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Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Neural Regeneration Research 2025年第3期20卷 646-659页

作者： Zhengyu An Aidi Jiang Jingqi Chen Institute of Science and Technology for brain-inspired intelligence Fudan UniversityShanghaiChina moe Frontiers Center for brain Science Fudan UniversityShanghaiChina moe key laboratory of computational neuroscience and brain-inspired intelligence Fudan UniversityShanghaiChina Zhangjiang Fudan International Innovation Center ShanghaiChina

Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

关键词： Alzheimer's disease ataxia deep learning long-read sequencing neurodegeneration neurodegenerative diseases Parkinson's disease repeat element structural variant

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Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

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Neural Regeneration Research 2025年第5期20卷 1495-1504页

作者： Yunting Xiang Xiangxin Xing Xuyun Hua Yuwen Zhang Xin Xue Jiajia Wu Mouxiong Zheng He Wang Jianguang Xu School of Rehabilitation Science Shanghai University of Traditional Chinese MedicineShanghaiChina Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghaiChina Department of Rehabilitation Medicine Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina Department of Traumatology and Orthopedics Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina Institute of Science and Technology for brain-inspired intelligence Fudan UniversityShanghaiChina key laboratory of computational neuroscience and brain-inspired intelligence(Fudan University) Ministry of EducationShanghaiChina Human Phenome Institute Fudan UniversityShanghaiChina

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected ***,differences among reconstruction strategies at the brain network level have not been *** study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve ***–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper ***-state brain functional magnetic resonance imaging was obtained 7 months after *** independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the *** in intra-network resting-state functional connectivity were compared among the *** muscle reinnervation was assessed by behavioral observation(elbow flexion)and *** results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural *** repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor *** thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve *** areas related to cognition and emotion were enhanced after end-to-side nerve *** study revealed important brain networks related to different nerve *** networks may be potential targets for enhancing motor recovery.

关键词： brain functional networks end-to-end nerve transfer end-to-side nerve transfer independent component analysis nerve repair peripheral plexus injury resting-state functional connectivity

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The brain Connectome for Chinese Reading

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neuroscience Bulletin 2022年第9期38卷 1097-1113页

作者： Wanwan Guo Shujie Geng Miao Cao Jianfeng Feng Institute of Science and Technology for brain-inspired intelligence Fudan UniversityShanghai200433China key laboratory of computational neuroscience and brain-inspired intelligence(Ministry of Education) Fudan UniversityShanghai200433China

Chinese,as a logographic language,fundamentally differs from alphabetic languages like *** neuroimaging studies have mainly focused on alphabetic languages,while the exploration of Chinese reading is still an emerging and fast-growing research ***,a growing number of neuroimaging studies have explored the neural circuit of Chinese ***,we summarize previous research on Chinese reading from a connectomic *** evidence indicates that the left middle frontal gyrus is a specialized hub region that connects the ventral with dorsal pathways for Chinese ***,the orthography-to-phonology and orthography-to-semantics mapping,mainly processed in the ventral pathway,are more specific during Chinese ***,in addition to the left-lateralized language-related regions,reading pathways in the right hemisphere also play an important role in Chinese ***,we comprehensively review prior findings and emphasize several challenging issues to be explored in future work.

关键词： brain connectome Chinese reading Word recognition Reading comprehension Structural connectivity Functional connectivity

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Geometry Flow-Based Deep Riemannian Metric Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1882-1892页

作者： Yangyang Li Chaoqun Fei Chuanqing Wang Hongming Shan Ruqian Lu key Lab of MADIS Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China IEEE Institute of Science and Technology for brain-inspired intelligence and key laboratory of computational neuroscience and brain-inspired intelligence(Ministry of Education)and moe Frontiers Center for brain Science Fudan UniversityShanghai 200433 Shanghai Center for brain Science and brain-inspired Technology Shanghai 200031China

Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural *** deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass ***,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding *** alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural *** that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its *** DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the *** several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.

关键词： Curvature regularization deep metric learning(DML) embedding learning geometry flow riemannian metric

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Role of the Anterior Cingulate Cortex in Translational Pain Research

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neuroscience Bulletin 2021年第3期37卷 405-422页

作者： Xiao Xiao Ming Ding Yu-Qiu Zhang key laboratory of computational neuroscience and brain-inspired intelligence Ministry of EducationInstitute of Science and Technology for Brain-Inspired IntelligenceBehavioral and Cognitive Neuroscience CenterFudan UniversityShanghai 200433China State key laboratory of Medical Neurobiology and moe Frontiers Center for brain Science Department of Translational NeuroscienceJing’an District Centre Hospital of ShanghaiInstitutes of Brain ScienceInstitute of Integrative MedicineFudan UniversityShanghai 200032China

As the most common symptomatic reason to seek medical consultation,pain is a complex experience that has been classified into different categories and *** pain processing,noxious stimuli may activate the anterior cingulate cortex(ACC).But the function of ACC in the different pain conditions is not well *** this review,we elaborate the commonalities and differences from accumulated evidence by a variety of pain assays for physiological pain and pathological pain including inflammatory pain,neuropathic pain,and cancer pain in the ACC,and discuss the cellular receptors and signaling molecules from animal *** further summarize the ACC as a new central neuromodulation target for invasive and non-invasive stimulation techniques in clinical pain *** comprehensive understanding of pain processing in the ACC may lead to bridging the gap in translational research between basic and clinical studies and to develop new therapies.

