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Kai Wu

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23 papers

2 author rows

AAAI Conference 2026 Conference Paper

Stochastic Universal Adversarial Perturbations with Fixed Optimization Constraint and Ensured High-probability Transferability

Yulin Jin
Xiaoyu Zhang
Haoyu Tong
Jian Lou
Kai Wu
Haibo Hu
Xiaofeng Chen

Adversarial perturbations (APs) have become a great concern in image classification tasks. The most challenging branch, universal adversarial perturbations (UAPs), are exploited to fool most of the unseen samples. Such one-to-all perturbations have the merit of transferability, which has strong practical significance. In this paper, we firstly define the transferability gap and the algorithm stability of the UAP algorithm, and prove the relationship between them. In analyzing the UAP algorithm stability, we prove that the convergence domain of existing UAP algorithms with dynamic constraints is excessively small, which degrades the capacity of UAPs. Thus, we further propose a new expected constraint and prove that UAPs in the expected constraint suit any sample in a high probability. Besides, we propose a Stochastic Universal Adversarial Perturbation (SUAP) that involves additive noise and the expected constraint. Finally, by treating the proposed algorithm as a stochastic differential equation, we prove an upper bound of the UAP algorithm stability of SUAP, which decreases exponentially at the beginning and then increases with a sublinear rate to at most a fixed constant. Experimental results show that SUAP is aligned with our analysis.

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AAAI Conference 2026 Conference Paper

Textual Self-Attention Network: Test-Time Preference Optimization Through Textual Gradient-Based Attention

Shibing Mo
Haoyang Ruan
Kai Wu
Jing Liu

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities, but aligning their outputs with human preferences typically requires expensive supervised fine-tuning. Recent test-time methods leverage textual feedback to overcome this, but they often critique and revise a single candidate response, lacking a principled mechanism to systematically analyze, weigh, and synthesize the strengths of multiple promising candidates. Such a mechanism is crucial because different responses may excel in distinct aspects (e.g., clarity, factual accuracy, or tone), and combining their best elements may produce a far superior outcome. This paper proposes the Textual Self-Attention Network (TSAN), a new paradigm for test-time preference optimization that requires no parameter updates. TSAN emulates self-attention entirely in natural language to overcome this gap: it analyzes multiple candidates by formatting them into textual keys and values, weighs their relevance using an LLM-based attention module, and synthesizes their strengths into a new, preference-aligned response under the guidance of the learned textual attention. This entire process operates in a textual gradient space, enabling iterative and interpretable optimization. Empirical evaluations demonstrate that with just three test-time iterations on a base SFT model, TSAN outperforms supervised models like Llama-3.1-70B-Instruct and surpasses the current state-of-the-art test-time alignment method by effectively leveraging multiple candidate solutions.

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AAAI Conference 2025 Conference Paper

AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks

Shibing Mo
Kai Wu
Qixuan Gao
Xiangyi Teng
Jing Liu

In real-world applications, spectral Graph Neural Networks (GNNs) are powerful tools for processing diverse types of graphs. However, a single GNN often struggles to handle different graph types—such as homogeneous and heterogeneous graphs—simultaneously. This challenge has led to the manual design of GNNs tailored to specific graph types, but these approaches are limited by the high cost of labor and the constraints of expert knowledge, which cannot keep up with the rapid growth of graph data. To overcome these challenges, we introduce AutoSGNN, an automated framework for discovering propagation mechanisms in spectral GNNs. AutoSGNN unifies the search space for spectral GNNs by integrating large language models with evolutionary strategies to automatically generate architectures that adapt to various graph types. Extensive experiments on nine widely-used datasets, encompassing both homophilic and heterophilic graphs, demonstrate that AutoSGNN outperforms state-of-the-art spectral GNNs and graph neural architecture search methods in both performance and efficiency.

