The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the *

The apolipoprotein E ε4 (APOE ε4) allele is the most important genetic risk factor for Alzheimer's disease (AD); however, the underlying mechanisms responsible for it remain controversial. We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to examine the influence of APOE ε4 dose on clinical and neuroimaging biomarkers across the AD spectrum (from cognitive normal to AD patients with severe cognitive impairment). A total of 1718 participants from the ADNI cohort were selected, and we evaluated the impact of ε4 dose on cerebrospinal fluid (CSF) levels' Abeta1-42 (Aβ1-42), tau, and phosphorylated-tau (p-tau); cortical amyloid deposition (Florbetapir-PET-AV45); brain atrophy (MRI); brain metabolism (FDG-PET); hippocampal metabolism; and cognitive declines, through different cognitive subgroups. We found that (1) ε4 was associated with decreased CSF beta-amyloid (Aβ1-42) and increased cerebral Aβ deposition across the AD spectrum; (2) increased CSF tau, P-tau and cerebral hypometabolism, hippocampal atrophy, and cognition decline were all associated with APOE ε4 in prodromal AD stage; (3) increased CSF tau, P-tau and cerebral hypometabolism appear to begin earlier than hippocampal atrophy and cognitive decline. We hypothesized that APOE ε4 increases cerebral amyloid-β (Aβ) deposition in all the stages of AD development, and also influences Aβ-initiated cascade of downstream neurodegenerative effects, thereby increasing the risk of AD.

This study supported that BIN1 contributes to the risk of AD by altering neural degeneration (abnormal tau, brain atrophy and glucose metabolism) but not Aβ pathology.

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional magnetic resonance imaging based schizophrenia diagnosis. However, previous studies usually measure the fALFF with specific bands from 0.01-0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. As we konow, fALFF data is intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multi-frequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multi-frequency bands (i.e., slow-5:0.01-0.027 Hz, slow-4:0.027-0.073 Hz, slow-3:0.073-0.198 Hz and slow-2:0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches, and select those significant patches related to schizophrenia using random forest-based importancescore. Moreover, we use tree-structured sparse learning method for feature selection with above patches spatial constraint. Finally, considering biomarkers from multi-frequency bands can reflect complementary information among multiple frequency bands, we adopt the multi-kernel learning (MKL) method to combine features of multi-frequency bands for classification. Our experimental results show that these biomarkers from multi-frequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating the multi-frequency bands analysis can better account for classification of schizophrenia.