Background: Alzheimer’s disease (AD) is a common neurodegenerative disease occurring in the elderly population. The effective and accurate classification of AD symptoms by using functional magnetic resonance imaging (fMRI) has a great significance for the clinical diagnosis and prediction of AD patients. Methods: Therefore, this paper proposes a new method for identifying AD patients fromhealthy subjects by using functional connectivities (FCs) between the activity voxels in the brain based on fMRI data analysis. Firstly, independent component analysis is used to detect the activity voxels in the fMRI signals of AD patients and healthy subjects; Secondly, the FCs between the common activity voxels of the two groups are calculated, and then the FCs with significant differences are further identified by statistical analysis between them; Finally, the classification of AD patients fromhealthy subjects is realized by using FCs with significant differences as the feature samples in support vector machine. Results: The results show that the proposed identification method can obtain higher classification accuracy, and the FCs between activity voxels within prefrontal lobe as well as those between prefrontal and parietal lobes play an important role in the prediction of AD patients. Furthermore, we also find that more brain regions and much more voxels in some regions are activity in AD group compared with health control group. Conclusion: It has a great potential value for the AD pathogenesis mechanism study.

Compared with independent component analysis (ICA), constrained ICA (CICA) has unique advantages in functional magnetic resonance image (fMRI) data analysis by introducing some priori information into the estimation process. However, there are still some controversies in the current CICA methods, such as how to choose the threshold parameter to restrain the similarity, and how to reduce the accuracy requirements for the priori information. In this paper, we propose a new constrained spatiotemporal ICA (CSTICA) method based on the framework of multi-objective optimization, where the inequality constraint of traditional CICA method is transformed into the objective optimization function of CSTICA, and both temporal and spatial priori information are included simultaneously. The simulated, hybrid and real fMRI data experiments are designed to evaluate the performance of the proposed CSTICA method in comparison with classical ICA and CICA methods. Compared with the traditional CICA methods, CSTICA has circumvented the problem of threshold parameter selection. Furthermore, the experimental results demonstrate that the source recovery ability of CSTICA has been improved to a certain extent especially in the cases of priori information with low accuracies. Meanwhile, the results also indicate that CSTICA reduces dependency on the accuracy of priori information.

Granger causality (GC) is one of the most popular measures to investigate causality influence among brain regions and has been achieved significant results for exploring brain networks based on functional magnetic resonance imaging (fMRI). However, the predictors and order selection of conventional GC are based on linear models which result in such restrictions as poorly detection of nonlinearity and so on, in the application. This paper proposes a novel GC model called back propagation (BP) based kernel function Granger causality (BP_KFGC), in which symplectic geometry is used for embedding dimension and fuzzy inference system for predicting time series. The proposed method doesn’t depend on the prediction of the vector auto-regression model, so that time series don’t need to be wide-sense stationary as linear GC and kernel GC. In addition, it is a multivariate approach which is applicable to both linear and nonlinear systems and eliminates the effects of latent variables. The performance of the new method is evaluated and compared with linear GC, partial GC, neural network GC and kernel GC by simulated data with multiple adjustments to the nonlinearity. The results show that BP_KFGC outperforms the other four methods in detecting both linear and nonlinear causalities. Furthermore, we applied BP_KFGC to construct directed weight network (DWN) of Alzheimer's disease (AD) patients and health controls (HCs), and then nine graph-based features of DWN were used for classification by the classifier of support vector machine with radial basis kernel function. The accuracy of 95.89%, sensitivity of 93.31%, and specificity of 94.97% were achieved which may provide an auxiliary mean for the clinical diagnosis of AD.

Objective: The study of pathogenic mechanism at the genetic level by imaging genetics methods enables to effectively reveal the association of histopathology and genetics. However, there is a lack of effective and accurate tools to establish association models from macroscopic to microscopic. Methods: The multi-constrained joint non-negative matrix factorization (MCJNMF) was developed for simultaneous integration of genomic data and image data to identify common modules related to disease. Two types of data matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. Meanwhile, the correlation between original data features was integrated by using regularization constraints to improve the biological relevance. Sparsity constraints and orthogonal constraints were performed on decomposition factors to minimize the redundancy between different bases and to reduce algorithm complexity. Results: This algorithm was successfully performed on the module identification of lung metastasis in soft tissue sarcomas (STSs) by integrating FDG-PET image and DNA methylation data features. Multilevel analysis on the top extracted modules revealed that these modules were closely related to the lung metastasis. Particularly, several genes with diagnostic potential for lung metastasis can be discovered from high score modules. Conclusion: This method not only can be applied for the accurate identification of patterns related to pathogenic mechanism of diseases, but also has a significant implication for discovering protein biomarkers. Significance: This method provides avenues for further studies of identifying complex association patterns of diseases according to different types of biological data.

Migraine is a common, chronic dysfunctional disease with recurrent headaches. Its etiology and pathogenesis have not been fully understood and there is a lack of objective diagnostic criteria and biomarkers. Meanwhile, resting-state functional magnetic resonance imaging (RS-fMRI) is increasingly being used in migraine research to classify and diagnose brain disorders. However, the RS-fMRI data is characterized by a large amount of data information and the difficulty of extracting high-dimensional features, which brings great challenges to relevant studies. In this paper, we proposed an automatic recognition framework based on static functional connectivity (sFC) strength features and dynamic functional connectome pattern (DFCP) features of migraine sufferers and normal control subjects, in which we firstly extracted sFC strength and DFCP features and then selected the optimal features using the recursive feature elimination based on the support vector machine (SVM−RFE) algorithm and, finally, trained and tested a classifier with the support vector machine (SVM) algorithm. In addition, we compared the classification performance of only using sFC strength features and DFCP features, respectively. The results showed that the DFCP features significantly outperformed sFC strength features in performance, which indicated that DFCP features had a significant advantage over sFC strength features in classification. In addition, the combination of sFC strength and DFCP features had the optimal performance, which demonstrated that the combination of both features could make full use of their advantage. The experimental results suggested the method had good performance in differentiating migraineurs and our proposed classification framework might be applicable for other mental disorders.