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Chunyan Li

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4 papers

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YNICL Journal 2024 Journal Article

Mapping grey matter and cortical thickness alterations associated with subjective cognitive decline and mild cognitive impairment among rural-dwelling older adults in China: A population-based study

Ziwei Chen
Qianqian Xie
Jiafeng Wang
Yan Wang
Huisi Zhang
Chunyan Li
Yongxiang Wang
Lin Cong

BACKGROUND: The structural brain alterations for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are poorly defined. We sought to characterize grey matter volume (GMV) and cortical thickness associated with SCD and MCI among rural-dwelling older adults in China. METHODS: This population-based cross-sectional study included 1072 dementia-free participants from the brain MRI sub-study of MIND-China (2018-2020). We defined MCI following the Petersen's criteria, and SCD as the self-rated Ascertain Dementia 8-item Questionnaire score ≥ 2. Data were analyzed using voxel-based morphometry (VBM), surface-based morphometry analysis (SBM), and logistic regression models. RESULTS: SCD was defined in 243 persons and MCI in 246 individuals. The VBM analysis showed that MCI (vs. normal cognition) was significantly associated with reduced GMV in brain regions such as the bilateral parahippocampus, bilateral hippocampus, and bilateral fusiform (P 0.05). The ROI-wise SBM analysis revealed that SCD was significantly associated with cortical thinning in the right paracentral sulcus, left caudal middle frontal gyrus, and left entorhinal cortex (P < 0.05) and that MCI was significantly associated with cortical thinning in the left temporal lobe, left frontal lobe, bilateral parietal lobe and bilateral fusiform (P < 0.05). CONCLUSIONS: The brain regions with reduced GMV or cortical thickness in older adults gradually expand from normal cognition through SCD to MCI, suggesting that characterizing structural brain alterations may help define the cognitive spectrum at the pre-dementia phase. These findings have potential implications for understanding the neuropathological process of cognitive deterioration in aging.

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AAAI Conference 2023 Conference Paper

LagNet: Deep Lagrangian Mechanics for Plug-and-Play Molecular Representation Learning

Chunyan Li
Junfeng Yao
Jinsong Su
Zhaoyang Liu
Xiangxiang Zeng
Chenxi Huang

Molecular representation learning is a fundamental problem in the field of drug discovery and molecular science. Whereas incorporating molecular 3D information in the representations of molecule seems beneficial, which is related to computational chemistry with the basic task of predicting stable 3D structures (conformations) of molecules. Existing machine learning methods either rely on 1D and 2D molecular properties or simulate molecular force field to use additional 3D structure information via Hamiltonian network. The former has the disadvantage of ignoring important 3D structure features, while the latter has the disadvantage that existing Hamiltonian neural network must satisfy the “canonial” constraint, which is difficult to be obeyed in many cases. In this paper, we propose a novel plug-and-play architecture LagNet by simulating molecular force field only with parameterized position coordinates, which implements Lagrangian mechanics to learn molecular representation by preserving 3D conformation without obeying any additional restrictions. LagNet is designed to generate known conformations and generalize for unknown ones from molecular SMILES. Implicit positions in LagNet are learned iteratively using discrete-time Lagrangian equations. Experimental results show that LagNet can well learn 3D molecular structure features, and outperforms previous state-of-the-art baselines related molecular representation by a significant margin.

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JBHI Journal 2022 Journal Article

3DMol-Net: Learn 3D Molecular Representation Using Adaptive Graph Convolutional Network Based on Rotation Invariance

Chunyan Li
Wei Wei
Jin Li
Junfeng Yao
Xiangxiang Zeng
Zhihan Lv

Studying the deep learning-based molecular representation has great significance on predicting molecular property, promoted the development of drug screening and new drug discovery, and improving human well-being for avoiding illnesses. It is essential to learn the characterization of drug for various downstream tasks, such as molecular property prediction. In particular, the 3D structure features of molecules play an important role in biochemical function and activity prediction. The 3D characteristics of molecules largely determine the properties of the drug and the binding characteristics of the target. However, most current methods merely rely on 1D or 2D properties while ignoring the 3D topological structure, thereby degrading the performance of molecular inferring. In this paper, we propose 3DMol-Net to enhance the molecular representation, considering both the topology and rotation invariance (RI) of the 3D molecular structure. Specifically, we construct a molecular graph with soft relations related to the spatial arrangement of the 3D coordinates to learn 3D topology of arbitrary graph structure and employ an adaptive graph convolutional network to predict molecular properties and biochemical activities. Comparing with current graph-based methods, 3DMol-Net demonstrates superior performance in terms of both regression and classification tasks. Further verification of RI and visualization also show better robustness and representation capacity of our model.

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YNICL Journal 2021 Journal Article

Volume quantification of endolymphatic hydrops in patients with vestibular schwannoma

Jingjing Wang
Chunyan Li
Yaoqian Liu
Yuxin Tian
Yuanping Xiong
Yanmei Feng
Dongzhen Yu
Zhengnong Chen

OBJECTIVE: The origin of vestibular symptoms in patients with vestibular schwannoma (VS) is uncertain. We used intratympanic gadolinium-enhanced magnetic resonance imaging (MRI) to confirm the labyrinthine lesions in patients with VS and to explore the features of endolymphatic hydrops (EH) in these patients. METHODS: In total, 66 patients diagnosed with unilateral VS were enrolled in this study and underwent intratympanic gadolinium-enhanced MRI. The borders of the vestibule and endolymph were mapped on the axial MRI images, and the area and volume of vestibule and endolymph were automatically calculated using Osirix software, and the area and volume percentage of vestibular endolymph were obtained. RESULTS: The area and volume percentages of vestibular endolymph on the affected side were significantly larger than those on the healthy side (both p < 0.001). Using Kendall's W test, we found that the area and volume percentages of vestibular endolymph on the affected side were consistent (p < 0.001), but the consistency was moderate (k = 0.574). The healthy side was also consistent (p < 0.001), and the degree of consistency was moderate (k = 0.444). We used 19.1% as the cut-off point to distinguish the presence or absence of vestibular EH; that is, the volume percentage of vestibular endolymph that was more than 19.1% were defined as the subgroup with hydrops, while the subgroup without hydrops included patients with a baseline level below 19.1%. No volume classification for vestibular EH was proposed. Based on this standard, 11/66 (16.7%) of the patients with VS in this study had vestibular EH. CONCLUSIONS: The volume percentage of the vestibular endolymph was more accurate than the area percentage for assessing vestibular EH. Using 19.1% as the cut-off point to distinguish the presence or absence of vestibular EH, we found that 16.7% of patients with VS had varying degrees of vestibular EH. We believe that the vestibular symptoms in patients with VS may originate from the peripheral lesions.

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