Conceived and designed the experiments: JL DD MC. Performed the experiments: JL JCT. Analyzed the data: JL JCT SP. Wrote the paper: JL JCT DD MC.
The authors have declared that no competing interests exist.
Most prior studies on selective attention in the setting of total sleep deprivation (SD) have focused on behavior or activation within fronto-parietal cognitive control areas. Here, we evaluated the effects of SD on the top-down biasing of activation of ventral visual cortex and on functional connectivity between cognitive control and other brain regions.
Twenty-three healthy young adult volunteers underwent fMRI after a normal night of sleep (RW) and after sleep deprivation in a counterbalanced manner while performing a selective attention task. During this task, pictures of houses or faces were randomly interleaved among scrambled images. Across different blocks, volunteers responded to house but not face pictures, face but not house pictures, or passively viewed pictures without responding. The appearance of task-relevant pictures was unpredictable in this paradigm. SD resulted in less accurate detection of target pictures without affecting the mean false alarm rate or response time. In addition to a reduction of fronto-parietal activation, attending to houses strongly modulated parahippocampal place area (PPA) activation during RW, but this attention-driven biasing of PPA activation was abolished following SD. Additionally, SD resulted in a significant decrement in functional connectivity between the PPA and two cognitive control areas, the left intraparietal sulcus and the left inferior frontal lobe.
SD impairs selective attention as evidenced by reduced selectivity in PPA activation. Further, reduction in fronto-parietal and ventral visual task-related activation suggests that it also affects sustained attention. Reductions in functional connectivity may be an important additional imaging parameter to consider in characterizing the effects of sleep deprivation on cognition.
Although a broad array of cognitive processes are affected when human beings are deprived of sleep, deficits in sustained or vigilant attention are particularly robust and are of great importance in predicting real-world cognitive errors
In the well-rested state, selective attention results in the biasing of sensory processing in favor of the attended stimulus over competing distracters
Deficits in selective attention are likely to arise from a reduction in the strength of top-down biasing of information-processing in the sensory cortex. In support of this hypothesis, several functional neuroimaging experiments have shown that sleep deprivation in humans often results in reduced activation of the dorsal fronto-parietal attention network
In a recent experiment, subjects viewed picture quartets containing alternating faces and scenes with instructions to attend to faces, scenes, or both. In this paradigm, sleep deprivation reduced functional connectivity between the intraparietal sulcus (IPS) and the parahippocampal place area (PPA)
To investigate this hypothesis, we studied the effect of sleep deprivation on the functional anatomy of selective attention using a task that did not provide subjects with a prior alerting cue. We predicted that in addition to decreased activation in fronto-parietal control areas, we would also uncover reduced biasing of activation in the PPA to relevant stimuli. We additionally anticipated a reduction in connectivity between cognitive control regions and ventral visual cortex in the sleep-deprived as compared to the well-rested state.
Twenty-seven undergraduates from the National University of Singapore were recruited for this within-subject study through advertisements on a campus website. From this original pool, two were removed from analysis due to excessive head-motion in the scanner, one was excluded based on near-chance performance in both states, and another was excluded on the basis of image problems, giving a final sample of N = 23 (12 male; mean age = 21.3 years, SD = 1.4 years). All subjects were right-handed, had no history of chronic physical or psychiatric disorders, or long-term medication use. They had regular sleep schedules and slept between 6.5–8 hours a night based on self-report, and were not extreme morning chronotypes as assessed by a modified Horne-Ostberg Chronotype Questionnaire
Upon entering the study, subjects visited the lab for a briefing to practice the experimental task and to collect an Actiwatch (Actiwatch, Philips Respironics, USA) that they were instructed to wear at all times until the conclusion of the experiment. Subjects were also issued sleep diaries on which they were to record the onset and offset of all sleep bouts. Sleep history was checked prior to each of the fMRI scanning sessions, and participants who did not comply with a regular sleep schedule (>6.5 hours of sleep/night; sleep time no later than 1:00 AM; wake time no later than 9:00 AM) were excluded.
At least five days after the briefing, subjects returned to the laboratory for the first of two experimental sessions. In the rested wakefulness (RW) condition, subjects reported to the lab at approximately 7:30 AM. After filling in a questionnaire to assess their subjective level of sleepiness (the Karolinska Sleepiness Scale), they underwent an fMRI scan during which they performed a task involving selective attention to two different classes of stimuli: faces and houses (see fMRI procedures below for detailed description). Anatomical scans were also acquired during this time. fMRI scanning in the RW state typically began at about 8:00 AM. In the sleep deprivation (SD) condition, subjects reported to the lab on the evening prior to their fMRI scan. Subjects' actigraphy records were used to confirm they had awakened at their regular time on that day, and had not taken any daytime naps. Subjects remained awake overnight in the laboratory under the constant supervision of a research assistant. They were permitted to engage in light recreational activities, but were not allowed to smoke or consume caffeine. Every hour, participants performed the Psychomotor Vigilance Test and rated their subjective sleepiness using the Karolinska Sleepiness Scale. In the SD condition, subjects underwent an fMRI scan as in the RW condition, but at 6:00 AM. The order of scanning sessions was counterbalanced across subjects (RW session first; N = 12) to minimize potential order confounds. Sessions were separated by at least one week, so that subjects undergoing the SD session first had sufficient time to fully recover from the effects of sleep loss.
Permission to conduct this study was granted by the Singapore General Hospital IRB, and all subjects provided written informed consent prior to participation. Subjects were financially compensated for their time. The individual providing the example face in
Three faces and three houses were presented during every task block. Inter-stimulus intervals varied randomly after each scrambled image, and were held constant at 2000 ms following each target. Subjects performed 6 task runs during each scanning session. AF = attend and respond to faces; AH = attend and respond to houses; OBS = passive observation of houses and faces.
