Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connection patterns. However, brain information processing is a hierarchical process that involves local and long-range interactions between brain regions, interactions between regions and sub-functional modules, and interactions among functional modules themselves. This hierarchical interaction mechanism enables the brain to efficiently integrate local computations and global information flow, supporting the execution of complex cognitive functions. To address this issue, we propose BrainHGT, a hierarchical Graph Transformer that simulates the brain’s natural information processing from local regions to global communities. Specifically, we design a novel long-short range attention encoder that utilizes parallel pathways to handle dense local interactions and sparse long-range connections, thereby effectively alleviating the over-globalizing issue. To further capture the brain’s modular architecture, we designe a prior-guided clustering module that utilizes a cross-attention mechanism to group brain regions into functional communities and leverage neuroanatomical prior to guide the clustering process, thereby improving the biological plausibility and interpretability. Experimental results indicate that our proposed method significantly improves performance of disease identification, and can reliably capture the sub-functional modules of the brain, demonstrating its interpretability.

High-quality neuroimaging data is vital for robust brain disease identification, yet the presence of outliers in functional magnetic resonance imaging (fMRI) datasets often degrades performance and reliability of identification models. Existing unsupervised outlier detection methods struggle with parameter sensitivity and data distributional complexity, while supervised detection is hindered by the scarcity of labeled outliers. To address this challenge, a novel self-supervised frame-work named Variational Autoencoder Graph Outlier Detection (VAGOD) is proposed, in which pseudo-labeled normal and outlier samples are first generated by a conditional variational autoencoder with hierarchical batch-wise attention, and then a reachability-based graph neural network uses these labels to learn local structures and find subtle outliers in the functional connectivity network. Extensive experiments on the ADHD-200 and ADNI2 datasets demonstrate that removing outliers with VAGOD significantly improves downstream brain disease classification accuracy and stability. Furthermore, group-level analysis reveals that detected outliers exhibit distinct neurobiological signatures, validating the method's interpretability and its practical value for clinical neuroimaging applications.

Dynamic temporal information and static connectivity information derived from functional magnetic resonance imaging (fMRI) can assist in the diagnosis of neurological disorders. However, existing disease diagnosis methods primarily rely on information from a single view, neglecting the advantages of multi-view information fusion. In this work, we propose an end-to-end multi-view fusion method that pre-trains on one view of fMRI data and fine-tunes on another view for disease identification. First, the dynamic temporal information and static connectivity information are integrated during the pre-training stage based on the consistency between the two views, effectively combining complementary information from both data types to improve disease identification accuracy. Finally, in the fine-tuning stage, for different fine-tuning datasets, we combine the residual connections in the model with the self-attention mechanism through the hadamard product. This guides the learning process and can be seen as a form of regularization or inductive bias, enhancing the models ability to learn from the data. Experiments conducted on the ADHD-200 dataset demonstrate that: 1) our method effectively fuses temporal and connectivity information from fMRI, improving the accuracy of brain disorder identification; 2) analyzing the consistency between the two views validates the effectiveness of the pre-training strategy and its positive impact on accuracy; 3) the residual attention maps of the model fine-tuned with functional connectivity networks (FCN) capture distinct symmetrical connections, which align with the inherent symmetry of FCN, supporting the rationale for using the hadamard product.

Abnormal brain functional network is an effective biomarker for brain disease diagnosis. Most existing methods focus on mining discriminative information from whole-brain connectivity patterns. However, multi-level collaboration is the foundation of efficient brain function, in addition to the whole-brain network, there are multiple sub-networks that can quickly integrate and process specific cognitive functions, forming the modular community structure of the brain. To address this gap, we propose a novel method, community-aware graph Transformer (CAGT), that integrates the community information of sub-networks and the topological information of brain graph into the Transformer architecture for better brain disorder identification. CAGT enhances information exchange within and between functional communities through dual-scale feature fusion, capturing interactive information across various scales. Additionally, it incorporates prior knowledge to design brain region position encoding and guide the self-attention, thereby enhancing the spatial awareness of the Transformer and aligning it with the brain's natural information transfer process. Experimental results indicate that our proposed method significantly improves performance on both large and small datasets, and can reliably capture the interactions between sub-networks, demonstrating its generalization and interpretability.

