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Renal impairment in Wilson’s disease

Kidney International Reports 2025年

作者： Zi-Wei Zheng Meng-Hui Xu Lu-Na Fan Rou-Min Wang Wan-Qing Xu Guo-Min Yang Ling-Yu Guo Qian Liu Yi Dong Zhi-Ying Wu Department of Medical Genetics and Center for Rare Diseases Second Affiliated Hospital Zhejiang University School of Medicine and Zhejiang Key Laboratory of Rare Diseases for Precision Medicine and Clinical Translation Hangzhou Zhejiang 31009 China Nanhu Brain-computer Interface Institute Hangzhou Zhejiang 314050 China MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou China

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Group-Specific Fusion Model and Its Application in Identifying Multimodal Co-varying Diagnostic Patterns for Psychiatric Disorders

Group-Specific Fusion Model and Its Application in Identifyi...

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4th International Workshop on Human brain and Artificial Intelligence, HBAI 2024

作者： Cao, Siyuan Liang, Chuang Zhu, Qi Jiang, Rongtao Zhang, Daoqiang Calhoun, Vince D. Qi, Shile Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9789819640003

Multimodal fusion offers a complementary perspective for understanding brain function and structure. Nevertheless, most existing multimodal fusion methods are unsupervised and thus ignore the diagnostic information. Previous supervised fusion models stack subjects from different diagnostic groups into a single feature matrix, resulting in potential interference between diagnostic groups. In this study, we propose a group-specific multimodal fusion method, called Group-specific Multiset Canonical Correlation Analysis with Logistic Regression (GMCCALR) that can effectively leverage group specific information by incorporating a logistic regression term into mCCA and utilizing a group-specific fusion strategy. Results in human neuroimaging data show that GMCCALR provide higher diagnostic accuracy across several psychiatric disorders, including schizophrenia, schizoaffective disorder, bipolar disorder and autism spectrum disorder. We also show the identified disorder-specific multimodal patterns are replicable across multiple cohorts. Overall, results demonstrate the reliability and generalizability of the proposed GMCCALR approach and its efficacy for identifying multimodal co-varying diagnostic patterns for psychiatric disorders. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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Developmental Impairments of Synaptic Refinement in the Thalamus of a Mouse Model of Fragile X Syndrome

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Neuroscience Bulletin 2024年第4期40卷 439-450页

作者： Xiaotong Wu Yali Liu Xiaomeng Wang Lu Zheng Libiao Pan Hao Wang Department of Neurosurgery of Second Affiliated Hospital and School of Brain Science and Brain Medicine Key Laboratory for Biomedical Engineering of Education MinistryZhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China NHC and CAMS Key Laboratory of Medical Neurobiology MOE Frontier Science Center for Brain Research and Brain Machine IntegrationKey Laboratory of Precise Treatment and Clinical Translational Research of Neurological DiseasesSchool of Brain Science and Brain MedicineZhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

While somatosensory over-reactivity is a common feature of autism spectrum disorders such as fragile X syndrome(FXS),the thalamic mechanisms underlying this remain ***,we found that the developmental elimination of synapses formed between the principal nucleus of V(PrV)and the ventral posterior medial nucleus(VPm)of the somatosensory system was delayed in fragile X mental retardation 1 gene knockout(Fmr1 KO)mice,while the developmental strengthening of these synapses was *** showed excessive VGluT2 puncta in mutants at P12–13,but not at P7–8 or P15–16,confirming a delay in somatic pruning of PrV-VPm *** synaptic function was associated with a reduction in the frequency of quantal AMPA events,as well as developmental deficits in presynaptic vesicle size and *** results uncovered the developmental impairment of thalamic relay synapses in Fmr1 KO mice and suggest that a thalamic contribution to the somatosensory over-reactivity in FXS should be considered.

关键词： Fragile X syndrome Synaptic refinement VPm Sensory over-reactivity

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Low-frequency Cortical Activity Reflects Context-dependent Parsing of Word Sequences

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iscience 2025年

作者： Honghua Chen Minhui Zhang Tianyi Ye Max A. Wolpert Nai Ding Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Sciences Zhejiang University Hangzhou 310027 China Department of Linguistics Georgetown University Washington DC 20057 USA Nanhu Brain-computer Interface Institute Hangzhou China The State Key Lab of Brain-Machine Intelligence； The MOE Frontier Science Center for Brain Science & Brain-machine Integration Zhejiang University Hangzhou 310027 China

During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context – the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.

关键词： speech language sentence processing frequency tagging MEG RSA

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Parallel exploration via negatively correlated search

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Frontiers of computer science 2021年第5期15卷 123-135页

作者： Peng YANG Qi YANG Ke TANG Xin YAO Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Effective exploration is key to a successful search *** recently proposed negatively correlated search(NCS)tries to achieve this by coordinated parallel exploration,where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited *** successful applications of NCS,the negatively correlated search behaviors were mostly devised by intuition,while deeper(e.g.,mathematical)understanding is *** this paper,a more principled NCS,namely NCNES,is presented,showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic *** learning,for which exploration is of particular importance,are considered for empirical *** proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari *** results show that the significant advantages of NCNES,especially on games with uncertain and delayed rewards,can be highly owed to the effective parallel exploration ability.

关键词： evolutionary computation reinforcement learning exploration

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Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1233-1247页

作者： Yiming Lei Haiping Zhu Junping Zhang Hongming Shan Shanghai Key Laboratory of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200433China with Huawei Shanghai 200120China Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 200031China IEEE

The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)***,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better *** improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning *** merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)***,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding ***,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction ***,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature *** training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian *** results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.

