Zening Fu

Jul 30, 2024 | nature.com | Zening Fu |Jingyu Liu

AbstractChildren’s brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age.

Jul 26, 2024 | digitalcommons.library.tmc.edu | Krishna Pusuluri |Zening Fu |Robyn Miller |Godfrey Pearlson

AbstractDespite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks.

Jul 24, 2024 | onlinelibrary.wiley.com | Krishna Pusuluri |Zening Fu |Robyn Miller |Godfrey Pearlson

1 INTRODUCTION Resting-state functional magnetic resonance imaging (rsfMRI) investigates spontaneous neural activity indirectly via blood-oxygen-level-dependent (BOLD) signal (Matsui et al., 2016; Schwalm et al., 2017).

Nov 5, 2023 | nature.com | Zening Fu |Jingyu Liu

AbstractBrain functional connectivity (FC) derived from functional magnetic resonance imaging has been serving as a potential ‘fingerprint’ for adults. However, cross-scan variation of FC can be substantial and carries biological information, especially during childhood. Here we performed a large-scale cross-sectional analysis on cross-scan FC stability and its associations with a diverse range of health measures in children.

Sep 18, 2023 | biorxiv.org | Zening Fu |Robyn Miller |Godfrey Pearlson |Krishna Pusuluri

AbstractDespite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks.