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Yuzheng Hu

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20 papers

2 author rows

TIST Journal 2026 Journal Article

An Improved Autoregressive Evaluation Paradigm for Large Language Models

Jipeng Zhang
Rui Pan
Yuzheng Hu
KaShun Shum
Guanyu Yao
Xiang Liu
Renjie Pi
Hanze Dong

The AI community has witnessed the emergence of various chat-style Large Language Models (LLMs) since the advent of ChatGPT. Despite significant progress in this area, evaluating these models remains a substantial challenge. The evaluations provided by humans or GPT-4 oracles are often taken as the gold standard, but they are neither automatic nor scalable. More recently, a series of (open source) LLM-based judge models have been introduced, yet they often exhibit model-specific biases, e.g., a LLaMA-family judge favors a LLaMA-family model. On the other hand, autoregressive evaluation metrics, which holds the potential to address the aforementioned issues, remains underexplored. Among them, likelihood-based metrics such as perplexity and Negative Log-Likelihood (NLL) are widely adopted and has proven effective in tracking the pre-training progress of LLMs. However, they struggle to evaluate the generation capabilities of fine-tuned models due to exposure bias, a phenomenon where the distribution of the model’s output gradually deviates from the ground-truth during inference. To address this key issue, in this article, we propose a novel autoregressive metric, Normalized Discounted Cumulative Gain (NDCG), to improve the evaluation of fine-tuned LLMs. Our experimental results demonstrate that NDCG significantly outperforms likelihood-based metrics: it shows over 45% improvement in both Spearman and Kendall’s tau correlation coefficients for commonsense QA tasks, and aligns more closely with GPT-4 Elo rankings for instruction-tuned models.

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YNIMG Journal 2026 Journal Article

Brain activity inhibition during Short Video Viewing: neurochemical insights

Tiantian Hong
Conghui Su
Hui Zhou
Fengji Geng
Yuzheng Hu

H-MRS, our study provides novel evidence that immersive viewing of preferred short videos deactivates the cognitive control network and that individual differences in this deactivation are linked to glutamate metabolism. These findings enhance our understanding of how digital media consumption interacts with neurochemical processes to influence self-regulation. Our study offers new insights into the neural mechanisms underlying short-video engagement and has implications for understanding excessive digital media use.

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NeurIPS Conference 2025 Conference Paper

A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning

Yuzheng Hu
Fan Wu
Haotian Ye
David Forsyth
James Zou
Nan Jiang
Jiaqi Ma
Han Zhao

Online reinforcement learning (RL) excels in complex, safety-critical domains but suffers from sample inefficiency, training instability, and limited interpretability. Data attribution provides a principled way to trace model behavior back to training samples, yet existing methods assume fixed datasets, which is violated in online RL where each experience both updates the policy and shapes future data collection. In this paper, we initiate the study of data attribution for online RL, focusing on the widely used Proximal Policy Optimization (PPO) algorithm. We start by establishing a local attribution framework, interpreting model checkpoints with respect to the records in the recent training buffer. We design two target functions, capturing agent action and cumulative return respectively, and measure each record's contribution through gradient similarity between its training loss and these targets. We demonstrate the power of this framework through three concrete applications: diagnosis of learning, temporal analysis of behavior formation, and targeted intervention during training. Leveraging this framework, we further propose an algorithm, iterative influence-based filtering (IIF), for online RL training that iteratively performs experience filtering to refine policy updates. Across standard RL benchmarks (classic control, navigation, locomotion) to RLHF for large language models, IIF reduces sample complexity, speeds up training, and achieves higher returns. Together, these results open a new direction for making online RL more interpretable, efficient, and effective.

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ICML Conference 2025 Conference Paper

Empirical Privacy Variance

Yuzheng Hu
Fan Wu 0011
Ruicheng Xian
Yuhang Liu
Lydia Zakynthinou
Pritish Kamath
Chiyuan Zhang
David A. Forsyth

We propose the notion of empirical privacy variance and study it in the context of differentially private fine-tuning of language models. Specifically, we show that models calibrated to the same $(\varepsilon, \delta)$-DP guarantee using DP-SGD with different hyperparameter configurations can exhibit significant variations in empirical privacy, which we quantify through the lens of memorization. We investigate the generality of this phenomenon across multiple dimensions and discuss why it is surprising and relevant. Through regression analysis, we examine how individual and composite hyperparameters influence empirical privacy. The results reveal a no-free-lunch trade-off: existing practices of hyperparameter tuning in DP-SGD, which focus on optimizing utility under a fixed privacy budget, often come at the expense of empirical privacy. To address this, we propose refined heuristics for hyperparameter selection that explicitly account for empirical privacy, showing that they are both precise and practically useful. Finally, we take preliminary steps to understand empirical privacy variance. We propose two hypotheses, identify limitations in existing techniques like privacy auditing, and outline open questions for future research.