关键词： Anterior cingulate cortex Deep brain stimulation Transcranial magnetic stimulation Transcranial direct current stimulation Pathological pain Inflammatory pain Neuropathic pain Cancer pain

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Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

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National Science Review 2024年第5期11卷 17-28页

作者： Wenlian Lu Longbin Zeng Jiexiang Wang Shitong Xiang Yang Qi Qibao Zheng Ningsheng Xu Jianfeng Feng DTB Consortium Institute of Science and Technology for brain-inspired intelligence Fudan University key laboratory of computational neuroscience and brain-inspired intelligence(Fudan University) Ministry of Education Fudan University Shanghai Center for Mathematical Sciences Fudan University Department of Computer Science University of Warwick Zhangjiang Fudan International Innovation Center Fudan University

A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex(V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart,despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

关键词： digital twin brain computational cortico-subcortical model fMRI data assimilation interoceptive circuit

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The Insular Cortex:An Interface Between Sensation,Emotion and Cognition

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neuroscience Bulletin 2024年第11期40卷 1763-1773页

作者： Ruohan Zhang Hanfei Deng Xiong Xiao Institute of neuroscience Key Laboratory of Brain Cognition and Brain-inspired Intelligence TechnologyCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China University of Chinese Academy of Sciences Shanghai200031China State key laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science and Institutes of Brain ScienceFudan UniversityShanghai200032China

The insula is a complex brain region central to the orchestration of taste perception,interoception,emotion,and *** research has shed light on the intricate connections between the insula and other brain regions,revealing the crucial role of this area in integrating sensory,emotional,and cognitive *** unique anatomical position and extensive connectivity allow the insula to serve as a critical hub in the functional network of the *** summarize its role in interoceptive and exteroceptive sensory processing,illustrating insular function as a bridge connecting internal and external *** on recent research,we delineate the insular involvement in emotional processes,highlighting its implications in psychiatric conditions,such as anxiety,depression,and *** further discuss the insular contributions to cognition,focusing on its significant roles in time perception and ***,the evidence underscores the insular function as a dynamic interface that synthesizes diverse inputs into coherent subjective experiences and decision-making *** this review,we hope to highlight the importance of the insula as an interface between sensation,emotion,and cognition,and to inspire further research into this fascinating brain region.

关键词： Insular cortex Interoception Emotion Reward-Timing Decision making

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Development of machine learning-based models to predict 10-year risk of cardiovascular disease:a prospective cohort study

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Stroke & Vascular Neurology 2023年第6期8卷 475-485页

作者： Jia You Yu Guo Ju-Jiao Kang Hui-Fu Wang Ming Yang Jian-Feng Feng Jin-Tai Yu Wei Cheng Institute of Science and Technology for brain-inspired intelligence Department of NeurologyHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina key laboratory of computational neuroscience and brain-inspired intelligence Fudan UniversityMinistry of EducationShanghaiChina Zhangjiang Fudan International Innovation Center Fudan UniversityShanghaiChina School of Data Science Fudan UniversityShanghaiChina Fudan ISTBI-ZJNU Algorithm Centre for brain-inspired intelligence Zhejiang Normal UniversityZhejiangChina Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center Fudan UniversityShanghaiChina

Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical *** study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction *** From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years *** implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident *** model was validated through a leave-one center-out *** During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of *** model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±***,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.

关键词： cardiovascular prediction learning

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Unveiling Dynamic System Strategies for Multisensory Processing: From Neuronal Fixed-Criterion Integration to Population Bayesian Inference

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Research 2023年第2期2022卷 81-100页

作者： Jiawei Zhang Yong Gu Aihua Chen Yuguo Yu State key laboratory of Medical Neurobiology and moe Frontiers Center for brain Science Shanghai Artificial Intelligence LaboratoryResearch Institute of Intelligent and Complex Systems and Institute of Science and Technology for Brain-Inspired IntelligenceHuman Phenome InstituteShanghai 200433China key laboratory of Primate Neurobiology Institute of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina key laboratory of brain Functional Genomics(Ministry of Education) East China Normal University3663 Zhongshan Road N.Shanghai 200062China

Multisensory processing is of vital importance for survival in the external *** circuits can both integrate and separate visual and vestibular senses to infer self-motion and the motion of other ***,it is largely debated how multisensory brain regions process such multisensory information and whether they follow the Bayesian strategy in this process.

关键词： Integration Fixed Criterion

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Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification

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Phenomics 2021年第6期1卷 257-268页

作者： Yiming Lei Junping Zhang Hongming Shan Shanghai key laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for brain-inspired intelligence and moe Frontiers Center for brain Science Fudan UniversityShanghai 200433China Shanghai Center for brain Science and brain-inspired Technology Shanghai 201210China key laboratory of computational neuroscience and brain-inspired intelligence(Fudan University) Ministry of EducationShanghai 201210China

Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer ***,LDCT images suffer from severe noise,largely influencing the performance of lung nodule *** methods combining denoising and classification tasks typically require the corresponding normal-dose CT(NDCT)images as the supervision for the denoising task,which is impractical in the context of clinical diagnosis using *** jointly train these two tasks in a unified framework without the NDCT images,this paper introduces a novel self-supervised method,termed strided Noise2Neighbors or SN2N,for blind medical image denoising and lung nodule classification,where the supervision is generated from noisy input *** specifically,the proposed SN2N can construct the supervision infor-mation from its neighbors for LDCT denoising,which does not need NDCT images *** proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for *** experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as ***,our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.

关键词： Convolutional neural network Medical image classification Self-supervised denoising Low-dose CT

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