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Alzheimer's Disease (AD) is a progressive irreversible neurodegeneration disease and thus timely identification is critical to delay its progression.In this work, we focus on the traditional branch to design discriminative feature extraction and selection strategies to achieve explainable AD identification. Specifically, a spatial pyramid based three-dimensional histogram of oriented gradient (3D-HOG) feature learning method is proposed. Both global and local texture changes are included in spatial pyramid 3D-HOG (SPHOG) features for comprehensive analysis. Then a modified wrapper-based feature selection algorithm is introduced to select the discriminative features for AD identification while reduce feature dimensions.Discriminative SPHOG histograms with various resolutions are selected, which can represent the atrophy characteristics of cerebral cortex with promising performance. As subareas corresponding to selected histograms are consistent with clinical experience, explanatory is emphasized and illustrated with Hippocampus.Experimental results illustrate the effectiveness of the proposed method on feature learning based on samples obtained from common dataset and a clinical dataset. The proposed method will be useful for further medical analysis as its explanatory on other region-of-interests (ROIs) of the brain for early diagnosis of AD.

Diffusion magnetic resonance imaging (dMRI) provides a noninvasive method for measuring brain tissue microstructure. q -Space deep learning( q -DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep networks are trained to learn the mapping directly from diffusion signals to tissue microstructure. However, the quality of tissue microstructure estimation can be limited not only by the reduced number of diffusion gradients but also by the low spatial resolution of typical dMRI acquisitions. Therefore, in this work we extend q -DL to super-resolved tissue microstructure estimation and propose super-resolved q -DL (SR- q -DL), where deep networks are designed to map low-resolution diffusion signals undersampled in the q -space to high-resolution tissue microstructure. Specifically, we use a patch-based strategy, where a deep network takes low-resolution patches of diffusion signals as input and outputs high-resolution tissue microstructure patches. The high-resolution patches are then combined to obtain the final high-resolution tissue microstructure map. Motivated by existing q -DL methods, we integrate the sparsity of diffusion signals in the network design, which comprises two functional components. The first component computes sparse representation of diffusion signals for the low-resolution input patch, and the second component maps the low-resolution sparse representation to high-resolution tissue microstructure. The weights in the two components are learned jointly and the trained network performs end-to-end tissue microstructure estimation. In addition to SR- q -DL, we further propose probabilistic SR- q -DL, which can quantify the uncertainty of the network output as well as achieve improved estimation accuracy. In probabilistic SR- q -DL, a deep ensemble strategy is used. Specifically, the deep network for SR- q -DL is revised to produce not only tissue microstructure estimates but also the uncertainty of the estimates. Then, multiple deep networks are trained and their results are fused for the final prediction of high-resolution tissue microstructure and uncertainty quantification. The proposed method was evaluated on two independent datasets of brain dMRI scans. Results indicate that our approach outperforms competing methods in terms of estimation accuracy. In addition, uncertainty measures provided by our method correlate with estimation errors, which indicates potential application of the proposed uncertainty quantification method in brain studies.

Background and Objective In recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer’s disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data. Methods A new subspace-based sparse feature learning method is proposed, which builds a union-of-subspace representation model to realize feature extraction and disease identification. Specifically, the proposed method estimates feature dimensions reasonably, at the same time, it protects local features for the specified ROIs of the brain, and realizes image-based feature extraction and classification automatically instead of computing the volume of GM or cortical thickness preliminarily. Results Experimental results illustrate the effectiveness and robustness of the proposed method on feature extraction and classification, which are based on the sampled clinical dataset from Peking University Third Hospital of China and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The extracted image-based feature parameters describe the atrophy of ROIs of the brain well as clinical parameters but show better performance in AD identification than clinical parameters. Based on them, the important ROIs for AD identification can be identified even for correlated variables. Conclusion The extracted features and the proposed identification parameters show high correlation with the volume of GM and the clinical mini-mental state examination (MMSE) score respectively. The proposed method will be useful in denoting the changes of cerebral pathology and cognitive function in AD patients.

Purpose To evaluate cerebral blood flow (CBF) in patients with Alzheimer's disease (AD) using a three-dimensional pseudocontinuous arterial spin labeling (PCASL). We aimed to study the effects of different post label delay on the resulting CBF maps and to investigate the characteristics and clinical applications of brain perfusion. Materials and methods Sixteen AD patients and nineteen healthy control subjects were recruited. 3D PCASL was performed using a 3.0 T MR scanner. ASL was performed twice with different post label delays (PLD). Comparisons of CBF were made between AD patients and healthy control subjects respectively with PLD of 1.5 s and PLD of 2.5 s. Relationship between the CBF values and cognition was investigated using correlation analysis. A receiver operating characteristic (ROC) curve was generated for CBF measurements in posterior cingulate region. Result AD patients with PLD of 1.5 s showed lower CBF values primarily in bilateral temporal lobes, precuneus, middle and posterior cingulate gyri, left inferior parietal gyrus, left angular gyrus and left superior frontal gyrus. Lowered cerebral values were also observed in similar regions with PLD of 2.5 s, but the clusters of voxel were smaller. CBF values were associated with cognition scores in most of gyri mentioned above. Conclusion Hypoperfusion areas were observed in AD patients. PLD of 1.5 s was sufficient to display CBF. Considering the complicated AD pathology, multiple PLDs are strongly recommended.