Biomedical subjects

Tianzi Jiang

Publications and source records attributed to Tianzi Jiang.

26 records · Page 2Linked to original sources

PubMed2004

Modulation of functional connectivity during the resting state and the motor task.

Quite a few studies in functional magnetic resonance imaging (fMRI) have tested that, even in a resting state, motor cortices constitute a network. It has never been investigated how the network modulates from the resting state to the motor task state. In this report, by a newly developed approach taking into account n-to-1 connectivity using 1-to-1 connectivity measures instead of conventional pairwise connectivity, we show the existence of a large organized functional connectivity network related to motor function in the resting brain with fMRI. More importantly, we found that such a network can be modulated from a conscious resting state to planning, initiation, coordination, guidance, and termination of voluntary movement state, exhibited by significant changes of functional connectivity of some brain regions in different brain activity. Moreover, a quantitative description of such a functional modulation has also been presented.

Adult↗

PubMed2004

A split-merge-based region-growing method for fMRI activation detection.

We introduce a hybrid method for functional magnetic resonance imaging (fMRI) activation detection based on the well-developed split-merge and region-growing techniques. The proposed method includes conjoining both of the spatio-temporal priors inherent in split-merge and the prior information afforded by the hypothesis-led component of region selection. Compared to the fuzzy c-means clustering analysis, this method avoids making assumptions about the number of clusters and the computation complexity is reduced markedly. We evaluated the effectiveness of the proposed method in comparison with the general linear model and the fuzzy c-means clustering method conducted on simulated and in vivo datasets. Experimental results show that our method successfully detected expected activated regions and has advantages over the other two methods.

Acoustic Stimulation↗

PubMed2004

Regional homogeneity approach to fMRI data analysis.

Kendall's coefficient concordance (KCC) can measure the similarity of a number of time series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present study, a new method was developed based on the regional homogeneity (ReHo), in which KCC was used to measure the similarity of the time series of a given voxel to those of its nearest neighbors in a voxel-wise way. Six healthy subjects performed left and right finger movement tasks in event-related design; five of them were additionally scanned in a rest condition. KCC was compared among the three conditions (left finger movement, right finger movement, and the rest). Results show that bilateral primary motor cortex (M1) had higher KCC in either left or right finger movement condition than in rest condition. Contrary to prediction and to activation pattern, KCC of ipsilateral M1 is significantly higher than contralateral M1 in unilateral finger movement conditions. These results support the previous electrophysiologic findings of increasing ipsilateral M1 excitation during unilateral movement. ReHo can consider as a complementary method to model-driven method, and it could help reveal the complexity of the human brain function. More work is needed to understand the neural mechanism underlying ReHo.

Adult↗

PubMed2003

Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images.

A local image model is proposed to eliminate the adverse impact of both artificial and inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based image segmentation methods. The estimation and correction procedures for intensity inhomogeneities are no longer indispensable because the highly convoluted spatial distribution of different tissues in the brain is taken into consideration. On the basis of the local image model, multicontext fuzzy clustering (MCFC) is proposed for classifying 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid automatically. In MCFC, multiple clustering contexts are generated for each pixel, and fuzzy clustering is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. To maintain the statistical reliability and spatial continuity of membership distributions, a fusion strategy is adopted to integrate the clustering outcomes from different contexts. The fusion result is taken as the final membership value of the pixel. Experimental results on both real MR images and simulated volumetric MR data show that MCFC outperforms the classic fuzzy c-means (FCM) as well as other segmentation methods that deal with intensity inhomogeneities.

Artifacts↗

PubMed2003

Region growing method for the analysis of functional MRI data.

Existing analytical techniques for functional magnetic resonance imaging (fMRI) data always need some specific assumptions on the time series. In this article, we present a new approach for fMRI activation detection, which can be implemented without any assumptions on the time series. Our method is based on a region growing method, which is very popular for image segmentation. A comparison of performance on fMRI activation detection is made between the proposed method and the deconvolution method and the fuzzy clustering method with receiver operating characteristic (ROC) methodology. In addition, we examine the effectiveness and usefulness of our method on real experimental data. Experimental results show that our method outperforms over the deconvolution method and the fuzzy clustering method on a number of aspects. These results suggest that our region growing method can serve as a reliable analysis of fMRI data.

Acoustic Stimulation↗

PubMed2003

A modified temporal self-correlation method for analysis of fMRI time series.

Temporal self-correlation has recentlybeen proposed as a measure for fMRI-activation detection. In this paper, a modified temporal self-correlation method is introduced. The modified temporal self-correlation is based on the expectation value and standard deviation of the correlation coefficients between all pairs of epochs, while the original temporal self-correlation method is only based on the expectation value. Performance of the proposed method is evaluated on both simulated and in vivo fMRI data. Compared with the original temporal self-correlation method, the proposed method shows a significant improvement. In addition, a technique for quantitative comparison of different fMRI data analysis methods is proposed.

Algorithms↗

PubMed2002

Volumetric segmentation of brain images using parallel genetic algorithms.

Active model-based segmentation has frequently been used in medical image processing with considerable success. Although the active model-based method was initially viewed as an optimization problem, most researchers implement it as a partial differential equation solution. The advantages and disadvantages of the active model-based method are distinct: speed and stability. To improve its performance, a parallel genetic algorithm-based active model method is proposed and applied to segment the lateral ventricles from magnetic resonance brain images. First, an objective function is defined. Then one instance surface was extracted using the finite-difference method-based active model and used to initialize the first generation of a parallel genetic algorithm. Finally, the parallel genetic algorithm is employed to refine the result. We demonstrate that the method successfully overcomes numerical instability and is capable of generating an accurate and robust anatomic descriptor for complex objects in the human brain, such as the lateral ventricles.

Algorithms↗

PubMedDate not supplied

Improving performance of reflectance diffuse optical imaging using a multicentered mode.

We propose a novel multicentered mode for arrangement of optical fibers to improve the imaging performance of reflectance diffuse optical imaging (rDOI). Simulations performed using a semi-infinite model show that the proposed multicentered geometries can achieve a maximum of 42 overlapping measurements. The contrast-to-noise ratio (CNR) analysis indicates that the best spatial resolution is 1 mm in radius and the contrast resolution is less than 1.05 for the multicentered geometries. The results from simulations indicate significant improvement in image quality compared to the single-centered mode and previous geometries. Additional experimental results on a single human subject lead to the conclusion that the proposed multicentered geometries are appropriate for exploring activations in the human brain. From the results of this research, we conclude that the proposed multicentered mode could advance the performance of rDOI both in image quality and practical convenience.

Algorithms↗