-seen negative MCCC signature is highly associated with the percentage of the HbS and total Hgb that are known to be correlated with disease severity. This might be due to the microvascular shunting (Juttukonda et al., JCBFM, 2019) that exists in patients who have high percentage of HbS. This study is the first to associate BOLD fMRI derived hemodynamic metrics with disease severity related outcomes in SCD. This never-before-seen anti-correlation MCCC signature may have tremendous promise as a novel biomarker for SCD severity and differentiate pain characteristics in different sickle cell phenotypes, and further investigation in larger number of subjects is needed to understand variations between SCD phenotypes and its relationship to clinical outcomes. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Study on inversion method of wall erosion information of on-orbit Hall thruster based on low-frequency oscillation signals and neural networks

Hall thrusters function as power plants on spacecraft, and its development is crucial for the aerospace industry. The wall erosion of the on-orbit Hall thruster cannot be measured by the control center through ground measurement, but can obtain the discharge current low-frequency oscillation data. Therefore, this study proposes an inversion method to obtain the wall erosion information based on low-frequency oscillation signals and neural networks. Firstly, we use an improved one-dimensional quasi-neutral dynamic fluid mathematical model to build a low-frequency oscillation simulation platform which obtains the corresponding data by varying the cross-sectional area. Secondly, a nonlinear neural network model is established based on the obtained low-frequency oscillation data to invert the wall erosion information. The training function, transfer function, number of hidden layer nodes, and other parameters affecting the results are analyzed and the best model parameters are obtained. The Elman neural network is established and compared with the BP neural network and RBF neural network. The training results of the Elman neural network algorithm present small and stable errors, and the results of multiple predictions remain consistent. The root means square error, average absolute error, and average absolute percentage are 0.0084, 0.0637, and 0.045%, respectively.

Cerebrovascular Dysregulation in Patients with Glioma Assessed with Time-shifted BOLD fMRI.

Background Microscopic vascular events, such as neovascularization and neurovascular uncoupling, are common in cerebral glioma. Mapping the cerebrovascular network remodeling at the macroscopic level may provide an alternative approach to assess hemodynamic dysregulation in patients with glioma. Purpose To investigate cerebrovascular dynamics and their relevance to tumor aggressiveness by using time-shift analysis (TSA) of the systemic low-frequency oscillation (sLFO) of the resting-state blood oxygenation level-dependent signal and a decision tree model. Materials and Methods In this retrospective study, 96 patients with histologically confirmed cerebral glioma were consecutively included (March 2012 to February 2017). TSA was performed to quantify the temporal properties of sLFO signals. Alteration in the time-shift properties was assessed in the tumor region and the contralesional hemisphere relative to the brains of healthy controls by using the Mann-Whitney U test. A decision tree model based on time-shift features was developed to predict the World Health Organization (WHO) glioma grade. Results A total of 88 patients with glioma (WHO grade II, 45; grade III, 21; grade IV, 22; mean age, 42 years; age range, 20-73 years; 51 men) and 40 healthy individuals from the 1000 Functional Connectomes Project (mean age, 32 years; age range, 24-49 years; 19 men) were included. The sLFO of the brain tissues was characterized by increased time shift in the tumor region and enhanced correlation with the global reference signal in the contralesional hemisphere compared with healthy brains. The proportion of tumor voxels with negative correlation to the reference signal significantly increased with the glioma malignancy grade. The decision tree model achieved an accuracy of 91% (80 of 88 patients) in predicting the glioma malignancy grade at the individual level (P = .004) based on the time-shift features. Conclusion Gliomas induced grade-specific cerebrovascular dysregulation in the entire brain, with altered time-shift features of systemic low-frequency oscillation signals. © RSNA, 2022 Online supplemental material is available for this article.

Experimental study of the evolution of water-entry cavity bubbles behind a hydrophobic sphere

This paper describes an experimental investigation of the cavity evolution and shedding wake behind a hydrophobic sphere during the water-entry process. Two distinct shedding phenomena are confirmed by varying the impact velocity and sphere size: regular air-bubble shedding and unstable air-cloud shedding. Both of these modes are highly dependent on the Weber and Bond numbers. Under the air-bubble shedding mode, approximately periodic big bubble shedding and low-frequency oscillation signals are observed. The relationship between big bubble shedding events and the corresponding acoustic signals is derived, and an empirical method for predicting the shedding period is proposed. The in-phase relationship between small bubble shedding and cavity rippling is confirmed, and we refer to the cavity shedding phenomenon as “acoustic” shedding. Unlike the observations of air-bubble shedding, the air-cloud shedding mode produces a group of disordered small bubbles from the rear of the cavity. Moreover, the cavity seal type has a significant effect on the cavity shedding mode. A deep seal always promotes the onset of air-cloud shedding, whereas surface seals with relatively low Bond numbers result in the air-bubble shedding mode. A surface seal suppresses resonance in the cavity volume. By observing the cavity motion, we find that air-cloud shedding is always accompanied by severe cavity resonance and a rapid decrease in cavity length. Under the air-bubble shedding mode, the cavity motion exhibits relatively weak oscillations.

