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Pan Wang

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12 papers

1 author row

YNICL Journal 2026 Journal Article

Brain topology alteration in Alzheimer’s disease brain networks: A multi-center study

Longhao Ma
Pan Wang
Dawei Wang
Hongxiang Yao
Bo Zhou
Yonghua Zhao
Zhengluan Liao
Yan Chen

Alterations in brain network centrality are key features of Alzheimer's disease (AD) and may offer insights into the disruption of network organization underlying cognitive decline. We introduce a novel centrality metric, DomiRank, to characterize dominance-driven connectivity patterns in the human brain network, using a multi-center MRI dataset comprising 809 participants. Compared with conventional metrics, DomiRank centrality showed greater sensitivity in detecting AD-related network disruptions, particularly within the cingulate gyrus, precuneus, and subcortical hubs such as the basal ganglia-regions critical for cognition. Regional DomiRank alterations were significantly correlated with clinical cognitive scores, indicating their potential relevance to disease severity. Gene enrichment analysis revealed that areas with reduced DomiRank centrality were enriched for genes involved in synaptic signaling and neuronal communication, suggesting molecular mechanisms underlying network vulnerability. These findings highlight DomiRank centrality as a promising biomarker for characterizing network disorganization in AD, linking changes in brain connectivity with underlying molecular processes.

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JBHI Journal 2026 Journal Article

Evaluating Large Language Models in Crisis Detection: A Real-World Benchmark from Psychological Support Hotlines

Guifeng Deng
Shuying Rao
Tianyu Lin
Anlu Dai
Pan Wang
Junyi Xie
Yue Pan
Ke Zhao

Psychological support hotlines serve as critical lifelines for crisis intervention but encounter significant challenges due to rising demand and limited resources. Large language models (LLMs) offer potential support in crisis assessments, yet their effectiveness in emotionally sensitive, real-world clinical settings remains underexplored. We introduce PsyCrisisBench, a comprehensive benchmark of 540 annotated transcripts from the Hangzhou Psychological Assistance Hotline, assessing four key tasks: mood status recognition, suicidal ideation detection, suicide plan identification, and risk assessment. 64 LLMs across 15 model families—including closed-source (e. g. , GPT, Claude, Gemini) and open-source (e. g. , Llama, Qwen, DeepSeek)— were evaluated using zero-shot, few-shot, and fine-tuning paradigms. LLMs showed strong results in suicidal ideation detection (F1 = 0. 880), suicide plan identification (F1 = 0. 779), and risk assessment (F1 = 0. 907), with notable gains from few shot prompting and fine-tuning. Compared to trained human operators, LLMs achieved comparable or superior performance on suicide plan identification and risk assessment, while humans retained advantages on mood status recognition and suicidal ideation detection. Mood status recognition remained challenging (max F1 = 0. 709), likely due to missing vocal cues and semantic ambiguity. Notably, a fine-tuned 1. 5B-parameter model (Qwen2. 5-1. 5B) outperformed larger models on mood and suicidal ideation tasks. LLMs demonstrate performance broadly comparable to trained human operators in text-based crisis assessment, with complementary strengths across task types. PsyCrisisBench provides a robust, real-world evaluation framework to guide future model development and ethical deployment in clinical mental health.

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AAAI Conference 2026 Conference Paper

Single-Stage fMRI-to-3D Reconstruction via Viewpoint-Aware Embedding and Hierarchical Guidance

Xun Zhang
Weihao Xia
Yulong Liu
Bo Yang
Alessandro Bozzon
Pan Wang

Understanding the neural basis of three-dimensional (3D) perception is a fundamental objective in cognitive neuroscience. Despite advances in decoding 2D visual stimuli from neural data, reconstructing high-fidelity 3D objects with detailed texture and geometry remains largely unexplored. In this work, we introduce NeuroSculptor3D, the first single-stage, end-to-end framework for reconstructing textured 3D shapes directly from brain activity. NeuroSculptor3D integrates a viewpoint-aware brain embedding module that captures fine-grained spatial variations across visual perspectives, and a hierarchical guidance mechanism that aligns brain-derived features with perceptual, semantic, and structural priors. Together, these components facilitate the generation of consistent multi-view embeddings, which are then decoded via TRELLIS to produce high-quality textured 3D reconstructions. Experiments on the fMRI-Shape dataset demonstrate that NeuroSculptor3D outperforms existing baselines across multiple settings, achieving significant improvements in both structural accuracy and semantic consistency. Code will be released to facilitate further research.

