The Neural Basis of Interindividual Variability in Inhibitory Efficiency after Sleep Deprivation

Participants.

Participants were selected from respondents to a web-based questionnaire. They had to (1) be right-handed, (2) be between 18 and 35 years of age, (3) have habitual good sleeping habits (sleeping no less than 6.5 h each night for the past 1 month), (4) score no more than 22 on the Morningness–Eveningness scale (Horne and Ostberg, 1976), (5) not be on any long-term medications, (6) have no symptoms associated with sleep disorders, and (7) have no history of any psychiatric or neurologic disorders. The sleeping habits of all participants were monitored throughout the 2 week duration of the study, and only those whose actigraphy data indicated habitual good sleep (i.e., they usually slept no later than 1:00 A.M. and woke no later than 9:00 A.M.) were recruited for the study after informed consent.

Twenty-seven persons successfully completed this study. They were right-handed, healthy, university undergraduates and graduates (12 females; mean ± SD age, 21.5 ± 1.70 years; range, 19–26 years). All participants indicated that they did not smoke or consume any medications, stimulants, caffeine, or alcohol for at least 24 h before scanning.

Experimental task.

The go/no-go task was based on previous work by Garavan and colleagues (Garavan et al., 2002, 2003; Hester et al., 2004). The letters X and Y were presented in a serial, alternating pattern at 1 Hz. Participants were instructed to make a button press to every letter except when the alternation was interrupted (i.e., they were to withhold the response when there is a repetition of a letter). To ensure a comparable number of stops and errors, the stimulus duration for each participant was determined during prescanning testing (see below).

A mixed design, incorporating block and event-related features, was used (Visscher et al., 2003; Donaldson, 2004). Each run consisted of four fixation blocks interleaved with three task blocks (Fig. 1). The first fixation block lasted 28 s (data for the first 8 s was discarded to allow for steady-state magnetization), whereas the remaining three fixation blocks lasted 20 s. Each task block consisted of 80 trials, of which 72 were go trials and eight were lure trials (i.e., a response was to be withheld). The inter-lure intervals were randomly distributed between 4, 6, 8, and 10 s (with an average of 7.5 s).

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Figure 1.

Schematic representation of the go/no-go task and the timing parameters of a single run. Participants performed four runs of the task in the scanner during each scanning session. A mixed design incorporating block and event-related features was used. Blocks of fixation were interspersed with task blocks. Within each task block, there were eight lure trials (as represented by the arrows) at random intervals of 4, 6, 8, and 10 s. Activation for the blocks represent tonic, sustained activity associated with task performance, whereas event-related activation represented the transient activity associated with stops and errors.

Study procedure.

All participants visited the laboratory three times. They first attended a briefing session during which they were informed of the protocol and requirements of the study and were given extensive practice on the go/no-go task. Each participant completed eight runs of the go/no-go task in which there was a decrement of 100 ms in stimulus presentation duration with each following run. In the first run, each stimulus was presented for 900 ms, followed by an interstimulus interval (ISI) of 100 ms fixation. In the second run, stimulus duration was 800 ms with an ISI of 200 ms fixation, and, in the final practice run, stimulus duration was 200 ms with an ISI of 800 ms fixation. This process ensured that participants were well practiced and additionally generated individualized timing parameters that would subsequently elicit an approximately equivalent number of stops and errors in the in-scanner experiment (Garavan et al., 2002).

The first scanning session took place ∼1 week later. The order of the two sessions (rested wakefulness and sleep deprivation) was counterbalanced across all of the participants to minimize possible effects of practice on brain activation (Van Dongen, 2005b). The 1 week interval between scan sessions (mean ± SD, 7.78 ± 2.36 d) sought to minimize the possibility of residual effects of sleep deprivation on cognition for those participants whose sleep deprivation session had preceded their rested wakefulness session (Van Dongen et al., 2003).

The scans at rested wakefulness took place at 8:00 A.M. For the sleep deprivation session, participants were monitored in the laboratory from 6:30 P.M. onward, and scanning took place at 7:00 A.M. the next morning. At the beginning of the sleep deprivation session, participants completed the Raven's Advanced Progressive Matrices (Raven et al., 1998), the Barrett Impulsiveness Scale-11 (Patton et al., 1995), the Cognitive Failures Questionnaire (Broadbent et al., 1982), and the Sixteen Personality Factor Scale (Cattell et al., 2002). Hereafter, at every hour from 8:00 P.M. to 5:00 A.M., they completed 10 min of the Psychomotor Vigilance Task (Dinges et al., 1997), the Karolinska Sleepiness Scale (Akerstedt and Gillberg, 1990), and a Likert-type rating scale (0–10) of motivation, fatigue, and mood, which were anchored by the terms motivated–unmotivated, fresh–exhausted, elated–depressed, congenial–irritable, relaxed–stressed, and calm–anxious (henceforth referred to as the mood scale). For the remaining time, they were allowed to engage in nonstrenuous activities such as reading and conversing.

Immediately before entering the scanner, participants completed 10 min of the Psychomotor Vigilance Task, the mood scale, and a practice run of the go/no-go task. Ratings on the Karolinska Sleepiness Scale were obtained after the practice run. Every participant was then scanned on four runs of the task. Performance on the scanner task was continuously monitored, and participants were prompted to respond through the intercom system whenever they failed to respond to three consecutive go trials. There was an average of 0.30 prompts given during the rested wakefulness session, and the average number of prompts for the sleep deprivation session was 1.33.

