Article
Clinical Neurology
Daniel Vereb, Marton Attila Kovacs, Szabolcs Antal, Krisztian Kocsis, Nikoletta Szabo, Balint Kincses, Bence Bozsik, Peter Farago, Eszter Toth, Andras Kiraly, Peter Klivenyi, Denes Zadori, Zsigmond Tamas Kincses
Summary: This study investigates functional connectivity modulation during a visuospatial attention task in patients with Parkinson's disease. The findings suggest that task-related networks function differently in these patients in association with motor symptoms, while impaired modulation of visual and default-mode network connectivity was not correlated with motor function.
FRONTIERS IN NEUROLOGY
(2022)
Article
Clinical Neurology
Xiaojuan Dan, Yang Hu, Junyan Sun, Linlin Gao, Yongtao Zhou, Jinghong Ma, Julien Doyon, Tao Wu, Piu Chan
Summary: This study found that altered cerebellar functional connectivity in early PD patients during resting state is influenced not only by motor deficits, but also by cognitive deficits, highlighting the interaction between motor and cognitive functioning and possibly reflecting compensatory mechanisms in early PD.
FRONTIERS IN NEUROLOGY
(2021)
Article
Clinical Neurology
William C. Palmer, Brenna A. Cholerton, Cyrus P. Zabetian, Thomas J. Montine, Thomas J. Grabowski, Swati Rane
Summary: The study found significant differences in cerebello-cortical functional connectivity between PD patients and normal controls, particularly in the somatomotor network. Cognitive function was found to be associated with connectivity of the cerebellar SMN and dorsal attention network. Altered cerebellar connectivity with frontoparietal and default mode networks was also correlated with the severity of motor function in PD.
FRONTIERS IN NEUROLOGY
(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
Clinical Neurology
Sahar Yassine, Ute Gschwandtner, Manon Auffret, Joan Duprez, Marc Verin, Peter Fuhr, Mahmoud Hassan
Summary: In this study, different sub-phenotypes of Parkinson's disease (PD) were identified based on electrophysiological profiles obtained from resting-state electroencephalography (RS-EEG). These sub-phenotypes showed distinct disruptions in various brain networks and were predictive of disease outcome. EEG features were also able to predict cognitive evolution of PD patients. The identification of these novel PD subtypes based on electrical brain activity signatures has important clinical implications and can support the development of brain-based therapeutic strategies.
MOVEMENT DISORDERS
(2023)
Article
Neurosciences
Aidi Shan, Heng Zhang, Mengxi Gao, Lina Wang, Xingyue Cao, Caiting Gan, Huimin Sun, Yongsheng Yuan, Kezhong Zhang
Summary: This study aimed to investigate changes in the inherent connectivity pattern of global functional networks in Parkinson's disease (PD) patients with fatigue. Voxel-wise degree centrality (DC) and seed-based functional connectivity (FC) analysis were conducted on 18 PD patients with fatigue (PD-F), 20 PD patients without fatigue (PD-NF), and 23 healthy controls (HCs). The results showed that PD-F patients had reduced DC values in the left postcentral gyrus and increased DC values in the bilateral precuneus. Altered FC was predominantly located in the sensorimotor network in the PD-F group. Cortical thickness did not differ significantly between the three groups.
CNS NEUROSCIENCE & THERAPEUTICS
(2023)
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
Clinical Neurology
Joni De Vleeschhauwer, Evelien Nackaerts, Nicholas D'Cruz, Britt Vandendoorent, Letizia Micca, Wim Vandenberghe, Alice Nieuwboer
Summary: Intensive writing training improved automaticity and retention of writing skills in Parkinson's disease patients. Resting-state networks in the brain underwent changes, particularly increased connectivity within the dorsal attentional network (DAN). These changes were associated with improved and sustained writing performance.
JOURNAL OF NEUROLOGY
(2022)
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
Engineering, Multidisciplinary
Sarah Leviashvili, Yael Ezra, Amgad Droby, Hao Ding, Sergiu Groppa, Anat Mirelman, Muthuraman Muthuraman, Inbal Maidan
Summary: The study found reduced connectivity in the Central Executive Network (CEN) and Dorsal Attention Network (DAN) in Parkinson's disease patients, while increased connectivity was observed in the Ventral Attention Network (VAN). These results indicate a complex pattern of DFC alteration within major brain networks, reflecting the co-occurrence of impairment and compensatory mechanisms processes taking place in PD.
