Biology subjects

Harper, N.

Publications and source records attributed to Harper, N..

3 recordsLinked to original sources

bioRxiv2021

Natural sound characteristics explain perceptual categorization

As we interact with our surroundings, we encounter the same or similar objects from different perspectives and are compelled to generalize. For example, we recognize dog barks as a distinct class of sound, despite the variety of individual barks. While we have some understanding of how generalization is done along a single stimulus dimension, such as frequency or color, natural stimuli are identifiable by a combination of dimensions. To understand perception, measuring the interaction across stimulus dimensions is essential. For example, when identifying a sound, does our brain focus on a specific dimension or a combination, such as its frequency and duration? Furthermore, does the relative relevance of each dimension reflect its contribution to the natural sensory environment? Using a 2- dimension discrimination task for mice we tested untrained generalization across several pairs of auditory dimensions in a naturalistic and automatized behavioral paradigm. We uncovered a perceptual hierarchy over the tested dimensions that was dominated by the sounds spectral composition. This hierarchy could reflect the relevance of the different dimensions in natural stimuli and their potentially associated differential shaping of neuronal tuning. Mice could learn to pay more attention to dimensions low in the hierarchy, but this learning was more rigid and did not generalize as flexibly. Stimuli are thus not perceived as a whole but as a combination of their features, each of which weights differently on the dentification of the stimulus according to an established hierarchy.

neuroscience↗

bioRxiv2021

Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies

Multiple studies have demonstrated less favourable childhood outcomes in infants born in early term (37-38 weeks gestation) compared to those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain and subsequent neurodevelopment in term-born babies. Here we characterise structural brain correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome. We used T2 and diffusion weighted Magnetic Resonance Images acquired in the neonatal period from a cohort (n=454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor based morphometry (TBM) and tract based spatial statistics (TBSS). Neurodevelopment was subsequently assessed at age 18 months using the Bayley-III Scales of Infant and Toddler Development, and the effects of gestational age at birth and related neuroimaging findings on outcome were analysed with linear regression. Infants born earlier had areas of higher relative ventricular volume, and lower relative brain volume in the basal ganglia, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Linear regression models predicting outcome from gestational age at birth were significantly improved by adding neuroimaging features associated with gestational age at birth. This work adds to the growing body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.

neuroscience↗

bioRxiv2020

Predicting age and clinical risk from the neonatal connectome

The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and are complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible, and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to be predominantly thalamocortical. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome, and suggest that a neural substrate for later developmental outcome is detectable at term equivalent age.

neuroscience↗