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TMLR Journal 2025 Journal Article

Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic

Yifei He
Yuzheng Hu
Yong Lin
Tong Zhang
Han Zhao

Model merging offers an effective strategy to combine the strengths of multiple finetuned models into a unified model that preserves the specialized capabilities of each. Existing methods merge models in a global manner, performing arithmetic operations across all model parameters. However, such global merging often leads to task interference, degrading the performance of the merged model. In this work, we introduce Localize-and-Stitch, a novel approach that merges models in a localized way. Our algorithm works in two steps: i) Localization: identify tiny ($1\%$ of the total parameters) localized regions in the finetuned models containing essential skills for the downstream tasks, and ii) Stitching: reintegrate only these essential regions back into the pretrained model for task synergy. We demonstrate that our approach effectively locates sparse regions responsible for finetuned performance, and the localized regions could be treated as compact and interpretable representations of the finetuned models (tasks). Empirically, we evaluate our method on various vision and language benchmarks, showing that it outperforms existing model merging methods under different data availability scenarios. Beyond strong empirical performance, our algorithm also facilitates model compression and preserves pretrained knowledge, enabling flexible and continual skill composition from multiple finetuned models with minimal storage and computational overhead.

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NeurIPS Conference 2025 Conference Paper

MergeBench: A Benchmark for Merging Domain-Specialized LLMs

Yifei He
Siqi Zeng
Yuzheng Hu
Rui Yang
Tong Zhang
Han Zhao

Model merging provides a scalable alternative to multi-task training by combining specialized finetuned models through parameter arithmetic, enabling efficient deployment without the need for joint training or access to all task data. While recent methods have shown promise, existing evaluations are limited in both model scale and task diversity, leaving open questions about their applicability to large, domain-specialized LLMs. To tackle the challenges, we introduce MergeBench, a comprehensive evaluation suite designed to assess model merging at scale. MergeBench builds on state-of-the-art open-source language models, including Llama and Gemma families at 2B to 9B scales, and covers five key domains: instruction following, mathematics, multilingual understanding, coding and safety. We standardize finetuning and evaluation protocols, and assess eight representative merging methods across multi-task performance, forgetting and runtime efficiency. Based on extensive experiments, we provide practical guidelines for algorithm selection and share insights showing that model merging tends to perform better on stronger base models, with techniques such as merging coefficient tuning and sparsification improving knowledge retention. However, several challenges remain, including the computational cost on large models, the gap for in-domain performance compared to multi-task models, and the underexplored role of model merging in standard LLM training pipelines. We hope MergeBench provides a foundation for future research to advance the understanding and practical application of model merging.

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YNIMG Journal 2025 Journal Article

Neural substrates of attack event prediction in video games: the role of ventral posterior cingulate cortex and theory of mind network

Zhiwei Ye
Chenyu Lv
Hui Zhou
Yanmeng Bao
Tiantian Hong
Qinghua He
Yuzheng Hu

Action anticipation, the ability to observe actions and predict the intent of others, plays a crucial role in social interaction and fields such as electronic sports. However, the neural mechanisms underlying the inference of purpose from action observation remain unclear. In this study, we conducted an fMRI experiment using video game combat scenarios to investigate the neural correlates of action anticipation and its relationship with task performance. The results showed that the higher level of ability to infer the purpose from action observation during experiment associates with higher level of proficiency in real world electric gaming competition. The action anticipation task activates visual streams, fronto-parietal network, and the ventral posterior cingulate cortex (vPCC), a key hub in the theory of mind network. The strength of vPCC activation during action anticipation, but not movement direction judgment, was positively correlated with gaming proficiency. Finite impulse response analysis revealed distinct dynamic response profiles in the vPCC compared to other theory of mind regions. These findings suggest that theory of mind ability may be an important factor influencing individual competitive performance, with the vPCC serving as a core neural substrate for inferring purpose from action observation.

