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MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes  [PDF]
Sergey M. Plis,Vince D. Calhoun,Michael P. Weisend,Tom Eichele,Terran Lane
Frontiers in Neuroinformatics , 2010, DOI: 10.3389/fninf.2010.00114
Abstract: The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system’s behavior by stabilizing the differential equations system and by requiring fewer computational resources.
Characteristics of fMRI BOLD signal and its neurophysiological mechanism
Zhao Xiaohu,Wu Yigen,Guo Shengli,
Zhao Xiaohu
,Wu Yigen and Guo Shengli

自然科学进展 , 2007,
Abstract: The functional magnetic resonance imaging (fMRI) based on blood oxygen level dependent (BOLD) contrast has emerged as one of the most potent noninvasive tools for mapping brain function and has been widely used to explore physiological, pathological changes and mental activity in the brain. Exploring the nature and property of BOLD signal has recently attracted more attentions. Despite that great progress has been made in investigation of the characteristics and neurophysiological basis, the exact nature of BOLD signal remains unclear. In this paper we discuss the characteristics of BOLD signals, the nonlinear BOLD response to external stimuli and the relation between BOLD signals and neural electrophysiological recordings. Furthermore, we develop our new opinions regarding nonlinear BOLD response and make some perspectives on future study.
Reliable and Efficient Approach of BOLD Signal with Dual Kalman Filtering  [PDF]
Cong Liu,Zhenghui Hu
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/961967
Abstract: By introducing the conflicting effects of dynamic changes in blood flow, volume, and blood oxygenation, Balloon model provides a biomechanical compelling interpretation of the BOLD signal. In order to obtain optimal estimates for both the states and parameters involved in this model, a joint filtering (estimate) method has been widely used. However, it is flawed in several aspects (i) Correlation or interaction between the states and parameters is incorporated despite its nonexistence in biophysical reality. (ii) A joint representation for states and parameters necessarily means the large dimension of state space and will in turn lead to huge numerical cost in implementation. Given this knowledge, a dual filtering approach is proposed and demonstrated in this paper as a highly competent alternative, which can not only provide more reliable estimates, but also in a more efficient way. The two approaches in our discussion will be based on unscented Kalman filter, which has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. 1. Introduction A thorough understanding of the dynamic relationship between cerebral blood flow (CBF), cerebral blood volume (CBV), and the blood oxygenation level dependent (BOLD) signal is essential for the physiological interpretation of fMRI activation data. The Balloon model described by Buxton et al. (1998) [1] is the first biomechanical plausible model to expound this relationship: increasing the flow (or perfusion rate) generally leads to dilution of venous deoxyhemoglobin (dHb), reducing the tendency of the blood to attenuate the magnetic resonance signal. The resultant increase in signal intensity is referred to as the BOLD response [2]. It is by extending this model to cover the dynamic coupling between CBF and synaptic activity, more sophisticated physiological realities are incorporated, for example, oxygen metabolism dynamics, both intra- and extravascular signal [3, 4], and more intricate models obtained. The Balloon model is an input-state-output model with three state variables: blood flow, and volume, deoxyhemoglobin content and several biologically reasonable parameters. The problems of state estimation and parameter estimation (sometimes referred to as system identification or machine learning) associated with Balloon model are often formulated in a state-space representation, where Balloon model serves as a set of continuous-time system equations to describe the hemodynamic process. The equations are nonlinear, corresponding to the fact that Balloon model is one of
Correcting the Variations of BOLD Signal Due to Susceptibility Gradients and Its Application  [PDF]
Lifang Zhao, Lianjun Zhang, Gang Liu
Journal of Computer and Communications (JCC) , 2019, DOI: 10.4236/jcc.2019.711002
Abstract: Blood oxygenation level dependence signal (BOLD) for functional magnetic resonance imaging (FMRI), is the use of blood magnetization depending on the oxygenation state of hemoglobin. Susceptibility gradient can shift and skew k-space trajectories and it leads to echo time shift and BOLD sensitivity change. FMRI can be used to detect the signal, the change of the susceptibility gradient of the signal and the distortion of k space trajectory, resulting in echo time shift and BOLD sensitivity change. Using the percentage signal change (PSC) and calibration function, it can be applied to many different fields, such as age-related research. In this paper, the performance of BOLD signal change correction based on sensitivity gradient was verified by real data group calculation, and methods of further improving the calculation speed were analyzed. This paper also analyzed the performance of correcting the variations of BOLD Signal due to susceptibility gradients with real data set, and identified the computational issues that need to be improved for further research.
