Wu Li

White Matter Microstructure Changes in the Thalamus in Parkinson Disease with Depression: A Diffusion Tensor MR Imaging Study

BACKGROUND AND PURPOSE: Depression occurs frequently in PD; however the neural basis of depression in PD remains unclear. The aim of this study was to characterize possible depression-related white matter microstructural changes in the thalamus of patients with DPD compared with those with NDPD. MATERIALS AND METHODS: FA and MD maps from DTI were obtained in 14 patients with DPD and 18 patients with NDPD. Region-of-interest−guided VBA was conducted on the FA maps to detect possible microstructural differences in the thalamus between these 2 patient groups. Moreover, mean FA and MD in regions with a detected difference were compared between DPD and NDPD groups, and correlations between diffusion quantities and the severity of depression were analyzed. RESULTS: White matter microstructure differences were found between the patients with DPD and NDPD in the bilateral mediodorsal thalamic regions. In these regions, patients with DPD showed significantly decreased FA values (P < .005) compared with patients with NDPD, and the mean values of FA were negatively correlated with the scores of depression severity (P < .05) for patients with PD. No significant differences of MD were found in the mediodorsal thalamus between these 2 groups. CONCLUSIONS: Our results provide preliminary evidence that the mediodorsal thalamus may play an important role in depression in PD and suggest a relationship between FA in the mediodorsal thalamus and the presence of depressive symptoms in patients with DPD. These findings may be helpful for further understanding the potential mechanisms of depression in PD.

Effective connectivities of cortical regions for top-down face processing: A Dynamic Causal Modeling study

To study top-down face processing, the present study used an experimental paradigm in which participants detected non-existent faces in pure noise images. Conventional BOLD signal analysis identified three regions involved in this illusory face detection. These regions included the left orbitofrontal cortex (OFC) in addition to the right fusiform face area (FFA) and right occipital face area (OFA), both of which were previously known to be involved in both top-down and bottom-up processing of faces. We used Dynamic Causal Modeling (DCM) and Bayesian model selection to further analyze the data, revealing both intrinsic and modulatory effective connectivities among these three cortical regions. Specifically, our results support the claim that the orbitofrontal cortex plays a crucial role in the top-down processing of faces by regulating the activities of the occipital face area, and the occipital face area in turn detects the illusory face features in the visual stimuli and then provides this information to the fusiform face area for further analysis.

Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification

Manual region tracing method for segmentation of infarction lesions in images from diffusion tensor magnetic resonance imaging (DT-MRI) is usually used in clinical works, but it is time consuming. A new unsupervised method has been developed, which is a multistage procedure, involving image preprocessing, calculation of tensor field and measurement of diffusion anisotropy, segmentation of infarction volume based on adaptive multiscale statistical classification (MSSC), and partial volume voxel reclassification (PVVR). The method accounts for random noise, intensity overlapping, partial volume effect (PVE), and intensity shading artifacts, which always appear in DT-MR images. The proposed method was applied to 20 patients with clinically diagnosed brain infarction by DT-MRI scans. The accuracy and reproducibility in terms of identifying the infarction lesion have been confirmed by clinical experts. This automatic segmentation method is promising not only in detecting the location and the size of infarction lesion in stroke patient but also in quantitatively analyzing diffusion anisotropy of lesion to guide clinical diagnoses and therapy.

Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method

It is a big challenge to segment magnetic resonance (MR) images with intensity inhomogeneity. The widely used segmentation algorithms are region based, which mostly rely on the intensity homogeneity, and could bring inaccurate results. In this paper, we propose a novel region-based active contour model in a variational level set formulation. Based on the fact that intensities in a relatively small local region are separable, a local intensity clustering criterion function is defined. Then, the local function is integrated around the neighborhood center to formulate a global intensity criterion function, which defines the energy term to drive the evolution of the active contour locally. Simultaneously, an intensity fitting term that drives the motion of the active contour globally is added to the energy. In order to segment the image fast and accurately, we utilize a coefficient to make the segmentation adaptive. Finally, the energy is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. Experiments on synthetic and real MR images show the effectiveness of our method.

Neural Correlates of Top-Down Letter Processing.

This fMRI study investigated top-down letter processing with an illusory letter detection task. Participants responded whether one of a number of different possible letters was present in a very noisy image. After initial training that became increasingly difficult, they continued to detect letters even though the images consisted of pure noise, which eliminated contamination from strong bottom-up input. For illusory letter detection, greater fMRI activation was observed in several cortical regions. These regions included the precuneus, an area generally involved in top-down processing of objects, and the left superior parietal lobule, an area previously identified with the processing of valid letter and word stimuli. In addition, top-down letter detection also activated the left inferior frontal gyrus, an area that may be involved in the integration of general top-down processing and letter-specific bottom-up processing. These findings suggest that these regions may play a significant role in top-down as well as bottom-up processing of letters and words, and are likely to have reciprocal functional connections to more posterior regions in the word and letter processing network.

A weighted-RV method to detect fine-scale functional connectivity during resting state.

