Biomedical subjects

Tianzi Jiang

Publications and source records attributed to Tianzi Jiang.

At least 19 recordsLinked to original sources

PubMed2007

Altered functional connectivity in early Alzheimer's disease: a resting-state fMRI study.

Previous studies have led to the proposal that patients with Alzheimer's disease (AD) may have disturbed functional connectivity between different brain regions. Furthermore, recent resting-state functional magnetic resonance imaging (fMRI) studies have also shown that low-frequency (<0.08 Hz) fluctuations (LFF) of the blood oxygenation level-dependent signals were abnormal in several brain areas of AD patients. However, few studies have investigated disturbed LFF connectivity in AD patients. By using resting-state fMRI, this study sought to investigate the abnormal functional connectivities throughout the entire brain of early AD patients, and analyze the global distribution of these abnormalities. For this purpose, the authors divided the whole brain into 116 regions and identified abnormal connectivities by comparing the correlation coefficients of each pair. Compared with healthy controls, AD patients had decreased positive correlations between the prefrontal and parietal lobes, but increased positive correlations within the prefrontal lobe, parietal lobe, and occipital lobe. The AD patients also had decreased negative correlations (closer to zero) between two intrinsically anti-correlated networks that had previously been found in the resting brain. By using resting-state fMRI, our results supported previous studies that have reported an anterior-posterior disconnection phenomenon and increased within-lobe functional connectivity in AD patients. In addition, the results also suggest that AD may disturb the correlation/anti-correlation effect in the two intrinsically anti-correlated networks.

Aged↗

PubMed2006

Exploring candidate genes for human brain diseases from a brain-specific gene network.

It is believed that large numbers of genes are involved in common human brain diseases. Here, we propose a novel computational strategy for simultaneously identifying multiple candidate genes for genetic human brain diseases from a brain-specific gene network-level perspective. By integrating diverse genomic and proteomic datasets based on Bayesian statistical model, we built a large-scale human brain-specific gene network. Based on this network and minor prior knowledge of a specific brain disease, we can effectively identify multiple candidate genes for this disease. When four known Alzheimer's disease genes were used as the prior knowledge, among the top 46 high-scoring genes that we have found, 37 were previously reported to be associated with Alzheimer's disease. And the higher score a gene has, the more likely this gene is a disease-related one. The results suggest that the proposed method is effective, convenient, and applicable in the future genetic studies.

Alzheimer Disease↗

PubMed2006

Quantitative analysis along the pyramidal tract by length-normalized parameterization based on diffusion tensor tractography: application to patients with relapsing neuromyelitis optica.

In this study, we introduced a length-normalized parameterization method to establish anatomical correspondence of white matter fiber tracts across subjects and applied this method to investigate the presence of abnormal diffusion along the pyramidal tract (PYT) of relapsing neuromyelitis optica (RNMO) patients without visible brain lesions. In this approach, the part of the PYT between the lowest slice of the cerebral peduncle and the uppermost slice of the lateral ventricle was reconstructed to establish the anatomical correspondence across subjects using diffusion tensor tractography. Then it was parameterized by normalizing its length and dividing equally the normalized length into a certain number of segments, so that the comparability of each segment across subjects along the PYT was established. Tract-specific diffusion indices, including directionally averaged diffusivity (D(av)), fractional anisotropy (FA), primary diffusivity (lambda(1)) and transverse diffusivity (lambda(23)), were obtained from each segment. Thus, the distribution maps of these indices along the PYT were obtained. The distribution maps of D(av), FA, and lambda(23) of RNMO patients were significantly different from those of healthy controls, especially in the lower part of the PYT. The differences may be caused by secondary degeneration to lesions in the spinal cord. In conclusion, a length-normalized parameterization method is proposed to establish anatomical correspondence for the PYT. Compared with existed methods, a major merit of our method is to provide comparability across subjects along the PYT on the basis of diffusion tensor tractography and to make it possible for the quantitative analysis along the fiber tract. This method can also be used to quantitatively analyze other white matter fiber tracts between two definite anatomic landmarks in many neurological or psychiatric diseases.

Adult↗

PubMed2006

Discriminative analysis of relapsing neuromyelitis optica and relapsing-remitting multiple sclerosis based on two-dimensional histogram from diffusion tensor imaging.