关键词： Terms-Convolutional neural network(CNNs) medical image classification meta-learning ordinal regression random forest

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EEG function network analysis of left and right hand motor imagery

EEG function network analysis of left and right hand motor i...

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2017 Chinese Automation Congress, CAC 2017

作者： Zhang, Rui Yan, Yamin Hu, Yuxia Shi, Li Wan, Hong Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electric Engineering Zhengzhou University Zhengzhou China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781538635247

Network analysis of signals originating from different parts of brain during motor imagery (MI) has gained lots of interest recently. In this paper, we used EEG to construct the brain network during MI, and analyzed the topological characteristics of the EEG function network. It is found that the node degree and clustering coefficient of the right hand MI is higher than the left hand MI on the nodes of right sensorimotor cortex, and the characteristic path length of the network is shorter for right hand MI than left hand MI. As for the right hand MI task, most subjects showed higher node degree and clustering coefficient on the right sensorimotor cortex than the left sensorimotor cortex. We conclude that EEG network based measures, which captures the brain information integrating characteristics during MI, may serve as useful features for classification in MI-BCI application, especially for BCI inefficiency subject. © 2017 IEEE.

关键词： Image analysis

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Language modulates vision: Evidence from neural networks and human brain-lesion models

arXiv

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arXiv 2025年

作者： Chen, Haoyang Liu, Bo Wang, Shuyue Wang, Xiaosha Han, Wenjuan Zhu, Yixing Wang, Xiaochun Bi, Yanchao State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain Research Beijing Normal University Beijing100875 China Department of Radiology First Hospital of Shanxi Medical University Shanxi Province Taiyuan030001 China College of Medical Imaging Shanxi Medical University Shanxi Province Taiyuan030001 China Shanxi Key Laboratory of Intelligent Imaging First Hospital of Shanxi Medical University Shanxi Province Taiyuan030001 China School of Computer Science & Technology Beijing Jiaotong University Beijing100044 China Institute for Artificial Intelligence Peking University Beijing100871 China State Key Laboratory of General Artificial Intelligence Peking University Beijing100871 China School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing100871 China IDG/McGovern Institute for Brain Research Peking University Beijing100871 China Chinese Institute for Brain Research Beijing102206 China Peking University Beijing100871 China

Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision-language DNN models, such as CLIP, with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited due to the "black box" nature of DNNs. To address this, we combined model–brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision–language DNNs to explain the activity of the VOTC. Across four diverse datasets, CLIP consistently outperformed both label-supervised (ResNet) and unsupervised (MoCo) models in predicting VOTC activity. This advantage was left-lateralized, aligning with the human language network. Analyses of the data of 33 stroke patients revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP performance and increased MoCo performance, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision–language DNN models. The sensitivity of model–brain similarity to specific brain lesions demonstrates that leveraging manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models. © 2025, CC BY.

关键词： brain models

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Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising

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Chinese Journal of Electronics 2014年第4期23卷 735-741页

作者： HOU Xin LI Jianwu LU Yao DONG Zhengchao Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and TechnologyBeijing Institute of Technology Brain Imaging Lab Columbia University

The patch matching of the traditional Nonlocal means(NLM) filter mainly depends on structure similarity and cannot adapt to the patch rotation or mirroring transformation. Therefore, designing a measure with rotationally invariant similarity is of significant importance for improving the effectiveness of patch comparison of NLM. This paper proposes to apply a no-reference image content metric with the rotation-invariance to NLM for denoising Magnetic resonance(MR) images. The metric measures quantitatively the content of a patch in an image, including sharpness, contrast, and geometric features such as textures and edges. The metric values for every patch are computed and added into the Gaussian matching kernel of NLM so as to effectively perform patch matching. The main advantage of the proposed method is that it does not need to rotate patches in different orientations during patch matching. Experimental results show that the proposed method is superior to the traditional NLM, the state-of-the-art method Block-matching and 3-D(BM3D) filtering and the Unbiased NLM(UNLM) for MRI denoising.

关键词： Nonlocal means Rotationally invariant similarity measure No-reference image content metric Magnetic resonance image denoising

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F2UE-NET: An Uncertainty-Driven Dynamic Expert Network for Long-Tail Retinal Disease Classification

F2UE-NET: An Uncertainty-Driven Dynamic Expert Network for L...

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IEEE International Symposium on Biomedical Imaging

作者： Junyong Zhao Tian Lin Meng Wang Liang Sun Haoyu Chen Daoqiang Zhang Huazhu Fu MOE Key Laboratory of Brain Computer Intelligence Technology College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Joint Shantou International Eye Center Shantou University and the Chinese University of Hong Kong Shantou Guangdong China Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Singapore

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The automatic diagnosis of retinal diseases presents a significant challenge in medical image analysis, primarily due to the long-tail distribution in existing retinal datasets. This imbalance creates substantial difficulties in model training and performance. To address this issue, we propose an uncertainty-driven dynamic expert network for long-tail retinal disease classification, called $\mathrm{F}^{2}\text{UE}$ -Net. Specifically, we apply Low-Rank Adaptation (LoRA) technology to fine-tune the foundation model, enhancing its feature extraction capabilities and overall performance. Additionally, we introduce an uncertainty-driven dynamic expert participation strategy, which assigns appropriate weights to different categories during training, ensuring high accuracy even for rare, tail-end diseases. Experimental results on a long-tail fundus disease dataset demonstrate that $\mathrm{F}^{2}$ UE outperforms state-of-the-art methods in long-tail retinal disease classification.

关键词： Training Heavily-tailed distribution Image analysis Foundation models Biological system modeling Tail Retina Feature extraction Medical diagnostic imaging Diseases

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