Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

The rich club, as a community of highly interconnected nodes, serves as the topological center of the network. However, the similarities and differences in how the rich club supports functional integration and segregation in the brain across different species remain unknown. In this study, we first detected and validated the rich club in the structural networks of mouse, monkey, and human brains using neuronal tracing or diffusion magnetic resonance imaging data. Further, we assessed the role of rich clubs in functional integration, segregation, and balance using quantitative metrics. Our results indicate that the presence of a rich club facilitates whole-brain functional integration in all three species, with the functional networks of higher species exhibiting greater integration. These findings are expected to help to understand the relationship between brain structure and function from the perspective of brain evolution.
Animals ; Humans ; Brain/diagnostic imaging* ; Mice ; Male ; Nerve Net/diagnostic imaging* ; Macaca ; Female ; Neural Pathways/diagnostic imaging* ; Magnetic Resonance Imaging ; Biological Evolution ; Adult ; Diffusion Magnetic Resonance Imaging ; Brain Mapping ; Species Specificity ; Mice, Inbred C57BL

Humans ; Artificial Intelligence ; Algorithms ; Brain ; Head

Objective To observe the alterations in functional complexity of brain regions in autism spectrum disorder(ASD)patients and correlations with the predicted brain age.Methods Open brain resting-state functional MRI(rs-MRI)data of 93 ASD patients and 96 typically developing adolescents(healthy subjects)were downloaded.The functional complexity in brain regions were extracted with self-developed virtual digital brain software,and the alterations in functional complexity of brain regions in ASD patients and correlations with their ages were analyzed.Two networks were prospectively trained with data of 65 ASD patients and 67 healthy subjects as the training set to predict brain age,and the results were evaluated,and the predicting errors were compared using test set,i.e.the other 28 ASD patients and 29 healthy subjects.Results Compared to healthy subjects,on the basis of anatomical automatic labeling(AAL)atlas,ASD patients exhibited significantly reduced functional complexity based on Shannon entropy in the left precuneus,left cuneus and right parahippocampal gyrus.Conversely,functional complexity of ASD patients based on permutation entropy significantly increased in the left cuneus and right cerebellar Crus Ⅱ region.The left hippocampus showed reduced functional complexity based on Pearson correlation coefficient,while the left middle temporal gyrus showed increased functional complexity based on Pearson correlation coefficient.The functional complexity in brain regions of ASD patients were not closely correlated with ages(all|r|＜0.4).According to the trained fully connected network,the predicted brain ages of ASD patients and healthy subjects in test set were all lower than their physiological ages,but no significant difference was found between the prediction errors of ASD patients and healthy subjects(P=0.283).Conclusion Functional complexity changed in some brain region functions in ASD patients.The predicted brain ages of ASD patients based on the obtained fully connected network were on the low side,but not obviously affected by the alterations of functional complexity in brain regions.

Objective To explore the value of artificial intelligence federated learning system based on chest X-ray films for pathogen diagnosis of community-acquired pneumonia(CAP)in children.Methods Totally 900 cases of CAP children from 2 hospitals were retrospectively enrolled,including bacterial,viral and mycoplasma CAP(each n=300),and chest posteroanterior X-ray films were collected.Meanwhile,chest posteroanterior X-ray films of 5856 children from the publicly available dataset GWCMCx were collected,including 4273 CAP images and 1583 healthy chest images.All above 6756 images were divided into training set(n=5359)and validation set(n=1397)at the ratio of 8∶2.Then a pathogen diagnosis model of children CAP was established based on attention mechanism.Binary and ternary diagnostic algorithms were designed,and federated deployment training was performed.The efficacy of this system for pathogen diagnosis of children CAP was analyzed and compared with DenseNet model.Results Based on all data,the accuracy of the obtained artificial intelligence federated learning system model for diagnosing children CAP was 97.00%,with the area under the curve(AUC)of 0.990.Based on hospital data,the AUC of this system using single imaging data and clinical-imaging data for pathogen diagnosis of children CAP was 0.858 and 0.836,respectively,both better than that of DenseNet model(0.740,both P＜0.05).Conclusion The artificial intelligence federated learning system based on chest X-ray films could be used for pathogen diagnosis of children CAP.

Objective　To establish a dual droplet digital PCR (ddPCR) assay for herpes simplex virus type I (HSV-1) and varicella-zoster virus (VZV). Methods The specific primers and probes were derived based on the conserved regions of HSV-1 and VZV genome. The primer-probe combinations were screened, and the annealing temperatures and primer-probe concentration ratios of the dual-droplet digital PCR reaction were optimized to establish a dual-droplet digital PCR reaction system for HSV-1 and VZV, which was tested for other viruses and validated for clinical samples. The sensitivity, specificity, and reproducibility of the established dual microtiter digital PCR method were analyzed. Results The optimal concentrations of primers and probes for the dual ddPCR detection method of HSV-I and VZV were determined to be 800 nmol/L and 250 nmol/L, respectively, with an optimal annealing temperature of 56 ℃. The correlation coefficient (R2) of the standard curve of the dual ddPCR assay was 0.99, showing a clear linear relationship. The method showed high sensitivity, with the lowest detection limit of herpes simplex virus type I being 2.97 copies/μL, and for VZV being 2.73 copies/μL. The repeatability was high with a small coefficient of variation and stable detection results; the specificity was excellent, and no cross-reaction was found with herpes simplex virus type Ⅱ, Epstein-Barr virus, Adenovirus, Coxsackievirus (CA6/CA10/CA16), Cytomegalovirus, Human Cytomegalovirus, Human enterovirus 71, Japanese Encephalitis virus, West Nile virus, Measles virus, Mumps virus, and human nucleic acids. Conclusions The dual droplet digital PCR assay for herpes simplex virus type I and varicella-zoster virus established in this experiment has strong sensitivity, specificity, and high repeatability, and can provide a solution for rapid quantitative detection of the two viruses in different scenarios.

Connectome/methods* ; Magnetic Resonance Imaging/methods* ; Brain/diagnostic imaging* ; Head ; Brain Mapping/methods* ; Nerve Net

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
Humans ; White Matter/diagnostic imaging* ; Diffusion Tensor Imaging/methods* ; Alzheimer Disease/complications* ; Reproducibility of Results ; Cognition ; Cognitive Dysfunction/complications* ; Brain/diagnostic imaging*

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal-occipital areas to the temporal-parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.

COVID-19 had caused the epidemic in Wuhan of China in December 2019. The asymptomatic infection of COVID-19 was found with the further research. This paper summarizes the discovery of the asymptomatic infection cases, analyzes their outcomes and transmission risks, and put forward the targeted suggestions for the prevention and control of asymptomatic infection of COVID-19 according to the existing problems in epidemic response.