It is difficult to completely differentiate patients with relapsing neuromyelitis optica (RNMO) from relapsing-remitting multiple sclerosis (RRMS) for their similarities in clinical manifestation. In this study, we proposed a novel approach, using two-dimensional histogram of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the brain derived from diffusion tensor imaging (DTI) as classification feature, to discriminate patients with RNMO from RRMS. In this approach, two-dimensional principal component analysis (2D-PCA) was used to extract feature and reduce dimensionality of matrix-formed data efficiently. Then linear discriminant analysis (LDA) was performed on these extracted features to find the best projection direction to separate patients with RNMO from RRMS. Finally, a minimum distance classifier was generated on the basis of projection scores. The correct recognition rate of our method reached 85.7%, validated by the leave-one-out method. This result was much higher than that using feature of ADC or FA separately (59.5% for ADC, 76.2% for FA). In conclusion, the proposed method on the basis of combined features is more effective for classification than those merely using the features separately, and it may be helpful in differentiating RNMO from RRMS patients.

Diffusion Magnetic Resonance Imaging↗

PubMed2006

Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder.

Dorsal anterior cingulate cortex (dACC) has been found to function abnormally in attention deficit hyperactivity disorder (ADHD) patients in several former functional MRI (fMRI) studies. Resting-state low-frequency fluctuations (LFFs) of blood oxygen level-dependent (BOLD) fMRI signals have been proved to be quite informative. This study used resting-state LFFs to investigate the resting-state functional connectivity pattern differences of dACC in adolescents with and without ADHD. As compared to the controls, the ADHD patients exhibited more significant resting-state functional connectivities with the dACC in bilateral dACC, bilateral thalamus, bilateral cerebellum, bilateral insula and bilateral brainstem (pons). No brain region in the controls was found to exhibit more significant resting-state functional connectivity with the dACC. We suggest these abnormally more significant functional connectivities in the ADHD patients may indicate the abnormality of autonomic control functions in them.

Adolescent↗

PubMed2006

Changes in hippocampal connectivity in the early stages of Alzheimer's disease: evidence from resting state fMRI.

A selective distribution of Alzheimer's disease (AD) pathological lesions in specific cortical layers isolates the hippocampus from the rest of the brain. However, functional connectivity between the hippocampus and other brain regions remains unclear in AD. Here, we employ a resting state functional MRI (fMRI) to examine changes in hippocampal connectivity comparing 13 patients with mild AD versus 13 healthy age-matched controls. Hippocampal connectivity was investigated by examination of the correlation between low frequency fMRI signal fluctuations in the hippocampus and those in all other brain regions. We found that functional connectivity between the right hippocampus and a set of regions was disrupted in AD; these regions are: medial prefrontal cortex (MPFC), ventral anterior cingulate cortex (vACC), right inferotemporal cortex, right cuneus extending into precuneus, left cuneus, right superior and middle temporal gyrus and posterior cingulate cortex (PCC). We also found increased functional connectivity between the left hippocampus and the right lateral prefrontal cortex in AD. In addition, rightward asymmetry of hippocampal connectivity observed in elderly controls was diminished in AD patients. The disrupted hippocampal connectivity to the MPFC, vACC and PCC provides further support for decreased activity in "default mode network" previously shown in AD. The decreased connectivity between the hippocampus and the visual cortices might indicate reduced integrity of hippocampus-related cortical networks in AD. Moreover, these findings suggest that resting-state fMRI might be an appropriate approach for studying pathophysiological changes in early AD.

Aged↗

PubMed2006

Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging.

Using resting-state functional magnetic resonance imaging, we examined the functional connectivity throughout the entire brain in schizophrenia. The abnormalities in functional connectivity were identified by comparing the correlation coefficients of each pair of 116 brain regions between 15 patients and 15 controls. Then, the global distribution of the abnormal functional connectivities was examined. Experimental results indicated, in general, a decreased functional connectivity in schizophrenia during rest, and such abnormalities were widely distributed throughout the entire brain rather than restricted to a few specific brain regions. The results provide a quantitative support for the hypothesis that schizophrenia may arise from the disrupted functional integration of widespread brain areas.

Adult↗

PubMed2006

Handedness-related functional connectivity using low-frequency blood oxygenation level-dependent fluctuations.

