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IntroductionAlzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI).MethodsIn this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD.ResultsThe experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age.DiscussionOur study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

ObjectiveTo evaluate the progression of brain glucose metabolism among participants with biological signature of Alzheimer’s disease (AD) and its relevance to cognitive decline.MethodWe studied 602 amyloid positive individuals who underwent 18F-fluorodeoxyglucose PET (FDG-PET) scan, 18F-AV-45 amyloid PET (AV45-PET) scan, structural MRI scan and neuropsychological examination, including 116 cognitively normal (CN) participants, 314 participants diagnosed as mild cognitive impairment (MCI), and 172 participants diagnosed as AD dementia. The first FDG-PET scan satisfying the inclusion criteria was considered as the baseline scan. Cross-sectional analysis were conducted with the baseline FDG-PET data to compare the regional differences between diagnostic groups after adjusting confounding factors. Among these participants, 229 participants (55 CN, 139 MCI, and 35 AD dementia) had two-year follow-up FDG-PET data available. Regional glucose metabolism was computed and the progression rates of regional glucose metabolism were derived from longitudinal FDG-PET scans. Then the group differences of regional progression rates were examined to assess whether glucose metabolism deficit accelerates or becomes stable with disease progression. The association of cognitive decline rate with baseline regional glucose metabolism, and progression rate in longitudinal data, were evaluated.ResultsParticipants with AD dementia showed substantial glucose metabolism deficit than CN and MCI at left hippocampus, in addition to the traditionally reported frontal and parietal–temporal lobe. More substantial metabolic change was observed with the contrast AD – MCI than the contrast MCI – CN, even after adjusting time duration since cognitive symptom onset. With the longitudinal data, glucose metabolism was observed to decline the most rapidly in the AD dementia group and at a slower rate in MCI. Lower regional glucose metabolism was correlated to faster cognitive decline rate with mild–moderate correlations, and the progression rate was correlated to cognitive decline rate with moderate-large correlations.Discussion and conclusionHippocampus was identified to experience hypometabolism in AD pathology. Hypometabolism accelerates with disease progression toward AD dementia. FDG-PET, particularly longitudinal scans, could potentially help predict how fast cognition declines and assess the impact of treatment in interventional trials.

Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.

IntroductionDue to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition. However, these methods ignore the unique spatial-temporal information of functional magnetic resonance imaging (fMRI) data, which is essential for FBN analysis.MethodsTo address this issue, we propose a spatial-temporal data-augmentation-based classification (STDAC) scheme that can fuse the spatial-temporal information, increase the samples, while improving the classification performance. Firstly, we propose a spatial augmentation module utilizing the spatial prior knowledge, which was ignored by previous augmentation methods. Secondly, we design a temporal augmentation module by random discontinuous sampling period, which can generate more samples than former approaches. Finally, a tensor fusion method is used to combine the features from the above two modules, which can make efficient use of spatial-temporal information of fMRI simultaneously. Besides, we apply our scheme to different types of classifiers to verify the generalization performance. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and REST-meta-MDD Project (MDD) dataset.ResultsExperimental results show that the proposed scheme achieves superior classification accuracy (ADNI: 82.942%, MDD: 63.406%) and feature interpretation on the benchmark datasets.DiscussionThe proposed STDAC scheme, utilizing both spatial and temporal information, can generate more diverse samples than former augmentation methods for brain disorder classification and analysis.

PurposeBrain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer’s disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient’s own MRI, and showcase its applicability in detecting AD pathology.MethodsWe included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities.ResultsThe model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects.ConclusionThis work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.

In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.

BackgroundAlzheimer’s Disease (AD) is an age-related neurodegenerative disease with a poorly understood etiology, shown to be partly genetic. Glucose hypometabolism, extracellular Amyloid-beta (Aβ) deposition, and intracellular Tau deposition are cardinal features of AD and display characteristic spatial patterns in the brain. We hypothesize that regional differences in underlying gene expression confer either resistance or susceptibility to AD pathogenic processes and are associated with these spatial patterns. Data-driven methods for the identification of genes involved in AD pathogenesis complement hypothesis-driven approaches that reflect current theories about the disease. Here we present a data driven method for the identification of genes involved in AD pathogenesis based on comparing spatial patterns of normal gene expression to Positron Emission Tomography (PET) images of glucose hypometabolism, Aβ deposition, and Tau deposition.MethodsWe performed correlations between the cerebral cortex microarray samples from the six cognitively normal (CN) post-mortem Allen Human Brain Atlas (AHBA) specimens and PET FDG-18, AV-45, and AV-1451 tracer images from AD and CN participants in the Alzheimer’s Disease and Neuroimaging Initiative (ADNI) database. Correlation coefficients for each gene by each ADNI subject were then entered into a partial least squares discriminant analysis (PLS-DA) to determine sets that best classified the AD and CN groups. Pathway analysisviaBioPlanet 2019 was then used to infer the function of implicated genes.ResultsWe identified distinct sets of genes strongly associated with each PET modality. Pathway analyses implicated novel genes involved in mitochondrial function, and Notch signaling, as well as genes previously associated with AD.ConclusionUsing an unbiased approach, we derived sets of genes with expression patterns spatially associated with FDG hypometabolism, Aβ deposition, and Tau deposition in AD. This methodology may complement population-based approaches for identifying the genetic underpinnings of AD.

Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.