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AAAI Conference 2025 Conference Paper

B2Opt: Learning to Optimize Black-box Optimization with Little Budget

Xiaobin Li
Kai Wu
Xiaoyu Zhang
Handing Wang

The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of the problem is how to design an efficient optimization strategy tailored to the target task. This paper designs a powerful optimization framework to automatically learn the optimization strategies from the target or cheap surrogate task without human intervention. However, current methods are weak for this due to poor representation of optimization strategy. To achieve this, 1) drawing on the mechanism of genetic algorithm, we propose a deep neural network framework called B2Opt, which has a stronger representation of optimization strategies based on survival of the fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task to guide the design of the efficient optimization strategies. Compared to the state-of-the-art BBO baselines, B2Opt can achieve multiple orders of magnitude performance improvement with less function evaluation cost.

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NeurIPS Conference 2025 Conference Paper

Enhancing Zero-Shot Black-Box Optimization via Pretrained Models with Efficient Population Modeling, Interaction, and Stable Gradient Approximation

Muqi Han
Xiaobin Li
Kai Wu
Xiaoyu Zhang
Handing Wang

Zero-shot optimization aims to achieve both generalization and performance gains on solving previously unseen black-box optimization problems over SOTA methods without task-specific tuning. Pre-trained optimization models (POMs) address this challenge by learning a general mapping from task features to optimization strategies, enabling direct deployment on new tasks. In this paper, we identify three essential components that determine the effectiveness of POMs: (1) task feature modeling, which captures structural properties of optimization problems; (2) optimization strategy representation, which defines how new candidate solutions are generated; and (3) the feature-to-strategy mapping mechanism learned during pre-training. However, existing POMs often suffer from weak feature representations, rigid strategy modeling, and unstable training. To address these limitations, we propose EPOM, an enhanced framework for pre-trained optimization. EPOM enriches task representations using a cross-attention-based tokenizer, improves strategy diversity through deformable attention, and stabilizes training by replacing non-differentiable operations with a differentiable crossover mechanism. Together, these enhancements yield better generalization, faster convergence, and more reliable performance in zero-shot black-box optimization.

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NeurIPS Conference 2025 Conference Paper

Synthetic Series-Symbol Data Generation for Time Series Foundation Models

Wenxuan Wang
Kai Wu
yujian li
Dan Wang
Xiaoyu Zhang

Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic expressions. To leverage series-symbol data pairs with strong correlations, we develop SymTime, a pre-trained foundation model for enhancing time series representation using symbolic information. SymTime demonstrates competitive performance across five major TSA tasks when fine-tunes with downstream tasks, rivaling foundation models pre-trained on real-world datasets. This approach underscores the potential of series-symbol data generation and pretraining mechanisms in overcoming data scarcity and enhancing task performance. The code is available at https: //github. com/wwhenxuan/SymTime.

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YNIMG Journal 2025 Journal Article

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia

Runlin Peng
Wei Wang
Liqin Liang
Rui Han
Yi Li
Haiyuan Wang
Yuran Wang
Wenhao Li

The association between the human brain and gut microbiota, known as the "brain-gut-microbiota axis", is involved in the neuropathological mechanisms of schizophrenia (SZ); however, its association patterns and correlations with symptom severity and neurocognition are still largely unknown. In this study, 43 SZ patients and 55 normal controls (NCs) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) and gut microbiota data were acquired for each participant. First, the brain features of brain images and functional brain networks were computed from rs-fMRI data; the gut features of gut microbiota abundance and the gut microbiota network were computed from gut microbiota data. Second, we propose a novel methodology to construct an individual brain-gut microbiota network (BGMN) for each participant by combining the brain and gut features via multiple strategies. Third, discriminative models between SZ patients and NCs were built using the connectivity matrices of the BGMN as input features. Moreover, the correlations between the most discriminative features and the scores of symptom severity and neurocognition were analyzed in SZ patients. The results showed that the best discriminative model between SZ patients and NCs was achieved using the connectivity matrices of the BGMN when all the brain and gut features were integrated, with an accuracy of 0.90 and an area under the curve value of 0.97. The most discriminative features were related primarily to the genera Faecalibacterium and Collinsella, in which the genus Faecalibacterium was linked to the visual system and subcortical cortices and the genus Collinsella was linked to the default network and subcortical cortices. Furthermore, parts of the most discriminative features were significantly correlated with the scores of neurocognition in the SZ patients. The methodology for constructing individual BGMNs proposed in this study can help us reveal the associations between the brain and gut microbiota and understand the neuropathology of SZ.

