Biology subjects

Bruchhage, M. M. K.

Publications and source records attributed to Bruchhage, M. M. K..

4 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

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↗

bioRxiv2020

The Development of Cognitive Control in Children with Autism Spectrum Disorders or Obsessive-Compulsive Disorder: A Longitudinal fMRI study

Repetitive behavior is a core symptom of Autism Spectrum Disorder (ASD) and Obsessive-Compulsive Disorder (OCD), and has been associated with impairments in cognitive control. However, it is unclear how cognitive control and associated neural circuitry relate to the development of repetitive behavior in children with these disorders. In a multicenter, longitudinal study (TACTICS; Translational Adolescent and Childhood Therapeutic Interventions in Compulsive Syndromes), the development of cognitive control was assessed during late childhood using a longitudinal fMRI design with a modified stop-signal task in children with ASD or OCD, and typically developing (TD) children (baseline: N=122 (8-12y), follow-up: N=72 (10-14y), average interval: 1.2y). Stop-signal reaction time (SSRT) decreased over development, regardless of diagnosis. Repetitive behavior in children with ASD and OCD was not associated with performance on the stop-signal task. There were no whole-brain between-group differences in brain activity, but ROI-analyses showed increases in activity in right precentral gyrus over development for children with OCD. In sum, even though subtle differences were observed in the development of brain activity in children with OCD, the findings overall suggest that the development of cognitive control, as assessed by the stop signal task, is similar in children with and without ASD or OCD.

neuroscience↗

bioRxiv2020

Gut microbial genes are associated with neurocognition and brain development in healthy children

The gastrointestinal tract, its resident microorganisms, and the central nervous system are connected by bio-chemical signaling, also known as the "microbiome-gut-brain-axis." Both the human brain and the gut microbiome have critical developmental windows in the first years of life, raising the possibility that their development is co-occurring and likely co-dependent. Emerging evidence implicates gut microorganisms and microbiota composition in cognitive outcomes and neurodevelopmental disorders (e.g., autism and anxiety), but the influence of gut microbial metabolism on typical neurodevelopment has not been explored in detail. We investigated the relationship of the microbiome with the neuroanatomy and cognitive function of 361 healthy children, demonstrating that differences in gut microbial taxa and gene functions are associated with overall cognitive function and with differences in the size of multiple brain regions. Using a combination of multivariate linear and machine learning (ML) models, we showed that many species, including Gordonibacter pamelae and Blautia wexlerae, were significantly associated with higher cognitive function, while some species such as Ruminococcus gnavus were more commonly found in children with low cognitive scores after controlling for sociodemographic factors. Microbial genes for enzymes involved in the metabolism of neuroactive compounds, particularly short-chain fatty acids such as acetate and propionate, were also associated with cognitive function. In addition, ML models were able to use microbial taxa to predict the volume of brain regions, and many taxa that were identified as important in predicting cognitive function also dominated the feature importance metric for individual brain regions. For example, B. wexlerae was the most important species in models predicting the size of the parahippocampal region in both the left and right hemispheres, while several species from the phylum Bacteroidetes, including GABA-producing B. ovatus, were important for predicting the size of the left accumbens area, but not the right. These findings provide potential biomarkers of neurocognition and brain development and may lead to the future development of targets for early detection and early intervention.

microbiology↗