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

One of the main reasons that affecting the stability of power systems is low frequency oscillation (LFO). The existence of noise influences the accuracy of LFO mode identification extracted from wide-area measurement system (WAMS). The wavelet threshold de-noising is widely used in signal processing. In this paper, wavelet soft threshold is illustrated to attenuate the noise of LFO signal, the optimal wavelet basis and decomposition level for de-noising LFO signal with noise are obtained and verified by experiments. Following the signal de-noising, an improved Matrix Pencil (MP) algorithm is used for mode identification of LFO. This improvement particularly lies in the ratio of adjacent singular entropy increment difference (RASEID) designed as an adaptive order determination method in the MP algorithm proposed in the paper. RASEID not only makes the MP algorithm adaptive, but also enhances the stability of the order determination in the mode identification process. The proposed method ensures the accuracy of mode identification with lower sensitivity to noise interference. Finally, the validity of the proposed method is verified by three cases studies. The first study is on the analysis of synthetic signal typically performed in many literatures. The second study is to identify the mode of active power LFO signal which is generated by IEEE four-generator and two-area system given with disturbance on RT-LAB experimental platform. The third study is the oscillation analysis of the actual LFO data in the North American power grid. The results validate the feasibility of the proposed method for mode identification of noisy LFO.

Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method

With increasing complexity of the power system, and the gradually expanding scales, the probability of low-frequency oscillation (LFO) is increasing, and its performance is more diverse. New oscillation types appear, such as close modes oscillation and intermittent modes oscillation. Due to the frequency resolution limitation, it is difficult to identify model parameters of the new oscillation types accurately by the classical methods. Based on this, this article proposes an improved analytical modal decomposition (IAMD) method. Analytical modal decomposition (AMD) has the advantage in processing low-frequency signals, narrow-band signals, and intermittent signals. However, when we use AMD method to process the above signals, the cut-off frequency is a fixed bisection frequency. This frequency cannot adjust adaptively according to the signals. This article proposes to utilize chaotic particle swarm optimization (CPSO) algorithm and auxiliary signal to obtain the optimal cut-off frequency of AMD, quickly and accurately. The optimal decomposed signals are also obtained at the same time. Finally, the Hilbert Transform (HT) is used to identify the parameters of the LFO signals. Through the analysis of simulated and measured signals, it is verified that the IAMD method is suitable not only for online analysis and monitoring of typical LFO signals, but also for new oscillation types such as close modes oscillation and intermittent modes oscillation.

Parameter Identification of Low Frequency Oscillation Based on Fourier Synchrosqueezing Transform

In this paper, the Fourier-based synchrosqueezing transform (FSST) method is introduced into the power system to realize low frequency oscillation identification. Firstly, the modal components of low frequency oscillation signal are separated by FSST, and calculating the instantaneous parameters including magnitude, frequency and phase of every mode component with Hilbert transform. Then, calculating the damping ration of every modal component based on above parameters and derived the formulas. Thus, it realized effective identification of modal parameters of low frequency oscillation signal. The experimental results of simulated data and 16-generator system demonstrate the validity and correctness of the FSST method. Comparing with the classical Prony method shows that the FSST method is better in the identification accuracy when the low frequency oscillation signal is polluted by noise. Finally, the effectiveness of the FSST in Actual power grid is verified based on the real data from Sichuan power grid.

