Kwangsun Yoo

Connectome-based Predictive Models of General and Specific Executive Functions

This study uses connectome-based predictive modeling to predict individual differences in three executive function components using fMRI data from the Human Connectome Project.

Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity

The paper defines the parietal memory network (PMN) using high-resolution fMRI and finds it extends beyond traditional regions to include multiple cortical areas.

High performers demonstrate greater neural synchrony than low performers across behavioral domains

The paper examines brain-behavior relationships using fMRI data and finds that neural similarity does not always correlate with behavioral similarity across different domains.

Double dissociation of sensorimotor interaction mediated by visual feedback during continuous de novo motor learning

This study investigates how visual feedback influences sensorimotor interactions during motor learning, finding a double dissociation in brain activity and reduced connectivity between somatosensory and visual cortices.

Edge-based general linear models capture high-frequency fluctuations in attention

The paper uses edge-based general linear models to analyze fMRI data and identifies high-frequency network fluctuations related to attention.

Connectome-based predictions reveal developmental change in the functional architecture of sustained attention and working memory

The paper shows that functional brain networks predict sustained attention and working memory in children and adults, with WM networks changing more from preadolescence to adulthood.

A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

This paper introduces a connectome-to-connectome state transformation model to quantify brain reorganization across cognitive states using fMRI data.

A brain-based universal measure of attention: predicting task-general and task-specific attention performance and their underlying neural mechanisms from task and resting state fMRI

The paper develops a universal attention measure from fMRI data that predicts both task-general and task-specific attention performance and their neural mechanisms across multiple tasks.

A connectome-based prediction model of long-term memory

This study develops a whole-brain functional connectivity model using fMRI data to predict individual differences in long-term memory, showing it is distinct from working memory networks.