An Overcomplete Signal Basis Approach to Nonlinear Time-Tone Analysis with Application to Audio and Speech Processing  [cached]
Reilly Richard B
EURASIP Journal on Advances in Signal Processing , 2006,
Abstract: Although a beating tone and the two pure tones which give rise to it are linearly dependent, the ear considers them to be independent as tone sensations. A linear time-frequency representation of acoustic data is unable to model these phenomena. A time-tone sensation approach is proposed for inclusion within audio analysis systems. The proposed approach extends linear time-frequency analysis of acoustic data, by accommodating the nonlinear phenomenon of beats. The method replaces the one-dimensional tonotopic axis of linear time-frequency analysis with a two-dimensional tonotopic plane, in which one direction corresponds to tone, and the other to its frequency of modulation. Some applications to audio prostheses are discussed. The proposed method relies on an intuitive criterion of optimal representation which can be applied to any overcomplete signal basis, allowing for many signal processing applications.
Resting State Brain Function Analysis Using Concurrent BOLD in ASL Perfusion fMRI  [PDF]
Senhua Zhu, Zhuo Fang, Siyuan Hu, Ze Wang, Hengyi Rao
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0065884
Abstract: The past decade has seen astounding discoveries about resting-state brain activity patterns in normal brain as well as their alterations in brain diseases. While the vast majority of resting-state studies are based on the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI), arterial spin labeling (ASL) perfusion fMRI can simultaneously capture BOLD and cerebral blood flow (CBF) signals, providing a unique opportunity for assessing resting brain functions with concurrent BOLD (ccBOLD) and CBF signals. Before taking that benefit, it is necessary to validate the utility of ccBOLD signal for resting-state analysis using conventional BOLD (cvBOLD) signal acquired without ASL modulations. To address this technical issue, resting cvBOLD and ASL perfusion MRI were acquired from a large cohort (n = 89) of healthy subjects. Four widely used resting-state brain function analyses were conducted and compared between the two types of BOLD signal, including the posterior cingulate cortex (PCC) seed-based functional connectivity (FC) analysis, independent component analysis (ICA), analysis of amplitude of low frequency fluctuation (ALFF), and analysis of regional homogeneity (ReHo). Consistent default mode network (DMN) as well as other resting-state networks (RSNs) were observed from cvBOLD and ccBOLD using PCC-FC analysis and ICA. ALFF from both modalities were the same for most of brain regions but were different in peripheral regions suffering from the susceptibility gradients induced signal drop. ReHo showed difference in many brain regions, likely reflecting the SNR and resolution differences between the two BOLD modalities. The DMN and auditory networks showed highest CBF values among all RSNs. These results demonstrated the feasibility of ASL perfusion MRI for assessing resting brain functions using its concurrent BOLD in addition to CBF signal, which provides a potentially useful way to maximize the utility of ASL perfusion MRI.
Alleviating Border Effects in Wavelet Transforms for Nonlinear Time-varying Signal Analysis  [cached]
SU, H.,LIU, Q.,LI, J.
