Seyed Mostafa Kia

Protocol Update: The Normative Modelling Paradigm for Computational Psychiatry

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

Lifespan Normative Modeling of Brain Microstructure

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.

Accelerated neural aging as a systems-level vulnerability for tinnitus

This study identifies accelerated neural aging as a risk factor for tinnitus by analyzing MEG and structural imaging data from multiple datasets.

A normative reference for large-scale human brain dynamics across the lifespan

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.

Distributional and Centile Calibration of Diffusion Tensor Imaging Normative Models as Training Sample Sizes Increase to 40,000 Subjects

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

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

GEDAI improves EEG denoising by removing artifacts without clean references, outperforming existing methods in simulations and enhancing neurobehavioral predictions.

MEGaNorm: Normative Modeling of MEG Brain Oscillations Across the Human Lifespan

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

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

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

Non-Gaussian Normative Modelling With Hierarchical Bayesian Regression

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

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

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.

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

This paper develops a hierarchical Bayesian model to account for site effects in neuroimaging data, showing improved predictive performance compared to harmonization techniques.

Charting Brain Growth and Aging at High Spatial Precision

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

The Normative Modeling Framework for Computational Psychiatry

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.

Normative modeling of neuroimaging data using generalized additive models of location scale and shape

Generalized additive models for location, scale and shape (GAMLSS) using SHASH distributions beat Gaussian brain normative models on log score and calibration in most of 150 UK Biobank cortical-thickness regions.

Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression

This paper introduces a federated probabilistic framework using hierarchical Bayesian regression for multi-site normative modeling of neuroimaging data, showing improved accuracy and adaptability.

Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets

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.