FMRI connectivity analysis of acupuncture effects on the whole brain network in mild cognitive impairment patients

The increased risk for the elderly with mild cognitive impairment (MCI) to progress to Alzheimer's disease makes it an appropriate condition for investigation. While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. We sought to investigate the acupuncture effects on the functional connectivity throughout the entire brain in MCI patients compared to healthy controls (HC). The functional magnetic resonance imaging experiment was performed with two different paradigms, namely, deep acupuncture (DA) and superficial acupuncture (SA), at acupoint KI3. We first identified regions showing abnormal functional connectivity in the MCI group compared to HC during the resting state and subsequently tested whether these regions could be modulated by acupuncture. Then, we made the comparison of MCI vs. HC to test whether there were any specific modulatory patterns in the poststimulus resting brain between the two groups. Finally, we made the comparisons of DA vs. SA in each group to test the effect of acupuncture with different needling depths. We found the temporal regions (hippocampus, thalamus, fusiform gyrus) showing abnormal functional connectivity during the resting state. These regions are implicated in memory encoding and retrieving. Furthermore, we found significant changes in functional connectivity related with the abnormal regions in MCI patients following acupuncture. Compared to HC, the correlations related with the temporal regions were enhanced in the poststimulus resting brain in MCI patients. Compared to SA, significantly increased correlations related with the temporal regions were found for the DA condition. The enhanced correlations in the memory-related brain regions following acupuncture may be related to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. The heterogeneous modulatory patterns between DA and SA may suggest that deep muscle insertion of acupuncture is necessary to achieve the appreciable clinical effect.     

Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease

We report the first application of a novel diffusion-based MRI method, called diffusional kurtosis imaging (DKI), to investigate changes in brain tissue microstructure in patients with mild cognitive impairment (MCI) and AD and in cognitively intact controls. The subject groups were characterized and compared in terms of DKI-derived metrics for selected brain regions using analysis of covariance with a Tukey multiple comparison correction. Receiver operating characteristic (ROC) and binary logistic regression analyses were used to assess the utility of regional diffusion measures, alone and in combination, to discriminate each pair of subject groups. ROC analyses identified mean and radial kurtoses in the anterior corona radiata as the best individual discriminators of MCI from controls, with the measures having an area under the ROC curve (AUC) of 0.80 and 0.82, respectively. The next best discriminators of MCI from controls were diffusivity and kurtosis (both mean and radial) in the prefrontal white matter (WM), with each measure having an AUC between 0.77 and 0.79. Finally, the axial diffusivity in the hippocampus was the best overall discriminator of MCI from AD, having an AUC of 0.90. These preliminary results suggest that non-Gaussian diffusion MRI may be beneficial in the assessment of microstructural tissue damage at the early stage of MCI and may be useful in developing biomarkers for the clinical staging of AD.     

Correlations between microstructural alterations and severity of cognitive deficiency in Alzheimer's disease and mild cognitive impairment: a diffusional kurtosis imaging study

Object

Diffusional kurtosis imaging (DKI), a natural extension of diffusion tensor imaging (DTI), can characterize non-Gaussian diffusion in the brain. We investigated the capability of DKI parameters for detecting microstructural changes in both gray matter (GM) and white matter (WM) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) and sought to determine whether these DKI parameters could serve as imaging biomarkers to indicate the severity of cognitive deficiency.

Materials and Methods

DKI was performed on 18 AD patients and 12 MCI patients. Fractional anisotropy, kurtosis and diffusivity parameters in the temporal, parietal, frontal and occipital lobes were compared between the two groups using Mann–Whitney U test. The correlations between regional DKI parameters and mini-mental state examination (MMSE) score were tested using Pearson's correlation.

Results

In ADs, significantly increased diffusivity and decreased kurtosis parameters were observed in both the GM and WM of the parietal and occipital lobes as compared to MCIs. Significantly decreased fractional anisotropy was also observed in the WM of these lobes in ADs. With the exception of fractional anisotropy and radial kurtosis, all the five other DKI parameters exhibited significant correlations with MMSE score in both GM and WM.

