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

Classification of Connective Tissues01:30

Classification of Connective Tissues

16.1K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
16.1K
Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

714
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
714
Functions of Connective Tissues01:17

Functions of Connective Tissues

16.9K
Connective tissues perform a broad range of functions in the body. Their primary function is to connect and link different tissues in the body and act as packaging material between tissues. The areolar tissue, a connective tissue prototype, commonly cements various tissue types in diverse body organs. In contrast, adipose tissue cushions internal organs while insulating the body from heat loss.
Hard connective tissues, such as bones and cartilage, provide structure and support to the body.
16.9K
Functional Classification of Joints01:09

Functional Classification of Joints

6.9K
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...
6.9K
Disorders of Acid-Base Balance01:29

Disorders of Acid-Base Balance

2.1K
The human body maintains a precise pH range of arterial blood between 7.35 and 7.45. Deviations result in either acidosis (pH < 7.35) or alkalosis (pH > 7.45). These conditions are further classified as respiratory or metabolic disorders based on their underlying cause.
Respiratory Acidosis and Alkalosis
Respiratory acidosis occurs due to an increase in the partial pressure of carbon dioxide PCO2 in the blood. It often arises from shallow breathing or impaired gas exchange caused by...
2.1K

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

Sparse time-varying log-ratios for longitudinal high-throughput sequencing data.

Frontiers in bioinformatics·2026
Same author

Nasal Instillation of Complex Metal Oxide Particles Induces Brain Metal Accumulation and Neurobehavioral Toxicity in Mice.

Environmental science & technology·2026
Same author

What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same author

Stage-Related Alterations in Cortical Functional Connectivity Gradients in Non-Dialysis Patients With Chronic Kidney Disease.

AJNR. American journal of neuroradiology·2026
Same author

Sexual health among patients with breast cancer undergoing endocrine therapy: an integrative review.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Towards a general-purpose foundation model for functional MRI analysis.

Nature biomedical engineering·2026

相关实验视频

Updated: Feb 8, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

超越功能连接:基于fMRI的脑疾病分类时间序列建模.

Guoqi Yu, Xiaowei Hu, Angelica I Aviles-Rivero

    IEEE transactions on medical imaging
    |February 6, 2026
    PubMed
    概括

    从功能磁共振成像 (fMRI) 分析血氧水平依赖 (BOLD) 信号的新时代模型显著改善了对传统方法的脑疾病分类. 这些先进的技术更有效地捕捉复杂的大脑动态.

    科学领域:

    • 神经成像是一种神经成像.
    • 机器学习 机器学习
    • 计算神经科学是一种神经科学.

    背景情况:

    • 功能磁共振成像 (fMRI) 使用血氧水平依赖 (BOLD) 信号进行非侵入性脑疾病分类.
    • 当前的方法通常依赖于通过皮尔森相关的功能连接 (FC),这简化了4D BOLD数据变成静态的2D矩阵,失去时间动态和线性关系.

    研究的目的:

    • 将最先进的时间模型与基于FC的传统方法进行fMRI脑疾病分类的比较.
    • 引入DeCI,一个新的框架,集成循环漂移分解和道独立,用于增强的fMRI分析.

    主要方法:

    • 在五个公共fMRI数据集的原始BOLD信号上对先进的时间序列模型 (PatchTST,TimesNet,TimeMixer) 的基准测试.
    • 开发和应用DeCI框架,包括每个感兴趣区域 (ROI) 的循环和漂移分解和频道独立建模.
    • 与传统的基于FC的方法和其他时间基线进行比较分析.

    主要成果:

    • 最先进的时间模型在分类准确性方面始终优于传统的基于FC的方法.
    • 与FC和时间基线相比,提出的DeCI框架显示出更高的分类准确性和概括能力.
    • 直接建模时间信息,包括振荡波动和缓慢的基线趋势,对于提高性能至关重要.

    更多相关视频

    Real-Time fMRI Brain Mapping in Animals
    04:05

    Real-Time fMRI Brain Mapping in Animals

    Published on: September 24, 2020

    4.1K
    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
    17:06

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

    Published on: November 8, 2012

    27.0K

    相关实验视频

    Last Updated: Feb 8, 2026

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.7K
    Real-Time fMRI Brain Mapping in Animals
    04:05

    Real-Time fMRI Brain Mapping in Animals

    Published on: September 24, 2020

    4.1K
    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
    17:06

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

    Published on: November 8, 2012

    27.0K

    结论:

    • 对BOLD信号的端到端时间建模为基于fMRI的脑疾病分类提供了比静态FC方法更有效的方法.
    • DeCI框架为捕捉复杂的大脑动态提供了强大而准确的方法,推进了神经影像分析领域.
    • 研究结果主张在fMRI分析中转向时间建模的范式转变,以更好地了解大脑功能和功能障碍.