Article
Neurosciences
Anders S. Olsen, Anders Lykkebo-Valloe, Brice Ozenne, Martin K. Madsen, Dea S. Stenbaek, Sophia Armand, Morten Morup, Melanie Ganz, Gitte M. Knudsen, Patrick M. Fisher
Summary: This study evaluated the impact of psilocin on the characteristics of resting-state time-varying functional connectivity in healthy individuals. The findings suggest that specific brain states showing negative associations with drug level and subjective drug intensity contribute to a better understanding of the acute effects of serotonergic psychedelics.
Article
Computer Science, Artificial Intelligence
Mingliang Wang, Jiashuang Huang, Mingxia Liu, Daoqiang Zhang
Summary: This study proposes a temporal dynamics learning (TDL) method for network-based brain disease identification using rs-fMRI time-series data. By integrating network feature extraction and classifier training into a unified framework, it addresses the issues of previous studies paying less attention to the evolution of global network structures over time and treating feature extraction and training as separate tasks.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Immunology
Johnna R. Swartz, Angelica F. Carranza, Laura M. Tully, Annchen R. Knodt, Janina Jiang, Michael R. Irwin, Camelia E. Hostinar
Summary: The study found associations between peripheral inflammation and adolescent brain connectivity, with higher TNF-α levels linked to changes in neural network connections. Associations with IL-6 and CRP were not significant, suggesting that inflammation may have unique effects on brain connectivity during adolescence.
BRAIN BEHAVIOR AND IMMUNITY
(2021)
Article
Neurosciences
Ignacio Cifre, Maria T. Miller Flores, Lucia Penalba, Jeremi K. Ochab, Dante R. Chialvo
Summary: The center stage of neuro-imaging is currently focused on studying functional correlations between brain regions, which define brain functional networks. This study proposes a new measure of nonlinear dynamic directed functional connectivity across regions of interest, providing directed information of functional correlations and a measure of temporal lags without extensive numerical complications. This approach offers a different and complementary perspective in analyzing brain co-activation patterns.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Neurosciences
Limin Peng, Zhiguo Luo, Ling-Li Zeng, Chenping Hou, Hui Shen, Zongtan Zhou, Dewen Hu
Summary: This study developed a brain parcellation method based on dynamic functional connectivity and created a new functional brain atlas. The atlas can reveal finer functional boundaries that static methods may overlook, and shows good agreement with cytoarchitectonic areas and task activation maps.
Article
Neurosciences
Anees Abrol, Zening Fu, Yuhui Du, Tony W. W. Wilson, Yu-Ping Wang, Julia M. M. Stephen, Vince D. D. Calhoun
Summary: The brain's functional architecture and organization undergo continuous development and modification throughout adolescence. This study systematically evaluated over 47,000 youth and adult brains to examine time-resolved functional connectivity patterns and found distinct differences between the two life stages, indicating an overall inverted U-shaped trajectory in the strengthening and modularization of functional coupling. These findings suggest greater synchrony and integration of the brain's functional connections beyond adolescence, with a gradual decline during healthy aging.
HUMAN BRAIN MAPPING
(2023)
Review
Neurosciences
Hongzan Sun, Yong He, Heqi Cao
Summary: NSFC has been funding various research programs related to fMRI over the past two decades, with increasing support particularly in the General Program and Key Program. Leading research institutes in economically developed provinces and municipalities received the most support and established close collaboration relationships. Notable achievements in data analysis methods, brain connectomes, and computational platforms as well as their applications in brain disorders were reviewed.
CNS NEUROSCIENCE & THERAPEUTICS
(2021)
Article
Neurosciences
Zhihong Lan, Shoujun Xu, Xiangrong Yu, Zhenjie Yu, Meng Li, Feng Chen, Yu Liu, Tianyue Wang, Yunfan Wu, Yungen Gan, Guihua Jiang
Summary: This study investigates the functional connectivity in single-sex children with autism spectrum disorders (ASDs) through resting-state functional magnetic resonance imaging (rs-fMRI), revealing enhanced connectivity in specific brain regions. The study suggests a possible relationship between atypical visual attention and poor learning ability in subjects with ASD.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Neurosciences
C. Ahrends, A. Stevner, U. Pervaiz, M. L. Kringelbach, P. Vuust, M. W. Woolrich, D. Vidaurre
Summary: Functional connectivity (FC) in the brain exhibits subtle but reliable modulations within a session. State-based models can estimate time-varying FC, but sometimes fail to capture changes effectively, resulting in model stasis. This study quantifies the impact of data nature and model parameters on detecting temporal changes in FC and provides practical recommendations for time-varying FC studies.
Article
Neurosciences
Zhiying Long, Xuanping Liu, Yantong Niu, Huajie Shang, Hui Lu, Junying Zhang, Li Yao
Summary: Dynamic functional connectivity (DFC) analysis is a method used to study the time-varying functional interactions between brain regions. In this study, an alternating HMM (aHMM) method was proposed, which showed better estimation of DFC with improved robustness to noise, parameter number, and sample size compared to the sliding window (SW) method and the standard hidden Markov model (HMM). Analysis of real fMRI data from patients with cerebral small vessel disease (CSVD) revealed that aHMM was able to detect significant differences in connectivity amplitude and fluctuations between patient groups, while HMM and SW failed to do so.