During the resting state, in the absence of external stimuli or goal-directed mental tasks, some functionally related discrete regions of the brain show complex low-frequency fluctuations in the blood oxygenation level dependent signal. Here we developed a novel ROI-based multivariate statistical framework to obtain the fine-grained patterns of functionally specialized brain networks in the resting state. Under this framework, the weighted-RV method is proposed and used to detect the spatial fine-scale patterns of functional connectivity. This approach overcomes several major problems of the traditional resting-state data analysis methods such as Pearson correlation and linear regression analysis. By using simulation and real fMRI experiment, we have found that the weighted-RV method is shown to be more sensitive in detecting the fine-scale based low-frequency connectivity even at a very low functional contrast-to-noise ratio (CNR), and this method can achieve much better performance in mapping the fine-grained patterns of functionally specialized brain networks compared to the traditional methods.

Robust Tract Skeleton Extraction of Cingulum Based on Active Contour Model from Diffusion Tensor MR Imaging

Cingulum is widely studied in healthy and psychiatric subjects. For cingulum analysis from diffusion tensor MR imaging, tractography and tract of interest method have been adopted for tract-based analysis. Because tractography performs fiber tracking according to local diffusion measures, they can be sensitive to noise and tracking errors can be accumulated along the fiber. For more accurate localization of cingulum, we attempt to define it by skeleton extraction using the tensors information throughout the tract of cingulum simultaneously, which is quite different from the idea of tractography. In this study, we introduce an approach to extract the skeleton of cingulum using active contour model, which allows us to optimize the location of cingulum in a global sense based on the diffusion measurements along the entire tract and contour regularity. Validation of this method on synthetic and experimental data proved that our approach is able to reduce the influence of noise and partial volume effect, and extract the skeleton of cingulum robustly and reliably. Our proposed method provides an approach to localize cingulum robustly, which is a very important feature for tract-based analysis and can be of important practical utility.

Segmentation and quantification of ischemic lesions on DWI images with different B-value at high magnetic field (3.0T)

Diffusion weighted imaging (DWI) is the gold standard for imaging of acute stroke. Today, high-field systems operating at 3T become increasingly available in clinical settings. But, with b-value increasing, lesion SNR of DWI image descends, and anisotropy increases significantly. Aim of the study is to develop an automatic volumetric measure method of ischemic lesions on diffusion weighted imaging (DWI) images at high magnetic field, without the disturbance of anisotropy. Using a home-built interactive platform, we rated SNR and anisotropy. The extent of anisotropy was evaluated by the intensity ratio of white matter versus gray matter. Based on this knowledge, we developed an automatic segmentation method, involving firstly non-linear anisotropic diffusion filtering, secondly expert pieces of information applied to determine the scopes of parameters according to different b-value, and finally multi-scale adaptive statistical classification with intensity inhomogeneity correction. Results of the automatic segmentation are compared with lesion delineations by experts, showing the rapid identification of ischemic lesion with accuracy and reproducibility, even in the presence of radio frequency (RF) inhomogeneity. There has been considerable interest in using DWI at 3T to detect ischemic lesion in stroke patients. The proposed method is promising for rapid, accurate, and quantitatively diagnosis of ischemic stroke.

Automatic segmentation of cerebral ischemic lesions from diffusion tensor MR images

There has been increasing interest in quantitatively analyzing diffusion anisotropy of ischemic lesions from diffusion tensor magnetic resonance imaging (DT-MRI). In this study, we develop and evaluate a novel method to automatically segment cerebral ischemic lesions from DT-MRI images. The method is a combination of image preprocessing, measures of diffusion anisotropy, multi-scale statistical classification (MSSC), and partial volume reclassification (PVRC). First, non-linear anisotropic diffusion filtering are applied to DT-MRI images to reduce image noise. Then, measures of diffusion anisotropy, such as fractional anisotropy and trace of the diffusion tensor, are calculated to acquire the diffusion properties of different brain tissues. Finally, ischemic lesions are accurately segmented using robust MSSC-PVRC, taking into account spatial information, intensity gradient, radio frequency (RF) inhomogeity and measures of diffusion anisotropy of DT-MRI images. After MSSC, PVRC is applied to overcome partial volume effect (PVE). Analyses of synthetic data and DT-MRI scans of 20 patients with ischemic stroke were carried out. It shows that the method got a satisfied segmentation of ischemic lesions, successfully overcoming the problem of intensity overlapping and reducing PVE, and that the method is robust to varying starting parameters. The results of the automated method are compared with lesion delineations by human experts, showing the rapid identification of ischemic lesion with accuracy and reproducibility. The proposed automatic technique is promising not only to detect the site and size of ischemic lesions in stroke patients but also to quantitatively analyze diffusion anisotropy of lesions for further clinical diagnoses and therapy.

Classification of brain disease in magnetic resonance images using two-stage local feature fusion.

Background Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. Methods Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. Results We apply this method to magnetic resonance images of Alzheimers disease and Parkinsons disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. Discussion This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.