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

In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group ( P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
Humans ; Electroencephalography/methods* ; Cognitive Dysfunction/diagnosis* ; Neural Networks, Computer ; Sensitivity and Specificity

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'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 recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.
Humans ; Bayes Theorem ; Neural Networks, Computer ; Algorithms ; Brain ; Cognitive Dysfunction/diagnosis*

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

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

Idiopathic normal pressure hydrocephalus (iNPH) is a significant cause of the severe cognitive decline in the elderly population. There is no cure for iNPH, but cognitive symptoms can be partially alleviated through cerebrospinal fluid (CSF) diversion. In the early stages of iNPH, cognitive deficits occur primarily in the executive functions and working memory supported by frontostriatal circuits. As the disease progresses, cognition declines continuously and globally, leading to poor quality of life and daily functioning. In this review, we present recent advances in understanding the neurobiological mechanisms of cognitive impairment in iNPH, focusing on (1) abnormal CSF dynamics, (2) dysfunction of frontostriatal and entorhinal-hippocampal circuits and the default mode network, (3) abnormal neuromodulation, and (4) the presence of amyloid-β and tau pathologies.
Aged ; Cognitive Dysfunction/etiology* ; Humans ; Hydrocephalus, Normal Pressure/diagnosis* ; Peptide Fragments ; Quality of Life ; tau Proteins

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD
Alzheimer Disease/diagnosis* ; Brain/diagnostic imaging* ; Cognitive Dysfunction/diagnosis* ; Early Diagnosis ; Humans ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Neurodegenerative Diseases