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Lei Guo

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28 papers

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TIST Journal 2026 Journal Article

Mining High Average Utility Nonoverlapping Patterns from Sequential Database

Meng Geng
Youxi Wu
Yan Li
Jing Liu
Lei Guo
Xingquan Zhu
Xindong Wu

As a crucial aspect of data mining, high average utility sequential pattern mining (SPM) aims to discover low frequency and high average utility patterns (subsequences) in sequence data. Most existing high average utility SPM methods overlook the repetitive occurrences of patterns in each sequence, resulting in some important patterns being ignored. To address this issue, we focus on the problem of mining high average utility nonoverlapping patterns (HUPs) from sequential database, and propose an HUP-Miner algorithm. To reduce the need for repeated scanning of the original database, we use a position dictionary to record the occurrence information of each item. To reduce the number of candidate patterns generated, we adopt a pattern join strategy and explore four pruning strategies. To efficiently calculate the average utility of a pattern, we propose an SPC algorithm that utilizes the occurrence positions of sub-patterns. When compared with 12 competitive algorithms, the experimental results on 14 databases show that HUP-Miner gives superior results. Furthermore, we use information gain as the utility for each item, and find that the HUPs discovered in this way can generate better performance via a clustering analysis. All of the algorithms and databases used here are available from https://github.com/wuc567/Pattern-Mining/tree/master/HUP-Miner.

JBHI Journal 2025 Journal Article

A Foundational fMRI Model for Representing Continuous Brain States

Li Yang
Lei Guo
Yixuan Yuan
Junwei Han
Xintao Hu
Tuo Zhang

Foundational models have significant potential to advance brain function research, particularly in understanding the dynamics of brain states. However, most existing models process brain signals within fixed time windows, restricting their ability to capture the full temporal complexity of brain activity. In this study, we propose BrainSN (Brain States Network), a novel fMRI foundational model designed to represent continuous brain state information and support diverse downstream tasks. First, leveraging a transformer-based architecture, BrainSN reconstructs input brain states across multiple time scales and predicts future brain activity, effectively capturing both short-term and long-term dependencies. Second, through multiple embeddings and a channel gating module, the model integrates brain state information and applies an attention mechanism to extract critical features. Additionally, we train BrainSN on 1, 256 hours of resting-state and naturalistic stimulus fMRI data, enabling it to learn large-scale brain dynamics without relying on task-based paradigms. Without fine-tuning, BrainSN achieves 75. 23% and 75. 82% accuracy in autism and attention disorder diagnosis tasks, respectively, matching the performance of leading models pretrained on disease-specific data. After fine-tuning, it surpasses these models. In mental state decoding, BrainSN attains 95. 31% accuracy without fine-tuning, outperforming the best models trained on large-scale task-based fMRI data. Furthermore, by analyzing BrainSN's embeddings in relation to movie stimuli, we demonstrate that the model effectively captures the semantic content of movie scenes embedded in fMRI signals and is highly sensitive to sequence. These results highlight BrainSN's ability to model brain state dynamics and underscore its potential advantages for clinical diagnosis, treatment evaluation, and cognitive neuroscience research.

EAAI Journal 2025 Journal Article

Anti-damage ability of biological plausible spiking neural network with synaptic time delay based on speech recognition under random attack