COGNITIVE NEURODYNAMICS
(2022)
Article
Neurosciences
Fei Jiang, Huaqing Jin, Yijing Gao, Xihe Xie, Jennifer Cummings, Ashish Raj, Srikantan Nagarajan
Summary: This article introduces a novel framework called TVDN for studying dynamic resting state functional connectivity. The framework includes a generative model and an inference algorithm that can automatically and adaptively learn the low-dimensional manifold of dynamic RSFC and detect dynamic state transitions in data. Experimental results demonstrate that TVDN is able to accurately capture the dynamics of brain activity and more robustly detect brain state switching.
Article
Psychology, Clinical
Lizhu Luo, Christelle Langley, Laura Moreno-Lopez, Keith Kendrick, David K. Menon, Emmanuel A. Stamatakis, Barbara J. Sahakian
Summary: This study examined the association between depressive symptoms in traumatic brain injury (TBI) patients and altered resting-state functional connectivity (rs-fc) or voxel-based morphology in brain regions involved in emotional regulation and associated with depression. The results showed a positive association between depression scores and rs-fc between limbic regions and cognitive control regions, while there was a negative association between depression scores and rs-fc between limbic and frontal regions involved in emotion regulation. These findings contribute to a better understanding of the mechanisms underlying depression following TBI and can inform treatment decisions.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Neurosciences
M. Fiona Molloy, Zeynep M. Saygin
Summary: This study used neonatal data to uncover the intrinsic functional brain networks and individual differences. The study found the most individual variability in different networks among neonates, and this variability was not influenced by noise differences or differences from adult networks. Differential gene expression provided a potential explanation for the emergence of these networks and identified potential genes of interest for future research. This study revealed that neonatal connectomes can reveal individual-specific information processing units and has the potential to improve prediction of behavior and future outcomes.
Article
Neurosciences
Elijah Agoalikum, Benjamin Klugah-Brown, Hang Yang, Pan Wang, Shruti Varshney, Bochao Niu, Bharat Biswal
Summary: In this study, dynamic functional network connectivity differences in adult, adolescent, and child ADHD were investigated using resting-state functional magnetic resonance imaging data. The findings suggest that there are connectivity differences among the three age groups, providing new insights for future case-control studies and treatment strategies.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Neurosciences
Jiahe Zhang, Aaron Kucyi, Jovicarole Raya, Ashley N. Nielsen, Jason S. Nomi, Jessica S. Damoiseaux, Deanna J. Greene, Silvina G. Horovitz, Lucina Q. Uddin, Susan Whitfield-Gabrieli
Summary: Functional connectivity (FC) has become a widely used tool for probing functional abnormalities in clinical populations, providing insights into intrinsic functional networks, neurodevelopmental patterns, and network-level changes in clinical conditions. FC studies support a dimensional approach to studying transdiagnostic clinical symptoms and enhance the understanding of symptom progression trajectories, offering unprecedented opportunities for investigating brain function in challenging clinical conditions.
Article
Biochemistry & Molecular Biology
Gaojian Li, Tao Zhang, Bin Hu, Shuyi Han, Chen Xiang, Guohui Yuan, Hongxuan He
Summary: Female mice can detect the urinary odors of male mice, but parasitic infection can reduce the attractiveness of male urine, inhibiting female attraction. In this study, Trichinella spiralis infection in male mice was found to decrease urine pheromone content and sperm quality, leading to reproductive injury. The downregulation of specific volatile compounds and genes related to spermatogenesis may contribute to these effects.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Analytical
Zhixin Wang, Chenming Li, Zhong Wang, Yuee Li, Bin Hu
Summary: The study demonstrates the combination of Raman spectroscopy and machine learning for traceability of garlic bulb species. Raman spectra of garlic bulbs were collected and analyzed for developing a classification model. The trained model achieved high accuracy, precision, and sensitivity. The study offers a novel approach for classification and origin identification of plant bulbs.
VIBRATIONAL SPECTROSCOPY
(2023)
Article
Computer Science, Information Systems
Zepeng Li, Rikui Huang, Minyu Zhai, Zhenwen Zhang, Bin Hu
Summary: This paper proposes a statistic-based algorithm for inferring missing entity types in knowledge graph, considering both performance and incrementality. The algorithm aggregates neighborhood information and type co-occurrence information to infer types, outperforming previous statistics-based algorithms and some other models.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoyan Yuan, Zhenyu Liu, Qiongqiong Chen, Gang Li, Zhijie Ding, Zixuan Shangguan, Bin Hu
Summary: This study proposes a framework for depression detection based on information regions and clips, which effectively reduces diagnosis error. It divides spatially informative regions and extracts features using temporal information, and introduces an improved attention mechanism to increase attention to information segments.
COGNITIVE COMPUTATION
(2023)
Article
Medical Informatics
Rui Li, Chao Ren, Sipo Zhang, Yikun Yang, Qiqi Zhao, Kechen Hou, Wenjie Yuan, Xiaowei Zhang, Bin Hu
Summary: The study focuses on EEG-based emotion recognition and proposes an end-to-end network called STSNet. It addresses the issue of obtaining complementary and discriminative data representation using the characteristics of EEG signals. STSNet utilizes manifold space and fusion of spatio-temporal-spectral features to improve classification ability. Experimental results on DEAP and DREAMER datasets demonstrate the good emotion recognition performance of the STSNet model.
HEALTH INFORMATION SCIENCE AND SYSTEMS
(2023)
Editorial Material
Computer Science, Cybernetics
Jian Lu, Chang Xu, Xiaoxing Ma, Bin Hu
Summary: This is the third issue of IEEE TCSS in 2023, and the authors are excited to share some exciting news with their esteemed readership.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Engineering, Biomedical
Jitao Zhong, Wenyan Du, Lu Zhang, Hong Peng, Bin Hu
Summary: Automatic detection of depression is crucial in today's society. This paper proposes an automatic feature extraction method called Sparse Graphs Embedding (SGE) for depression detection. The method addresses key challenges and achieves promising detection rates using fNIRS.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Yujuan Xing, Zhenyu Liu, Qiongqiong Chen, Gang Li, Zhijie Ding, Lei Feng, Bin Hu
Summary: Depression imposes a significant burden on families and society due to its high prevalence, recurrence, and disability mortality. Researchers are increasingly focusing on using efficient and objective methods to recognize depression. Subtle changes in the speaker's physical and mental state are subconsciously reflected in their vocal apparatus. Speech signals are easily influenced by emotional stimuli, making them a substantial factor in depression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yingying Shang, Qian Su, Rong Ma, Miao Chen, Ziyang Zhao, Chaofan Yao, Lin Han, Zhijun Yao, Bin Hu
Summary: This study investigated hierarchical perturbations in the functional connectomes of nurses with burnout and found that these perturbations may be influenced by genetic factors. The study also showed that regions affected by burnout severity were mainly distributed in the association and visual cortices.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Engineering, Chemical
Hongjing Li, Xu Hu, Kang Geng, Min Liu, Bin Hu, Qinghai Chen, Zhijian Jiang, Meizi He, Yingda Huang, Nanwen Li, Zushun Xu, Quanyuan Zhang
Summary: To solve the problems of high gas permeability and stability caused by commercial Zirfon separators, a highly hydrophilic polybenzimidazole-type composite porous separator was proposed. Compared to Zirfon separators, the proposed separators exhibited significantly reduced area resistance and hydrogen permeability, as well as higher water and liquid absorption rates, bubble pressure, and lower hydrogen permeability.
JOURNAL OF MEMBRANE SCIENCE
(2023)
Editorial Material
Computer Science, Cybernetics
Xinyi Li, Hanshu Cai, Guihua Tian, Bin Hu
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Jianxin Wang, Min Li, Edwin Wang, Jing Tang, Bin Hu
Summary: This edition includes 55 diverse articles that explore the interaction between computer technology and society.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Engineering, Biomedical
Kun Qian, Bin Hu, Yoshiharu Yamamoto, Bjorn W. Schuller
Summary: The sound generated by the human body carries valuable information about both physical and psychological health. Although there have been numerous successes in the field of body sound analysis in recent decades, the fundamental principles are not yet well-established. The lack of publicly accessible databases hinders sustainable research. Therefore, we are launching the Voice of the Body (VoB) archive to collect well-established body sound databases in a standardized manner. We also aim to organize challenges to promote the development of audio-driven healthcare methods. We believe that VoB can break barriers between different disciplines, leading to an era of Medicine 4.0 enriched by audio intelligence.
CYBORG AND BIONIC SYSTEMS
(2023)
Article
Engineering, Biomedical
Weijia Liu, Qunxi Dong, Shuting Sun, Jian Shen, Kun Qian, Bin Hu
Summary: Alzheimer's disease (AD) is a common neurodegenerative disease and it's important to assess the AD conversion risk of MCI individuals. This study proposes an AD conversion risk estimation system (CRES) that utilizes MRI data to estimate brain age and evaluate AD conversion risk. Results show that the MRI derived age gap significantly distinguishes different groups and each additional year in age gap is associated with a 4.57% greater risk of AD conversion for MCI subjects. Furthermore, a nomogram is drawn to predict the individual MCI conversion risk. The CRES provides valuable information for early intervention and diagnosis.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jianxiu Li, Yanrong Hao, Wei Zhang, Xiaowei Li, Bin Hu
Summary: Conflict control is impaired in major depression disorders (MDDs), leading to dysfunctional decision-making and social interactions. This study used dynamic causal modeling (DCM) technique on electroencephalography (EEG) to investigate the neural basis of conflict monitoring processes in MDD patients. The results showed abnormal neural activations and connections in MDD patients, suggesting a dysfunction in emotional conflict processing. Specifically, MDD patients exhibited lower N2 amplitudes and reduced ACC activation for incongruent stimuli. The findings provide insights into the neural mechanisms underlying emotional conflict processing in MDDs.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
