Biomedical subjects

Huafu Chen

Publications and source records attributed to Huafu Chen.

11 recordsLinked to original sources

PubMed2024

Shared differential factors underlying individual spontaneous neural activity abnormalities in major depressive disorder.

BACKGROUND: In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion. METHODS: To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization. RESULTS: Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability. CONCLUSIONS: This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

Humans↗

PubMed2006

Localization of latent epileptic activities using spatio-temporal independent component analysis of FMRI data.

Localizing interictal epileptic activities is a difficult problem in clinical practice. We report a novel noninvasive technique, resting functional magnetic resonance imaging (fMRI) with spatio-temporal independent component analysis (ICA), for localizing interictal epileptic activities. First, the fMRI data is separated into independent spatial patterns by spatial-ICA, and the patterns with Z-values larger than a threshold are selected as the potential spatial patterns of the epileptic activities. Second, the temporal series of the active points in the selected patterns are separated by temporal-ICA, and the component with the biggest Gaussian deviation (kurtosis) is selected as the representative of the epileptic discharge activity in a sub-region. Finally, those spatial sub-regions, which have distinct epileptic discharge activities confirmed by temporal-ICA are considered as the epileptic foci. This method was applied to fMRI data of six epileptic patients, and the results are consistent with the clinical assessment. Though more studies are required to validate this technique, the above preliminary results demonstrate the potential of using the resting fMRI with spatio-temporal ICA to detect and localize latent epileptic activities.

Adult↗

PubMed2006

An integrated neighborhood correlation and hierarchical clustering approach of functional MRI.

Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) time series, however, the huge computation load makes it difficult for practical use. In this paper, neighborhood correlation (NC) and hierarchical clustering (HC) methods are integrated as a new approach where fMRI data are processed first by NC to get a preliminary image of brain activations, and then by HC to remove some noises. In HC, to better use spatial and temporal information in fMRI data, a new spatio-temporal measure is introduced. A simulation study and an application to visual fMRI data show that the brain activations can be effectively detected and that different response patterns can be discriminated. These results suggest that the proposed new integrated approach could be useful in detecting weak fMRI signals.

Brain↗

PubMed2005

A projection-based image registration algorithm and its application.

Proposed is a projection-based image registration technique where, by rearranging the projections of characteristic images, the image registration is implemented with two independent steps - rotation and translation, to perform the two-dimensional or three-dimensional rigid-body image registration addressing the head motion problem in functional magnetic resonance imaging (fMRI). For a 2D problem, the approach is based on a one-dimensional projection of a segmented two-dimensional characteristic image, in which the translation and rotation parameters are obtained with a one-dimensional cross-correlation-based estimator. This is then used to compute the cross-correlation between the projection of an image and a registration table that is created by rearranged projections of a selected two-dimensional image with various rotation angles. In this approach, the translation registration table may be created by rearranged projections of sub-voxel level two-dimensional images with various sub-voxel level parameters, and so it may be applied into a sub-voxel registration. Such an approach replaced the general multi-dimensional optimization procedure with a linear projection calculation and a finite cross-correlation with a registration table, thus the amount of computation is considerably reduced. The performance of this method was confirmed by simulation study different SNRs and applications to 2D and 3D actual functional MRI images.

Algorithms↗

PubMed2005

A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response.

Blood oxygenation level-dependent (BOLD) contrast-based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is the comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian or Gamma function. The parameters of the two models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better than the Gamma model in fitting the data. The model parameters are different in the left and right occipital regions, which indicate that the dynamic processes seem different in various cerebral functional regions.

Algorithms↗

PubMed2005

Delay correlation subspace decomposition algorithm and its application in fMRI.

This paper reports a new delay subspace decomposition (DSD) algorithm. Instead of using the canonical zero-delay correlation matrix, the new DSD algorithm introduces a delay into the correlation matrix of the subspace decomposition to suppress noises in the data. The algorithm is applied to functional magnetic resonance imaging (fMRI) to detect the regions of focal activities in the brain. The efficiency is evaluated by comparing with independent component analysis and principal component analysis method of fMRI.

Algorithms↗

PubMed2004

Discussion on the choice of separated components in fMRI data analysis by spatial independent component analysis.

By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. Included simulations show the possibility of the spatial ICA on discriminating asynchronous activations or different response patterns in an fMRI data set. An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.

Algorithms↗

PubMed2004

A study on asymmetry of spatial visual field by analysis of the fMRI BOLD response.

The asymmetry of the left-right and upper-lower visual field is analyzed in this paper by a model approach based on the functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) response. The model consists of the convolution between a Gaussian function and the perfusion function of neural response to stimulus. The model parameters are estimated by a nonlinear optimal algorithm, and te asymmetry of the left-right and upper-lower visual field is investigated by the differences of the model parameters. The results from eight subjects show that reaction time is significant shorter and the response is significant stronger when the lower field is stimulated than that when the upper field is stimulated. For the left and right fields, the response is different. These results provide the fMRI BOLD response evidence of the asymmetry of spatial visual fields.

Algorithms↗

PubMed2003

Analysis of fMRI data by blind separation of data in a tiny spatial domain into independent temporal component.

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.

Algorithms↗

PubMed2003

[Advances in independent component analysis and its application].

The independent component analysis (ICA) is a new technique in statistical signal processing, which decomposes mixed signals into statistical independent components. The reported applications in biomedical and radar signal have demonstrated its good prospect in various blind signal separation. In this paper, the progress of ICA in such as its principle, algorithm and application and advance direction of ICA in future is reviewed. The aim is to promote the research in theory and application in the future.

Algorithms↗

PubMed2002

[A method based on independent component analysis for processing fMRI data].

Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.

Algorithms↗