Lei Guo
Weihang Ding
Youxi Wu
Menghua Man
Miaomiao Guo

The complex electromagnetic environment can reduce the performance of neuromorphic hardware. The robustness of a brain-inspired model is beneficial for maintaining its performance under exterior attack. However, the synaptic plasticity in a brain-inspired model still lacks biological plausibility. The objective of this paper is to enhance the anti-damage ability of brain-inspired model under random attack by improving the biological plausibility of its synaptic plasticity. In this study, we propose a new spiking neural network (SNN) as a brain-inspired model, in which the topology is constrained by functional magnetic resonance imaging (fMRI) data on the human brain, its nodes are Izhikevich neuron models, and its edges are synaptic plasticity models with a random time delay that conforms to the range of the biological synaptic time delay (STD), and called it as fMRI-SNN with synaptic random time delay (SRTD). Then, taking speech recognition (SR) as a case study, we certify the recognition performance of fMRI-SNN with SRTD. To evaluate the anti-damage ability of fMRI-SNN with SRTD, we compare its SR accuracy before and after random attack. To elucidate the anti-damage mechanism, we discuss the neuroelectric characteristics, adaptive regulation of synaptic plasticity, and dynamic topological characteristics of fMRI-SNN with SRTD under random attack. The results indicate that our approach enhances the anti-damage ability of the brain-inspired model, and our discussion elucidates its anti-damage mechanism. Our results prompt that the brain-inspired model with biological plausibility can enhance its information processing ability.

EAAI Journal 2025 Journal Article

Enhanced forest fire detection via dynamic multiscale fusion and contextual partial cross features

Lili Wang
Lei Guo
Haiyan Li
Bingbing He
Jundong Yang
Yaqun Huang

Timely and accurate detection of forest fires, particularly in the early stages when smoke and small flames are present, is crucial for minimizing ecological damage and improving the effectiveness of emergency response. However, existing methods face challenges such as missing edge information, ineffective multi-scale feature fusion, and low accuracy when identifying small or distant targets in complex forest conditions. To address these issues, a novel detection framework is proposed, called the Dynamic Contextual Shallow Network (DCSNet). This framework enhances detection performance and real-time efficiency. The proposed method incorporates three key components: (1) the Contextual Partial Cross Feature Network (CPCFNet), which employs a reparameterized non-local attention mechanism and partial channel separation to strengthen contextual representation; (2) the Dynamic Multiscale Fusion Pyramid Network (DMFPN), which uses dynamic sampling and deformable convolution to fuse multi-scale features adaptively; and (3) the Shallow Feature Detection Layer (SFDL), which refines shallow features to improve the detection of small smoke and flame targets. Experimental evaluations on visible-light and infrared remote sensing datasets collected by unmanned aerial vehicles (UAVs) demonstrate the effectiveness of DCSNet. Specifically, DCSNet achieves an average mean average precision (mAP) at 50 % intersection over union (IoU) of 76. 0 %, a frame rate of 283 frames per second (FPS), and an F1-score of 71. 9 % on the visible-light dataset. On the infrared dataset, the framework achieves an mAP@50 of 77. 5 %, an mAP@50: 95 of 45. 5 %, and an F1 score of 72. 9 %. These results suggest that DCSNet provides high accuracy and robustness in the real-time detection of forest fires under diverse environmental conditions. The code and datasets are available at https: //github. com/Lili-wang-del/DCSNet.

EAAI Journal 2025 Journal Article

Modeling temporal continuity of spatial interactions for vessel trajectories prediction in maritime transportation systems

Xiliang Zhang
Jin Liu
Chengcheng Chen
Peizhu Gong
Zhongdai Wu
Lei Guo

Learning the intricate spatio-temporal interactions among vessels poses a fundamental challenge to vessel trajectory prediction (VTP). Prevailing graph neural network-based methods typically employ a three-stage independent architecture, modeling spatial and temporal dependencies separately, which not only overlooks the temporal continuity of spatial interactions but also introduces higher computational overhead. Moreover, considering interaction semantics at distinct moments equally during the modeling process may restrict the effective learning for certain key features. To address these issues, our paper proposes an innovative Heterogeneous Spatio-temporal Graph Neural Network (HSTGN) that employs hierarchical aggregations, encompassing vessel node-level, temporal edge-level, and self-attention-level aggregations, tailored for VTP. Specifically, a heterogeneous graph is first constructed through the transformation of node and edge generators, allowing vessels to interact freely with each other across temporal dimensions. A key innovation is the vessel multi-relational graph aggregation module (VMGA), which selectively aggregates edge-specific semantics and assigns adaptive weights to enhance interaction modeling. Furthermore, we devise a gated temporal convolution module (GTCN) to compensate for potential global dependencies that VMGA might not capture. Finally, HSTGN integrates VMGA and GTCN in parallel to refine spatio-temporal correlations for more precise prediction. This study enhances VTP accuracy and efficiency, enabling safer navigation and optimized maritime route planning. Extensive experiments on three real-world Automatic Identification System (AIS) datasets demonstrate that HSTGN outperforms state-of-the-art approaches, achieving a 9. 47% improvement in mean absolute error (MAE), 1. 43% in root-mean-square error (RMSE), 2. 72% in mean absolute percentage error (MAPE), and a 43. 06% increase in inference speed, while utilizing fewer model parameters.

