Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis

Peyman Hosseinzadeh Kassani, Li Xiao, Gemeng Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu Ping Wang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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 studieson brain developmenthave 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 interactionmay 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 networkwas used as our proposed learner. Second, we used causality values as the weight for the directional connectivitybetween brain regions. Our findings indicated that the strength of connections was significantlyhigher in youngadults 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.

Original languageEnglish (US)
Pages (from-to)3290-33299
Number of pages30010
JournalIEEE transactions on medical imaging
Volume39
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Brain age prediction
  • brain maturation
  • causality
  • polynomial neural network

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications

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