Functional, structural, and phenotypic data fusion to predict developmental scores of pre-school children based on Canonical Polyadic Decomposition

Noramon Dron, María Navarro, Richard Chin, Javier Escudero

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Recent technological advances enable the acquisition of diverse datasets that demand data-driven analysis. In this context, we seek to take advantage of diverse data modalities to explore the links between childhood development, structure and function of the brain. We deploy a data fusion model using coupled matrix-tensor decomposition of electroencephalography (EEG), structural magnetic resonance imaging (sMRI), and phenotypic score data to investigate how functional, structural, and phenotypic variables reflect development in young children with epilepsy. Our model is based on Canonical Polyadic Decomposition and optimised with grid search to predict developmental scores of preschool children. The model is promising and able to show relationships between modalities that agree with clinical expectations. The score prediction yields a high similarity at the group level and potential to predict laborious and time-consuming developmental scores from routinely collected sMRI and/or EEG data, thus becoming a stepping-stone towards more ecient clinical assessment
of brain development in young children.
Original languageEnglish
Article number102889
JournalBiomedical Signal Processing and Control
Volume70
Early online date10 Jul 2021
DOIs
Publication statusPublished - Sept 2021

Keywords / Materials (for Non-textual outputs)

  • Data fusion
  • Tensor decomposition
  • Matrix decomposition
  • EEG
  • MRI
  • Child development

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