Patterns of thought: Population variation in the associations between large-scale network organisation and self-reported experiences at rest

Hao-Ting WangJonathan Smallwood


Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whether dimensions of population variation in different modes of unconstrained processing can be described by the associations between patterns of neural activity and self-reports of experience during the same period. We selected 258 individuals from a publicly available data set who had measures of resting-state functional magnetic resonance imaging, and self-reports of experience during the scan. We used machine learning to determine patterns of association between the neural and self-reported data, finding variation along four dimensions. 'Purposeful' experiences were associated with lower connectivity - in particular default mode and limbic networks were less correlated with attention and sensorimotor networks. 'Emotional' experiences were associated with higher connectivity, especially between limbic and ventral attention networks. Experiences focused on themes of 'personal importance' were associated with reduced functional connectivity within attention and control systems. Final...Continue Reading


Jun 13, 2020·Journal of Cognitive Neuroscience·Erin J Wamsley, Theodore Summer
Sep 5, 2020·Network Neuroscience·Deniz VatanseverJonathan Smallwood
Dec 15, 2020·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Theodoros KarapanagiotidisJonathan Smallwood
Mar 6, 2021·IScience·Jonathan SmallwoodElizabeth Jefferies
Apr 24, 2020·Cortex; a Journal Devoted to the Study of the Nervous System and Behavior·Adam TurnbullJonathan Smallwood

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