Functional neuroimaging studies of handedness have traditionally focused on exploring regionally activated discrepancies, yet little is known regarding the effect of handedness on functional connections of brain regions. Here, we reported the relationship between cerebellar functional connectivity patterns and handedness by low-frequency blood oxygenation level-dependent fluctuations. Compared with the right-handers, the left-handers show higher cerebellar-prefrontal, cerebellar-parietal, and cerebellar-temporal functional connectivity, and lower cerebellar-limbic connectivity. Previous non-human primate studies have shown that these regions with known contributions to higher cognitive functions have afferent or efferent connections with the cerebellum. Our findings suggest that these observed low-frequency fluctuation correlations may reflect actually anatomic connections between the cerebellum and cerebral cortex, and the cerebellar involvement in higher function may be associated with handedness.

Adult↗

PubMed2006

White matter integrity of the whole brain is disrupted in first-episode schizophrenia.

Diffusion tensor imaging studies in schizophrenia have demonstrated lower diffusion anisotropy within white matter that provides information about brain white matter integrity. We have examined whether white matter is abnormal in first-episode schizophrenia by using diffusion tensor imaging. Twenty-one schizophrenic patients and healthy controls underwent diffusion tensor imaging scans that analyzed by using a rigorous voxel-based approach. We found that fractional anisotropy in white matter of the patients was lower than that in controls at the cerebral peduncle, frontal regions, inferior temporal gyrus, medial parietal lobes, hippocampal gyrus, insula, right anterior cingulum bundle and right corona radiata. These results suggested that white matter integrity of the whole brain was disrupted in early illness onset of schizophrenia.

Adolescent↗

PubMed2006

Decreased regional homogeneity in schizophrenia: a resting state functional magnetic resonance imaging study.

We used a newly reported regional homogeneity approach to measure the temporal homogeneity of blood oxygen level-dependent signal for exploring the brain activity of schizophrenia in a resting state. The results showed decreased regional homogeneity in schizophrenia, which distributed over the bilateral frontal, temporal, occipital, cerebellar posterior, right parietal and left limbic lobes, similar to the findings reported in previous resting state functional studies. The brain regions that showed decreased regional homogeneity are believed to be involved in the psychopathology and pathophysiology of schizophrenia. Our results indicate that abnormal brain activity of schizophrenia may exist in a resting state and the regional homogeneity may be potentially helpful in understanding the resting state of schizophrenia.

Adult↗

PubMed2006

Fingerprint registration by maximization of mutual information.

Fingerprint registration is a critical step in fingerprint matching. Although a variety of registration alignment algorithms have been proposed, accurate fingerprint registration remains an unresolved problem. We propose a new algorithm for fingerprint registration using orientation field. This algorithm finds the correct alignment by maximization of mutual information between features extracted from orientation fields of template and input fingerprint images. Orientation field, representing the flow of ridges, is a relatively stable global feature of fingerprint images. This method uses the statistics and distribution of global feature of fingerprint images so that it is robust to image quality and local changes in images. The primary characteristic of this method is that it uses this stable global feature to align fingerprints, and that its behavior may resemble the way humans compare fingerprints. Experimental results show that the occurrence of misalignment is dramatically reduced and that registration accuracy is greatly improved at the same time, leading to enhanced matching performance.

Algorithms↗

PubMed2006

Chord-based image reconstruction in cone-beam CT with a curved detector.

Modern computed tomography (CT) scanners use cone-beam configurations for increasing volume coverage, improving x-ray-tube utilization, and yielding isotropic spatial resolution. Recently, there have been significant developments in theory and algorithms for exact image reconstruction from cone-beam projections. In particular, algorithms have been proposed for image reconstruction on chords; and advantages over the existing algorithms offered by the chord-based algorithms include the high flexibility of exact image reconstruction for general scanning trajectories and the capability of exact reconstruction of images within a region of interest from truncated data. These chord-based algorithms have been developed only for flat-panel detectors. Many cone-beam CT scanners employ curved detectors for important practical considerations. Therefore, in this work, we have derived chord-based algorithms for a curved detector so that they can be applied to reconstructing images directly from data acquired by use of a CT scanner with a curved detector. We have also conducted preliminary numerical studies to demonstrate and evaluate the reconstruction properties of the derived chord-based algorithms for curved detectors.

Algorithms↗

PubMed2005

Side and handedness effects on the cingulum from diffusion tensor imaging.

