有效的连接揭示了通过Insula和Pulvinar视觉预测精度的双路线机制
Linzhi Tao1, Trevor Steward1,2, Joshua Corbett1
1Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia.
Human brain mapping
|January 24, 2026
概括
这项研究揭示了大脑如何使用7-特斯拉fMRI对预测进行准确权衡. 研究结果表明,一种双路机制涉及insula,pulvinar和V1,以实现最佳的感官集成.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 预测处理依赖于大脑的权重预测,因为它们的精确性可以实现最佳的感官集成.
- 精确权衡的神经基础在很大程度上仍然是未知的.
- 了解这种机制对于解释贝叶斯整合和神经精神疾病中的潜在干扰至关重要.
研究的目的:
- 通过使用先进的神经成像技术,研究基于精度加权预测的神经电路.
- 确定岛屿皮层,脉动核和初级视觉皮层 (V1) 在编码预测精度中的作用.
- 阐明与不同级别的预测精度相关的功能连接模式.
主要方法:
- 在高精度和低精度条件下的视觉提示任务中使用了7-特斯拉功能磁共振成像 (fMRI).
- 利用动态因果建模 (DCM) 来推断关键大脑区域之间的有效连接.
- 进行了离开一个的交叉验证,以评估已识别的连接模式的行为相关性.
主要成果:
- 行为数据证实,在高精度条件下 (p < 0.001) 的准确性明显更高.
- 在高精度预测过程中,DCM揭示了不同的连接模式:刺激性insula-to-V1调制和抑制性insula-to-pulvinar和pulvinar-to-V1影响.
- 精度灵敏度的个体差异与脉冲到岛屿和脉冲到V1/V1-脉冲连接强度相关.
结论:
- 提出了一种双路线机制:insula直接增强了V1中的自上而下的预测,而间接地通过pulvinar抑制了自下而上的输入.
- 这种神经架构支持贝叶斯集成在不确定性下通过动态调整基于预测精度的感官证据来支持贝叶斯集成.
- 这些通路的干扰可能是神经精神疾病中观察到的改变精度权重的基础.
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