Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

Abstract

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

A connectome-based prediction model of long-term memory

“…Our finding that the LTM model extends beyond ROIs identified in previous studies is also consistent with the recent findings by Keerativittayayut et al(2018), who studied how dynamic FC during encoding captured within-individual fluctuations in memory performance using a classic incidental encoding task and a surprise recognition memory test. They found that within the subsequent memory ROIs, only FC between hippocampus and occipital cortex differed between good and bad encoding states.…”

“…Recent work showed that a sustained attention model built to predict attention performance across individuals generalized to predict the temporal fluctuations of attention within the same individual, demonstrating that the FC-based sustained attention model captures a combination of trait-like and state-like sustained attention (Rosenberg et al, 2020). The similarities in the LTM-related features identified by Keerativittayayut et al(2018) and our study suggest that the LTM network in our study may also contain both features that underlie trait-level differences in LTM and those that underlie state-level fluctuations. Future work can directly compare the within-and between-subject LTM networks to delineate the neural sources of within-and between-individual differences in LTM performance.…”

“…Why is this the case? Although there have been a rich body of work on the neural mechanism underlying within-subject fluctuations in LTM performance, such as the subsequent memory effect (Keerativittayayut et al, 2018;Kim, 2011;Paller & Wagner, 2002;Wagner et al, 1998), to date, only a few studies have investigated the neural basis of individual differences in LTM performance across subjects (Jangraw et al, 2018), most of which focused on the aging population (Meskaldji et al, 2016;Trelle et al, 2020). However, even among young adult population, there remains a large variance in LTM performance (for a recent review, see Unsworth, 2019).…”

Flexible network community organization during the encoding and retrieval of spatiotemporal episodic memories

“…A common finding from studies of task-related functional networks is that they deviate somewhat in structure compared with those derived from resting state (Bolt et al, 2017; Cohen & D’Esposito, 2016; Keerativittayayut et al, 2018; Spadone et al, 2015). Furthermore, some studies suggest that functional subnetworks, and even single regions within those subnetworks, can exhibit a large range of connectivity patterns over time (Braun et al, 2015; Inman et al, 2017) and over different tasks (Bassett et al, 2011; Cohen & D’Esposito, 2016; Spadone et al, 2015).…”

Linking flexibility of brain networks to cognitive development in preschool children

“…Importantly, numerous studies in adults have shown evidence for the dynamic nature of FC characterized by the variations of connection strengths, the sign of interactions or changes in node membership among modules Hutchison et al (2013) during task conditions as well as rest Calhoun et al (2014). Further such transient nature of brain network is reported to increase over development Chai et al (2017), and directly related with learning Bassett et al (2011), task performance Shine et al (2016); Keerativittayayut et al (2018), executive functions Medaglia et al (2018), and in general, with healthy cognitive functioning Thomas Yeo et al (2011) including general intelligence Barbey (2018) and creativity Li et al (2017); Sun et al (2018)…”