In particular, analyses revealed three TNS that shared cortical midline overlap with the default mode network (DMN), but these "complex" DMN states also encompassed distinct regions that fall beyond the prototypical DMN, suggesting that the DMN defined using static methods may represent the average of distinct complex-DMN states. ![]() ![]() These TNS spatially overlapped with prototypical resting state networks, but also diverged in notable ways. To fill this gap, two independent resting state scans collected in 462 healthy adults from the Human Connectome Project were evaluated using coactivation pattern analysis to identify (eight) TNS that recurred across participants and over time. In light of these new approaches, there is a need to classify common transient network states (TNS) in terms of their spatial and dynamic properties. Resting-state analyses evaluating large-scale brain networks have largely focused on static correlations in brain activity over extended time periods, however emerging approaches capture time-varying or dynamic patterns of transient functional networks.
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