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

Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.1K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
2.1K
Functional Classification of Joints01:09

Functional Classification of Joints

7.0K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
7.0K
Network Function of a Circuit01:25

Network Function of a Circuit

880
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
880
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Cluster Sampling Method01:20

Cluster Sampling Method

14.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.8K

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

Energy-Based Phase-Locking State Analysis in Brain State Identification.

Human brain mapping·2026
Same author

Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Transistor-Inspired Triboelectric Nanogenerators with Multiple Charging-Discharging Processes for Enhanced Transferred Charge.

ACS applied materials & interfaces·2026
Same author

From relay station to circuit hub: Thalamic subnuclear precision and the major depressive disorder dysfunctome.

Psychiatry and clinical neurosciences·2026
Same author

Gecko-Like Multi-Directional High-Power Magneto-Mechano-Electric Energy Harvesters for Self-Powered, Hundred-Meter-Scale LoRa Communication.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A Fully Self-Powered Digital Wearable System for the Auxiliary Treatment of Plantar Fasciitis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026
Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026
Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026
Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

相关实验视频

Updated: Feb 13, 2026

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

1.6K

学习功能大脑网络生成和分类的最佳光谱聚类.

Jiacheng Hou, Zhenjie Song, Chenfei Ye

    IEEE journal of biomedical and health informatics
    |February 11, 2026
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了学习最佳光谱集群 (LOSC) 用于功能大脑网络 (FBN) 分析. 通过有效利用大脑的小世界拓学,LOSC提高了神经和精神疾病的分类准确性.

    更多相关视频

    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
    10:40

    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

    Published on: April 25, 2022

    2.9K
    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
    07:28

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

    Published on: October 19, 2021

    3.7K

    相关实验视频

    Last Updated: Feb 13, 2026

    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

    1.6K
    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
    10:40

    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

    Published on: April 25, 2022

    2.9K
    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
    07:28

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

    Published on: October 19, 2021

    3.7K

    科学领域:

    • 神经科学是一个神经科学.
    • 计算生物学 计算生物学
    • 机器学习 机器学习

    背景情况:

    • 功能性大脑网络 (FBN) 分析对于理解大脑组织和诊断神经/精神疾病至关重要.
    • FBNs具有具有功能集群的小世界拓,其中异常与疾病有关.
    • 当前的方法往往无法充分利用这种拓,限制了性能和可解释性.

    研究的目的:

    • 提出一个新的框架,学习最佳光谱集群 (LOSC),集成FBN生成,集群和分类.
    • 通过图形理论基础的损失函数来利用FBN的小世界拓.
    • 提高FBN分析用于疾病诊断的准确性和可解释性.

    主要方法:

    • 在非线性空间-光谱嵌入空间中,LOSC学习了大脑的连接性,使用一个建议的雷利分数损失 (RQL).
    • 该框架在生成的FBN中保留了小世界属性.
    • 它将FBN分为功能集群,并使用集群内部和集群间的关系进行分类.

    主要成果:

    • 在ABIDE,ADHD-200和HCP数据集上,LOSC实现了2.0%,3.6%和2.6%的一致准确度增长.
    • 拟议的RQL桥梁图形理论和基于学习的FBN分析.
    • 发现的功能集群与已知的神经病理学一致,有助于识别新的生物标志物.

    结论:

    • 通过有效利用小世界功能集群,LOSC提供了更好的大脑网络分类准确性.
    • 该框架通过将图形理论原则集成到机器学习中来提供理论基础.
    • LOSC提高了FBN分析的解释性,有助于发现神经和精神疾病的生物标志物.