Biomedical subjects

Xiaohong Wan

Publications and source records attributed to Xiaohong Wan.

4 recordsLinked to original sources

PubMed2006

The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism.

It has been well recognized that the nonlinear hemodynamic responses of the blood oxygenation level-dependent (BOLD) functional MRI (fMRI) are important and ubiquitous in a series of experimental paradigms, especially for the event-related fMRI. Although this phenomenon has been intensively studied and it has been found that the post-capillary venous expansion is an intrinsically nonlinear mechanical process, the existence of an additional neural basis for the nonlinearity has not been clearly shown. In this paper, we assessed the correlation between the electric and vascular indices by performing simultaneous electroencephalography (EEG) and fMRI recordings in humans during a series of visual stimulation (i.e., radial checkerboard). With changes of the visual stimulation frequencies (from 0.5 to 16 Hz) and contrasts (from 1% to 100%), both the event related potentials (ERPs) and hemodynamic responses show nonlinear behaviors. In particular, the mean power of the brain electric sources and the neuronal efficacies (as originally defined in the hemodynamics model [Friston et al. Neuroimage, 12, 466-477, 2000], here represent the vascular inputs) in primary visual cortex consistently show a linear correlation for all subjects. This indicates that the hemodynamic response nonlinearity found in this paper primarily reflects the nonlinearity of underlying neural activity. Most importantly, this finding underpins a nonlinear neurovascular coupling. Specifically, it is shown that the transferring function of the neurovascular coupling is likely a power transducer, which integrates the fast dynamics of neural activity into the vascular input of slow hemodynamics.

Adult↗

PubMed2006

Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts.

OBJECTIVE: We present a new method of effectively removing imaging artifacts of electroencephalography (EEG) and extensively conserving the time-frequency features of EEG signals during simultaneous functional magnetic resonance imaging (fMRI) scanning under conventional conditions. METHODS: Under the conventional conditions of a 5000 Hz EEG sampling rate, but in the absence of the MRI slice-timing signals, the imaging artifact during each slice scanning is theoretically inferred to be a linear combination of the average artifact waveform and its derivatives, deduced by band-limited Taylor's expansion. Technically, the imaging artifact reduction algorithm is equivalent to an adaptive finite impulse response (FIR) filter. RESULTS: The capability of this novel method removing the imaging artifacts of EEG recording during fMRI scanning has been demonstrated by a phantom experiment. Moreover, the effectiveness of this method in conserving the time-frequency features of EEG activity has been evaluated by both visually evoked experiments and alpha waves. CONCLUSIONS: The adaptive FIR method is an effective method of removing the imaging artifacts under conventional conditions, and also conserving the time-frequency EEG signals. SIGNIFICANCE: The proposed adaptive FIR method, removing the imaging artifacts, combined with the wavelet-based non-linear noise reduction (WNNR) method [Wan X, Iwata K, Riera J, Ozaki T, Kitamura M, Kawashima R. Artifact reduction for EEG/fMRI recording: Nonlinear reduction of ballistocardiogram artifacts. Clin Neurophysiol 2006;117:668-80], reducing the ballistocardiogram artifacts (BAs), makes it feasible to obtain accurate EEG signals from the simultaneous EEG recordings during fMRI scanning.

Adult↗

PubMed2006

Artifact reduction for EEG/fMRI recording: nonlinear reduction of ballistocardiogram artifacts.

OBJECTIVE: We present a new method of effectively removing the ballistocardiogram artifacts (BAs) of electroencephalography (EEG), recorded inside a 1.5 T static magnetic field scanner with no fMRI scanning, which conserves the time and frequency features of event-related EEG activity. METHODS: The BAs are approximated as deterministically chaotic dynamics. A Wavelet-based nonlinear noise reduction (WNNR) method consisting of: (a) wavelet transformation, (b) nonlinear noise reduction and (c) spatial average subtraction, is developed to effectively reduce the BAs so that the residual artifacts are smaller than the EEG signals. RESULTS: The effectiveness of the WNNR method to remove the BAs with conservation of the temporal EEG signals is evaluated by simulations and experiments inside a 1.5 T static magnetic field, with the visual evoked EEG dynamics. The WNNR method is also demonstrated to effectively retrieve alpha waves while the subjects' eyes are closed. CONCLUSIONS: The WNNR method has the abilities to effectively remove the BAs and conserve the time-frequency features of EEG activity. SIGNIFICANCE: The WNNR method provides us a significant technique to obtain clean temporal EEG signals during recording with MRI, especially for the event-related EEG dynamics. Notably, it might work effectively at higher field strengths as well. Moreover, it can be also used to process many other biological data contaminated by the cardiac pulses.

Adult↗

PubMed2006

Nonlinear local electrovascular coupling. I: A theoretical model.

Here we present a detailed biophysical model of how brain electrical and vascular dynamics are generated within a basic cortical unit. The model was obtained from coupling a canonical neuronal mass and an expandable vasculature. In this proposal, we address several aspects related to electroencephalographic and functional magnetic resonance imaging data fusion: (1) the impact of the cerebral architecture (at different physical levels) on the observations; (2) the physiology involved in electrovascular coupling; and (3) energetic considerations to gain a better understanding of how the glucose budget is used during neuronal activity. The model has three components. The first is the canonical neural mass model of three subpopulations of neurons that respond to incoming excitatory synaptic inputs. The generation of the membrane potentials in the somas of these neurons and the electric currents flowing in the neuropil are modeled by this component. The second and third components model the electrovascular coupling and the dynamics of vascular states in an extended balloon approach, respectively. In the first part we describe, in some detail, the biophysical model and establish its face validity using simulations of visually evoked responses under different flickering frequencies and luminous contrasts. In a second part, a recursive optimization algorithm is developed and used to make statistical inferences about this forward/generative model from actual data.

Brain↗