Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017
Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
查看所有相关文章

相关实验视频

Updated: May 13, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

异步功能大脑网络构建与时空变压器用于MCI分类.

Jianjia Zhang, Xiaotong Wu, Xiang Tang

    IEEE transactions on medical imaging
    |October 24, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的方法来分析功能性大脑网络 (FBNs),使用静止状态功能磁共振成像 (rs-fMRI). 该方法模拟异步大脑活动,以改善对轻度认知障碍 (MCI) 等疾病的诊断.

    更多相关视频

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
    08:36

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

    Published on: March 21, 2019

    7.2K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    999

    相关实验视频

    Last Updated: May 13, 2026

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    7.9K
    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
    08:36

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

    Published on: March 21, 2019

    7.2K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    999

    科学领域:

    • 神经科学是一个神经科学.
    • 医疗成像医学成像
    • 人工智能的人工智能

    背景情况:

    • 休息状态功能磁共振成像 (rs-fMRI) 用于构建功能大脑网络 (FBNs) 用于疾病诊断.
    • 现有的方法通常假定是同步的大脑活动,并且缺乏诊断的联合优化,导致变化和精度降低.
    • 由于神经信息流中的时间滞后,模拟异步功能连接 (FC) 是至关重要的.

    研究的目的:

    • 使用rs-fMRI开发一种用于构建和分析异步FBN的新方法.
    • 为了解决现有的同步FC分析和个人级FBN构建的局限性.
    • 提高功能性脑疾病的诊断准确度,特别是轻度认知障碍 (MCI).

    主要方法:

    • 建议在变压器架构中使用基于滑动窗的方法来建模时空FCs.
    • 一种新的方法是通过适应性来学习共同的和个别的FBN,使用共同的FBN作为先前的知识来减少变异性.
    • 一个集成的网络可以共同构建和分析共同和单独的异步FBN,用于端到端的培训.

    主要成果:

    • 拟议的方法有效地模拟了异步的功能连接.
    • 共同和个体FBN的自适应学习减轻了变异性,并专注于疾病特异性模式.
    • 综合网络在诊断任务中提高了灵活性和区分能力.

    结论:

    • 开发的方法通过结合异步FCs,为FBN分析提供了更强大的方法.
    • 共同和个体FBN的适应性学习可以提高MCI等疾病的诊断准确性.
    • 这种综合,端到端的框架显示了大脑疾病诊断中临床应用的巨大潜力.