Advances in Electrical and Computer Engineering , 2011, DOI: 10.4316/aece.2011.03009
Abstract: Border effects are very common in many finite signals analysis and processing approaches using convolution operation. Alleviating the border effects that can occur in the processing of finite-length signals using wavelet transform is considered in this paper. Traditional methods for alleviating the border effects are suitable to compression or coding applications. We propose an algorithm based on Fourier series which is proved to be appropriate to the application of time-frequency analysis of nonlinear signals. Fourier series extension method preserves the time-varying characteristics of the signals. A modified signal duration expression for measuring the extent of border effects region is presented. The proposed algorithm is confirmed to be efficient to alleviate the border effects in comparison to the current methods through the numerical examples.
The CO5BOLD Analysis Tool  [PDF]
Sven Wedemeyer
Physics , 2013,
Abstract: The interactive IDL-based CO5BOLD Analysis Tool (CAT) was developed to facilitate an easy and quick analysis of numerical simulation data produced with the 2D/3D radiation magnetohydrodynamics code CO5BOLD. The basic mode of operation is the display and analysis of cross-sections through a model either as 2D slices or 1D graphs. A wide range of physical quantities can be selected. Further features include the export of models into VAPOR format or the output of images and animations. A short overview including scientific analysis examples is given.
One pair of hands is not like another: caudate BOLD response in dogs depends on signal source and canine temperament  [PDF]
Peter F. Cook,Mark Spivak,Gregory S. Berns
PeerJ , 2015, DOI: 10.7717/peerj.596
Abstract: Having previously used functional MRI to map the response to a reward signal in the ventral caudate in awake unrestrained dogs, here we examined the importance of signal source to canine caudate activation. Hand signals representing either incipient reward or no reward were presented by a familiar human (each dog’s respective handler), an unfamiliar human, and via illustrated images of hands on a computer screen to 13 dogs undergoing voluntary fMRI. All dogs had received extensive training with the reward and no-reward signals from their handlers and with the computer images and had minimal exposure to the signals from strangers. All dogs showed differentially higher BOLD response in the ventral caudate to the reward versus no reward signals, and there was a robust effect at the group level. Further, differential response to the signal source had a highly significant interaction with a dog’s general aggressivity as measured by the C-BARQ canine personality assessment. Dogs with greater aggressivity showed a higher differential response to the reward signal versus no-reward signal presented by the unfamiliar human and computer, while dogs with lower aggressivity showed a higher differential response to the reward signal versus no-reward signal from their handler. This suggests that specific facets of canine temperament bear more strongly on the perceived reward value of relevant communication signals than does reinforcement history, as each of the dogs were reinforced similarly for each signal, regardless of the source (familiar human, unfamiliar human, or computer). A group-level psychophysiological interaction (PPI) connectivity analysis showed increased functional coupling between the caudate and a region of cortex associated with visual discrimination and learning on reward versus no-reward trials. Our findings emphasize the sensitivity of the domestic dog to human social interaction, and may have other implications and applications pertinent to the training and assessment of working and pet dogs.
Exploiting Magnetic Resonance Angiography Imaging Improves Model Estimation of BOLD Signal  [PDF]
Zhenghui Hu, Cong Liu, Pengcheng Shi, Huafeng Liu
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0031612
Abstract: The change of BOLD signal relies heavily upon the resting blood volume fraction () associated with regional vasculature. However, existing hemodynamic data assimilation studies pretermit such concern. They simply assign the value in a physiologically plausible range to get over ill-conditioning of the assimilation problem and fail to explore actual . Such performance might lead to unreliable model estimation. In this work, we present the first exploration of the influence of on fMRI data assimilation, where actual within a given cortical area was calibrated by an MR angiography experiment and then was augmented into the assimilation scheme. We have investigated the impact of on single-region data assimilation and multi-region data assimilation (dynamic cause modeling, DCM) in a classical flashing checkerboard experiment. Results show that the employment of an assumed in fMRI data assimilation is only suitable for fMRI signal reconstruction and activation detection grounded on this signal, and not suitable for estimation of unobserved states and effective connectivity study. We thereby argue that introducing physically realistic in the assimilation process may provide more reliable estimation of physiological information, which contributes to a better understanding of the underlying hemodynamic processes. Such an effort is valuable and should be well appreciated.
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