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NeurIPS Conference 2025 Conference Paper

V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception

Lei Yang
Xinyu Zhang
Jun Li
Chen Wang
Jiaqi Ma
Zhiying Song
Tong Zhao
Ziying Song

Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on cameras and LiDAR, neglecting 4D Radar—a sensor used in single-vehicle autonomous driving to provide robust perception in adverse weather conditions. In this paper, to bridge the gap created by the absence of 4D Radar datasets in cooperative perception, we present V2X-Radar, the first large-scale, real-world multi-modal dataset featuring 4D Radar. V2X-Radar dataset is collected using a connected vehicle platform and an intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras. The collected data encompasses sunny and rainy weather conditions, spanning daytime, dusk, and nighttime, as well as various typical challenging scenarios. The dataset consists of 20K LiDAR frames, 40K camera images, and 20K 4D Radar data, including 350K annotated boxes across five categories. To support various research domains, we have established V2X-Radar-C for cooperative perception, V2X-Radar-I for roadside perception, and V2X-Radar-V for single-vehicle perception. Furthermore, we provide comprehensive benchmarks across these three sub-datasets.

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NeurIPS Conference 2024 Conference Paper

Pretrained Optimization Model for Zero-Shot Black Box Optimization

Xiaobin Li
Kai Wu
Yujian B. Li
Xiaoyu Zhang
Handing Wang
Jing Liu

Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It is crucial to ensure reliable and robust performance in various applications. Current optimizers often struggle with zero-shot optimization and require intricate hyperparameter tuning to adapt to new tasks. To address this, we propose a Pretrained Optimization Model (POM) that leverages knowledge gained from optimizing diverse tasks, offering efficient solutions to zero-shot optimization through direct application or fine-tuning with few-shot samples. Evaluation on the BBOB benchmark and two robot control tasks demonstrates that POM outperforms state-of-the-art black-box optimization methods, especially for high-dimensional tasks. Fine-tuning POM with a small number of samples and budget yields significant performance improvements. Moreover, POM demonstrates robust generalization across diverse task distributions, dimensions, population sizes, and optimization horizons. For code implementation, see https: //github. com/ninja-wm/POM/.

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NeurIPS Conference 2024 Conference Paper

Rapid Plug-in Defenders

Kai Wu
Yujian B. Li
Jian Lou
Xiaoyu Zhang
Handing Wang
Jing Liu

In the realm of daily services, the deployment of deep neural networks underscores the paramount importance of their reliability. However, the vulnerability of these networks to adversarial attacks, primarily evasion-based, poses a concerning threat to their functionality. Common methods for enhancing robustness involve heavy adversarial training or leveraging learned knowledge from clean data, both necessitating substantial computational resources. This inherent time-intensive nature severely limits the agility of large foundational models to swiftly counter adversarial perturbations. To address this challenge, this paper focuses on the \textbf{Ra}pid \textbf{P}lug-\textbf{i}n \textbf{D}efender (\textbf{RaPiD}) problem, aiming to rapidly counter adversarial perturbations without altering the deployed model. Drawing inspiration from the generalization and the universal computation ability of pre-trained transformer models, we propose a novel method termed \textbf{CeTaD} (\textbf{C}onsidering Pr\textbf{e}-trained \textbf{T}ransformers \textbf{a}s \textbf{D}efenders) for RaPiD, optimized for efficient computation. \textbf{CeTaD} strategically fine-tunes the normalization layer parameters within the defender using a limited set of clean and adversarial examples. Our evaluation centers on assessing \textbf{CeTaD}'s effectiveness, transferability, and the impact of different components in scenarios involving one-shot adversarial examples. The proposed method is capable of rapidly adapting to various attacks and different application scenarios without altering the target model and clean training data. We also explore the influence of varying training data conditions on \textbf{CeTaD}'s performance. Notably, \textbf{CeTaD} exhibits adaptability across differentiable service models and proves the potential of continuous learning.

