Aristeidis Sotiras

This paper introduces SURF-EPI, a real-time imaging technique that simultaneously captures CSF and neurovascular flow dynamics with high temporal resolution and without cardiac gating.

This study introduces MEDIC, a framewise distortion correction method for multi-echo fMRI that improves alignment and reduces motion impact without requiring field maps.

This study shows that resting-state functional connectivity better predicts sex than cortical thickness in preadolescents and relates to sex/gender alignment.

This study uses multimodal normative modeling on ATN biomarkers to analyze Alzheimer Disease heterogeneity, finding greater intra-group variation in more severe stages and a correlation between disease severity index and cognitive decline.

This study develops a framework to analyze CSF flow patterns using low-b dMRI and identifies factors influencing these patterns across different brain regions.

This paper introduces a multimodal introspective VAE for Alzheimer's Disease normative modeling, improving reconstruction of healthy controls and outlier detection in neuroimaging data.

This study identifies white matter fiber covariance networks in youth, showing their development aligns with executive function and hierarchical cortical maturation patterns.

This paper introduces Synth, a field map-less distortion correction method for fMRI data, showing comparable or better performance than field map-based approaches in pediatric and adult datasets.

The paper uses NMF on MRI data to identify white matter maturation patterns in newborns, showing distinct microstructural and histological features.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

This study uses a data-driven method to show that alcohol use in adolescents affects cortical thinning differently across age groups.

This study identifies three neurocognitive subtypes of depression in youth using machine learning, showing distinct neural signatures despite similar clinical symptoms.

This study identifies two neurobiologically distinct subtypes of youth with internalizing symptoms using structural brain imaging and machine learning.

This study links accelerated cortical thinning in structural brain networks to irritability in youth, highlighting associations in emotion-related brain regions.

The paper examines the association between cortical thickness and impulsive choice in adolescents, finding reduced cortical thickness in specific brain regions linked to impulsive decision-making.