Machine-learning to characterise neonatal functional connectivity in the preterm brain.
Brain development is adversely affected by preterm birth. Magnetic resonance image analysis has revealed a complex fusion of structural alterations across all tissue compartments that are apparent by term-equivalent age, persistent into adolescence and adulthood, and associated with wide-ranging neurodevelopment disorders. Although functional MRI has revealed the relatively advanced organisational state of the neonatal brain, the full extent and nature of functional disruptions following preterm birth remain unclear. In this study, we apply machine-learning methods to compare whole-brain functional connectivity in preterm infants at term-equivalent age and healthy term-born neonates in order to test the hypothesis that preterm birth results in specific alterations to functional connectivity by term-equivalent age. Functional connectivity networks were estimated in 105 preterm infants and 26 term controls using group-independent component analysis and a graphical lasso model. A random forest-based feature selection method was used to identify discriminative edges within each network and a nonlinear support vector machine was used to classify subjects based on functional connectivity alone. We achieved 80% cross-validated classification accuracy informed by a small set of discriminative edges. These edges connected a number of functional nodes in subcortical and cortical grey matter, and most were stronger in term neonates compared to those born preterm. Half of the discriminative edges connected one or more nodes within the basal ganglia. These results demonstrate that functional connectivity in the preterm brain is significantly altered by term-equivalent age, confirming previous reports of altered connectivity between subcortical structures and higher-level association cortex following preterm birth.
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
G
G
Ball
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
P
P
Aljabar
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
T
T
Arichi
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
N
N
Tusor
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
D
D
Cox
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
N
N
Merchant
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
P
P
Nongena
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
J V
JV
Hajnal
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom. Electronic address: ad.edwards@kcl.ac.uk.
A D
AD
Edwards
Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
S J
SJ
Counsell
D001921:Brain / Q000473:pathology* / Q000503:physiopathology*; D001931:Brain Mapping; D063132:Connectome / Q000379:methods; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male
Brain development; Prematurity; Support vector machines; fMRI