2010/01/01 by James M. Shine, Oluwasanmi Koyejo, Russell A. Poldrack +1
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · Neuroscience · Psychology · Social Sciences · #Cognition #Cognitive psychology #Computer network #Computer science #Crime Patterns and Interventions #Criminology #Data mining #Differential (mechanical device) #Dynamic functional connectivity #EEG and Brain-Computer Interfaces #Functional Brain Connectivity Studies #Functional connectivity #Law #Longitudinal data #Longitudinal study #Mathematics #Medicine #Nerve net #Network topology #Neural dynamics and brain function #Neuroimaging #Neuroscience #Physics #Political Conflict and Governance #Political science #Psychology #Resting state fMRI #Sociology #Terrorism #Terrorism, Counterterrorism, and Political Violence #Topology (electrical circuits) #q-bio.NC
paper · pdf · open access · doi:10.1073/pnas.1604898113
published in Proceedings of the National Academy of Sciences 113(35), 9888-91 (National Academy of Sciences) · 7 pages, 5 figures
openalex publication_date 2010/01/01 · arxiv created 2016/08/15 · arxiv updated 2017/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience.