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Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease

Neural Regeneration research 2025年第6期20卷 1628-1643页

作者： Yimiao Jiang Zengxin Qi Huixian Zhu Kangli Shen Ruiqi Liu Chenxin Fang Weiwei Lou Yifan Jiang Wangrui Yuan Xin Cao Liang Chen Qianxing Zhuang Department of Physiology School of MedicineNantong UniversityNantongJiangsu ProvinceChina Department of Neurosurgery Huashan HospitalShanghai Medical CollegeFudan UniversityShanghaiChina national center for Neurological Disorders ShanghaiChina Shanghai Key laboratory of brain Function Restoration and Neural Regeneration ShanghaiChina State Key laboratory of Medical Neurobiology and MOE Frontiers center for brain Science Institute of Brain ScienceFudan UniversityShanghaiChina

The globus pallidus plays a pivotal role in the basal ganglia circuit. Parkinson's disease is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore,bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico–striato–pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease,particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremordominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia–thalamus network. Therefore, the comp

关键词： anxiety basal ganglia bradykinesia deep brain stimulation depression globus pallidus externus globus pallidus internus lateral globus pallidus medial globus pallidus neural circuit Parkinson's disease

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Local structured representation for generic object detection

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Frontiers of Computer Science 2017年第4期11卷 632-648页

作者： Junge ZHANG Kaiqi HUANG Tieniu TAN Zhaoxiang ZHANG center for research on Intelligent Perception and Computing Chinese Academy of Sciences Beijing 100190 China research center lbr brain-inspired intelligence Chinese Academy of Sciences Beijing 100190 China national laboratory of pattern recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Structure information plays an important role in both object recognition and detection. This paper studies what visual structure is and addresses the problem of struc- ture modeling and representation from two aspects： visual feature and topology model. Firstly, at feature level, we pro- pose Local Structured Descriptor to capture the object＇s local structure effectively, and develop the descriptors from shape and texture information, respectively. Secondly, at topology level, we present a local strnctured model with a boosted fea- ture selection and fusion scheme. All experiments are conducted on the challenging PASCAL Visual Object Classes （VOC） datasets from VOC2007 to VOC2010. Experimental results show that our method achieves very competitive performance.

关键词： Local Structured Descriptor Local StructuredModel Object Representation Object Structure Object De-tection PASCAL VOC

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Exploring the brain-like Properties of Deep Neural Networks:A Neural Encoding Perspective

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Machine intelligence research 2022年第5期19卷 439-455页

作者： Qiongyi Zhou Changde Du Huiguang He research center for brain-inspired intelligence and national laboratory of pattern recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100190China center for Excellence in brain Science and intelligence Technology Chinese Academy of SciencesBeijing 100190China

Nowadays,deep neural networks(DNNs)have been equipped with powerful representation *** deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like ***,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to *** is natural to ask how the brain-like properties of ViTs *** the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to *** this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic *** on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual *** reveal hierarchical correspondences to the visual pathway as CNNs ***,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial *** study sheds light on the design principles for more brain-like *** code is available at https://***/QYiZhou/LWNeuralEncoding.

关键词： Convolutional neural network(CNN) vision transformer(Vi T) multi-modal networks spatial-temporal networks visual neural encoding brain-like neural networks

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Neural machine translation:Challenges,progress and future

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Science China(Technological Sciences) 2020年第10期63卷 2028-2050页

作者： ZHANG JiaJun ZONG ChengQing national laboratory of pattern recognition CASIABeijing 100190China University of Chinese Academy of Sciences Beijing 100190China CAS center for Excellence in brain Science and intelligence Technology Shanghai 200031China

Machine translation(MT)is a technique that leverages computers to translate human languages ***,neural machine translation(NMT)which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of *** article makes a review of NMT framework,discusses the challenges in NMT,introduces some exciting recent progresses and finally looks forward to some potential future research trends.

关键词： neural machine translation Transformer multimodal translation low-resource translation document translation

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Itch perception is reflected by neuronal ignition in the primary somatosensory cortex

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national Science Review 2022年第6期9卷 90-99页

作者： Xiao-Jun Chen Yan-He Liu Ning-Long Xu Yan-Gang Sun Institute of Neuroscience State Key Laboratory of Neuroscience Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shanghai center for brain Science and brain-inspired intelligence Technology

Multiple cortical areas including the primary somatosensory cortex(S1) are activated during itch signal processing, yet cortical representation of itch perception remains unknown. Using novel miniature two-photon microscopic imaging in free-moving mice, we investigated the coding of itch perception in *** found that pharmacological inactivation of S1 abolished itch-induced scratching behavior, and the itch-induced scratching behavior could be well predicted by the activity of a fraction of layer 2/3 pyramidal neurons, suggesting that a subpopulation of S1 pyramidal neurons encoded itch perception, as indicated by immediate subsequent scratching behaviors. With a newly established optogenetics-based paradigm that allows precisely controlled pruritic stimulation, we found that a small fraction of S1 neurons exhibited an ignition-like pattern at the detection threshold of itch perception. Our study revealed the neural mechanism underlying itch perceptual coding in S1, thus paving the way for the study of cortical representation of itch perception at the single-neuron level in freely moving animals.

