Article
Psychology, Clinical
Yuchao Jiang, Dezhong Yao, Jingyu Zhou, Yue Tan, Huan Huang, MeiLin Wang, Xin Chang, Mingjun Duan, Cheng Luo
Summary: This study examined the global topological disruptions of large-scale white matter (WM) and grey matter (GM) networks in patients with schizophrenia (SZ) using resting-state functional MRI and graph theoretical approaches. The results showed abnormal global topological properties in both WM and GM networks in SZ. Moreover, specific regions in WM and GM exhibited nodal disturbances. The findings suggest compensatory functional alterations in WM may occur in response to impairments in adjacent GM in SZ.
PSYCHOLOGICAL MEDICINE
(2022)
Article
Neurosciences
Wenyu Tu, Zilu Ma, Nanyin Zhang
Summary: This study demonstrates that inhibiting neural activity in a hub region of the brain can lead to changes in the topological architecture of the whole-brain network, affecting both hub-related connections and propagating to other neural connections. Selectively inhibiting excitatory neurons in the hub further alters network integration. These findings highlight the significant impact of hub dysfunction on large-scale network changes.
Article
Neurosciences
Emily R. Olafson, Keith W. Jamison, Elizabeth M. Sweeney, Hesheng Liu, Danhong Wang, Joel E. Bruss, Aaron D. Boes, Amy Kuceyeski
Summary: Motor recovery after ischemic stroke is dependent on the brain's ability to compensate for damaged tissue through functional reorganization. Research has shown that regions in the cerebellar/subcortical networks undergo the most frequent functional reorganization, and that areas with more disruption due to stroke experience more remapping over time. Additionally, greater motor impairment at baseline correlates with more extensive early subacute functional reorganization, leading to better motor recovery at 6 months.
Article
Computer Science, Artificial Intelligence
Yu Zhang, Nicolas Farrugia, Pierre Bellec
Summary: Decoding cognitive processes from brain activity recordings has been a popular topic in neuroscience research. Recent studies have shown that decoding with graph neural networks can achieve state-of-the-art performance on the human connectome project benchmark. This work investigates the impact of multiple path lengths, brain parcel homogeneity, and interaction types on decoding models. The findings suggest that integrating neural dynamics within empirical functional connectomes using high-order graph convolutions is optimal for large-scale cognitive decoding.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Neurosciences
Linda Douw, Ida A. Nissen, Sophie M. D. D. Fitzsimmons, Fernando A. N. Santos, Arjan Hillebrand, Elisabeth C. W. van Straaten, Cornelis J. Stam, Philip C. De Witt Hamer, Johannes C. Baayen, Martin Klein, Jaap C. Reijneveld, Djai B. Heyer, Matthijs B. Verhoog, Rene Wilbers, Sarah Hunt, Huibert D. Mansvelder, Jeroen J. G. Geurts, Christiaan P. J. de Kock, Natalia A. Goriounova
Summary: This study investigates the cellular substrates of functional network integration in patients with temporal lobe epilepsy (TLE). The findings show that functional network centrality is related to memory performance and cellular integrative properties. These findings provide insights into the focal pathology and network disturbances in TLE.
Article
Behavioral Sciences
Shiqi Lin, Xi Ye, Yuping Yang, Jingyi Yang, Guang Xu, Xinzhi Wang, Xiaofen Ma
Summary: This study aimed to investigate the topological abnormalities of the cerebellar functional connectome in individuals with chronic insomnia (CI). The findings suggest that abnormal global and nodal topological properties of the cerebellar functional connectome are associated with CI and could serve as an important biomarker for CI.
BRAIN AND BEHAVIOR
(2023)
Article
Chemistry, Multidisciplinary
Wenjun Bai
Summary: This study introduces the concept of smoothness harmonics to capture the slowly varying cortical dynamics in graph-based fMRI data, and showcases their application in differentiating the cortical dynamics of children and adults, as well as their empirical merit over static functional connectomes in classification analyses.
APPLIED SCIENCES-BASEL
(2023)
Article
Neurosciences
Qiang Xu, Yifei Weng, Chang Liu, Lianli Qiu, Yulin Yang, Yifei Zhou, Fangyu Wang, Guangming Lu, Long Jiang Zhang, Rongfeng Qi
Summary: The study aimed to investigate the distributed functional connectome of white matter in patients with FD. It revealed that FD patients exhibited a non-optimized functional organization of the WM brain network, with key regions in brain information exchange being the frontal lobe, insula, and thalamus. The findings provided novel imaging evidence for the mechanism of FD.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Neurosciences
Maham Saeidi, Waldemar Karwowski, Farzad Farahani, Krzysztof Fiok, P. A. Hancock, Ben D. Sawyer, Leonardo Christov-Moore, Pamela K. Douglas
Summary: This study introduces a method that uses a graph convolutional network (GCN) framework to classify task fMRI data, and compares the impact of different node embedding algorithms on the model's performance. The empirical results show that this method performs well in predicting individual differences, and there are significant differences in gender in classification predictions.
Article
Psychiatry
Cristiana Dimulescu, Serdar Gareayaghi, Fabian Kamp, Sophie Fromm, Klaus Obermayer, Christoph Metzner
Summary: The coordinated dynamic interactions of large-scale brain circuits and networks are associated with cognitive functions and behavior. The anatomical organization of these networks constrains the dynamical landscape of brain activity, affecting the states and transitions the brain can display. Large-scale dysconnectivity may play a crucial role in the pathophysiology of schizophrenia.
FRONTIERS IN PSYCHIATRY
(2021)
Article
Computer Science, Artificial Intelligence
Mustafa Yildirim, Feyza Yildirim Okay, Suat Ozdemir
Summary: This study introduces two new models for default prediction, using a Big Data Analytics platform and a combination of statistical and machine learning methods to predict default for one million companies in Turkey, achieving promising results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Neuroimaging
Mengye Shi, Shenghua Liu, Huiyou Chen, Wen Geng, Xindao Yin, Yu-Chen Chen, Liping Wang
Summary: The study investigated the topological properties of brain functional connectome in unilateral acute brainstem ischemic stroke using graph theory. The patients showed a shift towards a regular network with significant changes in clustering coefficient, local efficiency, normalized clustering coefficient, and global efficiency. Abnormal nodal centralities were mainly observed in the default mode network, subcortical network, frontal and occipital lobe.
BRAIN IMAGING AND BEHAVIOR
(2021)
Article
Neurosciences
Shijun Duan, Lei Liu, Guanya Li, Jia Wang, Yang Hu, Wenchao Zhang, Zongxin Tan, Zhenzhen Jia, Lei Zhang, Karen M. von Deneen, Yi Zhang, Yongzhan Nie, Guangbin Cui
Summary: Functional constipation (FCon) is a common functional gastrointestinal disorder. This study investigated differences in brain network connectivity and topology using resting-state functional magnetic resonance imaging (RS-fMRI) and graph theory method in patients with FCon associated with anxiety/depressive status (FCAD) and those without (FCNAD). The findings showed lower clustering coefficient and small-world-ness in FCAD/FCNAD, with altered nodal degree/efficiency mainly in the rostral anterior cingulate cortex (rACC), precentral gyrus (PreCen), supplementary motor area (SMA), and thalamus.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Neuroimaging
Lei Liu, Chunxin Hu, Yang Hu, Wenchao Zhang, Zhida Zhang, Yueyan Ding, Yuanyuan Wang, Karen M. von Deneen, Lijuan Sun, Huaning Wang, Shijun Duan, Kuanrong Mao, Fan Wang, Guangbin Cui, Jixin Liu, Yongzhan Nie, Yi Zhang
Summary: Functional constipation is associated with abnormalities in the thalamo-cortical network, with altered nodal degree and efficiency in regions such as the thalamus, rACC, and SMA in FCon patients. The topological network exhibits negative correlations with difficulties in defecation and sensation of incomplete evacuation in rACC.
BRAIN IMAGING AND BEHAVIOR
(2021)
Article
Anatomy & Morphology
Junji Ma, Ying Lin, Chuanlin Hu, Jinbo Zhang, Yangyang Yi, Zhengjia Dai
Summary: The study revealed that different brain regions exhibit distinct functional characteristics in different frequency ranges, leading to diverse roles in the functional network. Frequency variability is associated with a spectrum of behavioral functions, including sensorimotor, complex cognitive, and social functions.