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YNICL Journal 2024 Journal Article

Relationships among the gut microbiome, brain networks, and symptom severity in schizophrenia patients: A mediation analysis

Liqin Liang
Shijia Li
Yuanyuan Huang
Jing Zhou
Dongsheng Xiong
Shaochuan Li
Hehua Li
Baoyuan Zhu

The microbiome-gut-brain axis (MGBA) plays a critical role in schizophrenia (SZ). However, the underlying mechanisms of the interactions among the gut microbiome, brain networks, and symptom severity in SZ patients remain largely unknown. Fecal samples, structural and functional magnetic resonance imaging (MRI) data, and Positive and Negative Syndrome Scale (PANSS) scores were collected from 38 SZ patients and 38 normal controls, respectively. The data of 16S rRNA gene sequencing were used to analyze the abundance of gut microbiome and the analysis of human brain networks was applied to compute the nodal properties of 90 brain regions. A total of 1,691,280 mediation models were constructed based on 261 gut bacterial, 810 nodal properties, and 4 PANSS scores in SZ patients. A strong correlation between the gut microbiome and brain networks (r = 0.89, false discovery rate (FDR) -corrected p < 0.05) was identified. Importantly, the PANSS scores were linearly correlated with both the gut microbiome (r = 0.5, FDR-corrected p < 0.05) and brain networks (r = 0.59, FDR-corrected p < 0.05). The abundance of genus Sellimonas significantly affected the PANSS negative scores of SZ patients via the betweenness centrality of white matter networks in the inferior frontal gyrus and amygdala. Moreover, 19 significant mediation models demonstrated that the nodal properties of 7 brain regions, predominately from the systems of visual, language, and control of action, showed significant mediating effects on the PANSS scores with the gut microbiome as mediators. Together, our findings indicated the tripartite relationships among the gut microbiome, brain networks, and PANSS scores and suggested their potential role in the neuropathology of SZ.

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AAAI Conference 2024 Conference Paper

Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-Time Dynamics

Lanlan Chen
Kai Wu
Jian Lou
Jing Liu

Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy of dynamic modeling. The prevailing approach of integrating graph neural networks with ordinary differential equations has demonstrated promising performance. However, they disregard the crucial signed information potential on graphs, impeding their capacity to accurately capture real-world phenomena and leading to subpar outcomes. In response, we introduce a novel approach: a signed graph neural ordinary differential equation, adeptly addressing the limitations of miscapturing signed information. Our proposed solution boasts both flexibility and efficiency. To substantiate its effectiveness, we seamlessly integrate our devised strategies into three preeminent graph-based dynamic modeling frameworks: graph neural ordinary differential equations, graph neural controlled differential equations, and graph recurrent neural networks. Rigorous assessments encompass three intricate dynamic scenarios from physics and biology, as well as scrutiny across four authentic real-world traffic datasets. Remarkably outperforming the trio of baselines, empirical results underscore the substantial performance enhancements facilitated by our proposed approach. Our code can be found at https://github.com/beautyonce/SGODE.

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IJCAI Conference 2024 Conference Paper

UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation

Qingdong He
Jinlong Peng
Zhengkai Jiang
Kai Wu
Xiaozhong Ji
Jiangning Zhang
Yabiao Wang
Chengjie Wang

3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space. However, existing works not only fail to fully utilize all the available modal information in the 3D domain but also lack sufficient granularity in representing the features of each modality. In this paper, we propose a unified multimodal 3D open-vocabulary scene understanding network, namely UniM-OV3D, aligning point clouds with image, language and depth. To better integrate global and local features of the point clouds, we design a hierarchical point cloud feature extraction module that learns fine-grained feature representations. Further, to facilitate the learning of coarse-to-fine point-semantic representations from captions, we propose the utilization of hierarchical 3D caption pairs, capitalizing on geometric constraints across various viewpoints of 3D scenes. Extensive experimental results have demonstrated the effectiveness and superiority of our method in open-vocabulary semantic and instance segmentation, which achieves state-of-the-art performance on both indoor and outdoor benchmarks such as ScanNet, ScanNet200, S3IDS and nuScenes. Code is available at https: //github. com/hithqd/UniM-OV3D.

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AAAI Conference 2024 Conference Paper

Unsupervised Continual Anomaly Detection with Contrastively-Learned Prompt

Jiaqi Liu
Kai Wu
Qiang Nie
Ying Chen
Bin-Bin Gao
Yong Liu
Jinbao Wang
Chengjie Wang

Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. However, continual learning methods primarily rely on supervised annotations, while the application in UAD is limited due to the absence of supervision. Current UAD methods train separate models for different classes sequentially, leading to catastrophic forgetting and a heavy computational burden. To address this issue, we introduce a novel Unsupervised Continual Anomaly Detection framework called UCAD, which equips the UAD with continual learning capability through contrastively-learned prompts. In the proposed UCAD, we design a Continual Prompting Module (CPM) by utilizing a concise key-prompt-knowledge memory bank to guide task-invariant 'anomaly' model predictions using task-specific 'normal' knowledge. Moreover, Structure-based Contrastive Learning (SCL) is designed with the Segment Anything Model (SAM) to improve prompt learning and anomaly segmentation results. Specifically, by treating SAM's masks as structure, we draw features within the same mask closer and push others apart for general feature representations. We conduct comprehensive experiments and set the benchmark on unsupervised continual anomaly detection and segmentation, demonstrating that our method is significantly better than anomaly detection methods, even with rehearsal training. The code will be available at https://github.com/shirowalker/UCAD.

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YNIMG Journal 2023 Journal Article

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD

Aocai Yang
Hangwei Zhuang
Lei Du
Bing Liu
Kuan Lv
Jixin Luan
Pianpian Hu
Feng Chen

OBJECTIVE: ) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS: analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS: values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION: in the hippocampus may be a useful tool for monitoring cognitive impairment.

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ICRA Conference 2022 Conference Paper

Adaptable Action-Aware Vital Models for Personalized Intelligent Patient Monitoring

Kai Wu
Ee Heng Chen
Hao Xing
Felix Wirth
Keti Vitanova
Rüdiger Lange
Darius Burschka

Vital signs such as heart rate, oxygen saturation, and blood pressure are crucial information for healthcare workers to identify clinical deterioration of ward patients. Currently, medical devices monitor these vital signs and trigger alarms when the vital signs are not in the normal ranges based on predefined thresholds, which suggests the presence of clinical deterioration. However, such threshold-based approach is not robust for patient monitoring. This is because vital signs differ among patients due to human physiology and change across time based on the action performed by a patient. In this work, we want to tackle these problems by building adaptable action-aware vital models. These models can understand the changes in vital signs caused by patient's actions and can be adapted to the normal vital sign ranges of individual patients. Our experimental results show that general vital sign patterns for different actions exist and can be personalized to new patients. Additionally, we investigate the possibility of estimating the initial vital model for an unobserved action using models of observed actions for model personalization. The resulting adaptable action-aware vital models have the potential to improve patient monitoring by reducing false clinical alarms.