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NeurIPS Conference 2025 Conference Paper

Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining

Weiyi Wang
Junwei Deng
Yuzheng Hu
Shiyuan Zhang
Xirui Jiang
Runting Zhang
Han Zhao
Jiaqi Ma

Data attribution methods, which quantify the influence of individual training data points on a machine learning model, have gained increasing popularity in data-centric applications in modern AI. Despite a recent surge of new methods developed in this space, the impact of hyperparameter tuning in these methods remains under-explored. In this work, we present the first large-scale empirical study to understand the hyperparameter sensitivity of common data attribution methods. Our results show that most methods are indeed sensitive to certain key hyperparameters. However, unlike typical machine learning algorithms---whose hyperparameters can be tuned using computationally-cheap validation metrics---evaluating data attribution performance often requires retraining models on subsets of training data, making such metrics prohibitively costly for hyperparameter tuning. This poses a critical open challenge for the practical application of data attribution methods. To address this challenge, we advocate for better theoretical understandings of hyperparameter behavior to inform efficient tuning strategies. As a case study, we provide a theoretical analysis of the regularization term that is critical in many variants of influence function methods. Building on this analysis, we propose a lightweight procedure for selecting the regularization value without model retraining, and validate its effectiveness across a range of standard data attribution benchmarks. Overall, our study identifies a fundamental yet overlooked challenge in the practical application of data attribution, and highlights the importance of careful discussion on hyperparameter selection in future method development.

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ICRA Conference 2024 Conference Paper

HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V

Yuhang Liu
Boyi Sun
Yuke Li
Yuzheng Hu
Fei-Yue Wang 0001

To develop the next generation of intelligent LiDARs, we propose a novel framework of parallel LiDARs and construct a hardware prototype in our experimental platform, DAWN (Digital Artificial World for Natural). It emphasizes the tight integration of physical and digital space in LiDAR systems, with networking being one of its supported core features. In the context of autonomous driving, V2V (Vehicle-to-Vehicle) technology enables efficient information sharing between different agents which significantly promotes the development of LiDAR networks. However, current research operates under an ideal situation where all vehicles are equipped with identical LiDAR, ignoring the diversity of LiDAR categories and operating frequencies. In this paper, we first utilize OpenCDA and RLS (Realistic LiDAR Simulation) to construct a novel heterogeneous LiDAR dataset named OPV2V-HPL. Additionally, we present HPL-ViT, a pioneering architecture designed for robust feature fusion in heterogeneous and dynamic scenarios. It uses a graph-attention Transformer to extract domain-specific features for each agent, coupled with a cross-attention mechanism for the final fusion. Extensive experiments on OPV2V-HPL demonstrate that HPL-ViT achieves SOTA (state-of-the-art) performance in all settings and exhibits outstanding generalization capabilities.

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NeurIPS Conference 2024 Conference Paper

Most Influential Subset Selection: Challenges, Promises, and Beyond

Yuzheng Hu
Pingbang Hu
Han Zhao
Jiaqi W.

How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often fails to capture the more complex and pronounced collective influence of a set of samples. To tackle this challenge, we study the Most Influential Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence. We conduct a comprehensive analysis of the prevailing approaches in MISS, elucidating their strengths and weaknesses. Our findings reveal that influence-based greedy heuristics, a dominant class of algorithms in MISS, can provably fail even in linear regression. We delineate the failure modes, including the errors of influence function and the non-additive structure of the collective influence. Conversely, we demonstrate that an adaptive version of these heuristics which applies them iteratively, can effectively capture the interactions among samples and thus partially address the issues. Experiments on real-world datasets corroborate these theoretical findings, and further demonstrate that the merit of adaptivity can extend to more complex scenarios such as classification tasks and non-linear neural networks. We conclude our analysis by emphasizing the inherent trade-off between performance and computational efficiency, questioning the use of additive metrics such as the linear datamodeling score, and offering a range of discussions.