An Approach to Suppress Low Frequency Oscillation in the Traction Network of High-Speed Railway Using Passivity-Based Control

The traction blockade in depots of multiple electric multiple units (EMUs) is generally considered to be caused by the voltage low frequency oscillation (LFO) of the high-speed railway traction network. As a possible solution, a strategy using single-phase EMUs rectifiers with passivity-based control (PBC) is proposed in this paper. First, the mathematical model of the four-quadrant pulse width modulation rectifier is constructed based on the port controlled Hamiltonian with dissipation. Next, with the insertion of damping, the new energy function can be minimized at the equilibrium point of the system, which enables the derivation of the rectifier control law based on the interconnection and damping assignments PBC. Comparison of simulation results with those of the traditional proportional integral (PI) scheme, autodisturbance rejection control and multivariable control verifies that the proposed PBC controller has better dynamic and static performance. Among all strategies considered, the resulting line current has the least total harmonic distortion, and the dc-link voltage of single-phase EMUs rectifier has the least oscillation and the shortest adjustment time, when using the PBC. Meanwhile, the simulations of multi-EMUs accessed a traction network show that the proposed method significantly suppresses the voltage LFO of the traction network. Finally, the LFO signal modal analysis is performed using the fast Fourier transform and the estimating signal parameters via the rotational invariance techniques. It indicates that PBC can suppress the symmetrical frequency components and effectively reduce the third, fifth, and other harmonic components compared with the PI control in EMUs.

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.

Stability Research of High-Speed Railway EMUs and Traction Network Cascade System Considering Impedance Matching

Due to the low-frequency oscillation (LFO) between traction network and electric multiple units (EMUs) of high-speed railway, many traction blockades occur in different EMU depots. Aiming at this problem, the mathematical model and small-signal analysis model of EMUs-traction network cascade system (ETNCS) are built in this paper. The system stability mainly affected by the electrical coupling between source and load module is analyzed and discussed in detail. Considering the autotransformer leakage reactance, traction network impedance, and distributed capacitance, the output source module resistance of ETNCS is calculated based on the generalized symmetrical component method. Using the generalized Nyquist criterion and Bode diagram, the ETNCS's stability is analyzed and the quantitative indicators of system impedance matching are given. Then, the simulation model of full ETNCS is built in this paper, which can reproduce the LFO phenomenon. In addition, the theoretical analysis results and the feasibility of inhibition for LFO are verified based on MATLAB/simulink simulation. Finally, the LFO signal modal analysis is implemented by using Prony and estimating signal parameters via rotational invariance techniques (ESPRIT) methods. The conclusions show that the proposed stability and modal analysis methods could be used not only to analyze the LFO more clearly, but also to provide the technical reference for the stable operation of EMUs.

A Time-Frequency Analysis Method for Low Frequency Oscillation Signals Using Resonance-Based Sparse Signal Decomposition and a Frequency Slice Wavelet Transform

To more completely extract useful features from low frequency oscillation (LFO) signals, a time-frequency analysis method using resonance-based sparse signal decomposition (RSSD) and a frequency slice wavelet transform (FSWT) is proposed. FSWT can cut time-frequency areas freely, so that any band component feature can be extracted. It can analyze multiple aspects of the LFO signal, including determination of dominant mode, mode seperation and extraction, and 3D map expression. Combined with the Hilbert transform,the parameters of the LFO mode components can be identified. Furthermore, the noise in the LFO signal could reduce the frequency resolution of FSWT analysis, which may impact the accuracy of oscillation mode identification. Complex signals can be separated by predictable Q-factors using RSSD. The RSSD method can do well in LFO signal denoising. Firstly, the LFO signal is decomposed into a high-resonance component, a low-resonance component and a residual by RSSD. The LFO signal is the output of an underdamped system with high quality factor and high-resonance property at a specific frequency. The high-resonance component is the denoised LFO signal, and the residual contains most of the noise. Secondly, the high-resonance component is decomposed by FSWT and the full band of its time-frequency distribution are obtained. The 3D map expression and dominant mode of the LFO can be obtained. After that, due to its energy distribution, frequency slices are chosen to get accurate analysis of time-frequency features. Through reconstructing signals in characteristic frequency slices, separation and extraction of the LFO mode components is realized. Thirdly, high-accuracy detection for modal parameter identification is achieved by the Hilbert transform. Simulation and application examples prove the effectiveness of the proposed method.

Stimulus-dependent modulation of spontaneous low-frequency oscillations in the rat visual cortex.

Research on spontaneous low-frequency oscillations is important to reveal underlying regulatory mechanisms in the brain. The mechanism for the stimulus modulation of low-frequency oscillations is not known. Here, we used the intrinsic optical imaging technique to examine stimulus-modulated low-frequency oscillation signals in the rat visual cortex. The stimulation was presented monocularly as a flashing light with different frequencies and intensities. The phases of low-frequency oscillations in different regions tended to be synchronized and the rhythms typically accelerated within a 30-s period after stimulation. These phenomena were confined to visual stimuli with specific flashing frequencies (12.5-17.5 Hz) and intensities (5-10 mA). The acceleration and synchronization induced by the flashing frequency were more marked than those induced by the intensity. These results show that spontaneous low-frequency oscillations can be modulated by parameter-dependent flashing lights and indicate the potential utility of the visual stimulus paradigm in exploring the origin and function of low-frequency oscillations.