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YNICL Journal 2022 Journal Article

Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder

Yanxiang Ye
Chengyu Wang
Xiaofeng Lan
Weicheng Li
Ling Fu
Fan Zhang
Haiyan Liu
Kai Wu

BACKGROUND: The default mode network (DMN) is implicated in the pathophysiology of major depressive disorder (MDD), and functional connectivity (FC) involved in DMN is suggested to be associated with antidepressant remission. The goal of this study is to recognize relationships between FC within DMN and early amelioration in MDD patients and to further test the capacity of FC to predict early efficacy. METHODS: In total 66 MDD patients and 57 healthy controls were recruited for resting-state functional magnetic resonance imaging scans at baseline. After four weeks of treatment with Escitalopram or Venlafaxine, patients were divided into subgroups with remitters (R, n = 31) and non-remitters (NR, n = 35). Independent component analysis (ICA) was used to compare intranetwork functional connectivity (intra-FC) in DMN between the three groups. RESULTS: Relative to NR-MDD group and HCs, the R-MDD group showed significantly higher intra-FC in the right angular gyrus of DMN, and the intra-FC was positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that intra-FC exhibited a high diagnostic value for remission. CONCLUSION: These findings indicated that intra-FC related to the DMN is a prognostic marker that can potentially predict early remission of symptoms after antidepressant treatment.

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EAAI Journal 2022 Journal Article

Learning large-scale fuzzy cognitive maps under limited resources

Kai Wu
Jing Liu

Research on the problem of learning large-scale fuzzy cognitive maps (FCMs) with a limited computational budget is outstanding. To learn large-scale FCMs from time series, in most work, this problem is decomposed into learning local connections of each concept, respectively, and then one optimizer is employed to optimize each such sub-problem. Each sub-problem may have different requirements for the computational resource, but the existing methods ignore this issue and allocate the same amounts of computational resources for each sub-problem. In this paper, we propose two strategies to address this problem. We first develop a dynamic resource allocation strategy to maximize the performance of the decomposition-based optimizer under a limited computational budget. Second, we propose a half-thresholding memetic algorithm to improve the performance of the traditional evolutionary algorithm. We term our proposal as a half-thresholding memetic algorithm with a dynamic resource allocation strategy (HTMA-DRA). Finally, the experiments on large-scale synthetic data and DREAM datasets compared with the existing state-of-the-art methods demonstrate the effectiveness of the proposed HTMA-DRA.

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NeurIPS Conference 2022 Conference Paper

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Xi Jiang
Jianlin Liu
Jinbao Wang
Qiang Nie
Kai Wu
Yong Liu
Chengjie Wang
Feng Zheng

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.

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YNIMG Journal 2012 Journal Article

Correlation among body height, intelligence, and brain gray matter volume in healthy children

Yasuyuki Taki
Hiroshi Hashizume
Yuko Sassa
Hikaru Takeuchi
Michiko Asano
Kohei Asano
Yuka Kotozaki
Rui Nouchi

A significant positive correlation between height and intelligence has been demonstrated in children. Additionally, intelligence has been associated with the volume of gray matter in the brains of children. Based on these correlations, we analyzed the correlation among height, full-scale intelligence quotient (IQ) and gray matter volume applying voxel-based morphometry using data from the brain magnetic resonance images of 160 healthy children aged 5–18years of age. As a result, body height was significantly positively correlated with brain gray matter volume. Additionally, the regional gray matter volume of several regions such as the bilateral prefrontal cortices, temporoparietal region, and cerebellum was significantly positively correlated with body height and that the gray matter volume of several of these regions was also significantly positively correlated with full-scale intelligence quotient (IQ) scores after adjusting for age, sex, and socioeconomic status. Our results demonstrate that gray and white matter volume may mediate the correlation between body height and intelligence in healthy children. Additionally, the correlations among gray and white matter volume, height, and intelligence may be at least partially explained by the effect of insulin-like growth factor-1 and growth hormones. Given the importance of the effect of environmental factors, especially nutrition, on height, IQ, and gray matter volume, the present results stress the importance of nutrition during childhood for the healthy maturation of body and brain.