关键词： itch perception primary somatosensory cortex miniature two-photon microscopic imaging

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Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

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Machine intelligence research 2024年第2期21卷 257-271页

作者： Bo-Jing Feng Xi Cheng Hao-Nan Xu Wen-Fang Xue center for research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial *** models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating ***,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between *** addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial *** experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.

关键词： Corporate credit rating hierarchical relation heterogeneous graph neural networks adversarial learning

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Multimodal Fusion of brain Imaging Data: Methods and Applications

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Machine intelligence research 2024年第1期21卷 136-152页

作者： Na Luo Weiyang Shi Zhengyi Yang Ming Song Tianzi Jiang brainnetome center and national laboratory of pattern recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100049China center for Excellence in brain Science and intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China research center for Augmented intelligence Zhejiang LaboratoryHangzhou 311100China

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the *** lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality *** the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease *** this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning *** by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain ***,we discuss some exciting emerging trends and important future ***,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

关键词： Multimodal fusion supervised learning unsupervised learning brain atlas cognition brain disorders

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Scene text removal via cascaded text stroke detection and erasing

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Computational Visual Media 2022年第2期8卷 273-287页

作者： Xuewei Bian Chaoqun Wang Weize Quan Juntao Ye Xiaopeng Zhang Dong-Ming Yan national laboratory of pattern recognition Institute of AutomationBeijing 100049China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100084China

Recent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant *** this work,a novel end-to-end framework is proposed based on accurate text stroke ***,the text removal problem is decoupled into text stroke detection and stroke removal;we design separate networks to solve these two subproblems,the latter being a generative *** two networks are combined as a processing unit,which is cascaded to obtain our final model for text *** results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text.A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research,as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.

关键词： scene text removal text stroke detection generative adversarial networks cascaded network design real-world dataset

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Indirect building localization based on a prominent solid landmark from a forward-looking infrared imagery

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Chinese Optics Letters 2011年第4期9卷 34-37页

作者：汪小平张天序杨效余 Institute for pattern recognition and Artificial intelligence Huazhong University of Science and Technology

A novel indirect building localization technique based on a prominent solid landmark from a forward- looking infrared imagery is proposed to localize low, deeply buried, or carefully camouflaged buildings in dense urban areas. First, the widely used effective methods are applied to detect and localize the solid landmark. The building target is then precisely indirectly localized by perspective transformation according to the imaging parameters and the space constraint relations between the building target and the solid landmark. Experimental results demonstrate this technique can indirectly localize buildings in dense urban areas effectively.

关键词： mode DOM

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VFM： Visual Feedback Model for Robust Object recognition

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Journal of Computer Science & Technology 2015年第2期30卷 325-339页

作者：王冲黄凯奇 national laboratory of pattern recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Object recognition, which consists of classification and detection, has two important attributes for robustness： 1） closeness： detection windows should be as close to object locations as possible, and 2） adaptiveness： object matching should be adaptive to object variations within an object class. It is difficult to satisfy both attributes using traditional methods which consider classification and detection separately; thus recent studies propose to combine them based on confidence contextualization and foreground modeling. However, these combinations neglect feature saliency and object structure, and biological evidence suggests that the feature saliency and object structure can be important in guiding the recognition from low level to high level. In fact, object recognition originates in the mechanism of ＂what＂ and ＂where＂ pathways in human visual systems. More importantly, these pathways have feedback to each other and exchange useful information, which may improve closeness and adaptiveness. inspired by the visual feedback, we propose a robust object recognition framework by designing a computational visual feedback model （VFM） between classification and detection. In the ＂what＂ feedback, the feature saliency from classification is exploited to rectify detection windows for better closeness; while in the ＂where＂ feedback, object parts from detection are used to match object structure for better adaptiveness. Experimental results show that the ＂what＂ and ＂where＂ feedback is effective to improve closeness and adaptiveness for object recognition, and encouraging improvements are obtained on the challenging PASCAL VOC 2007 dataset.

关键词： object recognition object classification object detection visual feedback

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