Article
Computer Science, Information Systems
Jesper Pilmeyer, Georgios Hadjigeorgiou, Rolf M. J. N. Lamerichs, Marcel Breeuwer, Albert P. Aldenkamp, Svitlana Zinger
Summary: The application of multi-echo functional magnetic resonance imaging has increased in the last decade. Five multi-echo combination schemes were evaluated, and two of them were found to outperform the others in terms of functional resting-state network quality. Additionally, it was found that postprocessing steps were not necessary to achieve similar network quality.
Article
Neurosciences
Qinghua Liu, Yangyang Zhang, Lingyun Guo, ZhengXia Wang
Summary: In this study, a classification scheme based on spatial and temporal data augmentation was proposed, which aims to increase the sample size and improve the classification performance in functional brain network (FBN) analysis. By utilizing spatial and temporal information, a spatial augmentation module and a temporal augmentation module were designed, and a tensor fusion method was used to combine their features. Experimental results showed that the proposed scheme achieved superior classification accuracy and feature interpretation on benchmark datasets.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Neurosciences
Maxwell Shinn, Amber Hu, Laurel Turner, Stephanie Noble, Katrin H. Preller, Jie Lisa Ji, Flora Moujaes, Sophie Achard, Dustin Scheinost, R. Todd Constable, John H. Krystal, Franz X. Vollenweider, Daeyeol Lee, Alan Anticevic, Edward T. Bullmore, John D. Murray
Summary: High-throughput experimental methods in neuroscience have led to a surge in techniques for measuring complex interactions and multi-dimensional patterns. However, it is unclear whether these sophisticated measures can be traced back to simpler low-dimensional statistics. In this study, we examined rs-fMRI data using complex topology measures from network neuroscience, and found that spatial and temporal autocorrelation serve as reliable statistics that explain various network topology measures. These findings have important implications for understanding neurobiology and could help establish a connection between complexity measures and brain function.
NATURE NEUROSCIENCE
(2023)
Article
Clinical Neurology
Yanzhe Ning, Sisi Zheng, Sitong Feng, Hao Yao, Zhengtian Feng, Xinzi Liu, Linrui Dong, Hongxiao Jia
Summary: Acupuncture has been shown to improve sleep quality and cognitive function in individuals with insufficient sleep. This study found that acupuncture can modulate extensive brain networks and reverse the altered functional connectivity in individuals after acute sleep deprivation.
FRONTIERS IN NEUROLOGY
(2022)
Article
Neurosciences
Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu
Summary: In this study, we propose a biologically inspired architecture called Spatial Temporal-pyramid Graph Convolutional Network (STpGCN) to capture the spatial-temporal graph representation of functional brain activities for high brain decoding performance. We also introduce a sensitivity analysis method called BrainNetX for better explaining the decoding results. The experimental results show that STpGCN significantly improves brain-decoding performance and successfully annotates task-relevant brain regions.
HUMAN BRAIN MAPPING
(2023)
Article
Multidisciplinary Sciences
Foteini Simistira Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Scott Wellington, Holly Wilson, Marcus Liwicki, Johan Eriksson
Summary: This paper presents the first publicly available bimodal dataset of EEG and fMRI data acquired during inner-speech production. The aim is to contribute towards speech prostheses by decoding inner speech.
Article
Neurosciences
Linfeng Hu, Eliot S. Katz, Catherine Stamoulis
Summary: Metabolic, hormonal, autonomic and physiological rhythms can have significant effects on cerebral hemodynamics and intrinsic brain synchronization. Scan time parameters, such as time-of-day, time-of-week (school day vs weekend), and time-of-year (school year vs summer vacation) can also impact the functional circuits of the adolescent brain. Therefore, these factors should be taken into account in studying the associations between cognitive performance and brain connectivity.
