Seonjoo Lee

The paper identifies group-specific plasma proteomic signatures in Alzheimer's disease stages and links them to regional brain structural changes using machine learning and neuroimaging.

This study examines how apathy progresses in Alzheimer's disease and related dementias, finding that FTLD and HS are consistently linked to higher apathy odds, with sex-specific differences.

This study identifies two distinct cortical thickness subtypes in youth exposed to prenatal gestational diabetes, each associated with different neuroanatomical and clinical features.

The paper evaluates age and sex effects on regional functional brain network reconfiguration during childhood and adolescence using resting-state fMRI data.

The paper develops a 3D brain assembloid model to study Alzheimer's disease progression and identifies IGFBP pathways as therapeutic targets in APOE4 carriers.

The paper examines sex-specific effects of anxiety on cognition and neural networks in mice and humans, finding earlier cognitive decline in female AD mice and faster dementia progression in anxious female humans.

This paper introduces explainable variational autoencoders to interpret nonlinear patterns in resting state fMRI data from autism spectrum disorder and control groups.

The paper examines intrinsic neural timescales in AD and MCI using rs-fMRI data, finding altered hierarchical gradients and potential biomarker value for AD prediction.

This study finds that maternal childhood adversity is linked to accelerated epigenetic aging in male newborns but not females, using multiple epigenetic clocks.

This paper classifies PTSD vs. controls using MRI data from multiple sites, finding better performance when distinguishing PTSD from trauma-unexposed controls than trauma-exposed controls, and showing DVAE improves generalizability.

This study uses persistent homology-based functional connectivity to analyze resting-state brain networks and finds it better explains cognitive ability variance than traditional measures.

This paper uses a sliding-window clustering method to analyze age-related changes in resting-state functional connectivity (FC) from fMRI data, identifying sex-specific developmental patterns.

This paper develops a deep learning model to quantify cognitive reserve using structural MRI data from healthy cohorts and validates its generalizability across different populations and scanners using transfer learning.

This study examines how dynamic neural flexibility during rest and task-based scans relates to age and cognitive performance in adults, finding task-based flexibility is more strongly linked to cognitive outcomes.

This study assesses intra-session test-retest reliability of functional connectivity in infants, finding poor edge-level reliability but moderate-to-good subject-level reliability for major networks.

This study examines age-related differences in cognitive abilities using fMRI and diffusion imaging, finding that vocabulary-related brain regions are better maintained with age than fluid abilities.

This paper uses multimodal MRI and machine learning to classify Alzheimer's disease and mild cognitive impairment, showing high accuracy in diagnosis and progression prediction.

This paper uses machine learning on brain morphometry and connectomes from MRI to predict Alzheimer's disease risk and progression, achieving high accuracy in both single-site and multi-site datasets.