Mariam Zabihi

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.

The paper establishes a normative reference for large-scale human brain dynamics using resting-state fMRI data from over 10,000 individuals, revealing systematic reorganization across development and ageing and disorder-specific deviations in mental health conditions.

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.

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

This work develops normative models of brain growth and aging using neuroimaging data to quantify variability across disorders and improve case-control inferences.

This paper introduces a standardized protocol for normative modeling in computational psychiatry using the PCNtoolkit, enabling analysis of biological heterogeneity without relying on case-control comparisons.

The paper learns interpretable latent representations of task-fMRI data from large-scale datasets, capturing demographic and behavioral variability through a three-dimensional autoencoder and normative modeling.

The paper identifies atypical brain asymmetry in autism as a potential neurophenotype for stratification, showing individualized patterns linked to symptom severity and language delay.

This study uses normative models to analyze cortical thickness deviations in ASD, finding individualized patterns rather than common case-control differences.