Article
Clinical Neurology
Koji Yamashita, Takahiro Kuwashiro, Kensuke Ishikawa, Kiyomi Furuya, Shino Harada, Seitaro Shin, Noriaki Wada, Chika Hirakawa, Yasushi Okada, Tomoyuki Noguchi
Summary: This study aimed to discover common biomarkers correlating with the Mini-Mental State Examination (MMSE) scores from multi-country MRI datasets. A positive correlation was identified between right entorhinal cortex (ERC) thickness and MMSE score, indicating a potential biomarker for cognitive function assessment.
Article
Computer Science, Information Systems
Mingsen Du, Yanxuan Wei, Xiangwei Zheng, Cun Ji
Summary: Multivariate time series classification is widely used in various real-life applications and has attracted significant research interest. However, existing methods only focus on local or global features and overlook the spatial dependency features among multiple variables. In this study, we propose a multi-feature based network (MF-Net) that captures both local and global features through the attention-based mechanism and integrates them to capture spatial dependency features. Experimental results on UEA datasets demonstrate that our method performs competitively with state-of-the-art methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Summary: CortexODE is a deep learning framework that uses neural ordinary differential equations (ODEs) to reconstruct cortical surfaces. By modeling the trajectories of points on the surface as ODEs and parameterizing the derivatives with a learnable deformation network, CortexODE is able to prevent self-intersections. Integrated with an automatic learning-based pipeline, CortexODE can efficiently reconstruct cortical surfaces in less than 5 seconds.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Neurosciences
Quanjing Chen, Adam Turnbull, Martin Cole, Zhengwu Zhang, Feng Lin
Summary: Effective cognitive training should improve cognition beyond trained domains and be applicable to populations at risk of dementia. Enhancing brain integration, which is relatively preserved in individuals with amnesic mild cognitive impairment (aMCI), may provide a target for developing effective cognitive training.
Article
Geriatrics & Gerontology
Yuqing Wu, Hao Zhou, Xiaojiao Ci, Liuyu Lin, Da Zhang, Jie Lu
Summary: This study summarized the clinical and radiological features, glucocorticoid therapy, and recurrence outcomes in adult patients with cortical encephalitis associated with MOG antibody. The results showed that patients exhibited increased intracranial pressure, pleocytosis, and elevated cerebrospinal fluid protein. MRI images revealed cortical lesions closely related to the classification of cortical encephalitis. Glucocorticoid therapy showed effectiveness against MOG antibody-associated cortical encephalitis.
FRONTIERS IN AGING NEUROSCIENCE
(2023)
Article
Biochemistry & Molecular Biology
Abdulaziz Alshammari
Summary: Brain metastases are serious complications of brain malignancies, often originating from primary tumors in the lung, breast, and melanoma. Traditional treatment options for BM patients have been limited, but this study introduces a novel method for categorizing brain tumors using optimization algorithms. The suggested cancer categorization method showed promising results, with high accuracy and precision.
Article
Medicine, General & Internal
Venkatesan Rajinikanth, P. M. Durai Raj Vincent, C. N. Gnanaprakasam, Kathiravan Srinivasan, Chuan-Yu Chang
Summary: This research aims to develop an efficient deep-learning-based brain-tumor detection scheme using FLAIR- and T2-modality MRI slices. The scheme includes preprocessing, deep-feature extraction, tumor segmentation, feature optimization, and binary classification. Experimental results show that the integrated feature-based scheme achieves a classification accuracy of 99.6667% when using a support-vector-machine classifier.
Article
Clinical Neurology
Carina H. Fowler, Michael S. Gaffrey
Summary: This study investigates the association between cortical structure and depressive symptoms in preschoolers and finds that reduced cortical surface area, particularly in the lateral orbitofrontal cortex, is correlated with elevated depressive symptoms. These findings provide new insights into the early development of depression in young children.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Computer Science, Artificial Intelligence
Jie Gu, Bin Cui, Shan Lu
Summary: This paper proposes an effective framework for multivariate compositional data classification, utilizing Dirichlet feature embedding to remove data constraint, obtain high-quality training data, and reduce dimensionality, followed by employing support vector machine to build the classification model. Results from simulation study and real-world dataset demonstrate the proposed method achieves good performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Psychiatry
Jennifer L. Bruno, David S. Hong, Amy A. Lightbody, S. M. Hadi Hosseini, Joachim Hallmayer, Allan L. Reiss
Summary: Individuals with Fragile X syndrome who carry the BclI polymorphism of the glucocorticoid receptor gene NR3C1 may exhibit attenuated symptoms of anxiety/depression and externalizing behaviors, with structural neuroimaging data able to differentiate between genotypes. Key regions of anxiety/fear neurocircuitry play a significant role in distinguishing groups.
