With the growth of aging population and the heavier burden that people bear in the modern world, the incidence of neurological and mental disorders grows year by year worldwide, which draws the attention of more and more medical doctors and neuroimaging researchers. The development of neuroimaging technologies, especially magnetic resonance imaging (MRI) technology, makes it possible for people to observe the brain structures and functions non invasively, providing a new avenue for diagnosis of brain disease and researches on brain functions. However, due to the complexity of brain structure and the continuously increased amount of imaging data, it is difficult for researchers and medical doctors to efficiently extract helpful information that is relevant to the disease, or to make correct diagnosis. In recent years, the application of pattern recognition technologies in neuroimaging community makes possible the computer aided diagnosis and localization of disease focus. This paper introduces the recent researches related to pattern classification in neuroimaging community, and puts forward the classification frameworks for prediction of Alzheimer's patients and children with attention deficit/hyperactivity disorder. The main contributions of this paper are as the following: (1) For classification of Alzheimer's disease, this paper presents a classification framework based on cortical thickness networks. In this work, we construct a network for each subject using the mean cortical thickness of different brain regions, and the edge weight of these networks are used as features to train an SVM classifier. Because the network features pay more attention on the relationships of cortical thickness change between different brain areas which is caused by the diseases, they are not sensitive to the individual differences and therefore are more stable and reliable. The experiments on the public OASIS datasets show that the best accurary of our method is 90.4%, reaching or even exceeding the results of related studies at home and abroad. In addition, we also use a hybrid method for feature selection, which can ensure the performance and reduce the computational complexity of the feature selection process. (2) As for classification of attention deficit/hyperactivity disorder, this paper presents a classification framework based on multi-kernel learning to fuse the features extracted from multimodal imaging. First several features such as cortical thickness, gray...
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