Article
Neurosciences
Jung-Hoon Kim, Josepheen De Asis-Cruz, Kushal Kapse, Catherine Limperopoulos
Summary: The reliability and robustness of rs-fcMRI depend on minimizing the influence of head motion on brain signals. This study examined the impact of head motion on newborn brain connectivity using a large dataset. The findings revealed that head motion significantly affected connectivity, with specific effects observed in sensory-related and default mode networks. Implementing a motion correction strategy helped reduce the confounding effects of head motion on neonatal rs-fcMRI.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Luoyao Pang, Huidi Li, Quanying Liu, Yue-Jia Luo, Dean Mobbs, Haiyan Wu
Summary: Motivated dishonesty is a common social behavior that varies among individuals. This study explores the relationship between brain networks and dishonesty using resting-state functional magnetic resonance imaging (rsfMRI), and demonstrates the ability to predict dishonest behavior through a model based on functional connectivity.
Article
Clinical Neurology
Mario Stanziano, Nico Golfre Andreasi, Giuseppe Messina, Sara Rinaldo, Sara Palermo, Mattia Verri, Greta Demichelis, Jean Paul Medina, Francesco Ghielmetti, Salvatore Bonvegna, Anna Nigri, Giulia Frazzetta, Ludovico D'Incerti, Giovanni Tringali, Francesco DiMeco, Roberto Eleopra, Maria Grazia Bruzzone
Summary: Magnetic resonance-guided high-intensity focused ultrasound is a non-invasive alternative treatment for tremor. This study found that the treatment modulated resting state functional connectivity within the tremor network and these changes were associated with clinical outcomes.
FRONTIERS IN NEUROLOGY
(2022)
Article
Neurosciences
Yongfa Zhang, Fei Wang, Jie Sui
Summary: Recent research supports a fundamental self hypothesis, suggesting that the self is a baseline function of the brain that regulates cognitive processing and behavior. Understanding this hypothesis can help identify the emergence of self-biased behaviors and predict the influence of brain signals at rest on such behaviors.
Article
Neurosciences
Jeffrey M. Kenzie, Deepthi Rajashekar, Bradley G. Goodyear, Sean P. Dukelow
Summary: Around 50% of stroke patients have deficits in proprioception, but our understanding of the neurological mechanisms behind these deficits is limited. This study used resting-state functional magnetic resonance imaging (fMRI) to investigate changes in functional brain networks associated with proprioception deficits in stroke patients. The results showed reduced connectivity in specific brain regions, including the supplementary motor area and the supramarginal gyrus, in stroke patients compared to healthy controls. Functional connectivity of these regions, as well as the primary somatosensory cortex and the parietal opercular area, was significantly associated with proprioceptive function. The parietal lobe of the lesioned hemisphere was identified as an important node for proprioception after stroke, and evaluating the functional connectivity of this region could help predict recovery. The study also identified potential targets for therapeutic neurostimulation to aid in stroke recovery.
HUMAN BRAIN MAPPING
(2023)
Article
Psychology, Clinical
Debo Dong, Dezhong Yao, Yulin Wang, Seok-Jun Hong, Sarah Genon, Fei Xin, Kyesam Jung, Hui He, Xuebin Chang, Mingjun Duan, Boris C. Bernhardt, Daniel S. Margulies, Jorge Sepulcre, Simon B. Eickhoff, Cheng Luo
Summary: This study investigated the pathological interaction of sensory and cognitive function in schizophrenia and its relationship to system-level imbalance. The results revealed a compression of the cortical hierarchy organization, leading to a diminished separation between sensory and cognitive systems. Furthermore, the analysis showed reduced connectivity within unimodal regions and increased connectivity between unimodal regions and other areas. These findings suggest that disruptions in the somatosensory-motor system and inefficient integration of sensory information contribute to high-level cognitive deficits in schizophrenia.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Neurosciences
Yuchao Jiang, Wei Li, Yingjie Qin, Le Zhang, Xin Tong, Fenglai Xiao, Sisi Jiang, Yunfang Li, Qiyong Gong, Dong Zhou, Dongmei An, Dezhong Yao, Cheng Luo
Summary: This study used T1-weighted and T2-weighted MRI to investigate the myelination changes in temporal lobe epilepsy (TLE) patients. The mSCN analysis showed decreased myelination in frontotemporal regions, amygdala, and thalamus in both patient groups compared to healthy controls. Left TLE patients also had lower myelination in left medial temporal regions. The findings suggest that myelination alterations in TLE are related to epileptic seizures.
