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

Functional Brain Systems: Reticular Formation01:13

Functional Brain Systems: Reticular Formation

4.2K
The reticular formation is a complex network of gray and white matter located within the brainstem extending from the medulla to the midbrain.
Within the reticular formation, there are several distinct nuclei that can be classified into three broad categories. The Raphe nuclei are located along the midline of the brainstem. They are primarily known for their role in synthesizing and releasing serotonin, a neurotransmitter involved in regulating mood, appetite, sleep, and circadian rhythms. The...
4.2K

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

Focused Ultrasound Ablation for Neurological Disorders.

Biological psychiatry·2026
Same author

Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.

Biomedical engineering online·2025
Same author

Correction: Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.

Biomedical engineering online·2025
Same author

Precision connectivity in osteoarthritis pain with permutation and network analysis: a key step toward clinical application.

BMC medical imaging·2025
Same author

Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping review.

Digital health·2025
Same author

Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.

Biomedical engineering online·2025
Same journal

Adaptive memristor-based LIF neuron circuit for energy efficient SNN crossbar array.

Cognitive neurodynamics·2026
Same journal

Dynamic bi-domain discriminator adversarial network for EEG emotion recognition.

Cognitive neurodynamics·2026
Same journal

Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density FeaturesOlfactory perception and neural rhythms: a simulation-based eeg analysis using power spectral density features.

Cognitive neurodynamics·2026
Same journal

An event-related potentials account of brain predictive coding.

Cognitive neurodynamics·2026
Same journal

A recurrent neural network model for a decision-making task based on sequential evidence accumulation.

Cognitive neurodynamics·2026
Same journal

Synaptic neurotransmitter concentration modulation during learning in bio-inspired spiking neural network.

Cognitive neurodynamics·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.7K

在rs-fMRI中用于功能大脑网络分类的多式深度学习框架.

Belfin Robinson1, William Reuther1, Olivia Leggio1

  • 1Clinical Resting State fMRI Service, Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.

Cognitive neurodynamics
|November 12, 2025
PubMed
概括
此摘要是机器生成的。

这项研究使用休息状态fMRI数据的深度学习自动化脑网络分类. 该框架准确地确定了发作区域,改善了临床审查和手术规划.

关键词:
大脑绘制地图.计算机辅助的诊断 计算机辅助的诊断在Connectome中使用Connectome.功能连接性的功能连接性.磁共振成像技术 磁共振成像技术神经网络的神经网络的神经网络发作发作区是发作开始的区域.

更多相关视频

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.2K
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

1.5K

相关实验视频

Last Updated: Jan 11, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.7K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.2K
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

1.5K

科学领域:

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

背景情况:

  • 的诊断和手术规划依赖于功能性大脑网络的准确识别.
  • 休息状态功能磁共振成像 (rs-fMRI) 为分析大脑连接提供了宝贵的数据.
  • 自动化rs-fMRI衍生网络的分类可以减少临床实践中的主观性和工作量.

研究的目的:

  • 开发和验证一个深度学习框架,用于使用rs-fMRI在患者中功能性大脑网络的自动分类.
  • 区分发作发作区域 (SoZ),静止状态网络 (RSN) 和文物/噪声组件.
  • 评估空间,时间和光谱特征在网络分类中的贡献.

主要方法:

  • 采用混合深度学习架构,将空间特征的3D卷积神经网络 (3D-CNN) 和时间 (TS) 和频域 (FS) 信号的长短期记忆 (LSTM) 网络结合起来.
  • 使用独立组件分析 (ICA) 来从rs-fMRI数据中推导功能性大脑网络.
  • 一项废除研究评估了不同特征类型 (SF,TS,FS) 对分类性能的影响.
  • 专家神经病学家提供了对该模型可解释性和临床相关性的定性验证.

主要成果:

  • 混合模型在分类11种不同的ICA组件类型中达到高达70%的准确性,包括SoZ.
  • 结合频域信号 (SF+FS) 将ROC AUC提高到0.78,同时结合所有特征 (SF+TS+FS) 获得了最高的精度.
  • "噪音"类显示高性能 (高达0.94),而"叶"网络类的得分较低 (0.14-0.24).

结论:

  • 开发的数据驱动深度学习框架有效地自动化了RS-fMRI衍生的功能性脑网络在中的分类.
  • 空间,时间和光谱特征的整合提高了分类准确性,并支持临床应用,如手术规划.
  • 这种方法有可能减少主观性,提高患者大脑成像数据的临床审查效率.