BRAIN STRUCTURE & FUNCTION
(2021)
Article
Biology
Zhilei Xu, Mingrui Xia, Xindi Wang, Xuhong Liao, Tengda Zhao, Yong He
Summary: This study identifies consistent and reproducible connectome hubs in the human brain, particularly in the lateral parietal cortex, and reveals their importance in intra- and inter-network communications. Transcriptome analysis further suggests the involvement of neuropeptide signaling pathway, neurodevelopment, and metabolism in these hub regions.
COMMUNICATIONS BIOLOGY
(2022)
Article
Robotics
Yuanfan Xu, Zhaoliang Zhang, Jincheng Yu, Yuan Shen, Yu Wang
Summary: This letter presents a framework to co-optimize robot exploration and task planning in unknown environments. A unified structure called subtask is designed to decompose the exploration and planning phases, and a value function and value-based scheduler are developed to select the appropriate subtask each time. The framework is evaluated in a photo-realistic simulator, achieving a 25%-29% increase in task efficiency.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Zhaoliang Zhang, Jincheng Yu, Jiahao Tang, Yuanfan Xu, Yu Wang
Summary: This article explores multi-robot exploration in communication-constrained environments and proposes a method based on topological maps. The effectiveness of this method is confirmed through testing.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Editorial Material
Neurosciences
Mingrui Xia, Yong He
BIOLOGICAL PSYCHIATRY
(2023)
Article
Neurosciences
Zilong Zeng, Tengda Zhao, Lianglong Sun, Yihe Zhang, Mingrui Xia, Xuhong Liao, Jiaying Zhang, Dinggang Shen, Li Wang, Yong He
Summary: In this study, a new 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) was proposed for tissue segmentation of 6-month-old infant brain MRI images. Compared to traditional single-scale symmetric convolutions, this approach demonstrated better accuracy and achieved the best performance in the evaluation.
HUMAN BRAIN MAPPING
(2023)
Article
Automation & Control Systems
Jing Zhang, Yuan Shen, Yu Wang, Xudong Zhang, Jian Wang
Summary: Edge computing is important for future Internet of Things systems, as it can reduce service latency and energy consumption by offloading computational tasks to edge servers. Caching appropriate services in the edge server can improve the quality of service, but it requires joint optimization of resource allocation considering different timescales of caching and offloading operations. This article proposes a novel hierarchical deep reinforcement learning scheme to optimize collaborative service caching and computation offloading.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Wenjun Tang, Mingyen Lee, Juejian Wu, Yixin Xu, Yao Yu, Yongpan Liu, Kai Ni, Yu Wang, Huazhong Yang, Vijaykrishnan Narayanan, Xueqing Li
Summary: Bitwise logic-in-memory (BLiM) is a promising approach to efficient computing in data-intensive applications. This work proposes a new BLiM approach based on ferroelectric field-effect transistors (FeFETs), supporting various computing functions and achieving higher energy efficiency and speed. The evaluation shows significant improvements in latency and energy consumption for typical operations, such as in-memory XOR and the advanced encryption standard (AES).