Review
Neurosciences
Ashish Raj, Parul Verma, Srikantan Nagarajan
Summary: This article reviews recent advancements in using mathematical models to understand the relationship between brain structure and function, focusing on capturing various dynamic features. The need for models that can capture temporal, spatial, and spectral features of brain activity is emphasized. The article also presents recent work on spectral graph theory based models that accurately capture spectral and spatial patterns across multiple frequencies in MEG reconstructions.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Multidisciplinary Sciences
Fabian Eitel, Jan Philipp Albrecht, Martin Weygandt, Friedemann Paul, Kerstin Ritter
Summary: A new CNN architecture is proposed to combine hierarchical abstraction idea with spatial homogeneity in neuroimaging data, introducing patch individual filters (PIF) for faster learning of abstract features specific to regions. Results show that CNNs with PIF layers converge faster and achieve better performance than standard CNNs and patch-based CNNs for sex classification, Alzheimer's disease detection, and multiple sclerosis detection tasks on different data sets.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Marlena Duda, Danai Koutra, Chandra Sripada
Summary: This study investigates the presence of dynamic functional connectivity during rest and proposes a data-driven framework for studying cognitive neuroscience questions using connectivity changes. The framework outperforms the traditional sliding window approach in accuracy and computational efficiency when applied to working memory task data. Additionally, when applied to resting state fMRI data, the method consistently identifies five reliable FC states which show significant correlation with behavioral phenotypes.
HUMAN BRAIN MAPPING
(2021)
Article
Neurosciences
Evan M. Gordon, Timothy O. Laumann, Scott Marek, Dillan J. Newbold, Jacqueline M. Hampton, Nicole A. Seider, David F. Montez, Ashley M. Nielsen, Andrew N. Van, Annie Zheng, Ryland Miller, Joshua S. Siegel, Benjamin P. Kay, Abraham Z. Snyder, Deanna J. Greene, Bradley L. Schlaggar, Steven E. Petersen, Steven M. Nelson, Nico U. F. Dosenbach
Summary: This study utilized highly sampled resting-state functional connectivity MRI to map individual-specific corticostriatal connections. The researchers identified ten subnetworks linking frontal cortex and striatum, most of which were consistent with nonhuman primate tract-tracing work. Two subnetworks were connected to cortical regions associated with human language function.
Article
Neurosciences
Lu Zhang, Lorenzo Pini, DoHyun Kim, Gordon L. Shulman, Maurizio Corbetta
Summary: Recent evidence suggests that spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. This study examines whether replay patterns occur in resting-state activity in high-order cognitive networks not directly processing sensory inputs or motor outputs. The results show that spontaneous activity patterns in human attention networks code for hand movements.
JOURNAL OF NEUROSCIENCE
(2023)
Article
Neurosciences
Hong Gu, Kurt P. Schulz, Jin Fan, Yihong Yang
Summary: This study used a novel method to identify functional configuration patterns in the brain during a working memory task, showing that changes in brain states with increasing working memory load directly impact cognitive functioning.
Article
Neurosciences
Li Liang, Pengzheng Zhou, Chenfei Ye, Qi Yang, Ting Ma
Summary: Recent evidence suggests that white matter hyperintensities (WMHs) may contribute to cognitive dysfunction in Alzheimer's disease (AD) through their effects on brain networks. However, the vulnerability of specific neural connections related to WMHs in AD is not well understood. This study used a computational framework to assess the spatial-temporal patterns of WMH-related structural disconnectivity in AD. The results showed a specific pattern of brain disconnectome that predicted conversion from mild cognitive impairment (MCI) to dementia with high accuracy.