EAAI Journal 2024 Journal Article

A hybrid evolutionary algorithm for the stochastic human–robot collaborative disassembly line balancing problem considering carbon emission optimization

Tengfei Wu
Zeqiang Zhang
Lei Guo
Haoxuan Song
Xinlan Xie
Shiyi Ren

To drive a paradigm shift towards a human-centric approach in remanufacturing systems and promote energy-efficient, carbon-reduction-friendly upgrades, this paper introduces a stochastic human–robot collaborative disassembly line balancing problem for real-world disassembly lines characterized by uncertain operation times. The developed mathematical model takes into account task priority relationships, task attributes, human–robot collaboration, and cycle time constraints, with the goal of optimizing workstation numbers, smoothing indices, and carbon emissions. Subsequently, we propose a hybrid evolutionary algorithm (HEA) to efficiently address this problem. The algorithm includes methods for generating initial solutions and decoding them based on random disassembly time. Additionally, an enhanced differential evolution operation is integrated into the non-dominated sorting genetic algorithm II (NSGA-II) framework to improve the algorithm’s evolutionary and search capabilities. Validation experiments involving 21 benchmark cases demonstrate that HEA significantly outperforms seven other algorithms reported in the existing literature. Finally, the proposed algorithm is applied to a printer case comprising 55 components. The results underscore the algorithm’s superior search capabilities compared to the innovative hybrid algorithm, genetic simulated annealing algorithm, as well as traditional NSGA-II and evolutionary algorithms, under both low and high randomness conditions.

NeurIPS Conference 2024 Conference Paper

Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning

Chong Ma
Hanqi Jiang
Wenting Chen
Yiwei Li
Zihao Wu
Xiaowei Yu
Zhengliang Liu
Lei Guo

In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships in the medical context. This data-reliance may lead to low generalization of the learned alignment relationships. In this work, we propose the Eye-gaze Guided Multi-modal Alignment (EGMA) framework to harness eye-gaze data for better alignment of medical visual and textual features. We explore the natural auxiliary role of radiologists' eye-gaze data in aligning medical images and text, and introduce a novel approach by using eye-gaze data, collected synchronously by radiologists during diagnostic evaluations. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets, where EGMA achieved state-of-the-art performance and stronger generalization across different datasets. Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal alignment framework.

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EAAI Journal 2024 Journal Article

PDI-HFP: An intelligent method for heat flux prediction on hypersonic aircraft based on projection depth images

Tingrui Jiang
Lei Guo
Guopeng Sun
Wei Chang
Zhigong Yang
Yueqing Wang

We propose a novel intelligent method to predict the heat flux on hypersonic aircraft. This method considers the aircraft shape and the inflow conditions as inputs and directly outputs overall surface heat flux values. Specifically, PDI-HFP first projects the aircraft shape onto two-dimensional (2D) images in six directions and then utilizes well-trained neural networks to predict the corresponding heat flux images. Finally, PDI-HFP reconstructs the predicted surface heat flux from 2D space to 3D space, and an interpolation method is then implemented to obtain the heat flux distribution on the surface of the 3D aircraft. To the best of our knowledge, this is the first work to apply deep learning techniques to 3D heat flux prediction on arbitrary types of surface grid. Extensive experimental results demonstrate that the values of the heat flux predicted by our method are very close to those generated by CFD simulation. More importantly, compared with CFD simulation, the use of PDI-HFP effectively shortens the computational time, achieving a speedup by a factor of 200–1000, depending on the aircraft shape.

YNIMG Journal 2023 Journal Article

Arousal modulates the amygdala-insula reciprocal connectivity during naturalistic emotional movie watching

Liting Wang
Xintao Hu
Yudan Ren
Jinglei Lv
Shijie Zhao
Lei Guo
Tianming Liu
Junwei Han

Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.

YNIMG Journal 2023 Journal Article

Genetic Influence on Gyral Peaks

Ying Huang
Tuo Zhang
Songyao Zhang
Weihan Zhang
Li Yang
Dajiang Zhu
Tianming Liu
Xi Jiang

Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

NeurIPS Conference 2023 Conference Paper

SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting

Shane Bergsma
Tim Zeyl
Lei Guo

We propose SutraNets, a novel method for neural probabilistic forecasting of long-sequence time series. SutraNets use an autoregressive generative model to factorize the likelihood of long sequences into products of conditional probabilities. When generating long sequences, most autoregressive approaches suffer from harmful error accumulation, as well as challenges in modeling long-distance dependencies. SutraNets treat long, univariate prediction as multivariate prediction over lower-frequency sub-series. Autoregression proceeds across time and across sub-series in order to ensure coherent multivariate (and, hence, high-frequency univariate) outputs. Since sub-series can be generated using fewer steps, SutraNets effectively reduce error accumulation and signal path distances. We find SutraNets to significantly improve forecasting accuracy over competitive alternatives on six real-world datasets, including when we vary the number of sub-series and scale up the depth and width of the underlying sequence models.

NeurIPS Conference 2022 Conference Paper

C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting

Shane Bergsma
Tim Zeyl
Javad Rahimipour Anaraki
Lei Guo

We present coarse-to-fine autoregressive networks (C2FAR), a method for modeling the probability distribution of univariate, numeric random variables. C2FAR generates a hierarchical, coarse-to-fine discretization of a variable autoregressively; progressively finer intervals of support are generated from a sequence of binned distributions, where each distribution is conditioned on previously-generated coarser intervals. Unlike prior (flat) binned distributions, C2FAR can represent values with exponentially higher precision, for only a linear increase in complexity. We use C2FAR for probabilistic forecasting via a recurrent neural network, thus modeling time series autoregressively in both space and time. C2FAR is the first method to simultaneously handle discrete and continuous series of arbitrary scale and distribution shape. This flexibility enables a variety of time series use cases, including anomaly detection, interpolation, and compression. C2FAR achieves improvements over the state-of-the-art on several benchmark forecasting datasets.

IJCAI Conference 2021 Conference Paper

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

Lei Guo
Li Tang
Tong Chen
Lei Zhu
Quoc Viet Hung Nguyen
Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains. Existing work on solving SCSR mainly relies on mining sequential patterns via RNN-based models, which are not expressive enough to capture the relationships among multiple entities. Moreover, all existing algorithms try to bridge two domains via knowledge transfer in the latent space, and the explicit cross-domain graph structure is unexploited. In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two novel attention mechanisms are further developed to selectively guide the message passing process. Extensive experiments on two real-world datasets are conducted to demonstrate the superiority of our DA-GCN method.