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YNIMG Journal 2024 Journal Article

The brain markers of creativity measured by divergent thinking in childhood: Hippocampal volume and functional connectivity

Wenwen Xu
Liyuan Ren
Xiaoxin Hao
Donglin Shi
Yupu Ma
Yuzheng Hu
Long Xie
Fengji Geng

Creativity, a high-order cognitive ability, has received wide attention from researchers and educators who are dedicated to promoting its development throughout one's lifespan. Currently, creativity is commonly assessed with divergent thinking tasks, such as the Alternative Uses Task. Recent advancements in neuroimaging techniques have enabled the identification of brain markers for high-order cognitive abilities. One such brain structure of interest in this regard is the hippocampus, which has been found to play an important role in generating creative thoughts in adulthood. However, such role of the hippocampus in childhood is not clear. Thus, this study aimed to investigate the associations between creativity, as measured by divergent thinking, and both the volume of the hippocampus and its resting-state functional connectivity in 116 children aged 8-12 years. The results indicate significant relations between divergent thinking and the volume of the hippocampal head and the hippocampal tail, as well as the volume of a subfield comprising cornu ammonis 2-4 and dentate gyrus within the hippocampal body. Additionally, divergent thinking was significantly related to the differences between the anterior and the posterior hippocampus in their functional connectivity to other brain regions during rest. These results suggest that these two subregions may collaborate with different brain regions to support diverse cognitive processes involved in the generation of creative thoughts. In summary, these findings indicate that divergent thinking is significantly related to the structural and functional characteristics of the hippocampus, offering potential insights into the brain markers for creativity during the developmental stage.

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YNIMG Journal 2023 Journal Article

Exploring the neural mechanisms underlying achalasia: A study of functional connectivity and regional brain activity

Nina Zhang
Binyu Teng
Xinyi Lu
Liangliang Shi
Li Liu
Fan Zhou
Ni Jiang
Xin Zhang

BACKGROUND AND AIMS: The pathophysiology of achalasia, which involves central nuclei abnormalities, remains unknown. We investigated the resting-state functional MRI (rs-fMRI) features of patients with achalasia. METHODS: We applied resting-state functional MRI (rs-fMRI) to investigate the brain features in patients with achalasia (n = 27), compared to healthy controls (n = 29). Focusing on three regions of interest (ROIs): the dorsal motor nucleus of the vagus (DMV), the nucleus ambiguus (NA), and the nucleus of the solitary tract (NTS), we analyzed variations in resting-state functional connectivity (rs-FC), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo). RESULTS: Achalasia patients demonstrated stronger functional connectivity between the NA and the right precentral gyrus, left postcentral gyrus, and left insula. No significant changes were found in the DMV or NTS. The fMRI analysis showed higher rs-FC values for NA-DMV and NA-NTS connections in achalasia patients. Achalasia patients exhibited decreased fALFF values in the NA, DMV, and NTS regions, as well as increased ReHo values in the NA and DMV regions. A positive correlation was observed between fALFF values in all six ROIs and the width of the barium meal. The NTS fALFF value and NA ReHo value displayed a positive correlation with integrated relaxation pressure (IRP), while the ReHo value in the right precentral gyrus showed an inverse correlation with the height of the barium meal. CONCLUSIONS: Abnormal rs-FC and regional brain activity was found in patients with achalasia. Our study provides new insights into the pathophysiology of achalasia and highlights the potential of rs-fMRI in improving the diagnosis and treatment of this condition.

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NeurIPS Conference 2023 Conference Paper

Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective

Yuzheng Hu
Ruicheng Xian
Qilong Wu
Qiuling Fan
Lang Yin
Han Zhao

Linear scalarization, i. e. , combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recent years, there is a surge of interest in developing Specialized Multi-Task Optimizers (SMTOs) that treat MTL as a multi-objective optimization problem. However, it remains open whether there is a fundamental advantage of SMTOs over scalarization. In fact, heated debates exist in the community comparing these two types of algorithms, mostly from an empirical perspective. To approach the above question, in this paper, we revisit scalarization from a theoretical perspective. We focus on linear MTL models and study whether scalarization is capable of fully exploring the Pareto front. Our findings reveal that, in contrast to recent works that claimed empirical advantages of scalarization, scalarization is inherently incapable of full exploration, especially for those Pareto optimal solutions that strike the balanced trade-offs between multiple tasks. More concretely, when the model is under-parametrized, we reveal a multi-surface structure of the feasible region and identify necessary and sufficient conditions for full exploration. This leads to the conclusion that scalarization is in general incapable of tracing out the Pareto front. Our theoretical results partially answer the open questions in Xin et al. (2021), and provide a more intuitive explanation on why scalarization fails beyond non-convexity. We additionally perform experiments on a real-world dataset using both scalarization and state-of-the-art SMTOs. The experimental results not only corroborate our theoretical findings, but also unveil the potential of SMTOs in finding balanced solutions, which cannot be achieved by scalarization.