Dynamic functional connectivity network (FCN) can effectively mine meta-stable state transition within the period of data acquisition time, which is related to neurological diseases. However, conventional FCN directly describes the correlation between two brain regions in a meta-stable state, it is low-order FCN. In fact, the connection between two brain regions within the meta-stable state also has a changing pattern, which can reveal the functional consistency between two connections over time, we denote the changing pattern between two regions as high-order FCN. Here, we propose an end-to-end method that integrates low-order and high-order dynamic FCNs for better brain disorder identification. First, a sliding window operation is adopted to capture meta-stable states. Then, a matrix variate normal distribution based approach is employed to construct the low-order and high-order FCNs for each meta-stable state. Finally, a two-stage Transformer is designed to extract meta-stable state transition feature for classification. Experimental results on the ADHD-200 and ABIDE datasets indicate that: 1) our proposed method integrate multi-level connectivity information of dynamic brain functions, thereby effectively improving the identification of brain disorders; 2) the proposed two-stage Transformer is more effective in feature extraction than the well-known CNN-LSTM architecture; 3) high-order FCN help locate biomarkers that low-order FCN cannot be determine, including brain regions as well as functional connections between brain regions, which contribute significantly to the diagnosis of brain disorders.

Automatic speech depression level prediction (SDLP) is a very challenging problem in affective computing. There are many studies that have acquired quite good performances for SDLP. However, most of the input speech features of these studies are based on the amplitude spectrogram, which loses the phase spectrogram information. Therefore, these speech features may lose some important information related to depression. In order to make full use of speech information, this paper proposes a complex squeeze-and-excitation network (CSENet) for SDLP. The complex spectrogram is used as the input speech feature, which contains both amplitude and phase spectrogram. In addition, to acquire a discriminative feature, the squeeze-and-excitation residual network is employed to extract deep speech feature. Finally, the attentive temporal pooling is utilized to dynamically select more important information according to the attention mechanisms. Experimental results on the AVEC 2013 and AVEC 2014 datasets prove the effectiveness of our proposed method. As for the mean absolute error (MAE) evaluation metric on AVEC 2013, our proposed method acquires state-of-the-art performance.

BACKGROUND: The default mode network (DMN) in resting state has been increasingly used in disease diagnosis since it was found in 2001. Prior work has mainly focused on extracting a single DMN with various techniques. However, by using seeding-based analysis with more than one desirable seed, we can obtain multiple DMNs, which are likely to have complementary information, and thus are more promising for disease diagnosis. In the study, we used 18 early mild cognitive impairment (EMCI) participants and 18 late mild cognitive impairment (LMCI) participants of Alzheimer's disease (AD). First, we used seeding-based analysis with four seeds to extract four DMNs for each subject. Then, we conducted fusion analysis for all different combinations of the four DMNs. Finally, we carried out nonlinear support vector machine classification based on the mixing coefficients from the fusion analysis. RESULTS: We found that (1) the four DMNs corresponding to the four different seeds indeed capture different functional regions of each subject; (2) Maps of the four DMNs in the most different joint source from fusion analysis are centered at the regions of the corresponding seeds; (3) Classification results reveal the effectiveness of using multiple seeds to extract DMNs. When using a single seed, the regions of posterior cingulate cortex (PCC) extractions of EMCI and LMCI show the largest difference. For multiple-seed cases, the regions of PCC extraction and right lateral parietal cortex (RLP) extraction provide complementary information for each other in fusion, which improves the classification accuracy. Furthermore, the regions of left lateral parietal cortex (LLP) extraction and RLP extraction also have complementary effect in fusion. In summary, AD diagnosis can be improved by exploiting complementary information of DMNs extracted with multiple seeds. CONCLUSIONS: In this study, we applied fusion analysis to the DMNs extracted by using different seeds for exploiting the complementary information hidden among the separately extracted DMNs, and the results supported our expectation that using the complementary information can improve classification accuracy.