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JBHI Journal 2021 Journal Article

Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19

Guangyu Guo
Zhuoyan Liu
Shijie Zhao
Lei Guo
Tianming Liu

The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective detections like Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests or CT scans. This motivates us to develop a quick screening method via common clinical diagnosis results. However, the diagnostic items of different patients may vary greatly, and there is a huge variation in the dimension of the diagnosis data among different suspected patients, it is hard to process these indefinite dimension data via classical classification algorithms. To resolve this problem, we propose an Indefiniteness Elimination Network (IE-Net) to eliminate the influence of the varied dimensions and make predictions about the COVID-19 cases. The IE-Net is in an encoder-decoder framework fashion, and an indefiniteness elimination operation is proposed to transfer the indefinite dimension feature into a fixed dimension feature. Comprehensive experiments were conducted on the public available COVID-19 Clinical Spectrum dataset. Experimental results show that the proposed indefiniteness elimination operation greatly improves the classification performance, the IE-Net achieves 94. 80% accuracy, 92. 79% recall, 92. 97% precision and 94. 93% AUC for distinguishing COVID-19 cases from non-COVID-19 cases with only common clinical diagnose data. We further compared our methods with 3 classical classification algorithms: random forest, gradient boosting and multi-layer perceptron (MLP). To explore each clinical test item's specificity, we further analyzed the possible relationship between each clinical test item and COVID-19.

JBHI Journal 2019 Journal Article

Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network

Yan Cui
Shijie Zhao
Han Wang
Li Xie
Yaowu Chen
Junwei Han
Lei Guo
Fan Zhou

For decades, task functional magnetic resonance imaging has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for task fMRI data, including the general linear model, independent component analysis, and sparse representation methods. However, these shallow models are limited in faithful reconstruction and modeling of the hierarchical and temporal structures of brain networks, as demonstrated in more and more studies. Recently, recurrent neural networks (RNNs) exhibit great ability of modeling hierarchical and temporal dependence features in the machine learning field, which might be suitable for task fMRI data modeling. To explore such possible advantages of RNNs for task fMRI data, we propose a novel framework of a deep recurrent neural network (DRNN) to model the functional brain networks from task fMRI data. Experimental results on the motor task fMRI data of Human Connectome Project 900 subjects release demonstrated that the proposed DRNN can not only faithfully reconstruct functional brain networks, but also identify more meaningful brain networks with multiple time scales which are overlooked by traditional shallow models. In general, this work provides an effective and powerful approach to identifying functional brain networks at multiple time scales from task fMRI data.

YNICL Journal 2016 Journal Article

Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder

Yu Zhao
Hanbo Chen
Yujie Li
Jinglei Lv
Xi Jiang
Fangfei Ge
Tuo Zhang
Shu Zhang

Understanding the organizational architecture of human brain function and its alteration patterns in diseased brains such as Autism Spectrum Disorder (ASD) patients are of great interests. In-vivo functional magnetic resonance imaging (fMRI) offers a unique window to investigate the mechanism of brain function and to identify functional network components of the human brain. Previously, we have shown that multiple concurrent functional networks can be derived from fMRI signals using whole-brain sparse representation. Yet it is still an open question to derive group-wise consistent networks featured in ASD patients and controls. Here we proposed an effective volumetric network descriptor, named connectivity map, to compactly describe spatial patterns of brain network maps and implemented a fast framework in Apache Spark environment that can effectively identify group-wise consistent networks in big fMRI dataset. Our experiment results identified 144 group-wisely common intrinsic connectivity networks (ICNs) shared between ASD patients and healthy control subjects, where some ICNs are substantially different between the two groups. Moreover, further analysis on the functional connectivity and spatial overlap between these 144 common ICNs reveals connectomics signatures characterizing ASD patients and controls. In particular, the computing time of our Spark-enabled functional connectomics framework is significantly reduced from 240 hours (C ++ code, single core) to 20 hours, exhibiting a great potential to handle fMRI big data in the future.

YNIMG Journal 2015 Journal Article

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

Hanbo Chen
Tao Liu
Yu Zhao
Tuo Zhang
Yujie Li
Meng Li
Hongmiao Zhang
Hui Kuang

Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.