JOURNAL OF PSYCHIATRIC RESEARCH
(2021)
Article
Spectroscopy
Jiwei Xu, Jianjie Xu, Zhaoyang Tong, Siqi Yu, Bing Liu, Xihui Mu, Bin Du, Chuan Gao, Jiang Wang, Zhiwei Liu, Dong Liu
Summary: This study investigated the noise-tolerant capability of laboratory-measured fluorescence spectra as a database for detecting and identifying biological agents. The performance of classification schemes and feature descriptors were analyzed under different noise levels. The results showed that robust features extracted with corresponding techniques are critical for enhancing spectral differentiation capabilities and eliminating the noise effect.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2023)
Article
Neurosciences
Marcus Siems, Johannes Tuennerhoff, Ulf Ziemann, Markus Siegel
Summary: The study developed a novel unsupervised multistage analysis approach and successfully compared changes in brain-wide electrophysiological coupling between Multiple Sclerosis patients and healthy controls, achieving an accuracy of 84% in classifying patients and controls.
Article
Neurosciences
Marco de Curtis, Laura Librizzi, Laura Uva
Summary: Seizures affecting the limbic regions, such as the hippocampus, are common and often resistant to medication. This study examines the network mechanisms involved in the generation of olfactory-limbic epileptiform patterns and discusses the potential relevance of these findings for human focal epilepsy. The interactions within olfactory-limbic circuits, including region-specific seizure-like events and cortical control, are investigated using in vitro preparations and pro-convulsive drugs.
NEUROBIOLOGY OF DISEASE
(2023)
Article
Computer Science, Artificial Intelligence
Mona Sharifnezhad, Mohsen Rahmani, Hossein Ghaffarian
Summary: In this paper, a new distributed framework is proposed to address the multivariate feature selection problem. The framework calculates the relevance of each feature to class labels by interacting with classifiers and examines redundancy among features through multivariate filter algorithms. Experimental results show that the proposed framework can improve classification accuracy and outperform compared approaches in precision, recall, and runtime.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2023)
Article
Chemistry, Multidisciplinary
Shiqing Wu, Shiyu Zhao, Qianqian Zhang, Long Chen, Chenrui Wu
Summary: In this paper, a method combining feature extraction, feature transformation, and nearest neighbors is proposed to classify steel surface defects, achieving significant progress in addressing the degradation problem caused by network deepening.
APPLIED SCIENCES-BASEL
(2021)
Article
Clinical Neurology
Jiayi Liu, Jing Xu, Guangyuan Zou, Yong He, Qihong Zou, Jia-Hong Gao
Article
Multidisciplinary Sciences
Yuhan Chen, Qixiang Lin, Xuhong Liao, Changsong Zhou, Yong He
Summary: The study investigates the relationship between brain aerobic glycolysis (AG) and the macroscopic connectome, proposing a weighted regional distance-dependent model to estimate total axonal projection length of brain nodes. Results show significant associations between estimated axonal projection length and AG across brain nodes, with high-AG regions exhibiting a high degree of wiring optimization in the human brain.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Clinical Neurology
Ruixiang Cao, Xiangyun Yang, Jia Luo, Pengchong Wang, Fanqiang Meng, Mingrui Xia, Yong He, Tengda Zhao, Zhanjiang Li
Summary: Cognitive behavioral therapy (CBT) shows significant effects on the global brain network topology in OCD patients, particularly in responders to CBT. Changes in nodal clustering are positively correlated with improvements in obsessive-compulsive symptoms, indicating that CBT may help restore normal brain structural network function in OCD patients.
PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY
(2021)
Article
Neurosciences
Fengmei Fan, Xuhong Liao, Tianyuan Lei, Tengda Zhao, Mingrui Xia, Weiwei Men, Yanpei Wang, Mingming Hu, Jie Liu, Shaozheng Qin, Shuping Tan, Jia-Hong Gao, Qi Dong, Sha Tao, Yong He
Summary: A study on the developmental trajectories of the default-mode network (DMN) using longitudinal resting-state fMRI data revealed that connectivity strength and network efficiency increased from childhood to adolescence, particularly in midline structures. The identification of three subclusters within the DMN based on divergent developmental rates of nodal centrality highlights the complex developmental patterns of this network system. These findings have implications for understanding the network mechanisms underlying cognitive development in individuals.
Article
Neurosciences
Qiushi Wang, Yuehua Xu, Tengda Zhao, Zhilei Xu, Yong He, Xuhong Liao
Summary: The functional connectome is highly distinctive in neonates, with individual differences mainly seen in higher-order cortices. Individual uniqueness is primarily reflected in connections between different functional systems, and functional data longer than 3.5 minutes can capture this uniqueness.
Article
Neurosciences
Jintao Sheng, Liang Zhang, Junjiao Feng, Jing Liu, Anqi Li, Wei Chen, Yuedi Shen, Jinhui Wang, Yong He, Gui Xue
Summary: The study found that the mean-scaled fractional standard deviation of the BOLD signal (mfSD BOLD) demonstrated high reliability in test-retest, cross-site comparisons, and was less affected by head motion compared to existing variability measures. There was a strong reproducible coupling between mfSD BOLD and functional integration, as well as correlations with cognitive function and cerebral blood flow. This suggests that BOLD signal variability may serve as a meaningful index of local function underlying functional integration in the human brain.
