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解码初级视皮层诊断亚区的面部类别。

Decoding face categories in diagnostic subregions of primary visual cortex.

机构信息

Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, 58 Hillhead Street, Glasgow, G12 8QB, UK.

出版信息

Eur J Neurosci. 2013 Apr;37(7):1130-9. doi: 10.1111/ejn.12129. Epub 2013 Feb 3.

Abstract

Higher visual areas in the occipitotemporal cortex contain discrete regions for face processing, but it remains unclear if V1 is modulated by top-down influences during face discrimination, and if this is widespread throughout V1 or localized to retinotopic regions processing task-relevant facial features. Employing functional magnetic resonance imaging (fMRI), we mapped the cortical representation of two feature locations that modulate higher visual areas during categorical judgements - the eyes and mouth. Subjects were presented with happy and fearful faces, and we measured the fMRI signal of V1 regions processing the eyes and mouth whilst subjects engaged in gender and expression categorization tasks. In a univariate analysis, we used a region-of-interest-based general linear model approach to reveal changes in activation within these regions as a function of task. We then trained a linear pattern classifier to classify facial expression or gender on the basis of V1 data from 'eye' and 'mouth' regions, and from the remaining non-diagnostic V1 region. Using multivariate techniques, we show that V1 activity discriminates face categories both in local 'diagnostic' and widespread 'non-diagnostic' cortical subregions. This indicates that V1 might receive the processed outcome of complex facial feature analysis from other cortical (i.e. fusiform face area, occipital face area) or subcortical areas (amygdala).

摘要

枕颞叶皮质中的高级视觉区域包含用于面部处理的离散区域,但尚不清楚 V1 是否在面部辨别过程中受到自上而下的影响的调节,以及这种调节是否广泛存在于 V1 中,还是局限于处理与任务相关的面部特征的视网膜区域。我们采用功能磁共振成像 (fMRI) 技术,绘制了两个特征位置的皮质代表图,这两个特征位置在类别判断过程中调节高级视觉区域 - 眼睛和嘴巴。向受试者呈现快乐和恐惧的面孔,我们测量了 V1 区域处理眼睛和嘴巴的 fMRI 信号,同时受试者进行性别和表情分类任务。在单变量分析中,我们使用基于感兴趣区域的广义线性模型方法来揭示这些区域内的激活变化作为任务的函数。然后,我们训练线性模式分类器,根据“眼睛”和“嘴巴”区域以及其余非诊断性 V1 区域的 V1 数据对面部表情或性别进行分类。使用多变量技术,我们表明 V1 活动既可以在局部“诊断性”和广泛的“非诊断性”皮质子区域中区分面部类别。这表明 V1 可能从其他皮质(即梭状回面部区、枕部面部区)或皮质下区域(杏仁核)接收复杂面部特征分析的处理结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/786d/3816327/e6760855d6d1/ejn0037-1130-f1.jpg

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