YNIMG Journal 2014 Journal Article

Fusing DTI and fMRI data: A survey of methods and applications

Dajiang Zhu
Tuo Zhang
Xi Jiang
Xintao Hu
Hanbo Chen
Ning Yang
Jinglei Lv
Junwei Han

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain's white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come.

YNIMG Journal 2012 Journal Article

Inferring consistent functional interaction patterns from natural stimulus FMRI data

Jiehuan Sun
Xintao Hu
Xiu Huang
Yang Liu
Kaiming Li
Xiang Li
Junwei Han
Lei Guo

There has been increasing interest in how the human brain responds to natural stimulus such as video watching in the neuroimaging field. Along this direction, this paper presents our effort in inferring consistent and reproducible functional interaction patterns under natural stimulus of video watching among known functional brain regions identified by task-based fMRI. Then, we applied and compared four statistical approaches, including Bayesian network modeling with searching algorithms: greedy equivalence search (GES), Peter and Clark (PC) analysis, independent multiple greedy equivalence search (IMaGES), and the commonly used Granger causality analysis (GCA), to infer consistent and reproducible functional interaction patterns among these brain regions. It is interesting that a number of reliable and consistent functional interaction patterns were identified by the GES, PC and IMaGES algorithms in different participating subjects when they watched multiple video shots of the same semantic category. These interaction patterns are meaningful given current neuroscience knowledge and are reasonably reproducible across different brains and video shots. In particular, these consistent functional interaction patterns are supported by structural connections derived from diffusion tensor imaging (DTI) data, suggesting the structural underpinnings of consistent functional interactions. Our work demonstrates that specific consistent patterns of functional interactions among relevant brain regions might reflect the brain's fundamental mechanisms of online processing and comprehension of video messages.

YNIMG Journal 2012 Journal Article

Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles

Dajiang Zhu
Kaiming Li
Carlos Cesar Faraco
Fan Deng
Degang Zhang
Lei Guo
L. Stephen Miller
Tianming Liu

Segregation and integration are two general principles of the brain's functional architecture. Therefore, brain network analysis is of significant importance in understanding brain function. Critical to brain network construction and analysis is the identification of reliable, reproducible, and accurate network nodes, or Regions of Interest (ROIs). Task-based fMRI has been widely considered as a reliable approach to identify functionally meaningful ROIs in the brain. However, recent studies have shown that factors such as spatial smoothing could considerably shift the locations of detected activation peaks. As a result, structural and functional connectivity patterns can be significantly altered. Here, we propose a novel framework by which to optimize ROI sizes and locations, ensuring that differences between the structural connectivity profiles among a group of subjects is minimized. This framework is based on functional ROIs derived from task-based fMRI and diffusion tensor imaging (DTI) data. Accordingly, we present a new approach to describe and measure the fiber bundle similarity quantitatively within and across subjects which will facilitate the optimization procedure. Experimental results demonstrated that this framework improved the localizations of fMRI-derived ROIs. Through our optimization procedure, structural and functional connectivities were more consistent across different individuals. Overall, the ability to accurately localize network ROIs could facilitate many applications in brain imaging that rely on the accurate identification of ROIs.

YNIMG Journal 2012 Journal Article

Visual analytics of brain networks

Kaiming Li
Lei Guo
Carlos Faraco
Dajiang Zhu
Hanbo Chen
Yixuan Yuan
Jinglei Lv
Fan Deng

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging.

YNIMG Journal 2010 Journal Article

A hybrid approach to automatic clustering of white matter fibers

Hai Li
Zhong Xue
Lei Guo
Tianming Liu
Jill Hunter
Stephen T.C. Wong

Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.

YNIMG Journal 2010 Journal Article

A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation

Zhong Xue
Hai Li
Lei Guo
Stephen T.C. Wong

It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration.