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Computer Science, Hardware & Architecture
Jingbo Hu, Guohao Dai, Liuzheng Wang, Liyang Lai, Yu Huang, Huazhong Yang, Yu Wang
Summary: This paper proposes an adaptive multidimensional parallel fault simulation framework based on the CPU-GPU heterogeneous system. It addresses the challenges of path divergence, unbalanced workload, and poor scalability, and further accelerates by introducing a 4-D parallel architecture on multiple GPUs. Experimental results show that compared to the commercial tool, the fault simulator based on 8 GPUs achieves an average speedup of 105.7 times, and for millions of gate-level circuits, the fault simulator based on one GPU achieves a speedup of up to 25.9 times compared to the CPU single-threaded simulator.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Shulin Zeng, Guohao Dai, Niansong Zhang, Xinhao Yang, Haoyu Zhang, Zhenhua Zhu, Huazhong Yang, Yu Wang
Summary: This paper proposes the H3M framework to optimize the architecture, scheduling, and mapping for INFaaS on cloud FPGA. H3M outperforms other accelerators in terms of EDP reduction on the ASIC platform. On the Xilinx U200 and U280 FPGA platforms, H3M significantly reduces EDP compared to Herald.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Clinical Neurology
Lei Wang, Qing Ma, Xiaoyi Sun, Zhilei Xu, Jiaying Zhang, Xuhong Liao, Xiaoqin Wang, Dongtao Wei, Yuan Chen, Bangshan Liu, Chu-Chung Huang, Yanting Zheng, Yankun Wu, Taolin Chen, Yuqi Cheng, Xiufeng Xu, Qiyong Gong, Tianmei Si, Shijun Qiu, Ching-Po Lin, Jingliang Cheng, Yanqing Tang, Fei Wang, Jiang Qiu, Peng Xie, Lingjiang Li, Yong He, Mingrui Xia, Yihe Zhang
Summary: This study conducted frequency-resolved connectome analysis on a large sample of MDD patients and healthy controls, revealing significant frequency-dependent connectome alterations in MDD. These alterations mainly occur in the left parietal, temporal, precentral, and fusiform cortices, as well as bilateral precuneus. Additionally, the connectome alteration in the high frequency band (0.16-0.24 Hz) is significantly associated with illness duration.
JOURNAL OF AFFECTIVE DISORDERS
(2023)
Article
Neurosciences
Junling Wang, Lianglong Sun, Lili Chen, Junyan Sun, Yapei Xie, Dezheng Tian, Linlin Gao, Dongling Zhang, Mingrui Xia, Tao Wu
Summary: Neuroimaging studies have shown that dysfunction of the amygdala plays a crucial role in the non-motor symptoms of Parkinson's disease. However, the specific relationship between amygdala subregions and these symptoms has not been well-defined. Using resting-state functional MRI, researchers found that the amygdala subregions in Parkinson's disease exhibited altered functional connectivity, particularly with the frontal, temporal, insular cortex, and putamen. Each subregion also displayed distinct hypoconnectivity with different limbic systems, and this hypoconnectivity was associated with various non-motor symptoms such as emotion, pain, olfaction, cognition, and sleepiness. These findings provide new insights into the pathogenesis of non-motor symptoms in Parkinson's disease.
NPJ PARKINSONS DISEASE
(2023)
Correction
Neurosciences
Junling Wang, Lianglong Sun, Lili Chen, Junyan Sun, Yapei Xie, Dezheng Tian, Linlin Gao, Dongling Zhang, Mingrui Xia, Tao Wu
NPJ PARKINSONS DISEASE
(2023)
Article
Neurosciences
Yuxing Hao, Huashuai Xu, Mingrui Xia, Chenwei Yan, Yunge Zhang, Dongyue Zhou, Tommi Karkkainen, Lisa D. Nickerson, Huanjie Li, Fengyu Cong
Summary: This study proposes an effective and powerful harmonisation strategy based on dual-projection (DP) theory of independent component analysis (ICA) to remove scanner/site effects while preserving signals of interest. The method shows superior performance compared to GLM-based and conventional ICA harmonisation methods in both simulation and real datasets.
EUROPEAN JOURNAL OF NEUROSCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Hanbo Sun, Zhenhua Zhu, Chenyu Wang, Xuefei Ning, Guohao Dai, Huazhong Yang, Yu Wang
Summary: This paper introduces an efficient co-exploration framework, named Gibbon, for NN models and PIM architectures. It improves search efficiency through a carefully designed co-exploration space and an evolutionary search algorithm, ESAPP, and addresses the issue of time-consuming evaluation with a multilevel joint simulator. Experimental results show that Gibbon can find better NN models and PIM architectures in a short amount of time, improving the accuracy of co-design results and reducing the energy-delay-product.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xuefei Ning, Yin Zheng, Zixuan Zhou, Tianchen Zhao, Huazhong Yang, Yu Wang
Summary: Neural architecture search (NAS) can automatically discover well-performing architectures in a large search space and has been shown to bring improvements to various applications. To improve the sample efficiency of search space exploration, GATES++ incorporates multifaceted information about NN's operation-level and architecture-level computing semantics into its construction and training, and it can discover better architectures after evaluating the same number of architectures.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)