Stamatios N. Sotiropoulos

DHARM is an open-source tool that diagnoses, harmonizes, and evaluates batch effects in MRI data using standardized workflows and multivariate diagnostics.

The paper integrates 3D light-sheet microscopy with diffusion MRI to predict cellular features from MRI data across multiple mouse strains.

This paper presents high-resolution diffusion MRI techniques for in vivo and ex vivo macaque brain imaging at 10.5 Tesla, enabling detailed connectivity studies.

The paper develops an RF coil array system for high-resolution in vivo and ex vivo non-human primate brain imaging at 10.5 Tesla, achieving improved spatial resolution and imaging capabilities.

This paper develops a VAE-based framework to translate mouse brain gene expression to human, showing that mouse disease models predict human Alzheimer's and Parkinson's patterns and identify the best mouse model for AD patients.

The paper develops an offline dMRI reconstruction pipeline and compares PCA-based and AI-based denoising methods on high-resolution dMRI data.

This paper evaluates the diffusion-weighted steady-state free precession (DW-SSFP) sequence for ex vivo macaque brain imaging at 10.5T, showing improved SNR efficiency and geometric distortion reduction.

This paper introduces standardized tractography protocols for mapping cortico-subcortical tracts in human and macaque brains, showing robustness and applicability across species.

This study compares white matter connectivity in two anthropoids and one strepsirrhine using diffusion MRI, finding greater dorsal prefrontal innervation in anthropoids.

This paper introduces an SBI framework for diffusion MRI analysis, showing it provides fast and accurate parameter estimation and uncertainty mapping compared to classical Bayesian methods.

The paper compares white matter organization between humans, chimpanzees, and macaques, identifying uniquely human connectivity profiles in areas related to language and abstract reasoning.

The paper investigates connectivity patterns in the human and monkey brain, identifying changes in fornix-related connections associated with Mild Cognitive Impairment and Alzheimer's Disease.

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.

The paper evaluates the stability of CCA and PLS in high-dimensional datasets, finding instability in small samples and proposing a power calculator for reliable multivariate analysis.

The paper develops a biophysically-based circuit model to capture individual variation in cortical functional connectivity using resting-state fMRI data from the Human Connectome Project.

This paper evaluates dMRI denoising methods by defining performance criteria and comparing patch-based algorithms in complex and magnitude domains.

The paper develops a resource to evaluate MRI harmonisation approaches by comparing variability across scanners, within-scanners, and biological differences using multimodal data from multiple sites.

The study quantifies corticothalamic connectivity patterns in humans and macaques, revealing distinct spatial extents associated with sensorimotor and associative cortical areas.

QuNex provides a platform for reproducible neuroimaging analytics, integrating multi-modal tools and enabling scalable, high-throughput processing across species and datasets.

This paper introduces a framework to study brain ontogeny and phylogeny by integrating connectivity maps across species and developmental stages.

The paper shows that enriched environments reduce white matter dispersion in the right FPN, improving alertness and preventing age-related atrophy.

This study examines the relationship between sleep quality and brain ageing using neuroimaging data, finding that poor sleep is associated with accelerated brain ageing.

This paper introduces an algorithm to reduce tractography biases in white matter by modeling fibre density and orientation as a divergence-free vector field, improving cross-modal agreement with functional connectomes.

The paper examines structural and functional connectivity changes in a mouse model of vascular cognitive impairment, finding altered network connectivity and behavioural deficits.

This paper develops a data-driven framework using non-negative matrix factorisation to map structural connections in the neonatal brain and generates a cortical parcellation from 323 newborns.

The paper optimizes multi-shell diffusion MRI acquisition parameters using a data-driven approach, resulting in a specific protocol for neonatal imaging.

The paper introduces XTRACT, a software package with standardised tractography protocols for automated white matter tract extraction in human and macaque brains, validated across datasets and species.

The paper develops a 24-channel MRI coil and adapts HCP protocols for macaques, enabling high-quality in vivo imaging and preprocessing comparable to human studies.

The paper develops a dynamical circuit model of human cortex incorporating hierarchical heterogeneity to better explain large-scale neural dynamics and functional connectivity.

This paper presents GPU-accelerated frameworks for diffusion MRI applications, achieving significant speedups in microstructure estimation and tractography.