YNIMG Journal 2010 Journal Article

Gyral folding pattern analysis via surface profiling

Kaiming Li
Lei Guo
Gang Li
Jingxin Nie
Carlos Faraco
Guangbin Cui
Qun Zhao
L. Stephen Miller

Folding is an essential shape characteristic of the human cerebral cortex. Descriptors of cortical folding patterns have been studied for decades. However, many previous studies are either based on local shape descriptors such as curvature, or based on global descriptors such as gyrification index or spherical wavelets. This paper proposes a gyrus-scale folding pattern analysis technique via cortical surface profiling. Firstly, we sample the cortical surface into 2D profiles and model them using a power function. This step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. Secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical surface, and apply the affinity propagation clustering algorithm to parcellate the cortex into cortical regions with strict hierarchy and smooth transitions among them. Finally, a second-round surface profiling is performed on the parcellated cortical surface, and the number of hinges is detected to describe the gyral folding pattern. We have applied the surface profiling method to two normal brain datasets and a schizophrenia patient dataset. The experimental results demonstrate that the proposed method can accurately classify human gyri into 2-hinge, 3-hinge and 4-hinge patterns. The distribution of these folding patterns on brain lobes and the relationship between fiber density and gyral folding patterns are further investigated. Results from the schizophrenia dataset are consistent with commonly found abnormality in former studies by others, which demonstrates the potential clinical applications of the proposed technique.

NeurIPS Conference 2010 Conference Paper

Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

Kaiming Li
Lei Guo
Carlos Faraco
Dajiang Zhu
Fan Deng
Tuo Zhang
Xi Jiang
Degang Zhang

Functional segregation and integration are fundamental characteristics of the human brain. Studying the connectivity among segregated regions and the dynamics of integrated brain networks has drawn increasing interest. A very controversial, yet fundamental issue in these studies is how to determine the best functional brain regions or ROIs (regions of interests) for individuals. Essentially, the computed connectivity patterns and dynamics of brain networks are very sensitive to the locations, sizes, and shapes of the ROIs. This paper presents a novel methodology to optimize the locations of an individual's ROIs in the working memory system. Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints. The optimization problem is solved via the simulated annealing approach. Our experimental results show that the optimized ROIs have significantly improved consistency in structural and functional profiles across subjects, and have more reasonable localizations and more consistent morphological and anatomic profiles.

YNIMG Journal 2009 Journal Article

Automatic cortical sulcal parcellation based on surface principal direction flow field tracking

Gang Li
Lei Guo
Jingxin Nie
Tianming Liu

The human cerebral cortex is a highly convoluted structure composed of sulci and gyri, corresponding to the valleys and ridges of the cortical surface respectively. Automatic parcellation of the cortical surface into sulcal regions is of great importance in structural and functional mapping of the human brain. In this paper, a novel method is proposed for automatic cortical sulcal parcellation based on the geometric characteristics of cortical surface including its principal curvatures and principal directions. This method is composed of two major steps: 1) employing the hidden Markov random field model (HMRF) and the expectation maximization (EM) algorithm on the maximum principal curvatures of the cortical surface for sulcal region segmentation, and 2) using a principal direction flow field tracking method on the cortical surface for sulcal basin segmentation. The flow field is obtained by diffusing the principal direction field on the cortical surface mesh. A unique feature of this method is that the automatic sulcal parcellation process is quite robust and efficient, and is independent of any external guidance such as atlas-based warping. The method has been successfully applied to the inner cortical surfaces of twelve healthy human brain MR images. Both quantitative and qualitative evaluation results demonstrate the validity and efficiency of the proposed method.

YNIMG Journal 2008 Journal Article

Reconstruction of central cortical surface from brain MRI images: Method and application

Tianming Liu
Jingxin Nie
Ashley Tarokh
Lei Guo
Stephen T.C. Wong

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.

YNIMG Journal 2007 Journal Article

Brain tissue segmentation based on DTI data

Tianming Liu
Hai Li
Kelvin Wong
Ashley Tarokh
Lei Guo
Stephen T.C. Wong

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.