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使用机器学习和深度学习进行基于语言任务的fMRI分析.

Elaine Kuan1,2,3, Viktor Vegh1,2,3, John Phamnguyen1,3,4

  • 1Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.

Frontiers in radiology
|December 12, 2024
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 和深度学习 (DL) 在脑成像中有效地分类语言区域. 这些方法显示了从非结构化的功能性MRI (fMRI) 范式中识别语言激活的前景.

关键词:
大脑激活活动大脑激活深度学习是一种深度学习.语言语言语言语言语言语言.机器学习是机器学习.基于任务的fMRI.时间序列时间序列

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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:

  • 基于任务的功能性磁共振成像 (fMRI) 对于识别语言主导的大脑区域至关重要,特别是在雄辩区域附近的神经外科规划.
  • 非结构化的fMRI范式,像自然主义的fMRI一样,对语言映射越来越感兴趣,但由于难以定义任务回归器,因此需要先进的分析技术.
  • 机器学习 (ML) 和深度学习 (DL) 为分析来自这些范式的复杂fMRI数据提供了潜在的解决方案.

研究的目的:

  • 通过基于任务的fMRI数据,研究各种ML和DL算法的有效性,以识别与语言相关的大脑区域.
  • 评估ML和DL模型在分类语音智能fMRI时间序列的性能,用于语言映射.

主要方法:

  • 收集了来自七个基于任务的语言fMRI范式的26个人的fMRI数据.
  • 训练有素的ML和DL模型来分类voxel-wise fMRI时间序列.
  • 对其在语言区域识别中的表现进行评估的一般机器学习和基于间隔的方法.

主要成果:

  • 一般机器学习和基于间隔的方法在通过fMRI时间序列分类来识别语言区域方面表现出显著的希望.
  • 一般机器学习方法实现了全脑平均AUC为[值],平均Dice系数为[值],平均欧几里德距离为[值]mm.
  • 基于间隔的方法实现了全脑平均AUC[值],平均Dice系数[值],平均欧几里德距离[值]mm.

结论:

  • 这项研究证实了各种ML和DL方法用于分类基于任务的语言fMRI时间序列的实用性.
  • 这些先进的分析方法对识别语言激活具有重大潜力,特别是在非结构化的fMRI范式中.