Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

Abstract

Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in ongoing cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of spontaneous cognitive-task-like processes, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states-commonly used to summarize and interpret resting dFC-can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.

Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

“…Note also that for both mild and aggressive pipelines, pairs of scans from the same day exhibited higher accuracies compared to pairs of scans from different days, which cannot be attributed to potential residuals of nuisance fluctuations ( Figure 3A ). Possible explanations for this finding are that the functional connectome of a subject reflects some aspects of their vigilance levels (Tagliazucchi and Laufs, 2014; Thompson et al, 2013a; Wang et al, 2016), mind-wandering (Gonzalez-Castillo et al, 2019; Gorgolewski et al, 2014; Kucyi, 2018; Kucyi and Davis, 2014), or the effect of time of day (Hodkinson et al, 2014; Jiang et al, 2016; Orban et al, 2020; Shannon et al, 2013), which can differ across sessions. Overall, the high connectome-based identification accuracies reported in the literature do not appear to be driven by nuisance confounds, suggesting a neural origin underpinning the inter-individual differences in connectivity.…”

Neural bases of learning and recognition of statistical regularities

“…Therefore, we could be confident that any differences in neural responses to random and structured blocks were elicited because structured blocks included recognizable structure with extractable and learnable constituents. Contrasting structured blocks to rest would also have introduced the problem of individual variability because mental activity during rest can vary, engaging different mechanisms and different networks …”

“…Contrasting structured blocks to rest would also have introduced the problem of individual variability because mental activity during rest can vary, engaging different mechanisms and different networks. 43 The habituation syllabic stream was prepared by alternating structured and pseudorandom blocks three times. At the end of the stream, we added an additional 36 randomized syllables (each of the 18 syllables from the inventory repeated twice for a total of 8.64 s) and applied a fade-out filter.…”

Predictive modeling of neurobehavioral state and trait variation across development

“…An alternative is to consider state and trait relationships that occur ‘within’ a specific task or scan session. For example, a growing number of studies have focused on time-varying analyses of FC, which investigate how brain function reconfigures itself at the scale of seconds to minutes ( Gonzalez-Castillo et al, 2019 ; Saggar et al, 2018 ; Shine et al, 2016 ). In these studies, the term ‘state’ refers to whole-brain stable FC configurations recurring across participants and time ( Allen et al, 2014 ; Damaraju et al, 2014 ; Gonzalez-Castillo et al, 2015 ; Valsasina et al, 2019 ).…”