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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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相关实验视频

Updated: May 29, 2025

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

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

Published on: July 1, 2014

图像探险集的同时置信区域:一个验证研究与fMRI中的应用.

Jiyue Qin¹, Samuel Davenport¹, Armin Schwartzman^1,2

¹Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego.

bioRxiv : the preprint server for biology

|February 3, 2025

概括

本研究引入了一种用于功能磁共振成像 (fMRI) 分析的同时置信区域 (SCR) 的新方法. 该方法适用于所有激活值和大脑数据类型,改善大脑区域定位.

关键词:

在 Bootstrap 中使用 Bootstrap.同时使用的信心带.同时的信心区域同时的信心区域功能磁力共振成像 (fMRI) 是一种功能共振成像.

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

Published on: March 21, 2019

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

相关实验视频

Last Updated: May 29, 2025

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

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

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

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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科学领域:

神经成像是一种神经成像.
统计分析统计分析
大脑绘制地图.

背景情况:

功能磁共振成像 (fMRI) 对于在任务中定位大脑活动至关重要.
对于fMRI信任区域的现有方法仅限于单个值和体积数据,忽视了表面分析.
需要在fMRI研究中采用更敏感和多功能统计方法.

研究的目的:

开发一种方法来构建适用于所有激活值的同时置信区域 (SCR).
将fMRI分析扩展到包括敏感的皮质表面数据以及传统的体积数据.
为了评估fMRI数据分析的不同启动方法的性能.

主要方法:

开发了一种基于同时信任波段 (SCB) 的新方法来构建同时信任区域 (SCR).
通过使用fMRI数据进行广泛的2D模拟,评估了六种启动变体,包括非参数和乘数启动.
通过静止状态3D fMRI数据的现实噪声模拟来验证该方法,并将其应用于人类连接器项目数据.

主要成果:

拉德马切尔乘数启动式t变体表现出卓越的性能,实现了近乎名义的覆盖率,样本大小低至20.
拟议的SCR方法适用于所有激活值,并且适用于fMRI体积和表面数据.
该实现处理多维数据,并包括用于fMRI分析的交互可视化工具.

结论:

开发的SCR方法为fMRI研究中的统计推断提供了重大进展.
这种方法通过适应所有值和数据类型,提高了大脑区域分析的灵敏度和适用性.
这些发现支持使用Rademacher乘数启动-t用于神经成像中的强大的统计推理.