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ICML Conference 2023 Conference Paper

Understanding the Impact of Adversarial Robustness on Accuracy Disparity

Yuzheng Hu
Fan Wu 0011
Hongyang Zhang 0001
Han Zhao 0002

While it has long been empirically observed that adversarial robustness may be at odds with standard accuracy and may have further disparate impacts on different classes, it remains an open question to what extent such observations hold and how the class imbalance plays a role within. In this paper, we attempt to understand this question of accuracy disparity by taking a closer look at linear classifiers under a Gaussian mixture model. We decompose the impact of adversarial robustness into two parts: an inherent effect that will degrade the standard accuracy on all classes due to the robustness constraint, and the other caused by the class imbalance ratio, which will increase the accuracy disparity compared to standard training. Furthermore, we also show that such effects extend beyond the Gaussian mixture model, by generalizing our data model to the general family of stable distributions. More specifically, we demonstrate that while the constraint of adversarial robustness consistently degrades the standard accuracy in the balanced class setting, the class imbalance ratio plays a fundamentally different role in accuracy disparity compared to the Gaussian case, due to the heavy tail of the stable distribution. We additionally perform experiments on both synthetic and real-world datasets to corroborate our theoretical findings. Our empirical results also suggest that the implications may extend to nonlinear models over real-world datasets. Our code is publicly available on GitHub at https: //github. com/Accuracy-Disparity/AT-on-AD.

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ICLR Conference 2022 Conference Paper

Actor-critic is implicitly biased towards high entropy optimal policies

Yuzheng Hu
Ziwei Ji
Matus Telgarsky

We show that the simplest actor-critic method — a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or exploration — does not merely find an optimal policy, but moreover prefers high entropy optimal policies. To demonstrate the strength of this bias, the algorithm not only has no regularization, no projections, and no exploration like $\epsilon$-greedy, but is moreover trained on a single trajectory with no resets. The key consequence of the high entropy bias is that uniform mixing assumptions on the MDP, which exist in some form in all prior work, can be dropped: the implicit regularization of the high entropy bias is enough to ensure that all chains mix and an optimal policy is reached with high probability. As auxiliary contributions, this work decouples concerns between the actor and critic by writing the actor update as an explicit mirror descent, provides tools to uniformly bound mixing times within KL balls of policy space, and provides a projection-free TD analysis with its own implicit bias which can be run from an unmixed starting distribution.

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ICLR Conference 2022 Conference Paper

Towards Understanding the Data Dependency of Mixup-style Training

Muthu Chidambaram
Xiang Wang 0011
Yuzheng Hu
Chenwei Wu 0002
Rong Ge 0001

In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset on which minimizing the Mixup loss leads to learning a classifier that does not minimize the empirical loss on the data. On the other hand, we also give sufficient conditions for Mixup training to also minimize the original empirical risk. For generalization, we characterize the margin of a Mixup classifier, and use this to understand why the decision boundary of a Mixup classifier can adapt better to the full structure of the training data when compared to standard training. In contrast, we also show that, for a large class of linear models and linearly separable datasets, Mixup training leads to learning the same classifier as standard training.

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YNIMG Journal 2021 Journal Article

Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area

Conghui Su
Hui Zhou
Liangyu Gong
Binyu Teng
Fengji Geng
Yuzheng Hu

Cutting-edge recommendation algorithms have been widely used by media platforms to suggest users with personalized content. While such user-specific recommendations may satisfy users' needs to obtain intended information, some users may develop a problematic use pattern manifested by addiction-like undesired behaviors. Using a popular video sharing and recommending platform (TikTok) as an example, the present study first characterized use-related undesired behaviors with a questionnaire, then investigated how personally recommended videos modulated brain activity with an fMRI experiment. We found more undesired symptoms were related to lower self-control ability among young adults, and about 5.9% of TikTok users may have significant problematic use. The fMRI results showed higher brain activations in sub-components of the default mode network (DMN), ventral tegmental area, and discrete regions including lateral prefrontal, anterior thalamus, and cerebellum when viewing personalized videos in contrast to non-personalized ones. Psychophysiological interaction analyses revealed stronger coupling between activated DMN subregions and neural pathways underlying auditory and visual processing, as well as the frontoparietal network. This study highlights the functional heterogeneity of DMN in viewing personalized videos and may shed light on the neural underpinnings of how recommendation algorithms are able to keep the user's attention to suggested contents.