New Theories and Methods of Low Frequency Oscillation Analysis under Smart Grid Framework in Electrical Engineering

The low-frequency oscillation is one of the most important reasons that affects the stability of the power system. Under the background of building a unified and strong smart grid, the scale of the power system will continue to expand, the interconnection of systems will be more closely, the structure will be more complex, these factors will further increase the difficulty on the low-frequency oscillation control. This paper summarizes the methods to analysis low frequency oscillation signal, the way that suppresses the low frequency oscillation in electrical engineering. Further, based on smart grid framework, a on-line visualization technology and a self-recovery regulation strategy are proposed to damp low-frequency oscillation.

A box model of the Arctic natural variability

We consider a box model of the Arctic system to examine its natural variability pertaining to the decadal Arctic Oscillation (AO) and the multidecadal Low-Frequency Oscillation (LFO). We distinguish the hierarchical order of the winter over the summer open-areas with only the former perturbing the sea-level pressure to effect coupled balances. From such balances, we discern two feedback loops on the winter open-area: a positive ice-flux feedback that elevates its overall variance and a negative buoyancy feedback that suppresses its low-frequency variance to render a decadal AO peak when subjected to white atmospheric noise. This negative buoyancy feedback may also reproduce observed phasing among LFO signals forced by the AMV (Atlantic Multidecadal Variability), thus resolving some outstanding questions. For the summer open-area, its variance is induced mainly by the winter forcings and insensitive to the base state. Its decadal signal merely reflects the preconditioning winter open-area, but its LFO variance is induced additionally and in comparable measure by the winter SAT (surface air temperature) through the latter’s effect on the melt duration and the first-year ice thickness. As such, the summer open-area signal is dominantly multidecadal, which moreover is several times its winter counterpart, consistent with the observed disparity. Although the model is extremely crude, its explicit solution allows quantitative comparison with observations and the generally positive outcome suggests that the model has isolated the essential physics of the Arctic natural variability of our concern.

Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain

Low frequency oscillations (LFOs), characterized by frequencies in the range 0.01–0.1 Hz are commonly observed in blood-related brain functional measurements such as near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI). While their physiological origin and implications are not fully understood, these signals are believed to reflect some types of neuronal signaling, systemic hemodynamics, and/or cerebral vascular auto-regulation processes. Here, we examine a new method of integrated processing of concurrent NIRS and fMRI data collected on six human subjects during a whole brain resting state acquisition. The method combines the high spatial resolution offered by fMRI (~ 3 mm) and the high temporal resolution offered by NIRS (~ 80 ms) to allow for the quantitative assessment of temporal relationships between the LFOs observed at different spatial locations in fMRI data. This temporal relationship allowed us to infer that the origin of a large proportion of the LFOs is independent of the baseline neural activity. The spatio-temporal pattern of LFOs detected by NIRS and fMRI evolves temporally through the brain in a way that resembles cerebral blood flow dynamics. Our results suggest that a major component of the LFOs arise from fluctuations in the blood flow and hemoglobin oxygenation at a global circulatory system level.

Spatio-temporal analysis of stimuli-modulated spontaneous low frequency oscillations

In this paper, the spatio-temporal architecture of the stimulation-modulated spontaneous low frequency oscillation (LFO) in the SD rat’s somatosensory cortex is studied by optical imaging (OI) technology. After the electrical stimulation, it is observed that the phases of the LFO signals are changed, the amplitudes are increased, and most importantly, the signals in the bilateral somatosensory cortex tend to be synchronized. Based on these phenomena, the origin of the LFO signals is discussed. It is argued that the arteriole vasomotion may be the major contribution to the LFO signals under green illumination (546±10 nm). The phase relationship among the LFO signals of arteries, veins and cortex has also been studied. It is found that there are phase differences between the LFO signal of veins and that of cortex under red illumination (605±10 nm), the signal of cortex leads that of veins by 0.6–1.0 s, while under green illumination, no obvious differences are observed and the reason may be that the mechanism of the LFO signals of cortexes and vessels are different.