Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth

• Published in: Connectomics in NeuroImaging Vol. 11083; pp. 47 - 57
• Main Authors: Galdi, Paola, Blesa, Manuel, Sullivan, Gemma, Lamb, Gillian J., Stoye, David Q., Quigley, Alan J., Thrippleton, Michael J., Bastin, Mark E., Boardman, James P.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Brain developmental delay; Morphometric similarity networks; Multi-modal MRI; Preterm brain
• ISBN: 3030007545; 9783030007546
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-00755-3_6

Morphometric similarity networks (MSNs) have been recently proposed as a novel, robust, and biologically plausible approach to generate structural connectomes from neuroimaging data. In this work, we apply this method to multi-centre neonatal data (postmenstrual age range: 37–45 weeks) to predict brain dysmaturation in preterm infants. To achieve this goal, we combined different imaging sequences (diffusion and structural MRI) to extract a set of metrics from cortical and subcortical brain regions (e.g. regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging features) which were used to construct a similarity network. A regression model was then trained to predict postmenstrual age at the time of scanning from inter-regional connections. Finally, to quantify brain maturation, the Relative Brain Network Maturation Index (RBNMI) was computed as the difference between predicted and actual age. The model predicted chronological age with a mean absolute error of 0.88 (±0.63) weeks, and it consistently predicted preterm infants to have a lower RBNMI than term infants. We conclude that MSNs derived from multimodal imaging predict chronological brain development accurately, and provide a data-driven approach for defining cerebral dysmaturation associated with preterm birth.