Department of Physiology, Anatomy and Genetics, University of Oxford, UK.
Clin Neurophysiol. 2010 Apr;121(4):577-87. doi: 10.1016/j.clinph.2009.12.012. Epub 2010 Jan 27.
To decompose sensory event-related brain potentials (ERPs) into a set of independent components according to the modality and the spatial location of the eliciting sensory stimulus, and thus provide a quantitative analysis of their underlying components.
Auditory, somatosensory and visual ERPs were recorded from 124 electrodes in thirteen healthy participants. Probabilistic Independent Component Analysis (P-ICA) was used to decompose these sensory ERPs into a set of independent components according to the modality (auditory, somatosensory, visual or multimodal) and the spatial location (left or right side) of the eliciting stimulus.
Middle-latency sensory ERPs were explained by a large contribution of multimodal neural activities, and a smaller contribution of unimodal neural activities. While a significant fraction of unimodal neural activities were dependent on the location of the eliciting stimulus, virtually all multimodal neural activities were not (i.e. their scalp distributions and time courses were not different when stimuli were presented on the left and right sides).
These findings show that P-ICA can be used to dissect effectively sensory ERPs into physiologically meaningful components, and indicate a new approach for exploring the effect of various experimental modulations of sensory ERPs.
This approach offers a better understanding of the functional significance of sensory ERPs.
根据引发感觉刺激的模态和空间位置,将感觉事件相关脑电位(ERPs)分解为一组独立成分,从而对其潜在成分进行定量分析。
在 13 名健康参与者的 124 个电极上记录听觉、体感和视觉 ERPs。概率独立成分分析(P-ICA)用于根据引发刺激的模态(听觉、体感、视觉或多模态)和空间位置(左侧或右侧)将这些感觉 ERPs 分解为一组独立成分。
中潜伏期感觉 ERPs 主要由多模态神经活动解释,而较少由单模态神经活动解释。虽然单模态神经活动的很大一部分依赖于引发刺激的位置,但实际上所有多模态神经活动都不依赖于刺激的位置(即,当刺激出现在左侧和右侧时,其头皮分布和时程没有差异)。
这些发现表明,P-ICA 可有效用于将感觉 ERPs 分解为具有生理意义的成分,并为探索各种实验调制感觉 ERPs 的效果提供了一种新方法。
该方法有助于更好地理解感觉 ERPs 的功能意义。