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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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 7, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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用于rsfMRI数据分类的生成动态模型.

Grace Huckins1, Russell A Poldrack2

  • 1Neurosciences Interdepartmental Program, Stanford University, Stanford, CA, USA.

Network neuroscience (Cambridge, Mass.)
|December 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的动态方法,使用隐藏的马尔科夫模型来分类静态fMRI数据. 这种方法有效地利用大脑动态进行主体内部分类,为更大的可解释性提供了潜力.

关键词:
分类 分类 分类 分类.生成型模型是一种生成型模型.隐藏的马尔科夫模型网络动态 网络动态休息状态的fMRI.

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

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

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相关实验视频

Last Updated: May 7, 2025

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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:

  • 大规模的神经成像数据集和机器学习工具越来越多地用于从fMRI数据中预测心理/行为变量.
  • 大多数研究使用静态特征对fMRI数据进行分类,较少研究用于分类的大脑动态.

研究的目的:

  • 试验一种生成的,动态的方法来分类休息状态fMRI (rsfMRI) 数据.
  • 用隐藏的马尔科夫模型 (HMM) 来利用rsfMRI数据中的动态模式进行分类.

主要方法:

  • 在训练数据中将单独的HMM安装到类别中.
  • 在训练有素的HMM下,根据概率对测试数据进行分类.
  • 使用隐藏状态之间的过渡概率进行分类.

主要成果:

  • 在使用过渡概率的MyConnectome数据集上,HMMs成功执行了对象内部分类.
  • 在人类结合体项目的数据集上,单独使用HMM过渡概率无法实现个体对象的识别.
  • 一个矢量自回归模型实现了对主体识别的高性能.

结论:

  • 对rsfMRI数据的试点动态分类方法显示出有希望的性能,特别是在主体内分类方面.
  • 这种基于HMM的方法有可能比其他动态方法提供更好的解释性.
  • 需要进一步的研究来探索神经成像分析中的动态模型的全部潜力.