Article
Neurosciences
Lei Hao, Lei Li, Menglu Chen, Jiahua Xu, Min Jiang, Yanpei Wang, Linhua Jiang, Xu Chen, Jiang Qiu, Shuping Tan, Jia-Hong Gao, Yong He, Sha Tao, Qi Dong, Shaozheng Qin
Summary: This study used functional MRI data from 250 children aged 7 to 12 to create age-specific brain activity maps in four domains, and developed a toolbox for researchers to visualize and download these maps. The toolbox and maps are available on the Neuroimaging Informatics Tools and Resources Clearinghouse website, providing valuable resources for future developmental neuroimaging studies.
NEUROSCIENCE BULLETIN
(2021)
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
Yin Wang, Athanasia Metoki, Yunman Xia, Yinyin Zang, Yong He, Ingrid R. Olson
Summary: This study reveals the brain-wide organization and mechanisms of mentalizing processing, showing the detailed connectomic features of the mentalizing network. It demonstrates that mentalizing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, distinguishing it from other brain networks supporting related functions such as autobiographical memory and moral reasoning.
Article
Neurosciences
Yapei Xie, Zhilei Xu, Mingrui Xia, Jin Liu, Xiaojing Shou, Zaixu Cui, Xuhong Liao, Yong He
Summary: This study uncovers consistent alterations in brain network dynamics in individuals with Autism Spectrum Disorder (ASD) and identifies transcriptomic signatures associated with these changes, providing further insights into the biological basis of this disorder.
BIOLOGICAL PSYCHIATRY
(2022)
Article
Neurosciences
Lei Wang, Xiaodan Chen, Yuehua Xu, Miao Cao, Xuhong Liao, Yong He
Summary: The study discovered that functional hubs in the human brain network can be classified into three types (low-frequency, middle-frequency, and high-frequency) based on different frequency bands, with the hubs mainly distributed in regions such as the frontal lobe, parietal lobe, prefrontal cortex, temporal lobe, amygdala, and cerebellum.
NEUROSCIENCE BULLETIN
(2022)
Article
Neurosciences
Congying Chu, Haoran Guan, Sangma Xie, Yanpei Wang, Jie Luo, Gai Zhao, Zhiying Pan, Mingming Hu, Weiwei Men, Shuping Tan, Jia-Hong Gao, Shaozheng Qin, Yong He, Lingzhong Fan, Qi Dong, Sha Tao
Summary: School-age children often undergo ongoing brain development. Diffusion tensor imaging is commonly used to assess white matter properties during this period. However, there is a lack of standardized diffusion tensor templates for school-age children. In this study, researchers established the school-age children diffusion tensor (SACT) template and found that it showed higher spatial normalization accuracy and inter-subject coherence compared to adult templates. The SACT template could contribute to future studies on white matter development.
NEUROSCIENCE BULLETIN
(2022)
Article
Multidisciplinary Sciences
Yunman Xia, Mingrui Xia, Jin Liu, Xuhong Liao, Tianyuan Lei, Xinyu Liang, Tengda Zhao, Ziyi Shi, Lianglong Sun, Xiaodan Chen, Weiwei Men, Yanpei Wang, Zhiying Pan, Jie Luo, Siya Peng, Menglu Chen, Lei Hao, Shuping Tan, Jia-Hong Gao, Shaozheng Qin, Gaolang Gong, Sha Tao, Qi Dong, Yong He
Summary: This study utilizes longitudinal functional magnetic resonance imaging data to uncover significant changes in the primary-to-transmodal gradient in the brain network during childhood to adolescence. These gradient changes are linked to cognitive growth, topological reorganization, and gene expression profiles.
Review
Neurosciences
Fan Zhang, Alessandro Daducci, Yong He, Simona Schiavi, Caio Seguin, Robert E. Smith, Chun-Hung Yeh, Tengda Zhao, Lauren J. O'Donnell
Summary: This paper provides a high-level overview of how diffusion magnetic resonance imaging (dMRI) tractography is used for quantitative analysis of the brain's structural connectivity. The paper focuses on two types of quantitative analyses - tract-specific analysis and connectome-based analysis. It also reviews studies that have used quantitative tractography approaches to study the brain's white matter in various fields. The paper concludes that there is no consensus on the best methodology in quantitative analysis of tractography, and caution should be exercised when interpreting results.
Article
Psychiatry
Xiaoyi Sun, Jin Liu, Qing Ma, Jia Duan, Xindi Wang, Yuehua Xu, Zhilei Xu, Ke Xu, Fei Wang, Yanqing Tang, Yong He, Mingrui Xia
Summary: The study examined the intersubject variability of the functional connectome in schizophrenia patients and healthy controls based on resting-state fMRI data. The schizophrenia group showed higher IVFC in sensorimotor, visual, auditory, and subcortical regions compared to healthy controls, and these alterations were associated with clinical variables. Alterations in the sensorimotor, auditory, and subcortical cortices were specific to schizophrenia, suggesting potential implications for individualized clinical diagnosis and treatment.
SCHIZOPHRENIA BULLETIN
(2021)