Causality based Feature Fusion for Brain Neuro-Developmental Analysis

• URL: http://arxiv.org/abs/2001.08173v1
• Date: Wed, 22 Jan 2020 17:38:42 GMT
• Title: Causality based Feature Fusion for Brain Neuro-Developmental Analysis
• Authors: Peyman Hosseinzadeh Kassani, Li Xiao, Gemeng Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu Ping Wang
• Abstract summary: We propose to add the directional flow of information during brain maturation. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups. Our findings indicated that the strength of connections was significantly higher in young adults relative to children.
• Score: 26.218572787292427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human brain development is a complex and dynamic process that is affected 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 the 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.

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