HUMAN BRAIN MAPPING
(2023)
Article
Clinical Neurology
Jennifer A. Poon, James C. Thompson, Tara M. Chaplin
Summary: This fMRI study investigates the longitudinal mediation effects of young adolescents' emotion regulation abilities on the relationship between their task-based limbic-prefrontal functional connectivity values and subsequent levels of internalizing and externalizing symptoms. Results suggest that emotion regulation difficulties predict higher levels of psychological symptoms, indicating that emotion regulation may serve as a transdiagnostic risk factor for psychopathology.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Neurosciences
Jin-Bo Sun, Hui Deng, Si -Yu Wang, Ya-Peng Cui, Xue-Juan Yang, Chen-Yang Wang, Yi-Huan Chen, Qun Yang, Hua-Ning Wang, Wei Qin
Summary: This study compared sleep parameters between schizophrenia patients with auditory verbal hallucinations (AVHs) and those without AVHs, and found that patients with AVHs had more severe deficits in sleep architecture and spindle activities. These deficits were also correlated with the severity of AVH symptoms. These results provide insight into the sleep characteristics and clinical factors in schizophrenia patients and highlight the importance of further research in this area.
BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING
(2023)
Article
Computer Science, Interdisciplinary Applications
Yaxin Shang, Zechen Wei, Hui Hui, Xiaohu Li, Liang Li, Yongqiang Yu, Ligong Lu, Li Li, Hongjun Li, Qi Yang, Meiyun Wang, Meixiao Zhan, Wei Wang, Guanghao Zhang, Xiangjun Wu, Li Wang, Jie Liu, Jie Tian, Yunfei Zha
Summary: Automatic segmentation of infected regions is essential for the diagnosis and assessment of COVID-19 pneumonia. In this study, a two-stage hybrid UNet method is proposed, which achieves accurate segmentation of infected regions. Furthermore, a 3D-ResNet model is trained for COVID-19 pneumonia screening. The results show that the proposed approach performs well in various evaluation metrics and can serve as an efficient assisting tool for radiologists in COVID-19 diagnosis from CT images.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Liwen Zhang, Lianzhen Zhong, Cong Li, Wenjuan Zhang, Chaoen Hu, Di Dong, Zaiyi Liu, Junlin Zhou, Jie Tian
Summary: This study proposes a novel approach for predicting the overall survival risk of human cancer patients based on CT images. By using a multi-task network with tailored attention modules, the method improves the accuracy of OS risk prediction and achieves good results in clinical stage prediction.
Article
Engineering, Biomedical
Peng Zhang, Jie Liu, Lin Yin, Yu An, Suhui Zhang, Wei Tong, Hui Hui, Jie Tian
Summary: This study proposes an improved reconstruction method to recover small carotid atherosclerotic plaque targets with high resolution in rodents. The results demonstrate that the proposed method achieves higher accuracy in locating and quantifying plaques compared to traditional methods.
PHYSICS IN MEDICINE AND BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Xianghan Zhang, Jingkai Gao, Yingdi Tang, Jie Yu, Si Si Liew, Chaoqiang Qiao, Yutian Cao, Guohuan Liu, Hongyu Fan, Yuqiong Xia, Jie Tian, Kanyi Pu, Zhongliang Wang
Summary: This article presents a method for expanding the repertoire of responsive dyes in biorthogonal chemistry by developing a near-infrared fluorophore that turns on its fluorescence upon biorthogonal reaction. The study discovers a design mechanism for the construction of bioorthogonally activatable NIR fluorophores and demonstrates their potential in sensitive in vivo imaging of tumors.
NATURE COMMUNICATIONS
(2022)
Article
Oncology
Xiaojun Zeng, Wen Zhu, Wenjun Lin, Jie Tian, Jian Yang, Chihua Fang
Summary: This article introduces the feasibility of laparoscopic extended right posterior sectionectomy and related methods, and demonstrates that virtual liver segment projection and ICG fluorescence imaging can improve the accuracy of the surgery.
