Obsessive-compulsive disorder (OCD) is a mental disorder characterized by repeated thoughts or behaviors, which is also associated with anxiety and tics. Clinically, the diagnosis of OCD mainly depends on subjects symptoms and psychological rating scales. In this study, we proposed an imaging based diagnosis method using functional MRI to classify OCD patients and healthy controls, with a novel log Euclidean based kernel Principal Component Analysis (PCA) as feature extractor. In particular, functional connectivity (FC) matrix was computed for each subject as the FC correlations of each pair of brain regions of interest. To better reduce feature dimension and extract the most discriminative features, we propose to use log Euclidean geodesic distance as the distance of two matrices and apply a Gaussian kernel PCA to FC matrix for feature extraction, given the graph Laplacian matrix of a FC matrix is symmetric positive define (SPD) matrix and the set of SPD matrix forms a Riemannian manifold. We further employed gradient boosted decision trees (XGBoost) to classify the features extracted from log Euclidean based kernel PCA to diagnosis patient groups. Results show that the classification accuracy reaches 91.8% with 90.7% sensitivity and 92.6% specificity, which outperforms current start-of-the-art imaging based diagnosis methods such as 85% in an EEG study. Next, by evaluating the feature importance in the classifier, we found that most contributed connections are cerebellum related, such as cerebellar vermis. These findings may help the understanding of pathology of OCD and provide a surrogate means for clinical diagnosis.
Objective and efficient diagnosis of Alzheimer’s disease (AD) has been a major topic with extensive researches in recent years, and some promising results have been shown for imaging markers using magnetic resonance imaging (MRI) data. Beside conventional machine learning methods, deep learning based methods have been developed in several studies, where layer-by-layer neural network settings were purposed to extract features for disease classification from the patches or whole images. However, as the disease develops from subcortical nuclei to cortical regions, specific brain regions with morphological changes might contribute to the diagnosis of disease progress. Therefore, we propose a novel spatial and depth weighted neural network structure to extract effective features, and further improve the performance of AD diagnosis. Specifically, we first use group comparison to detect the most distinctive AD-related landmarks, and then sample landmark-based image patches as our training data. In the model structure, with a 15-layer DenseNet as backbone, we introduce a attention bypass to estimate the spatial weights in the image space to guide the network to focus on specific regions. A squeeze-and-excitation (SE) mechanism is also adopted to further weight the feature map channels. We used 2335 subjects from public datasets (i.e., ADNI-1, ADNI-2 and ADNI-GO) for experiment and results show that our framework achieves 90.02% accuracy, 81.25% sensitivity, and 96.33% specificity in diagnosis AD patients from normal controls.