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Biondo, F.

Publications and source records attributed to Biondo, F..

3 recordsLinked to original sources
bioRxiv2025

Latent Representations of Early Brain Development: A Multivariate Normative Model of Brian Structure and Behaviour

Individual variation in neurodevelopment plays a central role in shaping cognitive abilities and behavioural profiles, influencing both typical functioning and risk for neurodevelopmental conditions. While much research has focused on characterising trajectories of brain structure changes during development, this typically entails assessing brain regions individually, overlooking the multivariate nature of neuroimaging data. In this study, we trained an autoencoder to map latent representations of childrens brain development using T1-weighted MRI scans from a paediatric cohort (n = 564, 55% male, mean age 4.90 years, age range [0.11,15.38]). This approach enabled us to establish multivariate normative models of brain structure, providing a more comprehensive framework for understanding neurodevelopmental variation. The latent space representation from this model effectively captured demographic variables (age and sex), while preserving both global and local structural features. The model accurately reconstructed the data, having mean reconstruction of 0.04 {+/-} 0.01, whilst also capturing demographic features with classification accuracy for sex of 84% {+/-} 4%, and a mean absolute error of 0.79 {+/-} 0.06 years for age prediction, highlighting its sensitivity to developmental changes. Further, we validated the approach using correlation analysis to show that deviations from the latent norms were significantly associated with multiple cognitive and behavioural measures, suggesting that variations in brain structure may reflect individual differences in neurodevelopment. Finally, we generated reference brain images that represent typical development and used them to visualise structural differences in individuals who deviate from this normative pattern. Our findings demonstrate that semi-supervised autoencoders, combined with multivariate normative modelling, offer a framework for characterizing neurodevelopmental trajectories. This approach can identify meaningful deviations associated with cognition and behaviour and has potential future applications across the lifespan.

developmental biology↗
bioRxiv2025

Multi-cohort, multi-sequence harmonisation for cerebrovascular brain age

IntroductionHigher brain-predicted age gaps (BAG), based on anatomical brain scans, have been associated with cognitive decline among elderly participants. Adding a cerebrovascular component, in the form of arterial spin labelling (ASL) perfusion MRI, can improve the BAG predictions and potentially increase sensitivity to cardiovascular health, a contributor to brain ageing and cognitive decline. ASL acquisition differences are likely to influence brain age estimations, and data harmonisation becomes indispensable for multi-cohort brain age studies including ASL. In this multi-cohort, multi-sequence study, we investigate harmonisation methods to improve the generalisability of cerebrovascular brain age. MethodsA multi-study dataset of 2608 participants was used, comprising structural T1-weighted (T1w), FLAIR, and ASL 3T MRI data. The single scanner training dataset consisted of 806 healthy participants, age 50{+/-}17, 18-95 years. The testing datasets comprised four cohorts (n=1802, age 67{+/-}8, 37-90 years). Image features included grey and white matter (GM/WM) volumes (T1w), WM hyperintensity volumes and counts (FLAIR), and ASL cerebral blood flow (CBF) and its spatial coefficient of variation (sCoV). Feature harmonisation was performed using NeuroComBat, CovBat, NeuroHarmonize, OPNested ComBat, AutoComBat, and RELIEF. ASL-only and T1w+FLAIR+ASL brain age models were trained using ExtraTrees. Model performance was assessed through the mean absolute error (MAE) and mean BAG. ResultsASL feature differences between cohorts decreased after harmonisation for all methods (p<0.05), mostly for RELIEF. Negative associations between age and GM CBF (b=-0.37, R2=0.13, unharmonised) increased after harmonisation for all methods (b<-0.42, R2>0.12) but weakened for RELIEF (b=-0.28, R2=0.14). In the ASL-only model, MAE improved for all harmonisation methods from 11.1{+/-}7.5 years to less than 8.8{+/-}6.2 years (p<0.001), while BAGs changed from 0.6{+/-}13.4 years to less than -1.03{+/-}7.92 years (p<0.001). For T1w+FLAIR+ASL, MAE (5.9{+/-}4.6 years, unharmonised) increased for all harmonisation methods non-significantly to above 6.0{+/-}4.9 years (p>0.42) and significantly for RELIEF (6.4{+/-}5.2 years, p=0.02), while BAGs non-significantly differed from -1.6{+/-}7.3 years to between -1.3{+/-}4.7 and -2.0{+/-}8.0 years (p>0.82). In general, the ASL-specific parameter harmonisation method AutoComBat performed nominally best. DiscussionHarmonisation of ASL features improves feature consistency between studies and also improves brain age estimations when only ASL features are used. ASL-specific parameter harmonisation methods perform nominally better than basic mean and scale adjustment or latent-factor approaches, suggesting that ASL acquisition parameters should be considered when harmonising ASL data. Although multi-modal brain age estimations were improved less by ASL-only harmonisation, possibly due to weaker associations between age and ASL features compared to T1w features feature importance, studies investigating pathological ASL-feature distributions might still benefit from harmonisation. These findings advocate for ASL-parameter specific harmonisation to explore associations between cardiovascular risk factors, brain ageing, and cognitive decline using multi-cohort ASL and cerebrovascular brain age studies.

neuroscience↗
bioRxiv2025

BabyPy: a brain-age model for infancy, childhood and adolescence

Withdrawal StatementThe authors have withdrawn this manuscript because during the peer-review process, they realised that their interpretation of the brain-age model presented in this paper was not fully accurate. While the analyses, statistics, and results remain valid, their interpretation of the internal test set performance metrics was inaccurate due to the non-linear shape of the distribution. In other words, although the overall R{superscript 2} is correctly reported as 0.80, this value does not capture the variability of the metrics across different age bins. For this reason, the authors are withdrawing the preprint. Therefore, the authors do not wish this work to be cited as reference for the project. The authors aim to re-run the analysis to provide a more robust version of the model and a new DOI will be linked on this page once the revised work is available. If you have any questions, please contact the corresponding author.

neuroscience↗