Ilya Milos Veer

Inhibitory control and error processing in Obsessive-Compulsive Disorder: A mega-analysis of task-based fMRI data by the ENIGMA-OCD consortium

This study analyzes fMRI data from OCD patients and controls to identify age-specific differences in brain activation during inhibitory control and error processing.

Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis

IBMMA improves neuroimaging analysis by handling missing data and large datasets, revealing new brain region findings overlooked by traditional methods.

Executive control in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium

This study analyzes fMRI data from OCD patients and controls to identify neural activation patterns during executive control tasks, finding reduced activation in frontoparietal regions and increased default mode network activity in OCD.

A Fast And Intuitive Method For Calculating Dynamic Network Reconfiguration And Node Flexibility

The paper introduces a computationally efficient method to measure brain network flexibility using fixed modular frameworks, showing it matches results from a data-driven method with less computational cost.

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data

HALFpipe provides a reproducible, interactive, and efficient analysis pipeline for fMRI data, supporting preprocessing, post-processing, and quality assessment.

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The paper investigates how changes in resting-state functional connectivity predict depression relapse after antidepressant discontinuation, finding that increased connectivity between the dorsolateral prefrontal cortex and parietal cortex is associated with relapse risk.

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

This paper examines brain aging in major depressive disorder (MDD) using a large dataset, finding that MDD patients show increased brain age differences compared to controls.

Subcortical Shape Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.