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Guoyuan Yang

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5 papers

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AAAI Conference 2026 Conference Paper

BrainLMM: A Label-Free Framework for Mapping Multi-Semantic Representation in the Human Visual Cortex

Tan Gao
Mufan Xue
Haofang Zheng
Shuo Lv
Jia Xu
Dabin Sheng
Ziming Mao
Xinyu Wu

Previous studies leveraging artificial neural networks have been used to investigate the semantic coding within human visual cortex. However, building an interpretable label-free framework that can effectively map brain responses to multiple coexisting semantic concepts remains largely unexplored. Here, we propose BrainLMM, a label-free framework for multi-semantic mapping of voxel responses by combining diverse vision encoders with the Describe-and-Dissect strategy, enabling a hypothesis-free analysis of the human high-level visual cortex. First, we construct voxel-wise encoding models leveraging diverse vision encoders to predict visual cortical responses to natural scene images. Then, we use BrainLMM to map individual brain voxels to multiple semantics without requiring any predefined labels. To evaluate the effectiveness of our method, we compute Pearson correlation coefficients to compare the multi-semantic mappings produced by BrainLMM and CLIP-MSM with ground-truth voxel responses within selective cortical areas. Our findings indicate that BrainLMM achieves more accurate predictions of visual responses compared to CLIP-MSM. Finally, to demonstrate the multi-semantic mapping capability of our method, we project multiple representative semantic concepts onto the cortical surface for visualization. Our method enables the discovery of voxels that exhibit strong activation in response to previously undefined semantic concepts across two independent datasets: the Natural Scenes Dataset (NSD) and the Natural Object Dataset (NOD).

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EAAI Journal 2026 Journal Article

Secondary decomposition with temporal convolutional gated recurrent unit network for strong wind prediction in high-speed railway system

Wei Gu
Hongyan Xing
Guoyuan Yang
Tongyuan Liu
Yajing Shi

Wind speed prediction (WSP) provides future wind speed information and is vital for ensuring the operational safety of high-speed railway (HSR) systems. However, accurate forecasting wind speed is challenging due to its non-linearity and high volatility. To address these issues, a novel framework SD-TCGRU (secondary decomposition With temporal convolutional gated recurrent unit network) for WSP is proposed in this study. SD-TCGRU combines a secondary decomposition (SD) strategy with a hybrid network model. The SD component employs complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for initial decomposition. Subsequently, the decomposed data are reconstructed into three groups based on sample entropy (SE). The sub-signals with high SE values are further processed using variational mode decomposition (VMD), yielding a series of relatively stationary sub-signals. These decomposed sub-signals, consisting of those obtained from VMD and the remaining reconstructed signals, are then processed by our hybrid network TCGRU. This network integrates gated recurrent units (GRU) and temporal convolutional Networks (TCN) to capture both short-term fluctuations and long-term trends in wind speed data. Finally, we compare the proposed SD-TCGRU framework with various state-of-the-art works on three wind speed datasets. Extensive experiments demonstrate that SD-TCGRU outperforms state-of-the-art works. The experimental results confirm that the SD-TCGRU framework is an effective approach for enhancing the safety and efficiency of HSR operations.

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AAAI Conference 2025 Conference Paper

CLIP-MSM: A Multi-Semantic Mapping Brain Representation for Human High-Level Visual Cortex

Guoyuan Yang
Mufan Xue
Ziming Mao
Haofang Zheng
Jia Xu
Dabin Sheng
Ruotian Sun
Ruoqi Yang