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YNIMG Journal 2012 Journal Article

Sleep duration during weekdays affects hippocampal gray matter volume in healthy children

Yasuyuki Taki
Hiroshi Hashizume
Benjamin Thyreau
Yuko Sassa
Hikaru Takeuchi
Kai Wu
Yuka Kotozaki
Rui Nouchi

Sleep is essential for living beings, and sleep loss has been shown to affect hippocampal structure and function in rats by inhibiting cell proliferation and neurogenesis in this region of the brain. We aimed to analyze the correlation between sleep duration and the hippocampal volume using brain magnetic resonance images of 290 healthy children aged 5–18years. We examined the volume of gray matter, white matter, and the cerebrospinal fluid (CSF) space in the brain using a fully automated and established neuroimaging technique, voxel-based morphometry, which enabled global analysis of brain structure without bias towards any specific brain region while permitting the identification of potential differences or abnormalities in brain structures. We found that the regional gray matter volume of the bilateral hippocampal body was significantly positively correlated with sleep duration during weekdays after adjusting for age, sex, and intracranial volume. Our results indicated that sleep duration affects the hippocampal regional gray matter volume of healthy children. These findings advance our understanding of the importance of sleep habits in the daily lives of healthy children.

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YNIMG Journal 2011 Journal Article

Correlation between baseline regional gray matter volume and global gray matter volume decline rate

Yasuyuki Taki
Shigeo Kinomura
Kazunori Sato
Ryoi Goto
Kai Wu
Ryuta Kawashima
Hiroshi Fukuda

Evaluating whole-brain or global gray matter volume decline rate is important in distinguishing neurodegenerative diseases from normal aging and in anticipating cognitive decline over a given period in non-demented subjects. Whether a significant negative correlation exists between baseline regional gray matter volume of several regions and global gray matter volume decline in the subsequent time period in healthy subjects has not yet been clarified. Therefore, we analyzed the correlation between baseline regional gray matter volumes and the rate of global gray matter volume decline in the period following baseline using magnetic resonance images of the brains of 381 healthy subjects by applying a longitudinal design over 6years using voxel-based morphometry. As a result, the annual percentage change in gray matter ratio (GMR, APCGMR), in which GMR represents the percentage of gray matter volume in the intracranial volume, showed a significant negative correlation with the baseline regional gray matter volumes of the right posterior cingulate cortex/precuneus and the left hippocampus. Additionally, baseline regional gray matter volume of both the right PCC/precuneus and the left hippocampus significantly distinguished whether the APCGMR was above or below the mean of APCGMR. Our results suggest that baseline regional gray matter volume predicts the rate of global gray matter volume decline in the subsequent period in healthy subjects. Our study may contribute to distinguishing neurodegenerative diseases from normal aging and to predicting cognitive decline.

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YNIMG Journal 2011 Journal Article

Gender differences in partial-volume corrected brain perfusion using brain MRI in healthy children

Yasuyuki Taki
Hiroshi Hashizume
Yuko Sassa
Hikaru Takeuchi
Kai Wu
Michiko Asano
Kohei Asano
Hiroshi Fukuda

To investigate gender differences in brain perfusion, this study utilized pulsed arterial spin-labeling magnetic resonance imaging (MRI) in a large number of healthy children. Data on structural and perfusion MRI in the brain were collected from 202 healthy children aged 5–18years. Gender differences in brain perfusion using partial volume correction (PVC), which was calculated by dividing the normalized perfusion MRI by the normalized gray-matter segments, were analyzed by applying voxel-based analysis and region-of-interest (ROI) analysis. Girls showed significantly higher brain perfusion with PVC in the bilateral medial aspect of the parietal lobes, including the posterior cingulate cortex and precuneus, as compared to boys using voxel-based analysis. In addition, brain perfusion with PVC in the bilateral posterior cingulate cortex, bilateral precuneus, and left thalamus was significantly higher in girls than in boys in the ROI analysis. In contrast, no regions were seen in which boys exhibited higher brain perfusion with PVC than girls in both analyses. The findings showed significant differences between boys and girls in brain perfusion with PVC, and these differences may contribute to gender differences in the cognitive ability of healthy children.

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