Short‐time windows of correlation between large‐scale functional brain networks predict vigilance intraindividually and interindividually

Abstract

A better understanding of how behavioral performance emerges from interacting brain systems may come from analysis of functional networks using functional magnetic resonance imaging. Recent studies comparing such networks with human behavior have begun to identify these relationships, but few have used a time scale small enough to relate their findings to variation within a single individual's behavior. In the present experiment we examined the relationship between a psychomotor vigilance task and the interacting default mode and task positive networks. Two time-localized comparative metrics were calculated: difference between the two networks' signals at various time points around each instance of the stimulus (peristimulus times) and correlation within a 12.3-s window centered at each peristimulus time. Correlation between networks was also calculated within entire resting-state functional imaging runs from the same individuals. These metrics were compared with response speed on both an intraindividual and an interindividual basis. In most cases, a greater difference or more anticorrelation between networks was significantly related to faster performance. While interindividual analysis showed this result generally, using intraindividual analysis it was isolated to peristimulus times 4 to 8 s before the detected target. Within that peristimulus time span, the effect was stronger for individuals who tended to have faster response times. These results suggest that the relationship between functional networks and behavior can be better understood by using shorter time windows and also by considering both intraindividual and interindividual variability.

Ongoing dynamics in large-scale functional connectivity predict perception

“…This effect was expressed in both decreased within-module degree (each node's connectivity within its own module) in a variety of regions and widespread increase in across-module participation coefficient (each node's connectivity to other modules) before misses. These whole-brain results expand previous observations in prestimulus functional connectivity confined to task-relevant regions (15) or networks (19).…”

“…To investigate ongoing nontask-locked changes in baseline connectivity, we minimized contributions from stimulus-evoked activity. This study, therefore, extends beyond important previous investigations of connectivity dynamics that occur after changes in the external environment (such as stimulation, cues, instructions, or feedback) at infraslow (17)(18)(19)(20) and fast electrophysiological timescales (21,22). Specifically, we asked (i) whether ongoing dynamics of large-scale functional connectivity relate to perceptual performance and (ii) which properties of baseline functional connectivity distinguish brain states that support perceptual accuracy from those that do not.…”

Dynamic Properties of Functional Connectivity in the Rodent

“…Two possible approaches suggest themselves: first, linking the dynamic patterns to an external measurement of behavior; and second, finding a direct neural analog of the changes in functional connectivity. The first of these possibilities is addressed in part by a recent paper which shows that in human subjects, network relationships within a short 12 second window before the onset of a psychomotor vigilance task predict performance on that task (Thompson et al, 2012), a finding that is consistent with previous work linking behavioral variability to functional connectivity and network activity calculated from entire scans (e.g., Fox et al, 2007;Kelly et al, 2008;Li et al, 2007). The second possibility is appealing in that it would directly link the BOLD signal to electrical measures of neural activity, but it is difficult to perform in humans.…”

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

“…Most commonly, the latter has been addressed by looking for correlations between the extracted indexes of connectivity and some behavioral measure ([e.g. Raz et al, 2012; Thompson et al, 2013; see also Krishnan et al, 2011] for a review of Partial Least Squares [PLS] methods, including behavior‐PLS and task‐PLS that can test for relationships between inter‐regional patterns of activity and behavior/task).…”

“…For example, using short time‐windows, Thompson et al showed that the level of (anti‐)correlation between “the default mode network” and the “task positive network” in a 12.5 s window centered just before the target onset predicted the speed of target detection on a trial‐by‐trial basis [Thompson et al, 2013]. Recently, Raz et al used 30 s sliding‐windows to extract dynamic patterns of inter‐regional connectivity during movie watching [Raz et al, 2012].…”