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JBHI Journal 2025 Journal Article

Disentangled Representation Learning for Capturing Individualized Brain Atrophy via Pseudo-Healthy Synthesis

Zhuangzhuang Li
Kun Zhao
Pindong Chen
Dawei Wang
Hongxiang Yao
Bo Zhou
Jie Lu
Pan Wang

Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into “realistic” healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92. 50 $\pm$ 2. 70%.

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AAAI Conference 2025 Conference Paper

DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis

Pan Wang
Qiang Zhou
Yawen Wu
Tianlong Chen
Jingtong Hu

Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approaches can introduce redundancy and conflicts due to equal treatment of all modalities and the mutual transfer of information between modality pairs. To address these issues, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework, which incorporates a feature disentanglement module to separate modality-shared and modality-specific information. To further reduce redundancy and enhance language-targeted features, four geometric measures are introduced to refine the disentanglement process. A Language-Focused Attractor (LFA) is further developed to strengthen language representation by leveraging complementary modality-specific information through a language-guided cross-attention mechanism. The framework also employs hierarchical predictions to improve overall accuracy. Extensive experiments on two popular MSA datasets, CMU-MOSI and CMU-MOSEI, demonstrate the significant performance gains achieved by the proposed DLF framework. Comprehensive ablation studies further validate the effectiveness of the feature disentanglement module, language-focused attractor, and hierarchical predictions.

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TAAS Journal 2024 Journal Article

FASDSA: A Flexible Adaptive and Secure Data Sharing Architecture

Zixuan Wang
Pan Wang
Zhixin Sun
Xiaokang Zhou
MengYi Fu
MinYao Liu
Xintong Wang
Lu Chen

With the development of Web 3.0 and Metaverse technologies, the ability of autonomous vehicles has been dramatically improved. These technologies have decentralized features that break the traditional data-sharing mode, grant users control over their data, and achieve benefits through data sharing, promoting the widespread circulation of data. To ensure data exchange security, flexibility, and reliability, this paper proposes FASDSA: A Flexible, Adaptive, and Secure Data Sharing Architecture for CAVs with Web 3.0 and Metaverse. This architecture has three advantages: First, it adopts a decentralized, federated learning and CAV role division method, which allows different computational power CAVs to participate in data sharing according to their roles, achieving flexible data privacy protection. Second, it has the ability of tampered model detection based on interpretable analysis, which can effectively ensure that the model is not tampered with. Third, it has a reward mechanism based on work contribution and trust assessment, which uses blockchain technology to ensure the continuous security operation of this architecture. To verify the performance of FASDSA, we used the UNSW-NB15 dataset to conduct three experiments. The experimental results indicate that compared to traditional methods, FASDSA possesses greater flexibility and security while maintaining similar or even superior model performance.

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YNIMG Journal 2024 Journal Article

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Hongzhou Wu
Zhenzhen Yang
Qingquan Cao
Pan Wang
Bharat B. Biswal
Benjamin Klugah-Brown

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

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AAAI Conference 2024 Conference Paper

RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation

Haiming Zhang
Xu Yan
Dongfeng Bai
Jiantao Gao
Pan Wang
Bingbing Liu
Shuguang Cui
Zhen Li

3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. However, image-based scene perception encounters significant challenges in achieving accurate prediction due to the absence of geometric priors. In this paper, we address this issue by exploring cross-modal knowledge distillation in this task, i.e., we leverage a stronger multi-modal model to guide the visual model during training. In practice, we observe that directly applying features or logits alignment, proposed and widely used in bird's-eye-view (BEV) perception, does not yield satisfactory results. To overcome this problem, we introduce RadOcc, a Rendering assisted distillation paradigm for 3D Occupancy prediction. By employing differentiable volume rendering, we generate depth and semantic maps in perspective views and propose two novel consistency criteria between the rendered outputs of teacher and student models. Specifically, the depth consistency loss aligns the termination distributions of the rendered rays, while the semantic consistency loss mimics the intra-segment similarity guided by vision foundation models (VLMs). Experimental results on the nuScenes dataset demonstrate the effectiveness of our proposed method in improving various 3D occupancy prediction approaches, e.g., our proposed methodology enhances our baseline by 2.2% in the metric of mIoU and achieves 50% in Occ3D benchmark.