In order to explore the microstructure of bilateral cingulum bundles associated with side and handedness, fractional anisotropy was extracted from diffusion tensor imaging. Distinguished from the conventional region of interest-based method, a fiber-based analysis method called scale-invariant parameterization method was employed to ascertain the anisotropy along the cingulum bundle in 31 normal right-handers and 14 normal left-handers. The statistical results showed a remarkable left-greater-than-right asymmetry pattern of anisotropy in most segments of cingulum bundles except the most posterior segment, for both right-handers and left-handers. Interestingly, higher anisotropy of the right-hander than the left-hander was found in the bilateral cingulum bundles. No significant handedness-by-side interaction was obtained in the present study, however.

Adult↗

PubMed2005

Asymmetry analysis of cingulum based on scale-invariant parameterization by diffusion tensor imaging.

Current analysis of diffusion tensor imaging (DTI) is based mostly on a region of interest (ROI) in an image dataset, which is specified by users. This method is not always reliable, however, because of the uncertainty of manual specification. We introduce an improved fiber-based scheme rather than an ROI-based analysis to study in DTI datasets of 31 normal subjects the asymmetry of the cingulum, which is one of the most prominent white matter fiber tracts of the limbic system. The present method can automatically extract the quantitative anisotropy properties along the cingulum bundles from tractography. Moreover, statistical analysis was carried out after anatomic correspondence specific to the cingulum across subjects was established, rather than the traditional whole-brain registration. The main merit of our method compared to existing counterparts is that to find such anatomic correspondence in cingulum, a scale-invariant parameterization method by arc-angle was proposed. It can give a continuous and exact description on any segment of cingulum. More interestingly, a significant left-greater-than-right asymmetry pattern was obtained in most segments of cingulum bundle (-50-25 degrees), except in the most posterior portion of cingulum (25-50 degrees).

Adult↗

PubMed2005

Single-trial variable model for event-related fMRI data analysis.

Most methods for fMRI data analysis assume that the hemodynamic responses (HRs) across similar experimental events are same. This assumption is not appropriate when HRs vary unpredictably from trial to trial. Here, we introduce a new method for fMRI data analysis. The main features of the proposed method are as follows: 1) The trial-to-trial variability is modeled as meaningful signal rather than assuming that the same HR is evoked in each trial; 2) Since the proposed method is a constrained optimization based general framework, it could be extended by utilizing prior knowledge of HR; 3) The traditional deconvolution method can be included into our method as a special case. A comparison of performance on simulated fMRI datasets is made using the general linear model, the deconvolution method and the proposed method with receiver operating characteristic (ROC) methodology. In addition, we examined the effectiveness and usefulness of our method on real experimental data.

Adult↗

PubMed2004

Characterizing the dynamic connectivity between genes by variable parameter regression and Kalman filtering based on temporal gene expression data.

MOTIVATION: One popular method for analyzing functional connectivity between genes is to cluster genes with similar expression profiles. The most popular metrics measuring the similarity (or dissimilarity) among genes include Pearson's correlation, linear regression coefficient and Euclidean distance. As these metrics only give some constant values, they can only depict a stationary connectivity between genes. However, the functional connectivity between genes usually changes with time. Here, we introduce a novel insight for characterizing the relationship between genes and find out a proper mathematical model, variable parameter regression and Kalman filtering to model it. RESULTS: We applied our algorithm to some simulated data and two pairs of real gene expression data. The changes of connectivity in simulated data are closely identical with the truth and the results of two pairs of gene expression data show that our method has successfully demonstrated the dynamic connectivity between genes. CONTACT: jiangtz@nlpr.ia.ac.cn.

Computer Simulation↗

PubMed2004

A combinational feature selection and ensemble neural network method for classification of gene expression data.

BACKGROUND: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this paper, we attempt to introduce a combinational feature selection method in conjunction with ensemble neural networks to generally improve the accuracy and robustness of sample classification. RESULTS: We validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets. CONCLUSIONS: Thus, we conclude that our methods can obtain more information in microarray data to get more accurate classification and also can help to extract the latent marker genes of the diseases for better diagnosis and treatment.

Acute Disease↗

PubMed2004

Esub8: a novel tool to predict protein subcellular localizations in eukaryotic organisms.

BACKGROUND: Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed. RESULTS: Esub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test. CONCLUSIONS: Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.

Computational Biology↗