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ICML Conference 2020 Conference Paper

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

Fangcheng Fu
Yuzheng Hu
Yihan He
Jiawei Jiang 0001
Yingxia Shao
Ce Zhang 0001
Bin Cui 0001

Recent years have witnessed intensive research interests on training deep neural networks (DNNs) more efficiently by quantization-based compression methods, which facilitate DNNs training in two ways: (1) activations are quantized to shrink the memory consumption, and (2) gradients are quantized to decrease the communication cost. However, existing methods mostly use a uniform mechanism that quantizes the values evenly. Such a scheme may cause a large quantization variance and slow down the convergence in practice. In this work, we introduce TinyScript, which applies a non-uniform quantization algorithm to both activations and gradients. TinyScript models the original values by a family of Weibull distributions and searches for ”quantization knobs” that minimize quantization variance. We also discuss the convergence of the non-uniform quantization algorithm on DNNs with varying depths, shedding light on the number of bits required for convergence. Experiments show that TinyScript always obtains lower quantization variance, and achieves comparable model qualities against full precision training using 1-2 bits less than the uniform-based counterpart.

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YNIMG Journal 2020 Journal Article

Modular segregation of task-dependent brain networks contributes to the development of executive function in children

Chunjie Wang
Yuzheng Hu
Jian Weng
Feiyan Chen
Huafeng Liu

Executive function (EF) refers as to a set of high-level cognitive abilities that are critical to many aspects of daily life. Despite its importance in human daily life, the neural networks responsible for the development of EF in childhood are not well understood. The present study thus aimed to examine the development of task-dependent brain network organization and its relationship to age-related improvements in EF. To address this issue, we recruited eighty-eight Chinese children ranging in age from 7 to 12 years old, and collected their functional magnetic resonance imaging (fMRI) data when they performed an EF task. By utilizing graph theory, we found that the task-dependent brain network modules became increasingly segregated with age. Specifically, the intra-module connections within the default-mode network (DMN), frontal-parietal network (FPN) and sensorimotor network (SMN) increased significantly with age. In contrast, the inter-module connections of the visual network to both the FPN/SMN decreased significantly with age. Most importantly, modular segregation of the FPN significantly mediated the relationship between age and EF performance. These findings add to our growing understanding of how development changes in task-dependent brain network organization support vast behavioral improvements in EF observed during childhood.

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YNIMG Journal 2019 Journal Article

Regional excitation-inhibition balance predicts default-mode network deactivation via functional connectivity

Hong Gu
Yuzheng Hu
Xi Chen
Yong He
Yihong Yang

Deactivation of the default mode network (DMN) is one of the most reliable observations from neuroimaging and has significant implications in development, aging, and various neuropsychiatric disorders. However, the neural mechanism underlying DMN deactivation remains elusive. As the coordination of regional neurochemical substrates and interregional neural interactions are both essential in support of brain functions, a quantitative description of how they impact DMN deactivation may provide new insights into the mechanism. Using an n-back working memory task fMRI and magnetic resonance spectroscopy, we probed the pairwise relationship between task-induced deactivation, interregional functional connectivity and regional excitation-inhibition balance (evaluated by glutamate/GABA ratio) in the posterior cingulate cortex/precuneus (PCC/PCu). Task-induced PCC/PCu deactivation correlated with its excitation-inhibition balance and interregional functional connectivity, where participants with lower glutamate/GABA ratio, stronger intra-DMN connections and stronger antagonistic DMN-SN (salience network)/ECN (executive control network) inter-network connections had greater PCC/PCu deactivation. Mediation analyses revealed that the DMN-SN functional interactions partially mediated the relationship between task-induced deactivation and the excitation-inhibition balance at the PCC/PCu. The triple-relationship discovered in the present study has the potential to bridge DMN-deactivation related findings from various neuroimaging modalities and may provide new insights into the neural mechanism of DMN deactivation. Moreover, this finding may have significant implications for neuropsychiatric disorders related to the DMN dysfunction and suggests an integrated application of pharmacological and neuromodulation-based strategies for rescuing DMN deactivation deficits.

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