Recent advances in neuroimaging have enabled studies in functional connectivity (FC) of human brain, alongside investigation of the neuronal basis of cognition. One important FC study is the representation of vision in human brain. The release of publicly available dataset BOLD5000 has made it possible to study the brain dynamics during visual tasks in greater detail. In this paper, a comprehensive analysis of fMRI time series (TS) has been performed to explore different types of visual brain networks (VBN). The novelty of this work lies in (1) constructing VBN with consistently significant direct connectivity using both marginal and partial correlation, which is further analyzed using graph theoretic measures, (2) classification of VBNs as formed by image complexity-specific TS, using graphical features. In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than that using negative correlation. This result not only reflects the distinguishing graphical characteristics of each image complexity-specific VBN, but also highlights the importance of studying both positively correlated and negatively correlated VBN to understand the how differently brain functions while viewing different complexities of real-world images.

翻译：神经影像学的最新进展使得研究人脑功能连接（FC）成为可能，同时也有助于探索认知的神经基础。其中一项重要的功能连接研究是人脑对视觉的表征。公开数据集BOLD5000的发布为更详细地研究视觉任务期间的脑动态提供了可能。本文通过对fMRI时间序列（TS）进行综合分析，探索了不同类型的视觉脑网络（VBN）。本工作的创新点在于：(1) 使用边际相关和偏相关构建具有一致显著直接连接的VBN，并通过图论度量进行进一步分析；(2) 利用图像复杂度特定时间序列的图形特征对VBN进行分类。在图像复杂度特定的VBN分类中，XGBoost在正相关VBN上的平均准确率达到86.5%至91.5%，比使用负相关方法高出2%。这一结果不仅反映了每种图像复杂度特定VBN的独特图形特征，还强调了研究正相关和负相关VBN对于理解人脑在观看不同复杂度的真实世界图像时功能差异的重要性。