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视觉皮层区域对过渡性,多焦点和稳定状态VEP的贡献:基于前模型的分析

Kieran S Mohr¹, Anna C Geuzebroek¹, Simon P Kelly¹

¹School of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College Dublin, Dublin, Ireland.

Imaging neuroscience (Cambridge, Mass.)

|August 13, 2025

概括

像C1和稳定状态VEP (SSVEPs) 这样的视觉唤起潜能 (VEPs) 主要来自主视觉皮层 (V1),有一些外层贡献. 我们的研究结果支持使用这些VEP来研究V1活动.

科学领域:

神经科学是一个神经科学.
视觉感知视觉感知视觉感知
电子生理学电子生理学

背景情况:

了解视觉唤起潜能 (VEP) 的解剖来源对于视觉处理研究至关重要.
像C1和稳定状态VEP (SSVEPs) 这样的常见VEP测量与视觉皮层活动有关,但它们的确切起源仍在争论中.
现有的源建模技术在解决这些来源方面存在局限性.

研究的目的:

在统一的框架内描述过渡性C1,多焦点C1和SSVEPs的解剖来源.
调查初级视觉皮层 (V1) 和外层区域 (V2,V3) 对这些VEP信号的贡献.
开发方法来区分VEP贡献与不同的视觉领域.

主要方法:

利用基于视网膜和皮层折叠的V1活动的"十字形模型".
测量了瞬态C1,多焦C1和SSVEPs (18.75Hz和7.5Hz) 作为对视觉刺激的反应.
回归VEP地形与V1,V2和V3的前向模型相对应,这些模型来源于本森-2014地图.

主要成果:

V1模型比V2/V3模型解释了VEP地形变化的2-6倍多的变异.
包括所有三个视觉区域 (V1-V3) 改善了模型的合适性,但时间分析显示了后来的外层贡献.
所有测试的VEP信号都显示出更强的V1地形捕获.

结论:

峰值C1振幅和SSVEP适用于探测初级视觉皮层 (V1) 活动.
与C1.1相比,SSVEP表现出更显著的外层贡献.
基于横向化和极性逆转的启发式可以帮助在视觉区域中区分VEP源.

关键词:

C1 一个人的心态在外层皮质上,外层皮质是外层的.期货模型的前性模型.主要视觉皮层的主要视觉皮层.视网膜地图的地图视觉唤起的潜力 (VEP)

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