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

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

Multimodal subspace independent vector analysis effectively captures latent relationships between brain structure and function.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Multimodal Fusion of Structural and Diffusion MRI for Intelligence Prediction.

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation·2026
Same author

Large-scale brain dynamics are organized by a directional coordination hierarchy.

bioRxiv : the preprint server for biology·2026
Same author

Structure-function coupling of large-scale cortical networks across the lifespan is spectrally specific.

Communications biology·2026
Same author

Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.

bioRxiv : the preprint server for biology·2026
Same author

Brain regions with gestational age differences mediate cognition in adolescents born very premature.

Communications biology·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Jun 25, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

针对分布式神经成像数据的高效联合学习.

Bishal Thapaliya, Riyasat Ohib, Eloy Geenjar

    bioRxiv : the preprint server for biology
    |May 27, 2024
    PubMed
    概括
    此摘要是机器生成的。

    分散的稀疏联合学习使得协作神经成像分析无需数据传输. 这种方法提高了大规模研究数据集的数据隐私和通信效率.

    更多相关视频

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
    08:19

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

    Published on: October 20, 2023

    1.1K
    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
    10:35

    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

    Published on: June 3, 2013

    32.7K

    相关实验视频

    Last Updated: Jun 25, 2025

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
    08:19

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

    Published on: October 20, 2023

    1.1K
    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
    10:35

    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

    Published on: June 3, 2013

    32.7K

    科学领域:

    • 神经科学是一个神经科学.
    • 计算机科学 计算机科学
    • 数据科学数据科学数据科学

    背景情况:

    • 神经成像研究越来越多地涉及数据共享,但由于隐私和问责问题,机构数据控制限制了合作.
    • 现有的数据分析方法通常需要集中敏感数据集,这带来了重大的后勤和道德挑战.

    研究的目的:

    • 提出一种新的去中心化稀疏联合学习 (FL) 策略,用于分析没有直接数据传输的合并神经成像数据集.
    • 解决大规模科学研究中需要高效且保护隐私的协作分析的需求.

    主要方法:

    • 实施了分散的稀疏联合学习 (FL) 战略,专注于稀疏模型的本地培训.
    • 强调客户端站点之间选择性共享模型参数,以减少通信开销.
    • 利用模型稀疏度来优化通信效率,特别是在较大的模型和异质计算环境中.

    主要成果:

    • 与传统的FL方法相比,拟议的稀疏FL策略显著降低了通信开销.
    • 该方法在分析大型分布式数据集方面表现出有效性,正如青少年大脑认知发展 (ABCD) 数据集所示.
    • 该方法具有可扩展性,并且可以适应不同研究地点的不同资源能力.

    结论:

    • 分散的稀疏联合学习为协作神经成像研究提供了有效的解决方案,同时保护数据隐私并减少通信负担.
    • 这种方法促进了对大型多机构数据集的分析,推动了神经科学领域的发展.
    • 该战略对未来的合作研究倡议具有前景,需要安全和高效的数据分析.