The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of functional changes in the brain throughout development and aging. Specifically, global metastable state provides a overall perspective on the flexibility of brain reorganization, while the evolution trajectories of transient functional patterns capture detailed changes in brain activity. The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of highdimensional raw brain data may overlook non-linear information and lose some relationships between features. We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. This approach clusters to identify more distinct metastable brain states and is applied to the analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data across the human lifespan. This paper investigates age-related differences and continuity changes from different perspectives: metastable state indicators and state trajectory indicators (occurrence probability, lifetime, and state transition metrics). Global metastable state shows a linear decline with age, while both linear and quadratic effects of age-related changes are observed in detailed state metastable and state trajectory indicators. Finally, the proposed feature extraction scheme demonstrates good classification performance for categorizing brain age groups. These findings can help us understand the self-organizing reorganization characteristics associated with aging and their complex dynamic changes, providing new insights into brain development throughout the entire lifespan.

功能脑网络中不同的模板定义导致网络规模差异极大,进一步影响所构建网络的结构及其拓扑属性。但是,在机器学习方法中网络规模差异是如何影响特征选择策略及分类准确率并不清楚。研究中采用5种不同节点规模的模板进行脑网络构建,在此基础上选择脑网络的三个局部特征用SVM方法构建分类器进行抑郁症患者的识别。结果表明,节点规模较大的模板的分类准确率较高;同时,在不同节点规模下传统的P值的特征选择方法均是可行的,但其阈值设置过于严格。

Objective: The unclear propagation pathway of seizures is one of the main reasons for failure of surgical treatment, and the propagation process involves the directional brain networks. However, few network analysis techniques have successfully traced specific seizure propagation pathways. This study proposed a stepwise transfer entropy (STE) approach to describe the propagation of effective connections in brain networks. Methods: The proposed STE technique is applied to stereoelectroencephalography (SEEG) data collected from patients with epilepsy, which can identify characteristic regions connected to specific seed brain regions at different link-step levels. Importantly, the underlying TE-based network construction method can accurately obtain electrode-to-electrode connections, and the stepwise approach can capture the interactions between electrodes at the connection level. Finally, simulation and clinical data were used to evaluate the STE approach according to the similarity, confusion matrix, accuracy and recall. Results: We used three datasets: a simulation dataset, a clinical dataset, and a public dataset. We compared the results of different network construction methods with multiple datasets, and the STE approach successfully captured node changes in epilepsy patients, effectively identified early and late propagation nodes, and determined the propagation pathway. Moreover, the STE approach is superior to other methods in all evaluation indices, achieving 97.9% accuracy and 0.93 similarity. Significance: Compared with the existing methods, the STE approach has significant advantages in accurately tracking propagation pathways. Moreover, the STE approach is simple and quickly performs calculations, making it an easy-to-use promising method for determining propagation pathways in clinical settings. © 2023 The Author(s)

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression.; PURPOSE: This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance.; METHODS: Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method.; RESULTS: The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression.; CONCLUSIONS: This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD. © 2024 American Association of Physicists in Medicine.

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called Leading Eigenvector Dynamics Analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, inter group differences in brain dynamic activity indicators are examined. And the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual regions activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state, and more active in limbic regions activity state and visual regions activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.

为了解决图像压缩感知重建研究领域中通过有效的图像先验信息重构与原图相似性高且保留细节消除伪影的高质量图像的问题,针对不足采样的K空间数据,在经典的CNN算法CBDNet算法的基础上,通过融合深度学习先验信息及传统图像恢复各自优势的方法,研究了基于深度神经网络去噪先验和BM3D块压缩感知算法的混合式重构算法.该算法采用交互式方法训练多尺度残差网络抑制噪声水平,借优化选择的方式将深度学习与传统块匹配多尺度结合以提取图像不同尺度的特征数据从而实现抑制伪影、快速重建高质量MRI.实结果表明深度学习结合BM3D在MR图像重构领域能够有效降低伪影保留细节信息,加强重构效果.与此同时,通过采用GPU的加速运算,算法的计算复杂度较使用单一算法并未增加很多.可见基于卷积盲降噪的混合式核磁共振成像效果更佳.

在阿尔兹海默症分类问题中,超图神经网络可以从被试间的超图关系中提取特征,在表示学习复杂图结构方面具有很好的优势,但大多数模型都直接或间接地将超图所表示的被试间的高阶复杂关系分解,转化为简单的二元关系进行特征学习,没有有效利用超边的高阶信息,因此提出了基于线-超图神经网络（line-hypergraph neural network, L-HGNN）的阿尔兹海默症分类模型,该模型利用稀疏线性回归表征被试间多元相关性,借助超图和线图的转换在神经网络模型中实现节点的高阶邻域信息传递和超边整体结构特征学习,同时,结合注意力机制生成更具区分性的节点嵌入,进而用于阿尔兹海默症的辅助诊断.在ADNI数据上与常用的两种方法比较,实验结果表明,该方法能有效提高分类准确率,在阿尔兹海默症早期诊断上具有重要的应用价值.

非增强CT扫描是急诊室诊断疑似脑出血的首选方法，医疗人员通常借助CT图像对疑似急性脑出血患者病灶部位进行手动分割，进而根据临床经验进行分类，这种人工诊断的方式对医师的经验要求较高，主观性较强，将分割和分类任务分开执行，不能充分利用两个任务间相关联的特征信息，时间成本高，增大了基于CT图像快速进行脑出血病灶部位分割及分类的难度。针对上述问题，文中提出了一种共享浅层参数多任务学习的脑出血图像分割及分类模型，一方面，根据不同任务学习的难易程度对损失函数的权值进行优化，另一方面，在多任务学习网络的浅层实现公有信息共享，深层提取不同任务的私有信息，获取更具代表性的特征，从而快速、准确地对脑出血患者的CT图像进行分割及分类。实验结果表明，共享浅层参数多任务学习网络生成的分割标注与真实标注有较好的视觉一致性。在最优权值下所有被试的平均Dice系数（DSC）为0.828,敏感度为0.842,特异度为0.985,阳性预测值（PPV）为0.838。共享浅层参数多任务学习网络分类的准确率、敏感度、特异度和AUC值分别为95.00%,90.48%,100.00%和0.982。与单任务深度学习、Y-Net以及借助分类辅助的多任务学习相比，该方法更加有效地利用了相关任务信息，同时通过调节损失函数权值，提升了出血病灶区域的分割和分类精度。

网络数据中出现的大量节点属性和随时间变化的特征，给链路预测提出了新挑战。基于注意力机制和循环神经网络对随时间演化网络进行建模，提出了DTA-LP模型。与传统的静态链路预测算法相比，DTA-LP使用LSTM捕获时序信息，动态预测可以更好应用于现实网络；与基于网络拓扑的动态链路预测算法相比，DTA-LP可以聚集高阶拓扑特征，有效挖掘网络邻域信息；与基于属性网络的动态链路预测算法相比，DTA-LP可以加权融合网络拓扑属性，提高预测精度。在4种真实数据上的实验结果表明，该方法能结合网络已有先验知识，以较高的MAP值来预测未来网络中的边，验证了模型的有效性。

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.