HUMAN BRAIN MAPPING
(2023)
Article
Oncology
Lu Yang, Lei Wang, Yuchuan Tan, Hanli Dan, Peng Xian, Yipeng Zhang, Yong Tan, Meng Lin, Jiuquan Zhang
Summary: This study investigated the value of a novel functional magnetic resonance imaging (MRI) technique, amide proton transfer (APT)-weighted MRI, combined with serum prostate-specific antigen (PSA) levels in differentiating malignant and benign prostate lesions. The results showed that the combination of APTmax, APTmean, ADCmean, and PSAD had the highest diagnostic value.
Article
Endocrinology & Metabolism
He Liu, Jiao Wang, Tao Luo, Zhiming Zhen, Li Liu, Yalan Zheng, Chaobin Zhang, Xiaofei Hu
Summary: This study investigated the expression and prognostic significance of ITGB2 in glioma. The results showed that ITGB2 can be a potential marker for mesenchymal molecular subtype gliomas and an independent predictive marker of overall survival in malignant glioma patients. In addition, ITGB2 is closely related to glioma immune-related activities. Methylation of ITGB2 negatively regulates its expression in low-grade glioma tissues and can be used to predict the overall survival of low-grade glioma patients.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ying Cao, Xiaoxia Wang, Jinfang Shi, Xiangfei Zeng, Lihong Du, Qing Li, Dominik Nickel, Xiaoyu Zhou, Jiuquan Zhang
Summary: The purpose of this study was to assess the diagnostic performance of Ultrafast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) parameters compared to Apparent Diffusion Coefficient (ADC) for distinguishing benign from malignant breast lesions, and to investigate the complementarity of ultrafast DCE-MRI with Diffusion Weighted Imaging (DWI). The results showed that ultrafast DCE-MRI semi-quantitative parameters had better classification performance than quantitative parameters, and the combination of ultrafast DCE-MRI semi-quantitative parameters and ADC further improved the diagnostic value of ultrafast DCE-MRI.
DIAGNOSTIC AND INTERVENTIONAL IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hongfei Sun, Ge Ren, Xinzhi Teng, Liming Song, Kang Li, Jianhua Yang, Xiaofei Hu, Yuefu Zhan, Shiu Bun Nelson Wan, Man Fung Esther Wong, King Kwong Chan, Hoi Ching Hailey Tsang, Lu Xu, Tak Chiu Wu, Feng-Ming Spring Kong, Yi Xiang J. Wang, Jing Qin, Wing Chi Lawrence Chan, Michael Ying, Jing Cai
Summary: In this study, an AI-assisted multistrategy image enhancement technique was developed to improve the accuracy of COVID-19 classification on chest X-ray (CXR) images. The new strategy consisted of three parts: segmentation, bone signal suppression, and super-resolution reconstruction. Comparative experiments showed that the multistrategy enhanced CXR images had better performance in terms of peak signal-to-noise ratio and root mean square error. The classification accuracy of COVID-19 was improved using the improved CXR images.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Neurosciences
Changyue Hou, Sisi Jiang, Mei Liu, Hechun Li, Lang Zhang, Mingjun Duan, Gang Yao, Hui He, Dezhong Yao, Cheng Luo
Summary: This study investigates the spatiotemporal dynamics of schizophrenia and its relationship with psychiatric symptoms. A total of 98 patients with schizophrenia underwent resting-state functional magnetic resonance imaging, and the variations in functional connectivity density were evaluated. The findings show that patients exhibit altered temporal and spatial variations in different brain networks, and the variations in perceptual and attentional systems are associated with symptom severity. Moreover, the differences between patients and healthy subjects are related to the densities of dopamine, serotonin and mu-opioid receptors, as well as the densities of serotonin reuptake transporter, dopamine transporter, and dopamine synthesis capacity. Overall, this study highlights the importance of brain dynamics in understanding the pathological mechanism of schizophrenia.
Article
Neurosciences
Yun Qin, Sisi Jiang, Siwei Xiong, Sipei Li, Qiankun Fu, Lili Yang, Peishan Du, Cheng Luo, Dezhong Yao
Summary: In this study, the features of theta oscillations and the functional interactions among activation/deactivation networks during the n-back working memory (WM) task were examined in patients with idiopathic generalized epilepsy (IGE) using simultaneous EEG-fMRI. The results showed enhanced frontal theta power and increased activations in high-load WM tasks in IGE patients, as well as decreased counteraction between the activation network and deactivation network. These findings suggest the important role of the interactions between activation and deactivation networks in WM processing and provide insights into the pathophysiological mechanism of cognitive dysfunction in generalized epilepsy.
