Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.
Alzheimer Disease/diagnosis* ; Humans ; Neuroimaging/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Magnetic Resonance Imaging ; Deep Learning ; Machine Learning

Silicon-based microring resonator (SMRR), as a typical application of label-free detection in optical biosensors, performs advantages such as small size, high sensitivity, and ease of integration, making it suitable for detecting physical, chemical, and biological signals. Alzheimer's disease (AD) is a common neurodegenerative disorder with high concealment, while early intervention can effectively slow its progression. For early diagnosis of AD, optical detection platforms based on SMRR can effectively overcome the challenges posed by low-abundance biomarkers and interfering factors in blood screening, demonstrating the potential for ultra-sensitive detection with low false positives. However, the clinical application of SMRR in AD detection is currently limited due to significant differences in optimized designs, the lack of commercial off-the-shelf chips and unified detection platforms, and high costs. Additionally, the biomarkers for early AD diagnosis are controversial, limiting their diagnostic utility. This paper reviews the sensing principles of SMRR and summarizes its research progress in the biomedical field. With AD as the research focus, we have discussed the main application limitations of SMRR-based detection technology and its clinical potential in early AD diagnosis. In the future, the standardization, integration, and universal applicability of silicon-based microring resonator technology may emerge as key development directions, aiming to develop mature commercial detection instruments and promote their widespread application in clinical diagnosis.
Silicon/chemistry* ; Biosensing Techniques/methods* ; Humans ; Alzheimer Disease/diagnosis* ; Biomarkers/analysis*

Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer's disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer's disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.
Humans ; Electroencephalography/methods* ; Alzheimer Disease/physiopathology* ; Dementia/physiopathology* ; Early Diagnosis ; Algorithms ; Brain/physiopathology* ; Machine Learning ; Frontotemporal Dementia/physiopathology* ; Diagnosis, Computer-Assisted

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Humans ; Mental Disorders/diagnosis* ; Alzheimer Disease/diagnosis* ; Brain Injuries ; Electroencephalography ; Recognition, Psychology

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.
Humans ; Alzheimer Disease/diagnosis* ; Neurodegenerative Diseases ; Early Diagnosis ; Cognitive Dysfunction ; Biomarkers

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.
Humans ; Alzheimer Disease/diagnosis* ; Neurodegenerative Diseases ; Early Diagnosis ; Cognitive Dysfunction ; Biomarkers

INTRODUCTION: This study aimed to elucidate the cognitive profile of patients with mild cognitive impairment with Lewy bodies (MCI-LB) and to compare it to that of patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD). METHODS: Subjects older than 60 years with probable MCI-LB (n = 60) or MCI-AD (n = 60) were recruited. All patients were tested with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) to assess their global cognitive profile. RESULTS: The MCI-AD and MCI-LB patients did not differ in total MMSE and MoCA scores. However, some sub-items in MMSE and MoCA were shown to be screening markers for differentiating MCI-LB from MCI-AD. In the visuoconstructive test, the total score and hands subitem score in the clock-drawing test were significantly lower in MCI-LB than in MCI-AD. As for the executive function, the 'animal fluency test', 'repeat digits backward test' and 'take paper by your right hand' in MMSE all showed lower scores in MCI-LB compared with MCI-AD. As for memory, 'velvet' and 'church' in MoCA and 'ball' and 'national flag' in MMSE had lower scores in MCI-AD than in MCI-LB. CONCLUSION This study presents the cognitive profile of patients with MCI-LB. In line with the literature on Dementia with Lewy bodies, our results showed lower performance on tests for visuoconstructive and executive function, whereas memory remained relatively spared in the early period.
Humans ; Cognitive Dysfunction ; Alzheimer Disease/diagnosis* ; Neuropsychological Tests ; Cognition

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neural Networks, Computer ; Magnetic Resonance Imaging/methods* ; Neuroimaging/methods* ; Diagnosis, Computer-Assisted ; Brain ; Cognitive Dysfunction

Alzheimer Disease/diagnosis* ; Cognitive Dysfunction ; Disease Progression ; Humans ; Neuroimaging