Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies.

中文翻译：

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基于因果关系的大脑神经发育分析特征融合。

人脑发育是一个复杂、动态的过程，由遗传、性激素、环境变化等多种因素引起。最近许多关于大脑发育的研究检查了由不同大脑区域的时间序列之间的时间相关性定义的功能连接（FC）。我们建议在大脑成熟过程中添加信息的定向流动。为此，我们通过格兰杰因果关系（GC）为两组不同的受试者（即儿童和年轻人）提取有效连通性（EC）。动机是，纳入因果关系可能会进一步区分两个年龄组之间的大脑连接，并有助于发现大脑区域之间的新连接。这项研究的贡献有三个方面。首先，在大脑发育的背景下，人们对基于 EC 的特征提取缺乏关注。为此，我们提出了一种新的基于内核的GC（KGC）方法来学习复杂大脑网络的非线性，其中使用简化的正弦双曲多项式（RSP）神经网络作为我们提出的学习器。其次，我们使用因果关系值作为大脑区域之间的方向连接的权重。我们的研究结果表明，年轻人的联系强度明显高于儿童。此外，我们基于 EC 的新功能优于费城神经队列 (PNC) 研究中基于 FC 的分析，可以更好地区分不同年龄组。而且，这两组特征（FC + EC）的融合将大脑年龄预测的准确性提高了4%以上，表明它们应该一起用于大脑发育研究。