Ratings on the Karolinska Sleepiness Scale were also obtained (using the button box) at the end of each in-scanner run. Self-perceived sleepiness at each state was obtained by averaging the five ratings of sleepiness given on this scale after the five runs of the go/no-go task (one practice run and four in-scanner test runs). Ratings of motivation and mood were similarly derived by averaging the responses before and after each scanning session for each item on the mood scale. The number of lapses on the trial performed just before each scanning session was treated as the index of psychomotor vigilance (Dinges et al., 1997).

Imaging procedure and analysis.

Stimuli were projected onto a screen using a liquid crystal display projector and viewed by participants through a rearview mirror. Participants responded using a button box held in the right hand. A bite bar and foam padding were used to reduce head motion. Images were acquired on a 3T Allegra system (Siemens, Erlangen, Germany). A gradient echo-planar imaging sequence was used with a repetition time of 2000 ms, field of view of 192 × 192 mm, and a 64 × 64 mm pixel matrix. Thirty-two oblique axial slices (3 mm thick with a 0.3 mm interslice gap), approximately parallel to the anterior commissure–posterior commissure line, were acquired. High-resolution coplanar T1 anatomical images were also obtained. For the purpose of image display on Talairach space, an additional high-resolution anatomical reference image was acquired using a three-dimensional magnetization-prepared rapid-acquisition gradient echo sequence.

The functional images were processed using Brain Voyager QX version 1.5.2 (Brain Innovation, Maastricht, The Netherlands). Intra-session image alignment to correct for motion across runs was performed using, as the reference image, the first image of the functional run that was acquired immediately before the coplanar T1-weighted image. Interslice timing differences attributable to slice acquisition order were adjusted using linear interpolation. Gaussian filtering was applied in the spatial domain using a smoothing kernel of 4 mm full-width at half-maximum (FWHM) for individual activation maps and at 8 mm FWHM for group level activation maps. After linear trend removal, a high-pass filter of 160 s was applied. The T1 images were used to register the functional dataset to the volunteers' own three-dimensional image, and the resulting aligned dataset was transformed into Talairach space. The group-level anatomical image was an arithmetical average of the volunteers' structural images.

The functional image data were analyzed for both sessions using a general linear model with one block predictor (task) and two event-related predictors (stops, errors). Because there was an increased tendency for omissions after sleep deprivation (Table 1), only stop trials that were not preceded or followed by an omission were modeled. This was to increase the likelihood that the lure trials without responses were stops as opposed to errors of omission. All predictors were convolved using a canonical hemodynamic response function and analyzed using a mixed-effects model. In this model, modulation of blood oxygenation level-dependent signal at the time of task blocks, relative to signal recorded during periods of fixation, was taken to represent tonic or sustained activation associated with sustained attention required to respond to the go/no-go task. In these task blocks, go trials comprised 90% of the stimuli. Transient or phasic signal changes elicited by successful inhibition associated with stops or errors were modeled as discrete events occurring within the task blocks.

Data analysis.

Inhibitory efficiency was indexed using the intra-individual variability in reaction time on the go trials [intra-individual coefficient of variation (ICV)] (West et al., 2002; Stuss et al., 2003; Castellanos et al., 2005). The appropriateness of ICV as an index of inhibitory efficiency was corroborated by the correlations between ICV during rested wakefulness and other subjective measures of impulsiveness and self-control (supplemental Table 1, available at www.jneurosci.org as supplemental material).

To characterize the effects of sleep deprivation on brain activation, direct contrasts were obtained across the two sessions for event-related changes associated with stops and errors as well as the sustained task-related activation (covering both go and lure trials). Unless otherwise specified, a threshold of p < .001 (uncorrected) was used in the analysis of contrasts in this study.

Vulnerability to sleep deprivation was computed on the basis of the extent of an individual's change in inhibitory efficiency on the go/no-go task after sleep deprivation, taking into account their performance at rested wakefulness, using the following equation: (ICV_SD − ICV_RW) × ICV_RW, where SD is sleep deprivation and RW is rested wakefulness. Participants were divided into tertiles, based on the extent of their change in performance, to facilitate visualization and discussion of the relationships between activation and vulnerability to sleep deprivation. These three groups, comprising nine subjects each, were termed low vulnerable, moderately vulnerable, and highly vulnerable. Because the focus was on the relationship between inhibitory efficiency and interindividual variability in vulnerability to sleep loss and evaluating whether activation at rested wakefulness can help predict vulnerability to sleep loss, parameter estimates were obtained from functionally defined regions-of-interest (ROIs) that were significantly activated at rested wakefulness for stops and errors (for ROI, see Table 2). Using such a functional ROI approach helps to constrain hypothesis testing concerning state effects to regions known to be robustly activated by the cognitive process of interest. The voxels contributing to each ROI lay within a bounding cube of edge 10 mm surrounding the peak activation for that ROI. These parameter estimates were then analyzed separately using a 2 (state) by 3 (group) repeated-measures ANOVA. All analyses involving behavioral variables and parameter estimates were conducted using SPSS version 13, and significance was determined using an α level of 0.05.