Prior work employing deep neural networks (DNNs) with explainable techniques has identified human visual cortical selective representation to specific categories. However, constructing high-performing encoding models that accurately capture brain responses to coexisting multi-semantics remains elusive. Here, we used CLIP models combined with CLIP Dissection to establish a multi-semantic mapping framework (CLIP-MSM) for hypothesis-free analysis in human high-level visual cortex. First, we utilize CLIP models to construct voxel-wise encoding models for predicting visual cortical responses to natural scene images. Then, we apply CLIP Dissection and normalize the semantic mapping score to achieve the mapping of single brain voxels to multiple semantics. Our findings indicate that CLIP Dissection applied to DNNs modeling the human high-level visual cortex demonstrates better interpretability accuracy compared to Network Dissection. In addition, to demonstrate how our method enables fine-grained discovery in hypothesis-free analysis, we quantify the accuracy between CLIP-MSM’s reconstructed brain activation in response to categories of faces, bodies, places, words and food, and the ground truth of brain activation. We demonstrate that CLIP-MSM provides more accurate predictions of visual responses compared to CLIP Dissection. Our results have been validated using two large natural image datasets: the Natural Scenes Dataset (NSD) and the Natural Object Dataset (NOD).

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AAAI Conference 2024 Conference Paper

A Convolutional Neural Network Interpretable Framework for Human Ventral Visual Pathway Representation

Mufan Xue
Xinyu Wu
Jinlong Li
Xuesong Li
Guoyuan Yang

Recently, convolutional neural networks (CNNs) have become the best quantitative encoding models for capturing neural activity and hierarchical structure in the ventral visual pathway. However, the weak interpretability of these black-box models hinders their ability to reveal visual representational encoding mechanisms. Here, we propose a convolutional neural network interpretable framework (CNN-IF) aimed at providing a transparent interpretable encoding model for the ventral visual pathway. First, we adapt the feature-weighted receptive field framework to train two high-performing ventral visual pathway encoding models using large-scale functional Magnetic Resonance Imaging (fMRI) in both goal-driven and data-driven approaches. We find that network layer-wise predictions align with the functional hierarchy of the ventral visual pathway. Then, we correspond feature units to voxel units in the brain and successfully quantify the alignment between voxel responses and visual concepts. Finally, we conduct Network Dissection along the ventral visual pathway including the fusiform face area (FFA), and discover variations related to the visual concept of `person'. Our results demonstrate the CNN-IF provides a new perspective for understanding encoding mechanisms in the human ventral visual pathway, and the combination of ante-hoc interpretable structure and post-hoc interpretable approaches can achieve fine-grained voxel-wise correspondence between model and brain. The source code is available at: https://github.com/BIT-YangLab/CNN-IF.

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YNIMG Journal 2020 Journal Article

Sample sizes and population differences in brain template construction

Guoyuan Yang
Sizhong Zhou
Jelena Bozek
Hao-Ming Dong
Meizhen Han
Xi-Nian Zuo
Hesheng Liu
Jia-Hong Gao

Spatial normalization or deformation to a standard brain template is routinely used as a key module in various pipelines for the processing of magnetic resonance imaging (MRI) data. Brain templates are often constructed using MRI data from a limited number of subjects. Individual brains show significant variabilities in their morphology; thus, sample sizes and population differences are two key factors that influence brain template construction. To address these influences, we employed two independent groups from the Human Connectome Project (HCP) and the Chinese Human Connectome Project (CHCP) to quantify the impacts of sample sizes and population on brain template construction. We first assessed the effect of sample size on the construction of volumetric brain templates using data subsets from the HCP and CHCP datasets. We applied a voxel-wise index of the deformation variability and a logarithmically transformed Jacobian determinant to quantify the variability associated with the template construction and modeled the brain template variability as a power function of the sample size. At the system level, the frontoparietal control network and dorsal attention network demonstrated higher deformation variability and logged Jacobian determinants, whereas other primary networks showed lower variability. To investigate the population differences, we constructed Caucasian and Chinese standard brain atlases (namely, US200 and CN200). The two demographically matched templates, particularly the language-related areas, exhibited dramatic bilaterally in supramarginal gyri and inferior frontal gyri differences in their deformation variability and logged Jacobian determinant. Using independent data from the HCP and CHCP, we examined the segmentation and registration accuracy and observed significant reduction in the performance of the brain segmentation and registration when the population-mismatched templates were used in the spatial normalization. Our findings provide evidence to support the use of population-matched templates in human brain mapping studies. The US200 and CN200 templates have been released on the Neuroimage Informatics Tools and Resources Clearinghouse (NITRC) website (https: //www. nitrc. org/projects/us200_cn200/).

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