Conclusion

Bearing additional information, the DKI model can provide sensitive imaging biomarkers for assessing the severity of cognitive deficiency in reference to MMSE score and potentially improve early detection and progression monitoring of AD based on characterizing microstructures in both the WM and especially the GM.     

Real-time animal functional magnetic resonance imaging and its application to neuropharmacological studies

In pharmacological magnetic resonance imaging (phMRI) with anesthetized animals, there is usually only a single time window to observe the dynamic signal change to an acute drug administration since subsequent drug injections are likely to result in altered response properties (e.g., tolerance). Unlike the block-design experiments in which fMRI signal can be elicited with multiple repetitions of a task, these single-event experiments require stable baseline in order to reliably identify drug-induced signal changes. Such factors as subject motion, scanner instability and/or alterations in physiological conditions of the anesthetized animal could confound the baseline signal. The unique feature of such functional MRI (fMRI) studies necessitates a technique that is able to monitor MRI signal in a real-time fashion and to interactively control certain experimental procedures. In the present study, an approach for real-time MRI on a Bruker scanner is presented. The custom software runs on the console computer in parallel with the scanner imaging software, and no additional hardware is required. The utility of this technique is demonstrated in manganese-enhanced MRI (MEMRI) with acute cocaine challenge, in which temporary disruption of the blood-brain barrier (BBB) is a critical step for MEMRI experiments. With the aid of real-time MRI, we were able to assess the outcome of BBB disruption following bolus injection of hyperosmolar mannitol in a near real-time fashion prior to drug administration, improving experimental success rate. It is also shown that this technique can be applied to monitor baseline physiological conditions in conventional fMRI experiments using blood oxygenation level-dependent (BOLD) contrast, further demonstrating the versatility of this technique.     

Semiautomated detection of cerebral microbleeds in magnetic resonance images

Cerebral microbleeds (CMBs) are increasingly being recognized as an important biomarker for neurovascular diseases. So far, all attempts to count and quantify them have relied on manual methods that are time-consuming and can be inconsistent. A technique is presented that semiautomatically identifies CMBs in susceptibility weighted images (SWI). This will both reduce the processing time and increase the consistency over manual methods. This technique relies on a statistical thresholding algorithm to identify hypointensities within the image. A support vector machine (SVM) supervised learning classifier is then used to separate true CMB from other marked hypointensities. The classifier relies on identifying features such as shape and signal intensity to identify true CMBs. The results from the automated section are then subject to manual review to remove false-positives. This technique is able to achieve a sensitivity of 81.7% compared with the gold standard of manual review and consensus by multiple reviewers. In subjects with many CMBs, this presents a faster alternative to current manual techniques at the cost of some lost sensitivity.     

Surface-based functional magnetic resonance imaging analysis of partial brain echo planar imaging data at 1.5 T

Surface-based functional magnetic resonance imaging (fMRI) analysis is more sensitive and accurate than volume-based analysis for detecting neural activation. However, these advantages are less important in practical fMRI experiments with commonly used 1.5-T magnetic resonance devices because of the resolution gap between the echo planar imaging data and the cortical surface models. We expected high-resolution segmented partial brain echo planar imaging (EPI) data to overcome this problem, and the activation patterns of the high-resolution data could be different from the low-resolution data. For the practical applications of surface-based fMRI analysis using segmented EPI techniques, the effects of some important factors (e.g., activation patterns, registration and local distortions) should be intensively evaluated because the results of surface-based fMRI analyses could be influenced by them. In this study, we demonstrated the difference between activations detected from low-resolution EPI data, which were covering whole brain, and high-resolution segmented EPI data covering partial brain by volume- and surface-based analysis methods. First, we compared the activation maps of low- and high-resolution EPI datasets detected by volume- and surface-based analyses, with the spatial patterns of activation clusters, and analyzed the distributions of activations in occipital lobes. We also analyzed the high-resolution EPI data covering motor areas and fusiform gyri of human brain, and presented the differences of activations detected by volume- and surface-based methods.     

In vivo structural analysis of articular cartilage using diffusion tensor magnetic resonance imaging

Purpose

The articular cartilage is a small tissue with a matrix structure of three layers between which the orientation of collagen fiber differs. A diffusion-weighted twice-refocused spin-echo echo-planar imaging (SE-EPI) sequence was optimized for the articular cartilage, and the structure of the three layers of human articular cartilage was imaged in vivo from diffusion tensor images.

Materials and Methods

The subjects imaged were five specimens of swine femur head after removal of the flesh around the knee joint, five specimens of swine articular cartilage with flesh present and the knee cartilage of five adult male volunteers. Based on diffusion-weighted images in six directions, the mean diffusivity (MD) and the fractional anisotropy (FA) values were calculated.

Results

Diffusion tensor images of the articular cartilage were obtained by sequence optimization. The MD and FA value of the specimens (each of five examples) under different conditions were estimated. Although the articular cartilage is a small tissue, the matrix structure of each layer in the articular cartilage was obtained by SE-EPI sequence with GRAPPA. The MD and FA values of swine articular cartilage are different between the synovial fluid and saline. In human articular cartilage, the load of the body weight on the knee had an effect on the FA value of the surface layer of the articular cartilage.

Conclusion

This method can be used to create images of the articular cartilage structure, not only in vitro but also in vivo. Therefore, it is suggested that this method should support the elucidation of the in vivo structure and function of the knee joint and might be applied to clinical practice.     

Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition

The analysis of information derived from magnetic resonance imaging (MRI) and spectroscopy (MRS) has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to investigate the efficiency of the combination of textural MRI features and MRS metabolite ratios by means of a pattern recognition system in the task of discriminating between meningiomas and metastatic brain tumors. The data set consisted of 40 brain MR image series and their corresponding spectral data obtained from patients with verified tumors. The pattern recognition system was designed employing the support vector machines classifier with radial basis function kernel; the system was evaluated using an external cross validation process to render results indicative of the generalization performance to “unknown” cases. The combination of MR textural and spectroscopic features resulted in 92.15% overall accuracy in discriminating meningiomas from metastatic brain tumors. The fusion of the information derived from MRI and MRS data might be helpful in providing clinicians a useful second opinion tool for accurate characterization of brain tumors.     

Discriminative analysis of multivariate features from structural MRI and diffusion tensor images

Imaging markers derived from magnetic resonance images, together with machine learning techniques allow for the recognition of unique anatomical patterns and further differentiating Alzheimer's disease (AD) from normal states. T1-based imaging markers, especially volumetric patterns have demonstrated their discriminative potential, however, rely on the tissue abnormalities of gray matter alone. White matter abnormalities and their contribution to AD discrimination have been studied by measuring voxel-based intensities in diffusion tensor images (DTI); however, no systematic study has been done on the discriminative power of either region-of-interest (ROI)-based features from DTI or the combined features extracted from both T1 images and DTI. ROI-based analysis could potentially reduce the feature dimensionality of DTI indices, usually from more than 10e + 5, to 10–150 which is almost equal to the order of magnitude with respect to volumetric features from T1. Therefore it allows for straight forward combination of intensity based landmarks of DTI indices and volumetric features of T1. In the present study, the feasibility of tract-based features related to Alzheimer's disease was first evaluated by measuring its discriminative capability using support vector machine on fractional anisotropy (FA) maps collected from 21 subjects with Alzheimer's disease and 15 normal controls. Then the performance of the tract-based FA + gray matter volumes-combined feature was evaluated by cross-validation. The combined feature yielded good classification result with 94.3% accuracy, 95.0% sensitivity, 93.3% specificity, and 0.96 area under the receiver operating characteristic curve. The tract-based FA and the tract-based FA + gray matter volumes-combined features are certified their feasibilities for the recognition of anatomical features and may serve to complement classification methods based on other imaging markers.     

