1 School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2 School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Brain Connect. 2019 May;9(4):329-340. doi: 10.1089/brain.2018.0624. Epub 2019 Apr 22.
Neuroimaging studies have shown that discrete regions in ventral visual pathway respond selectively to specific object categories. For example, the fusiform face area (FFA) in humans is consistently more responsive to face than nonface images. However, it is not clear how other cortical regions contribute to this preferential response in FFA. To address this question, we performed a functional magnetic resonance imaging study on human subjects watching naturalistic movie clips from human actions. We then used correlation and multivariate regression (partial least-squares regression) analyses to estimate/predict the mean BOLD (blood oxygenation level-dependent) activity in FFA, from the mean and pattern of responses in 24 visual cortical areas. Higher tier retinotopic areas V3, hV4, and LO2, motion-selective area middle temporal, body-selective areas, and non-FFA face-selective areas had the best prediction accuracy particularly when they were located ipsilateral to FFA. All non-FFA collectively could explain up to 75% of variance in the FFA response. The regression models were also designed to predict the mean activity in one face area from the pattern of activity in another face area. The prediction power was significantly higher between the occipital face area and FFA. The multivariate regression analysis provides a new framework for investigating functional connectivity between cortical areas, and it could inform hierarchical models of visual cortex.
神经影像学研究表明，腹侧视觉通路的离散区域对特定的物体类别有选择性的反应。例如，人类的梭状回面孔区（FFA）对面孔图像的反应明显强于非面孔图像。然而，目前尚不清楚其他皮质区域如何对 FFA 中的这种优先反应做出贡献。为了解决这个问题，我们对人类受试者观看来自人类动作的自然主义电影片段进行了功能磁共振成像研究。然后，我们使用相关和多元回归（偏最小二乘回归）分析，从 24 个视觉皮质区域的平均和模式反应中，估计/预测 FFA 中的平均 BOLD（血氧水平依赖）活动。较高层次的视网膜区域 V3、hV4 和 LO2、运动选择性区域中颞叶、身体选择性区域以及非 FFA 面孔选择性区域具有最佳的预测准确性，特别是当它们位于 FFA 的同侧时。所有非 FFA 区域加起来可以解释高达 75%的 FFA 反应的方差。回归模型还被设计用于从另一个面孔区域的活动模式预测一个面孔区域的平均活动。在枕叶面孔区和 FFA 之间，预测能力显著更高。多元回归分析为研究皮质区域之间的功能连接提供了一个新的框架，并为视觉皮质的层次模型提供了信息。
