IntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity.MethodsHere, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation.ResultsAll inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology.DiscussionOur findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization.

中文翻译：

---

Intra-V1 功能网络和观察到的刺激的分类

简介之前的研究表明，V1 内神经活动的共同波动（通过功能磁共振成像测量）携带有关观察到的刺激的信息，可能反映各种认知机制。本研究通过使用不同的功能磁共振成像预处理方法探索塑造这些信息的神经源。所有个体对刺激的共同反应可以通过使用受试者间相关性来强调，或者通过在计算相关性之前将 fMRI 与血流动力学反应函数 (HRF) 去卷积来弱化。后一种方法将平衡转向参与者特殊活动。方法在这里，我们使用 V1 内相关矩阵的多元模式分析来预测使用上述相关类型观察到的 Navon 字母的水平或形状。我们评估了特定属性连接的受试者间预测的准确性，并尝试对刺激属性进行受试者内交叉分类（即，尽管另一个特征发生变化，仍预测一个特征）。成功分类器的权重图被投影到视野上。对照实验研究了刺激呈现期间的眼睛运动模式。结果所有主体间分类器都能准确预测特定观察到的刺激的水平和形状。然而，无论预处理方案如何，仅针对刺激水平实现了成功的受试者内交叉分类，而不是针对形状。成功级别分类的权重图在主体间相关性和去卷积相关性之间有所不同。后者揭示了与已知的感知不对称相对应的视野链接强度的不对称。眼球 fMRI 信号的事后测量没有发现刺激条件之间凝视的差异，并且对照实验（带有派生模拟）也表明眼球运动不能解释 V1 拓扑中与刺激相关的变化。讨论我们的研究结果表明，两者之间-受试者的共同反应和参与者特定的活动有助于 V1 内共同波动的信息，尽管是通过不同的子网络。反卷积增强了特定主题的活动，突出了全局刺激的半球间联系。对 V1 内网络的进一步探索有望深入了解注意力和感知组织的神经基础。