Neural dynamics at rest associated with patterns of ongoing thought

Abstract

Conscious experience is dynamic, and its fluidity is particularly marked when attention is not occupied by events in the external world and our minds are free to wander. Our study used measures of neural function, and advanced analyses techniques to examine how unconstrained neural state transitions relate to patterns of ongoing experience. Neural activity was recorded during wakeful rest using functional magnetic resonance imaging and Hidden Markov modelling identified recurrent patterns of brain activity constituting functional dynamic brain states. Individuals making more frequent transitions between states subsequently described experiences highlighting problem solving and lacking unpleasant intrusive features. Frequent switching between states also predicted better health and well-being as assessed by questionnaire. These data provide evidence that the fluidity with which individuals shift through dynamic neural states has an impact on the nature of ongoing thought, and suggest that greater flexibility at rest is an important indicator of a healthy mind.William James (James, 1890) emphasised experience unfolds dynamically over time, 2 using the analogy of a "stream of consciousness". The fluidity of experience is clearly 3 illustrated by the fact that our attention tends to flit from topic to topic, particularly 4 when we are not focused on events in the external world (Smallwood and Schooler, 2006, 5

Dynamic brain network configurations during rest and an attention task with frequent occurrence of mind wandering

“…While the probes are part of the task and an indication of whether the participant is paying attention, we acknowledge that including them in the analyses may have added some additional confounding elements related to memory and decision‐making processes. Finally, because of the general limitations of the dichotomous nature of collecting MW reports used herein, specific investigation of the brain mechanisms of MW as a function of the content of “off task” reports is beyond the scope or capability of this methodology (Karapanagiotidis et al, ; Sormaz et al, for focus on the content of MW).…”

“…Time‐varying functional connectivity analyses have been used in few resting‐state and/or task‐based studies related to MW (Karapanagiotidis et al, ; Kucyi & Davis, ; Mooneyham et al, ). For example, Kucyi and Davis () investigated both static and dynamic functional connectivity of the DN during rest and a painful stimulation task with MW probes.…”

Static and Dynamic Aspects of Cerebro-Cerebellar Functional Connectivity are Associated with Self-reported Measures of Impulsivity: A Resting-State fMRI Study

“…A first limitation, we believe, is the use of a low-model order parcellation that only included bilateral large-scale RSNs both at the cerebral and cerebellar levels. Although this choice served our goals and interests, it was based on the fact that the HMM method is prone to over-parametrise as a function of dimensionality and may not converge reliably when the number of considered regions becomes larger (Karapanagiotidis et al, 2018;Vidaurre et al, 2017). Developing new methods that can reliably process high-dimensional data is needed in order to overcome this limitation.…”

“…Particularly, we performed an additional iteration of the Bayesian inference process at the subject-level, given the initial group-level estimates and the subjects' BOLD data as The rationale behind this approach was based on the observation that static FC matrices across subjects highly resembled the frequency-weighted mean of the dynamic FC matrices: cosine similarity > 0.98 for full correlation matrices and > 0.94 for partial correlation matrices (see supplementary figure S4). Therefore, static FC could be considered as a superposition of dynamic FC states identified via HMM, as was also observed by Karapanagiotidis et al (2018). In other words, the dynamic FC Assessing the robustness of FC dynamics In order to confirm the presence of robust and genuine dynamic FC patterns in the rs-fMRI data, we generated 100 null datasets from a multivariate Gaussian distribution fitted to the real data of each individual subject as in (Vidaurre et al, 2017).…”

Human brain function during pattern separation follows hippocampal and neocortical connectivity gradients

“…As a main principle of functional cortical organization, gradients have also been leveraged to interrogate structure-function coupling (Huntenburg, Bazin et al 2017), brain development (Ball, Seidlitz et al 2019, Lariviere, Vos de Wael et al 2019, aging (Lowe, Paquola et al 2019, and disease effects , Tian, Zalesky et al 2019 . Furthermore, gradients have proven useful in assessing distributed patterns of task-related neural activity underlying cognitive functions (Karapanagiotidis, Vidaurre et al 2018, Murphy, Jefferies et al 2018, Wang, Margulies et al 2020. These findings suggest that gradientbased analysis may advance our understanding of principal organizational axes underlying neural function.…”