ANNALS OF SURGICAL ONCOLOGY
(2023)
Editorial Material
Oncology
Xiaojun Zeng, Wen Zhu, Wenjun Lin, Jie Tian, Jian Yang, Chihua Fang
ANNALS OF SURGICAL ONCOLOGY
(2023)
Article
Oncology
Jingbo Wang, Siyi Li, Kun Wang, Ling Zhu, Lin Yang, Yunjing Zhu, Zhen Zhang, Longwei Hu, Yuan Yuan, Qi Fan, Jiliang Ren, Gongxin Yang, Weilong Ding, Xiaoyu Zhou, Junqi Cui, Chunye Zhang, Ying Yuan, Ruimin Huang, Jie Tian, Xiaofeng Tao
Summary: Real-time fluorescence imaging can improve the detection rate of tumor margins in oral squamous cell carcinoma (SCC) patients. The feasibility of using c-MET-binding peptide-indocyanine green (cMBP-ICG) for intraoperative detection of tumor margins was evaluated in a clinical trial. The application of cMBP-ICG showed high sensitivity and moderate specificity in detecting tumor margins in oral SCC patients.
ANNALS OF SURGICAL ONCOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Changjian Li, Jiahui Mi, Yueqi Wang, Zeyu Zhang, Xiaoyong Guo, Jian Zhou, Zhenhua Hu, Jie Tian
Summary: Fast identification of tumor area is crucial during lung cancer surgery. This study developed a new technology using fluorescent probes to define tumor boundaries and distinguish metastatic lymph nodes, thus improving surgical accuracy.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Xiangjun Wu, Bingxi He, Pengli Gao, Peng Zhang, Yaxin Shang, Liwen Zhang, Jing Zhong, Jingying Jiang, Hui Hui, Jie Tian
Summary: This article proposes a deep learning method to address the sparse-view reconstruction problem in projection MPI. By constructing a simulated data set and using novel projections generated by a projection generative network (PGNet), the temporal resolution of 3D imaging in projection MPI is improved while suppressing streaking artifacts.
Article
Radiology, Nuclear Medicine & Medical Imaging
Yueqi Wang, Changjian Li, Jiaming Zhuo, Hui Hui, Bing Zhou, Jie Tian
Summary: This study presents a dual-reaction fluorescent probe named DR-1, which visualizes Fe(II) and ROS simultaneously, offering great potential to explore the mechanism of ferroptosis both in vitro and in vivo.
MOLECULAR IMAGING AND BIOLOGY
(2023)
Article
Oncology
Zeyu Zhang, Cheng Fang, Yang Zhang, Song Su, Bo Li, Gang Liu, Zhenhua Hu, Jie Tian
Summary: The application of the nano-scale probe IgG-IRDye800CW allows for precise resection of hepatic carcinoma in patients with cirrhosis. Near-infrared window II (NIR-II) fluorescence imaging can detect HCC lesions and provide clear surgical margins, improving surgical precision.
PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY
(2022)
Article
Biology
Jingyuan Li, Wenfang Sun, Karen M. von Deneen, Xiao Fan, Gang An, Guangbin Cui, Yi Zhang
Summary: In this study, we propose a deep learning network with enhanced global-awareness for automatic thymoma segmentation in preoperative contrast-enhanced computed tomography (CECT) images. The network enhances the global-awareness of convolutional neural networks (CNNs) through multi-level feature interaction and integration. Evaluation results show that the network has superior segmentation performance and generalization ability compared to other state-of-the-art models.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Jingyuan Li, Wenfang Sun, Xiulong Feng, Karen M. von Deneen, Wen Wang, Guangbin Cui, Yi Zhang
Summary: A hybrid CNN-Transformer architecture, TA-Net, was developed for automatic thymoma segmentation. The proposed method achieved better results compared to previous methods, with no significant differences among different tumor types and enhanced phases.
INTELLIGENT MEDICINE
(2023)
Review
Computer Science, Interdisciplinary Applications
Lin Yin, Wei Li, Yang Du, Kun Wang, Zhenyu Liu, Hui Hui, Jie Tian
Summary: This review provides a detailed overview of the research status and future research trends of image reconstruction methods, including system matrix- and x-space-based methods, in Magnetic Particle Imaging (MPI). It also reviews the application of deep learning methods in MPI reconstruction and the current open sources of MPI. Research opinions on MPI reconstruction are presented, aiming to promote the use of MPI in clinical applications.
VISUAL COMPUTING FOR INDUSTRY BIOMEDICINE AND ART
(2022)
