Christian F. Beckmann

The paper introduces a standardized protocol for manually aligning structural MR images to the clinically used Schaltenbrand AC-PC plane to improve anatomical consistency and validation of automated methods.

This paper updates the normative modelling protocol for computational psychiatry, including new methods, software tools, and models for neuroimaging data analysis.

The paper uses connectopic mapping on UK Biobank data to analyze variation in language-related brain connectivity and identifies genetic loci associated with these patterns.

This study integrates brain structure and function to identify genetic loci associated with language-related brain components using UK Biobank data.

This study uses longitudinal normative modelling of white matter fractional anisotropy in the EU-AIMS LEAP cohort to show individual deviations in white matter development are linked to clinical variation in autism, with significant cross-sectional and longitudinal brain-behaviour associations.

The paper introduces a multiscale resource integrating genetics, brain imaging, and clinical data from autistic individuals and their relatives to explore genetic and neurobiological patterns in autism.

This paper introduces Independent Filter Analysis (IFA), a supervised method for group discrimination in fMRI that outperforms traditional ICA in detecting group differences.

This paper develops lifespan normative models of white matter fractional anisotropy using diffusion MRI data and applies them to identify white matter deviations in early psychosis.

This paper introduces a framework using extreme value statistics to model extreme deviations from normative neuroimaging models, demonstrating its application to the UK Biobank dataset for risk estimation and anomaly detection.

The paper investigates why large-scale neuroimaging tasks yield weak brain-mental health associations, finding a signal evocation bottleneck in common fMRI tasks.

This paper addresses structured missingness in biomedical data by introducing a hierarchical imputation framework and advocating for alternative evaluation metrics.

This paper introduces polygenic component scores derived from genomICA components to link brain imaging genetics to diverse phenotypes, showing they predict physical and cognitive traits but not mental health outcomes.

This study investigates the neural correlates of dream recall frequency using multimodal neuroimaging in 258 individuals, finding associations between DRF and brain structure and function.

The paper examines white matter volume deviations in psychiatric disorders using normative modeling, finding heterogeneous patterns with some aggregation in autism and schizophrenia.

The paper applies genomICA to GWAS data from brain MRI phenotypes to identify genetic components associated with neurobiological processes and behaviors.

This paper introduces a framework using normative models to assess intra-individual longitudinal changes in neuroimaging data, revealing significant grey matter normalization in schizophrenia patients over time.

This study replicates and extends findings linking social dysfunction to reduced default mode network connectivity in schizophrenia, Alzheimer's disease, and major depressive disorder.

This study establishes normative trajectories of R1, R2*, and magnetic susceptibility in the basal ganglia across healthy ageing using Gaussian Process Regression.

The paper uses unsupervised machine learning to analyze fMRI data from an emotional face matching task, identifying large-scale brain networks and their relation to cognition and mental health.

The paper maps cerebellar anatomical variations across the lifespan and clinical illnesses, revealing heterogeneity in conditions like autism, schizophrenia, and Alzheimer's.

The paper uses normative modeling to analyze task-evoked brain activation in the Emotional Face Matching Task, revealing individual differences and multivariate associations with mental health symptoms.

This paper introduces a normative modelling approach to detect diffusion MRI artefacts and processing errors using UK Biobank data, outperforming traditional visual and quantitative QC methods.

This paper extends hierarchical Bayesian regression to model non-Gaussian data with skewness and kurtosis, showing improved performance on neuroimaging datasets.

This paper uses transfer learning from normative models to estimate cortical thickness trajectories in children across different scanners, showing it can detect pre-term morphological heterogeneity.

This paper introduces genomic principal and independent component analysis to decompose GWAS statistics of brain traits, showing improved reproducibility and interpretability of genetic effects.

This study investigates the effects of long-term sleep disruption on brain biomarkers in maritime pilots and finds no evidence of preclinical Alzheimer's disease markers compared to healthy controls.

This study maps individual brain deviation scores in individuals with pathogenic CNVs using normative modeling and shows correlations between negative deviations and cognitive function.

The paper uses multimodal MRI data to explore brain-behavior relationships, finding some components linked to intoxication and working memory but no significant shared variance between structural and functional organisation.

The paper maps functional connectivity patterns in the somatosensory cortex during rest and emotional tasks in ASD and neurotypical individuals, linking them to adaptive functioning skills.

The paper shows that functional connectivity gradients in resting state networks are present in the human fetal brain from 25 weeks gestation, with spatial consistency increasing in primary sensory and motor networks.