Godfrey Pearlson

Jan 14, 2025 | biorxiv.org | qiang li |Jingyu Liu |Godfrey Pearlson |Jiayu Chen

AbstractPsychotic disorders, such as schizophrenia and bipolar disorder, pose significant diagnostic challenges with major implications on mental health. The measures of resting-state fMRI spatiotemporal complexity offer a powerful tool for identifying irregularities in brain activity. To capture global brain connectivity, we employed information-theoretic metrics, overcoming the limitations of pairwise correlation analysis approaches.

Oct 30, 2024 | biologicalpsychiatryjournal.com | Godfrey Pearlson

CommentaryVolume 97, Issue 2p102-103 1Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 2Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut 3Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut Publication History: Received October 30, 2024; Revised October 31, 2024; Accepted November 1, 2024 DOI: 10.1016/j.biopsych.2024.11.001Also available on ScienceDirect Copyright: © 2024 Society of...

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).

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.