André F. Marquand

This paper uses a semi-supervised autoencoder to create multivariate normative models of brain structure and behaviour in children, showing associations between structural deviations and cognitive/behavioural differences.

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

This paper develops a lifespan normative model of brain white matter microstructure using diffusion MRI data from 54,583 individuals, enabling detection of microstructural deviations in diseases like Alzheimer's and 22q11.2 deletion syndrome.

The paper uses longitudinal neuroimaging and behavioural data to show how brain-behaviour relationships shift from externalising to internalising symptoms across adolescence, identifying age 14 as a critical transition point.

This paper develops voxelwise normative models of brain morphometry across the lifespan and applies them to study brain development after preterm birth and neurodegenerative disorders.

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

The paper introduces FUNCOIN, a whole-brain normative model for functional connectivity, and shows it detects abnormal patterns in bipolar disorder and Parkinson's disease better than localized models.

The paper examines how increasing training sample size improves the calibration of diffusion tensor imaging normative models using hierarchical Bayesian regression.

SUITPy improves cerebellar imaging analysis by isolating the cerebellum with a U-Net model, enhancing alignment and reducing contamination from surrounding cortical structures.

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 DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

This study maps cerebellar volumetric growth from infancy to young adulthood and links it to cerebral development and socio-linguistic behaviors.

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.

MEGaNorm models lifespan trajectories of resting-state MEG brain oscillations, revealing disease-related abnormalities in Parkinson's disease patients.

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

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

This study uses normative modeling to analyze cortical thickness deviations in MDD patients and healthy controls, finding large overlaps and significant heterogeneity in extreme deviations across individuals.

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.

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 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 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 develops population-wide cerebellar growth models using neuroimaging data, revealing an anterior-posterior growth gradient linked to cognitive and motor development.

The paper investigates whether simple trait measures correlate with striatal dopamine synthesis capacity and finds no significant correlations.

This paper expands a normative model repository to include lifespan trajectories of brain structure and function, showing their advantage over raw data in neuroimaging tasks.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.