Queensland Brain Institute, The University of Queensland, Brisbane, Qld. Australia.
Queensland Institute of Medical Research, Brisbane, Qld, Australia; School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; The Black Dog Institute, Sydney, NSW, Australia; The Royal Brisbane and Womans Hospital, Brisbane, Qld, Australia.
Neuroimage. 2014 Nov 15;102 Pt 1:60-70. doi: 10.1016/j.neuroimage.2013.06.083. Epub 2013 Jul 9.
Despite the wealth of research on face perception, the interactions between core regions in the face-sensitive network of the visual cortex are not well understood. In particular, the link between neural activity in face-sensitive brain regions measured by fMRI and EEG markers of face-selective processing in the N170 component is not well established. In this study, we used dynamic causal modeling (DCM) as a data fusion approach to integrate concurrently acquired EEG and fMRI data during the perception of upright compared with inverted faces. Data features derived from single-trial EEG variability were used as contextual modulators on fMRI-derived estimates of effective connectivity between key regions of the face perception network. The overall construction of our model space was highly constrained by the effects of task and ERP parameters on our fMRI data. Bayesian model selection suggested that the occipital face area (OFA) acted as a central gatekeeper directing visual information to the superior temporal sulcus (STS), the fusiform face area (FFA), and to a medial region of the fusiform gyrus (mFG). The connection from the OFA to the STS was strengthened on trials in which N170 amplitudes to upright faces were large. In contrast, the connection from the OFA to the mFG, an area known to be involved in object processing, was enhanced for inverted faces particularly on trials in which N170 amplitudes were small. Our results suggest that trial-by-trial variation in neural activity at around 170 ms, reflected in the N170 component, reflects the relative engagement of the OFA to STS/FFA network over the OFA to mFG object processing network for face perception. Importantly, the DCMs predicted the observed data significantly better by including the modulators derived from the N170, highlighting the value of incorporating EEG-derived information to explain interactions between regions as a multi-modal data fusion method for combined EEG-fMRI.
尽管对面部感知的研究很多，但视觉皮层中面部敏感网络的核心区域之间的相互作用还不是很清楚。特别是，功能磁共振成像（fMRI）测量的面部敏感脑区的神经活动与 N170 成分中面部选择性处理的脑电图（EEG）标记之间的联系还没有很好的建立。在这项研究中，我们使用动态因果建模（DCM）作为一种数据融合方法，在感知正立和倒立面孔时，同时采集 EEG 和 fMRI 数据。从单次试验 EEG 变异性中提取的数据特征作为上下文调制器，作用于 fMRI 衍生的面部感知网络关键区域之间的有效连接的估计值。我们的模型空间的总体构建受到任务和 ERP 参数对 fMRI 数据影响的高度约束。贝叶斯模型选择表明，枕部面部区（OFA）作为一个中央门控器，将视觉信息引导到颞上沟（STS）、梭状回面部区（FFA）和梭状回内侧区（mFG）。当 N170 对正立面孔的振幅较大时，OFA 到 STS 的连接增强。相反，OFA 到 mFG 的连接（已知参与物体处理）在 N170 振幅较小时增强，特别是在倒立面孔的试验中。我们的结果表明，在大约 170ms 处的神经活动的逐次试验变化，反映在 N170 成分中，反映了在面孔感知中，OFA 与 STS/FFA 网络的相对参与程度，而不是 OFA 与 mFG 物体处理网络的相对参与程度。重要的是，通过包含从 N170 中提取的调制器，DCMs 显著地更好地预测了观测数据，突出了将 EEG 衍生信息纳入解释区域之间相互作用的价值，作为一种用于 EEG-fMRI 联合的多模态数据融合方法。
