A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.

BACKGROUND: Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. METHODS: This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan-Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. RESULTS: The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aß) deposition (bootstrapped average causal mediation effect: ß = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: ß = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. CONCLUSIONS: This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network.

OBJECTIVE: The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. METHODS: The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. RESULTS: The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. INTERPRETATION: The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.

Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal-to-noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine.

Evidence-based treatment is the basis of traditional Chinese medicine (TCM), and the accurate differentiation of syndromes is important for treatment in this context. The automatic differentiation of syndromes of unstructured medical records requires two important steps: Chinese word segmentation and text classification. Due to the ambiguity of the Chinese language and the peculiarities of syndrome differentiation, these tasks pose a daunting challenge. We use text classification to model syndrome differentiation for TCM, and use multi-task learning (MTL) and deep learning to accomplish the two challenging tasks of Chinese word segmentation and syndrome differentiation. Two classic deep neural networks­bidirectional long short-term memory (Bi-LSTM) and text-based convolutional neural networks (TextCNN)­are fused into MTL to simultaneously carry out these two tasks. We used our proposed method to conduct a large number of comparative experiments. The experimental comparisons showed that it was superior to other methods on both tasks. Our model yielded values of accuracy, specificity, and sensitivity of 0.93, 0.94, and 0.90, and 0.80, 0.82, and 0.78 on the Chinese word segmentation task and the syndrome differentiation task, respectively. Moreover, statistical analyses showed that the accuracies of the non-joint and joint models were both within the 95% confidence interval, with pvalue < 0.05. The experimental comparison showed that our method is superior to prevalent methods on both tasks. The work here can help modernize TCM through intelligent differentiation.

Inner surface of Nepenthes slippery zone: ratchet effect of lunate cells causes anisotropic superhydrophobicity.

Inner surface of Nepenthes slippery zone shows anisotropic superhydrophobic wettability. Here, we investigate what factors cause the anisotropy via sliding angle measurement, morphology/structure observation and model analysis. Static contact angle of ultrapure-water droplet exhibits the value of 154.80°-156.83°, and sliding angle towards pitcher bottom and up is 2.82 ± 0.45° and 5.22 ± 0.28°, respectively. The slippery zone under investigation is covered by plenty of lunate cells with both ends bending downward, and a dense layer of wax coverings without directional difference in morphology/structure. Results indicate that the slippery zone has a considerable anisotropy in superhydrophobic wettability that is most likely caused by the lunate cells. A model was proposed to quantitatively analyse how the structure characteristics of lunate cells affect the anisotropic superhydrophobicity, and found that the slope/precipice structure of lunate cells forms a ratchet effect to cause ultrapure-water droplet to roll towards pitcher bottom/up in different order of difficulty. Our investigation firstly reveals the mechanism of anisotropic superhydrophobic wettability of Nepenthes slippery zone, and inspires the bionic design of superhydrophobic surfaces with anisotropic properties.

KCNQ1 and KCNH2 mutations associated with long QT syndrome in a Chinese population.

The long QT syndrome (LQTS) is a cardiac disorder characterized by prolongation of the QT interval on electrocardiograms (ECGs), syncope and sudden death caused by a specific ventricular tachyarrhythmia known as torsade de pointes. LQTS is caused by mutations in ion channel genes including the cardiac sodium channel gene SCN5A, and potassium channel subunit genes KCNQ1, KCNH2, KCNE1, and KCNE2. Little information is available about LQTS mutations in the Chinese population. In this study, we characterized 42 Chinese LQTS families for mutations in the two most common LQTS genes, KCNQ1 and KCNH2. We report here the identification of four novel KCNQ1 mutations and three novel KCNH2 mutations. The KCNQ1 mutations include L191P in the S2-S3 cytoplasmic loop, F275S and S277L in the S5 transmembrane domain, and G306V in the channel pore. The KCNH2 mutations include L413P in transmembrane domain S1, E444D in the extracellular loop between S1 and S2, and L559H in domain S5. The location and character of these mutations expand the spectrum of KCNQ1 and KCNH2 mutations causing LQTS. Excitement, exercises, and stress appear to be the triggers for developing cardiac events (syncope, sudden death) for LQTS patients with KCNQ1 mutations F275S, S277L, and G306V, and all three KCNH2 mutations L413P, E444D and L559H. In contrast, cardiac events for an LQTS patient with KCNQ1 mutation L191P occurred during sleep or awakening from sleep. KCNH2 mutations L413P and L559H are associated with the bifid T waves on ECGs. Inderal or propanolol (a beta blocker) appears to be effective in preventing arrhythmias and syncope for an LQTS patient with the KCNQ1 L191P mutation.