Exploration of the relationship between brain function and cortical development in the human preterm period with simultaneous EEG-fMRI

In animal models, spontaneous bursts of synchronized neuronal activity play an instructive
role in key developmental processes that set early cortical circuits. Experimental disruption
leads to permanent loss of healthy cortical organization, suggesting this may represent a key
pathological process in conditions like autism which originate in early life and are associated
with cortical dysfunction.
Although similar bursts of neural activity are seen on scalp electroencephalography [EEG] in
preterm human infants, they cannot be directly related to cortical development due to
limitations in spatial specificity. Simultaneously acquired EEG and functional MRI [fMRI] data
overcomes these restrictions and crucially can be combined with data from other MRI
contrasts. This can provide detailed characterisation of the underlying brain anatomy [eg,
cortical folding and key developmental regions like the subplate], and allows analysis of the
relationship with key deep brain structures [ie, thalamus and cerebellum] and MR derived
measures of tissue microstructure [such as neurite density and orientation dispersion index].
This project will test the hypothesis that spontaneous bursting neural activity localises to
developing brain regions in preterm infants. In addition to 50 existing simultaneous EEG-fMRI
neonatal datasets [the only dataset in the world], new data will be collected to provide a
deeper characterisation of brain development at different ages. A machine learning
classification algorithm will be used to characterise spontaneous bursting events within the
EEG data and then a combination of source localization algorithms and traditional fMRI
timeseries analysis used to identify spatial relationships. Resulting spatial maps can then be
aligned to other subject-specific and population level MRI data.