Ceren Tozlu

This study uses longitudinal multimodal neuroimaging to analyze changes in brain injury biomarkers over time, revealing dynamic relationships between structural, functional, and PET-derived measures.

The paper uses TSPO-PET to show higher inflammation in white matter disrupted by paramagnetic rim lesions in MS patients, linked to disability.

This study finds that cocaine use disorder is linked to reduced brain state transition energy in specific networks, particularly those involved in excitatory and higher-order functions.

This study uses Network Control Theory to analyze differences in brain energy landscapes between MCI patients and healthy controls, finding lower transition energy in the limbic network and reduced global entropy in MCI.

This study uses estimated structural and functional connectivity networks from MRI and AI to predict cognition in multiple sclerosis patients, finding strong predictive accuracy for follow-up scores.

This study examines sex-specific brain activity dynamics in youth with and without a family history of substance use disorder, finding differences in transition energy across brain networks.

The paper introduces Krakencoder, a tool that translates and fuses structural and functional brain connectomes across atlases and processing strategies, achieving higher identifiability than existing models.

The paper identifies early structural disconnection biomarkers in multiple sclerosis linked to disability and cognitive impairment progression.

This study examines how brain lesion volume in multiple sclerosis patients correlates with changes in brain activity dynamics and entropy.

This paper compares models using observed and estimated connectivity networks to classify multiple sclerosis patients into disability groups, finding estimated networks perform better.

The paper develops a patient-specific model to predict disability evolution in MS using clinical and imaging data, finding the clinical model performs better than imaging models.

The paper compares five classification methods for identifying high-risk infarction tissue in ischemic stroke using MRI data, finding no significant difference in precision-recall performance.

This study uses machine learning to predict motor improvement in chronic stroke patients after therapy, finding that pre-intervention assessments and motor thresholds are key predictors.