通过协同适应性培训增强fMRI解码的神经反:模拟和原则证明证据
Najmeddine Abdennour1, Pedro Margolles2, David Soto3
1Basque Center on Cognition, Brain and Language, Paseo Mikeletegi 69, 2nd Floor, 20009, San Sebastian, Spain. n.abdennour@bcbl.eu.
Neuroinformatics
|February 5, 2026
概括
这项研究引入了一种共同适应方法,以改善实时fMRI神经反 (DecNef) 培训. 这种自适应解码器增强了参与者实现目标大脑状态的能力,提高了DecNef的精度和可靠性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 神经反训练,特别是基于fMRI的解码神经反 (DecNef),在参与者学习控制特定的大脑模式时面临挑战.
- 解码器训练数据和实时神经反数据之间的差异,包括噪音和不同的环境,有助于学习困难.
研究的目的:
- 开发和验证一个共同适应程序,以提高参与者在DecNef培训中的表现.
- 提高DecNef协议的精度和可靠性,以准特定的大脑表示.
主要方法:
- 使用标准机器学习算法与实时自适应解码器开发了协同适应程序.
- 在以前的DecNef数据集上使用模拟测试了该程序.
- 通过DecNef培训课程的实时fMRI数据验证了同适应方法.
主要成果:
- 模拟表明解码器协同适应在神经反训练期间显著提高了性能.
- 漂移分析证实了共适应解码器在整个培训期间的稳定性.
- 实时fMRI数据提供了概念证明证据,即同适应增强了参与者诱导目标大脑状态的能力.
结论:
- 通过共同适应创建的个性化解码器可以提高DecNef培训协议的有效性.
- 这种方法为针对特定的大脑表示提供了更高的精度和可靠性,具有潜在的翻译研究应用.
- 开发的工具是科学界公开使用的.
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