A preliminary study of functional abnormalities in aMCI subjects during different episodic memory tasks

Functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of mild cognitive impairment (MCI) and early Alzheimer's disease (AD), because the memory dysfunction may occur before structural degeneration is obvious. In this research, we investigated the functional abnormalities of subjects with amnestic MCI (aMCI) using three episodic memory paradigms that are relevant to different memory domains in both encoding and recognition phases. Both whole-brain analysis and region-of-interest (ROI) analysis of the medial temporal lobes (MTL), which are central to the memory formation and retrieval, were used to compare the efficiency of the different memory paradigms and the functional difference between aMCI subjects and normal control subjects. We also investigated the impact of using different functional activation measurements in ROI analysis. This pilot study could facilitate the use of fMRI activations in the MTL as a marker for early detection and monitoring progression of AD.     

GUW-based structural damage detection using WPT statistical features and multiclass SVM

Recently, guided ultrasonic waves (GUW) are widely used for damage detection in structural health monitoring (SHM) of different engineering structures. In this study, an intelligent damage detection method is proposed to be used in SHM applications. At first, GUW signal is de-noised by discrete wavelet transform (DWT). After that, wavelet packet transform (WPT) is employed to decompose the de-noised signal and the statistical features of decomposed packets are extracted as damage-sensitive features. Finally, a multiclass support vector machine (SVM) classifier is used to detect the damage and estimate its severity. The proposed method is employed for GUW-based structural damage detection of a thick steel beam. The effects of different parameters on the sensitivity of the method are surveyed. Furthermore, by comparing with some other similar algorithms, the performance of the proposed method is verified. The experimental results demonstrate that the proposed method can appropriately detect a structural damage and estimate its severity.     

流形判别分析和支持向量机的恒星光谱数据自动分类方法

尽管经典的分类方法支持向量机SVM在天文学领域广泛应用,但其只考虑类间的绝对间隔而忽略类内的分布性状,因而分类性能有待于进一步提升。鉴于此,提出一种新颖的基于流形判别分析和支持向量机的恒星光谱数据自动分类方法。该方法引入流形判别分析的两个重要概念：基于流形的类内离散度M_W和基于流形的类间离散度M_B。所提方法找到的分类面同时保证M_W最小且M_B最大。可建立相应最优化问题,然后将原最优化问题转化为QP对偶形式求得支持向量和判别函数,最后利用判别函数判断测试样本的类属。该方法的最大优势在于进行分类决策时,不仅考虑样本的类间信息和分布特征,而且还保持了各类的局部流形结构。SDSS恒星光谱数据上的比较实验表明该方法的有效性。     

Region of interest and pixel-by-pixel analysis of dynamic contrast enhanced magnetic resonance imaging parameters and time-intensity curve shapes: a comparison in chondroid tumors

Dynamic contrast enhanced (DCE) MRI is widely acknowledged to be a helpful tool in the diagnosis and differentiation of tumors. In common clinical settings, the dynamic changes described by the time-intensity curves (TICs) are evaluated to find patterns of atypical tissue behavior, i.e., areas characterized by rapid contrast wash-in and wash-out. Despite the ease of this approach, there is no consensus about the specificity of the TIC shapes in discriminating tumor grades. We explore a new way of looking at TICs, where these are not averaged over a selected region of interest (ROI), but rendered pixel-by-pixel. In this way, the characteristic of the tissue is not given as a single TIC classification but as a distribution of the different TIC patterns. We applied this method in a group of patients with chondroid tumors and compared its outcome with the outcome of the standard ROI-based averaged TIC analysis. Furthermore, we focused on the problem of ROI selection in these tumors and how this affects the outcome of the TIC analysis. Finally, we investigated what relationship exists between the "standard" DCE-MRI parameter maximum enhancement (ME) and the TIC shape. CONCLUSIONS: We demonstrate that, where the ROI approach fails to show the presence of areas of rapid contrast wash-in and wash-out, the pixel-by-pixel approach reveals the coexistence of a heterogeneous pattern of TIC shapes. Secondly, we point out the differences in the DCE MRI parameters and tumor volume that can result when selecting the tumor based on DCE parameter maps or post-contrast T1-weighted images. Finally, we show that ME maps and TIC shape maps highlight different tissue areas and, therefore, the use of the ME maps is not appropriate for the correct identification of areas of atypical TICs.