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Junqi Wang

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6 papers

2 author rows

YNIMG Journal 2025 Journal Article

Altered neurobehavioral white matter integrity in preterm children: A confounding-controlled analysis using the adolescent brain and cognitive development (ABCD) study

Hailong Li
Yuwen Hung
Junqi Wang
Nicole Rudberg
Nehal A. Parikh
Lili He

INTRODUCTION: Children born preterm face elevated risks of atypical brain development and neurodevelopmental difficulties. However, little is known about childhood outcomes specifically associated with premature birth that are unconfounded by medical complications. This study takes a systematic approach to examine neural and behavioral outcomes in non-medically complex preterm children. The aim is to identify unconfounded neurobehavioral biomarkers and mechanisms that contribute to childhood vulnerability following premature birth, ultimately informing the development of effective interventions to mitigate adverse outcomes in this population. METHODS: This study leverages the largest publicly available prospective dataset on child brain health in the U.S.-the Adolescent Brain and Cognitive Development (ABCD) Study-using a case-control design. Applying rigorous, systematic confounding control procedures, the study includes 612 children aged 9-11 who have been free of medical and developmental complications since birth. The cohort comprises 306 children born preterm and 306 full-term children matched for age, sex, and socioeconomic status. A comprehensive range of neurocognitive outcomes is examined in relation to the integrity of brain connectomes, as measured by diffusion tensor imaging (DTI). RESULTS: Preterm children and full-term control children are significantly differentiated by altered microstructural and axonal integrity, in major frontal-limbic tracts in the whole brain (p < .05). In particular, the strength of structural connectivity in the anterior thalamic radiation, which connects the frontal lobe and the thalamic sensory relay circuit, shows altered brain-behavioral regulatory relationship with the performance on the attention and processing speed task (p < .05). CONCLUSION: This secondary analysis of the ABCD Study identified unconfounded neurobehavioral risk biomarkers associated with premature birth, along with underlying neurobiological and cognitive mechanisms. Children born preterm demonstrated reduced neurobehavioral white matter integrity within the frontal-limbic connectome, particularly in tasks requiring sustained attention and processing speed. This diminished adaptability places them at elevated risk for developing related neurodevelopmental difficulties. These findings highlight the urgent need for routine screening and preventive neuro-rehabilitative interventions-such as attention-focused and sensory-feedback-based training-for preterm-born populations.

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AIIM Journal 2025 Journal Article

VAE-GANMDA: A microbe-drug association prediction model integrating variational autoencoders and generative adversarial networks

Bo Wang
Yang He
Xiaoxin Du
Lei Zhu
Junqi Wang
Tongxuan Wang

Traditional biological experimental methods typically require weeks or even months of experimentation, and the cost of each experiment can reach hundreds or even thousands of dollars, which is quite expensive and time-consuming. To address this, a model called VAE-GANMDA, which integrates variational autoencoders (VAE) and generative adversarial networks (GAN) for predicting microbe-drug associations, has been proposed. Firstly, a heterogeneous network of microbes and drugs is established to enrich the association information. Secondly, by fusing VAE and GAN, the model learns the manifold distribution of data through association features, obtaining nonlinear manifold features. Furthermore, the VAE generation module is improved by integrating the Convolutional Block Attention Module (CBAM) and Gaussian kernel function, enhancing the smooth perception of manifold features, thus endowing VAE with stronger feature extraction capabilities. Then, singular value decomposition (SVD) technique is employed to extract linear features of the data. Finally, by combining linear and nonlinear features, the k-means++ algorithm is used to select balanced and high-quality negative samples for training the MLP classifier. Through performance evaluation, the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of VAE-GANMDA reach 0. 9724 and 0. 9635 respectively, outperforming classical machine learning methods and the majority of deep learning methods. Case studies demonstrate that VAE-GANMDA accurately predicts candidate drugs related to SARS-CoV-2 and candidate microbes related to ciprofloxacin.

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ICLR Conference 2024 Conference Paper

CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents

Siyuan Qi
Shuo Chen 0006
Yexin Li
Xiangyu Kong
Junqi Wang
Bangcheng Yang
Pring Wong
Yifan Zhong

The generalization of decision-making agents encompasses two fundamental elements: learning from past experiences and reasoning in novel contexts. However, the predominant emphasis in most interactive environments is on learning, often at the expense of complexity in reasoning. In this paper, we introduce CivRealm, an environment inspired by the Civilization game. Civilization’s profound alignment with human society requires sophisticated learning and prior knowledge, while its ever-changing space and action space demand robust reasoning for generalization. Particularly, CivRealm sets up an imperfect-information general-sum game with a changing number of players; it presents a plethora of complex features, challenging the agent to deal with open-ended stochastic environments that require diplomacy and negotiation skills. Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning. To catalyze further research, we present initial results for both paradigms. The canonical RL-based agents exhibit reasonable performance in mini-games, whereas both RL- and LLM-based agents struggle to make substantial progress in the full game. Overall, CivRealm stands as a unique learning and reasoning challenge for decision-making agents. The code is available at https://github.com/bigai-ai/civrealm.

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YNIMG Journal 2024 Journal Article

Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome

Hailong Li
Junqi Wang
Zhiyuan Li
Kim M. Cecil
Mekibib Altaye
Jonathan R. Dillman
Nehal A. Parikh
Lili He

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

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NeurIPS Conference 2023 Conference Paper

Generalized Belief Transport

Junqi Wang
Pei Wang
Patrick Shafto

Human learners have ability to adopt appropriate learning approaches depending on constraints such as prior on the hypothesis, urgency of decision, and drift of the environment. However, existing learning models are typically considered individually rather than in relation to one and other. To build agents that have the ability to move between different modes of learning over time, it is important to understand how learning models are related as points in a broader space of possibilities. We introduce a mathematical framework, Generalized Belief Transport (GBT), that unifies and generalizes prior models, including Bayesian inference, cooperative communication and classification, as parameterizations of three learning constraints within Unbalanced Optimal Transport (UOT). We visualize the space of learning models encoded by GBT as a cube which includes classic learning models as special points. We derive critical properties of this parameterized space including proving continuity and differentiability which is the basis for model interpolation, and study limiting behavior of the parameters, which allows attaching learning models on the boundaries. Moreover, we investigate the long-run behavior of GBT, explore convergence properties of models in GBT mathematical and computationally, document the ability to learn in the presence of distribution drift, and formulate conjectures about general behavior. We conclude with open questions and implications for more unified models of learning.

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NeurIPS Conference 2020 Conference Paper

A mathematical theory of cooperative communication

Pei Wang
Junqi Wang
Pushpi Paranamana
Patrick Shafto

Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise due to differences between beliefs of agents. Through a connection to the theory of optimal transport, we establishing a mathematical framework for cooperative communication. We derive prior models as special cases, statistical interpretations of belief transfer plans, and proofs of robustness and instability. Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. The results show that cooperative communication provably enables effective, robust belief transmission which is required to explain feats of human learning and improve human-machine interaction.

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