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YNIMG Journal 2021 Journal Article

Structural and functional connectivity mapping of the human corpus callosum organization with white-matter functional networks

Pan Wang
Jianlin Wang
Qin Tang
Tara L. Alvarez
Zedong Wang
Yi-Chia Kung
Ching-Po Lin
Huafu Chen

The corpus callosum serves as a crucial organization for understanding the information integration between the two hemispheres. Our previous study explored the functional connectivity between the corpus callosum and white-matter functional networks (WM-FNs), but the corresponding physical connectivity remains unknown. The current study uses the resting-state fMRI of Human Connectome Project data to identify ten WM-FNs in 108 healthy subjects, and then independently maps the structural and functional connectivity between the corpus callosum and above WM-FNs using the diffusion tensor images (DTI) tractography and resting-state functional connectivity (RSFC). Our results demonstrated that the structural and functional connectivity between the human corpus callosum and WM-FNs have the following high overall correspondence: orbitofrontal WM-FN, DTI map = 89% and RSFC map = 92%; sensorimotor middle WM-FN, DTI map = 47% and RSFC map = 77%; deep WM-FN, DTI map = 50% and RSFC map = 79%; posterior corona radiata WM-FN, DTI map = 82% and RSFC map = 73%. These findings reinforce the notion that the corpus callosum has unique spatial distribution patterns connecting to distinct WM-FNs. However, important differences between the structural and functional connectivity mapping results were also observed, which demonstrated a synergy between DTI tractography and RSFC toward better understanding the information integration of primary and higher-order functional systems in the human brain.

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IJCAI Conference 2020 Conference Paper

An AI-empowered Visual Storyline Generator

Chang Liu
Zhao Yong Lim
Han Yu
Zhiqi Shen
Ian Dixon
Zhanning Gao
Pan Wang
Peiran Ren

Video editing is currently a highly skill- and time-intensive process. One of the most important tasks in video editing is to compose the visual storyline. This paper outlines Visual Storyline Generator (VSG), an artificial intelligence (AI)-empowered system that automatically generates visual storylines based on a set of images and video footages provided by the user. It is designed to produce engaging and persuasive promotional videos with an easy-to-use interface. In addition, users can be involved in refining the AI-generated visual storylines. The editing results can be used as training data to further improve the AI algorithms in VSG.

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AAAI Conference 2020 Short Paper

Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

Chang Liu
Han Yu
Yi Dong
Zhiqi Shen
Yingxue Yu
Ian Dixon
Zhanning Gao
Pan Wang

There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao. com. Current video production workﬂow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e. g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging ﬁlm-making principles to improve viewing experience and perceived persuasiveness.

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YNIMG Journal 2012 Journal Article

Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity

Zengqiang Zhang
Yong Liu
Tianzi Jiang
Bo Zhou
Ningyu An
Haitao Dai
Pan Wang
Yixuan Niu

Alzheimer's disease (AD), the most prevalent cause of dementia in the elderly, is characterized by progressive cognitive and intellectual deficits. Most patients with mild cognitive impairment (MCI) are thought to be in a very early stage of AD. Resting-state functional magnetic resonance imaging reflects spontaneous brain activities and/or the endogenous/background neurophysiological process of the human brain. Regional Homogeneity (ReHo) can provide a fast method for mapping regional activity across the whole brain. Little has been previously published about where or how spontaneous activity differs between MCI and AD, although many previous fMRI studies have shown that the activity pattern is altered in MCI/AD. In the present study, we first used the ReHo method to explore differences in regional spontaneous activities throughout the whole brain between normal controls (NC) and people with MCI and with AD. A one-way ANOVA was performed to determine the regions in which the ReHo differs between the three groups, and then a post hoc analysis was performed to evaluate differences in the pattern among the three groups. Finally a correlation analysis was done between the ReHo index of these regions and clinical variables in order to evaluate the relationship between ReHo and cognitive measures in the AD and MCI groups. An exploratory classification analysis also demonstrated that ReHo measures were able to correctly separate subjects in 71. 4% of the cases. Altered brain spontaneous activations were found in the medial prefrontal cortex, the bilateral posterior cingulate gyrus/precuneus and the left inferior parietal lobule (IPL) in both MCI and AD. In MCI, the ReHo index in the left IPL was higher than that of the NC, which could indicate the presence of a compensatory mechanism in MCI. More obviously, the correlation analysis indicated that the lower the memory and other cognitive abilities, the lower the ReHo in patients with MCI and AD. Combining our findings with the results in earlier studies, we propose that the spontaneous activity pattern in the resting state could potentially be used as a clinical marker for MCI/AD.

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