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Xi Jiang

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11 papers

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JBHI Journal 2024 Journal Article

Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates

Wei Mao
Yuzhong Chen
Zhibin He
Zifan Wang
Zhenxiang Xiao
Yusong Sun
Liang He
Jingchao Zhou

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

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EAAI Journal 2024 Journal Article

WSRGAN: A wavelet-based GAN for super-resolution of plane-wave ultrasound images without sampling loss

Yuchao Lyu
Yinghao Xu
Cuiyun Yang
Xi Jiang
Xiaoyan Zhao
Xijun Zhu.

Plane-wave ultrasound imaging has ultra-fast temporal resolution and can image at frame rates exceeding 1 kHz, enabling breakthrough technologies like shear wave elastography. However, the imaging process of this technology lacks focus and poor imaging quality and usually requires beamforming technology to assist imaging, such as Coherent Plane-Wave Compounding (CPWC). The imaging quality of this method is often directly related to the number of beams, sacrificing the time advantage of single-beam plane wave imaging. This study proposes a Wavelet-based Generative Adversarial Network for Super-Resolution (WSRGAN) to meet the growing demand for high-quality imaging of single-beam plane waves. WSRGAN uses the encoding and decoding network as the generator and uses wavelet transform to replace the sampling process. Furthermore, we further designed adaptive wavelet attention to enable the model to focus attention on different levels of features at different stages. Wavelet GAN loss was proposed as GAN loss, and a new combined loss function was designed for the generator. The experiment was conducted on the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS) 2016 dataset. On the point target, the Full Width at Half Maximum (FWHM) of WSRGAN reached 0. 268 mm to 0. 502 mm. On the cyst target, the contrast of WSRGAN reaches 26. 156 dB to 37. 223 dB. Among invivo targets, the Peak Signal-to-Noise Ratio (PSNR) of WSRGAN is 41. 518 dB, and the Structural Similarity Index Measurement (SSIM) is 0. 984. Experiments show that each module proposed by the model has a total contribution and is helpful for various experiments. All targets performed well.

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EAAI Journal 2023 Journal Article

A deeper generative adversarial network for grooved cement concrete pavement crack detection

Jingtao Zhong
Ju Huyan
Weiguang Zhang
Hanglin Cheng
Jing Zhang
Zheng Tong
Xi Jiang
Baoshan Huang

Periodic grooved cement concrete pavement crack detection is of great importance for pavement condition monitoring and maintenance. The current state-of-the-art (SOTA) detection solutions highly depend on datasets. However, due to the limited access to crack images, more efficient methods are urgently needed to advance the detection of cracking on grooved cement concrete pavement. This study proposes an improved deeper Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to generate datasets of pavement images with a size of 512 × 512 pixels 2. Poisson bleeding is adopted to create the synthesized grooved cement concrete pavement crack images based on the generated crack images and groove images. The robustness of the proposed improved deeper WGAN-GP model is validated by Faster R-CNN, YOLOv3, and YOLOv4 models trained on original crack images and generated crack images for region-level detection. U-Net and W-segnet are used to achieve pixel-level crack detection to evaluate the effectiveness of proposed model. Results show that the improved deeper WGAN-GP could generate more realistic transverse, longitudinal and oblique crack images. In addition, the Poisson bleeding algorithm contributes to synthesizing grooved cement concrete pavement crack images. Moreover, it is observed that YOLOv3 trained by the augmented dataset could achieve a mean average precision (MAP) of 81. 98%, 6% MAP higher than the non-augmented dataset. U-Net and W-segnet benefit from augmented dataset with a better pixel-level segmentation result. Based on the results, it can be concluded that the improved deeper WAGN-GP image generation method can provide a straightforward way to fill the data shortage gap of grooved cement concrete pavement cracks, thus increasing the problem-solving capability of the SOTA crack detection models.

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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.

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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 2021 Journal Article

Intrinsic, dynamic and effective connectivity among large-scale brain networks modulated by oxytocin

Xi Jiang
Xiaole Ma
Yayuan Geng
Zhiying Zhao
Feng Zhou
Weihua Zhao
Shuxia Yao
Shimin Yang

The neuropeptide oxytocin is a key modulator of social-emotional behavior and its intranasal administration can influence the functional connectivity of brain networks involved in the control of attention, emotion and reward reported in humans. However, no studies have systematically investigated the effects of oxytocin on dynamic or directional aspects of functional connectivity. The present study employed a novel computational framework to investigate these latter aspects in 15 oxytocin-sensitive regions using data from randomized placebo-controlled between-subject resting state functional MRI studies incorporating 200 healthy subjects. In order to characterize the temporal dynamics, the 'temporal state' was defined as a temporal segment of the whole functional MRI signal which exhibited a similar functional interaction pattern among brain regions of interest. Results showed that while no significant effects of oxytocin were found on brain temporal state related characteristics (including temporal state switching frequency, probability of transitions between neighboring states, and averaged dwell time on each state) oxytocin extensively (n = 54 links) modulated effective connectivity among the 15 regions. The effects of oxytocin were primarily characterized by increased effective connectivity both between and within emotion, reward, salience, attention and social cognition processing networks and their interactions with the default mode network. Top-down control over emotional processing regions such as the amygdala was particularly affected. Oxytocin also increased effective homotopic interhemispheric connectivity in almost all these regions. Additionally, the effects of oxytocin on effective connectivity were sex-dependent, being more extensive in males. Overall, these findings suggest that modulatory effects of oxytocin on both within- and between-network interactions may underlie its functional influence on social-emotional behaviors, although in a sex-dependent manner. These findings may be of particular relevance to potential therapeutic use of oxytocin in psychiatric disorders associated with social dysfunction, such as autism spectrum disorder and schizophrenia, where directionality of treatment effects on causal interactions between networks may be of key importance .

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YNIMG Journal 2018 Journal Article

Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs

Jing Yuan
Xiang Li
Jinhe Zhang
Liao Luo
Qinglin Dong
Jinglei Lv
Yu Zhao
Xi Jiang

Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks’ behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.

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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.

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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.

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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.

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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.

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