Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI

Abstract

The temporal structure of self-generated cognition is a key attribute to the formation of a meaningful stream of consciousness. When at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 22sec. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective enhanced activity in temporal, parietal and occipital areas during the memory compared to the mental arithmetic condition, evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts, previously associated to "scene-reconstruction" and "self-experience" subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in the memory and in the mental arithmetic condition, respectively. EEG source analysis revealed similar spatial distributions of the sources of these microstates and the regions identified with fMRI. These findings imply a functional link between BOLD activity changes in regions related to a certain mental activity and the temporal dynamics of mentation, and support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring brief periods of integrated coherent neuronal activity, lasting only fractions of seconds.

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…In contrast to microstate temporal dynamics, the topographic configurations of microstates are strikingly consistent across published reports (Michel and Koenig, 2018). For the present study, microstate clusters A through E matched canonical patterns found in the literature (Michel and Koenig, 2018), including those observed in studies using data-driven approaches to determine an optimal number of clusters (Bréchet et al, 2019;Custo et al, 2017). Together, these five clusters explained 85% of the variance among subject-level cluster centroids, and 63% of the topographic variance (on average) seen in participants' resting EEG recordings.…”

“…Cognitive performance was also predictive of microstate temporal parameters. Microstate duration was positively associated with psychomotor alertness (i.e., faster reaction times) for configurations A and B, and positively associated with response compatibility (i.e., greater response interference) for configurations A, B, C, and E. In prior work, the neural generators of microstates A and B have been linked to the spontaneous activity of visual and auditory sensory networks, respectively (Bréchet et al, 2019;Britz et al, 2010;Custo et al, 2017). As such, our findings would appear to indicate that the speeded responding and detection of target stimuli is facilitated by individual differences in the tendency for sensory networks to predominate at rest.…”

“…Despite their topographic similarity across individuals and studies (Michel and Koenig, 2018); their association with fMRI-derived resting state networks (Bréchet et al, 2019;Britz et al, 2010;Custo et al, 2017); and the clear neurophysiological interpretation of microstate temporal parameters (Khanna et al, 2015;Murray et al, 2008)-the functional significance of microstates remains largely unknown in the resting state context. Ultimately, careful phenomenological investigation and experimental manipulation of conscious cognitive acts will be needed to better understand the millisecond fluctuations of microstates and their real-time associations with felt experience and cognition (Varela, 1996).…”

“…After 100 iterations, the set of centroids with maximal GEV were identified. For each individual recording, across all levels of k, the optimal number of k clusters was then selected from these sets of maximal GEV centroids, as determined using a metacriterion defined by 7 independent optimization criteria (see Custo et al, 2017;Bréchet et al, 2019). Figure 1 provides a schematic depicting the selection of voltage maps for clustering at GFP maxima from 1 second of eyes closed data from a random participant, and the result of the k-means clustering procedure for one full 8 minute recording.…”

“…The topographic configurations of clusters of resting EEG microstates are remarkably consistent within and between individuals, and the same clusters have been commonly identified across studies. Moreover, the electrical brain sources of microstates have been suggested to align in part with common fMRI-derived resting state functional networks (Bréchet et al, 2019;Britz et al, 2010;Custo et al, 2017), making microstate analysis a relevant and complementary approach to fMRI techniques for defining the spontaneous organization of the brain.…”