JOURNAL OF NEUROSCIENCE RESEARCH
(2023)
Article
Oncology
Jie Zhou, Guanming Chen, Jiuling Wang, Bo Zhou, Xuemin Sun, Jinsong Wang, Shu Tang, Xiangju Xing, Xiaofei Hu, Yang Zhao, Yu Peng, Wenjiong Shi, Tingting Zhao, Yuzhang Wu, Hanbing Zhong, Ni Hong, Zhihua Ruan, Yi Zhang, Wenfei Jin
Summary: Anti-PD-1 therapy shows effective outcomes in both liver cancer patients and non-liver cancer patients infected with HBV. Through a retrospective multicenter study, it is found that HBV+ non-liver cancer patients have better responses to anti-PD-1 therapy compared to HBV- non-liver cancer patients. Additionally, in HBV+ ESCC patients, the cytotoxicity score of T cells and MHC score of B cells significantly increase after anti-PD-1 therapy. CX3CR1(high) T-EFF, a subset of CD8(+) T-EFF, is also associated with better clinical outcomes in HBV+ ESCC patients.
Article
Immunology
Jingyun Yang, Xiaofei Hu, Yu Wang, Wenying Liu, Mengjie Zhang, Anmei Zhang, Bing Ni
Summary: This study identified shared genetic signatures and immune microenvironment between multiple sclerosis (MS) and non-small cell lung cancer (NSCLC) and revealed the molecular mechanisms underlying their relationship. Phosphodiesterase 4A (PDE4A) was identified as the most significant shared gene, and its high expression was associated with poor prognosis in NSCLC patients. Therefore, PDE4A may serve as a potential therapeutic target and immune-related biomarker for patients with both MS and NSCLC.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Oncology
Ming Xiong, Yaona Xu, Yang Zhao, Si He, Qihan Zhu, Yi Wu, Xiaofei Hu, Li Liu
Summary: A comprehensive and quantitative report on the research of liver disease using artificial intelligence was compiled using bibliometrics. China has the largest number of publications in this field, while the United States has the highest impact. The study of AI in liver cancer began in 2003 and has rapidly developed since 2017. The most common research goals are the diagnosis and differential diagnosis of liver cancer, while comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are relatively rare. Convolutional neural networks are the main technical method used in AI research on liver cancer.
FRONTIERS IN ONCOLOGY
(2023)
Article
Neurosciences
Jing Yang, Lei Li, Tao Luo, Chengsong Nie, Rui Fan, Deqiang Li, Rui Yang, Changru Zhou, Qian Li, Xiaofei Hu, Wei Chen
Summary: This study aimed to develop a predictive model for CDKN2A/B homozygous deletion in gliomas and investigate the prognostic value of this biomarker and radiomic features in IDH-mutant LGGs. The results showed that radiomic features accurately predict CDKN2A/B homozygous deletion, and CDKN2A/B homozygous deletion can be used as an independent predictor of prognosis in LGGs.
Article
Radiology, Nuclear Medicine & Medical Imaging
Fanrong Cheng, Yan Liu, Lihong Du, Lei Wang, Lan Li, Jinfang Shi, Xiaoxia Wang, Jiuquan Zhang
Summary: The purpose of this study was to assess the optimal characteristics of monoenergetic image (MEI (+)) from dual-energy CT (DECT) and its diagnostic performance for T staging in patients with thoracic esophageal cancer (EC). The results showed that MEI (+) at 40 keV in the venous phase had the best tumor delineation. The MEI (+) images had significantly higher signal-to-noise ratio (SNR) and tumor contrast-to-noise ratio (CNR) compared to the polyenergetic image (PEI). The agreement between MEI (+) (40 keV) and pathological T categories reached 81.63%.
INSIGHTS INTO IMAGING
(2023)
Article
Medicine, General & Internal
Sisi Jiang, Huan Huang, Jingyu Zhou, Hechun Li, Mingjun Duan, Dezhong Yao, Cheng Luo
Summary: This study aimed to explore the progressive trajectories of patterns of dysfunction after diagnosis in patients with schizophrenia. Five stage-specific phenotypes were identified, and dysfunctions shifted from primary and subcortical regions to higher-order cortices. Genetic factors related to neurodevelopmental and neurodegenerative processes were found to be relevant to schizophrenia progression, suggesting potential targets for interventions.
Article
Psychiatry
Yuling Luo, Debo Dong, Huan Huang, Jingyu Zhou, Xiaojun Zuo, Jian Hu, Hui He, Sisi Jiang, Mingjun Duan, Dezhong Yao, Cheng Luo
Summary: This study proposes a research framework that combines multimodal meta-analysis and genetic/molecular architecture to solve the consistency in neuroimaging biomarkers of schizophrenia and their link to molecular genetics.
SCHIZOPHRENIA BULLETIN
(2023)
