Elucidating age-specific patterns from background electroencephalogram pediatric datasets via PARAFAC

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

Identification of informative signatures from electrophysiological signals is important for understanding brain developmental patterns, where techniques such as magnetoencephalography (MEG) are particularly useful. However, less attention has been given to fully utilizing the multidimensional nature of MEG data for extracting components that describe these patterns. Tensor factorizations of MEG yield components that encapsulate the data's multidimensional nature, providing parsimonious models identifying latent brain patterns for meaningful summarization of neural processes. To address the need for meaningful MEG signatures for studies of pediatric cohorts, we propose a tensor-based approach for extracting developmental signatures of multi-subject MEG data. We employ the canonical polyadic (CP) decomposition for estimating latent spatiotemporal components of the data, and use these components for group level statistical inference. Using CP decomposition along with hierarchical clustering, we were able to extract typical early and late latency event-related field (ERF) components that were discriminative of high and low performance groups ([Formula: see text]) and significantly correlated with major cognitive domains such as attention, episodic memory, executive function, and language comprehension. We demonstrate that tensor-based group level statistical inference of MEG can produce signatures descriptive of the multidimensional MEG data. Furthermore, these features can be used to study group differences in brain patterns and cognitive function of healthy children. We provide an effective tool that may be useful for assessing child developmental status and brain function directly from electrophysiological measurements and facilitate the prospective assessment of cognitive processes.

Preliminary fusion of EEG and MRI with phenotypic scores in children with epilepsy based on the Canonical Polyadic Decomposition

Cognitive and behavioural impairments in early-onset epilepsy affect the children and families' quality of life. Our ability to detect these impairments is limited, and it requires laborious questionnaires. Here, we describe a pilot study exploring the fusion of resting-state EEG, volumetric MRI, and phenotypic scores of child development based on the Canonical Polyadic Decomposition, expanding the recently presented Joint EEG-Development Inference (JEDI) model. Pilot data fusion was performed on functional, structural and developmental brain features of 29 preschool children diagnosed with epilepsy. The results suggest that combining multimodal brain data towards a comprehensive analysis of brain development in young children is plausible.