Subjects were shown blocks consisting of 6 novel targets (grayscale images of three faces and three houses) and 30 scrambled images that were of approximately equivalent luminance as the target pictures (
At the start of each block, an instruction screen lasting 2 s was presented to the subject, informing them to either attend to faces, attend to houses, or passively observe the stimuli. This was followed by a further 2 s delay before the first stimulus appeared. In each of the ‘attend’ conditions, subjects were instructed to respond to the target by pressing a button with the right hand. In the ‘observe’ condition, subjects simply viewed the stimuli without making any response (
Finally, at the end of the RW session, subjects were scanned while they viewed blocks of faces and houses; data from these scans served as functional localizers that allowed us to identify the fusiform face area (FFA) and parahippocampal place area (PPA) for each individual subject
MR imaging was conducted using a 3T Siemens Tim Trio scanner (Siemens, Erlangen, Germany) fitted with a 12-channel head coil. Participants viewed stimuli through a set of MR-compatible LCD goggles (Resonance Technology, Los Angeles, USA) and responded using their right index finger via a MR-compatible button box. Performance was continually monitored by a research assistant who noted all lapses and eye closures (through use of an eye tracking device). Subjects were prompted to attend to the task through an intercom system when they failed to respond to two consecutive trials, or when epochs of eye closure exceeded 3 seconds. Functional images were collected using a gradient echo-planar imaging sequence (TR: 2000 ms; TE: 30 ms; flip angle: 90°; field-of-view: 192×192 mm; matrix size: 64×64). Twenty-eight 3-mm axial slices aligned to the intercommisural plane and covering the whole brain were acquired. Directly following the functional data collection, a high-resolution T1 coplanar image was acquired. Finally, a high-resolution 3D MPRAGE sequence was obtained so that anatomical images could be normalized into common stereotactic space.
MRI data were analyzed using Brain Voyager QX version 1.10.1 (Brain Innovation) and Matlab R13 (Mathworks). Functional images were aligned across scanning runs to the first image of the final run. Intrasession image alignment to correct for motion was performed using the first acquisition of the final functional run as the reference scan. Interslice timing differences within each functional acquisition were corrected using cubic spline interpolation. We performed Gaussian filtering in the spatial domain by applying an 8 mm FWHM smoothing kernel. Linear signal drift, and signals of lower than 3 cycles/functional run were removed. Finally, all images were registered to their respective individual 3D high-resolution T1 anatomical image, and normalized to Talairach space
Functional imaging data were analyzed using a general linear model with 13 predictors in an event-related analysis. Twelve of these predictors were created with a 2×2×3 model using all combinations of state (RW/SD), stimulus type (house/face) and trial type (attend/observe/ignore). We modeled events by convolving a stick function with a double-gamma, canonical hemodynamic response. Only correct ‘attend’ responses were analyzed. A thirteenth predictor was created to model all lapses (non-responses within 2 s) in each state; these events were not subsequently analyzed any further. As we did not want to include periods of data that included frequent microsleeps, runs in which there were >50% of undetected targets were not entered into the model. We excluded 14 out of 288 runs (4.9%) from the analysis for this reason.
In order to identify cognitive control regions activated above threshold by selective attention to houses as well as faces, we computed the conjunction of two contrasts: attend house (AH) vs. baseline and attend face (AF) vs. baseline in the RW state. To control for Type I error, voxels were processed using an iterative cluster size thresholding procedure
To characterize state-related differences in control region activation during task performance, we compared activation within a 10×10×10 mm cube of voxels surrounding the peak voxels obtained from the conjunction analysis described above in addition to running an ANOVA-based analysis. The frontal and parietal regions selected from the conjunction analysis have previously been identified as important areas involved in selective attention
Analysis of object-selective attention within the ventral visual cortex was ROI-based. The PPA and FFA were defined by a separately conducted localizer scan performed for each individual as described previously. PPA ROIs comprised a 10×10×10 mm cube of voxels that surrounded the one voxel showing maximum difference in activation between house and face blocks. We focused our analysis on the PPA as it has been shown to yield more discriminating and spatially more consistent, selectivity data
Psychophysiological interaction (PPI) analysis
To carry out PPI analysis, we used a linear model with three predictors: the time course of activity in the seed ROI, a task predictor coding for activity within task blocks (AH vs. IH or AH vs. OH) and a PPI term. To construct the PPI term, the deconvolved time-course of the relevant seed region was multiplied with a vector containing the psychological variables of interest. This product was then re-convolved with a canonical hemodynamic response function
To evaluate the robustness of the findings, we compared PPI in the AH vs. IH as well as AH vs. OH contexts as both comparisons evaluate object-selective attention.
In the RW state, subjects were able to perform the task accurately with high hit rates (mean = 91.0%, SD = 11.0%) and low rates of false alarms (mean = 4.1%, SD = 4.6%). After sleep deprivation, there was a significant decline in the percentage of hits (
Behavioral variable | RW | SD | t value |
Hits (%) | 91.05 (10.98) | 75.48 (17.13) | 5.30 |
False alarms (%) | 4.11 (4.57) | 4.95 (5.09) | −0.63 |
Mean reaction time (ms) | 574.08 (82.97) | 593.48 (83.99) | −1.24 |
Subjective sleepiness | 4.65 (1.78) | 8.40 (0.71) | −10.1 |
Data were collapsed across Attend House (AH) and Attend Face (AF) blocks. Subjective sleepiness was measured using the Karolinska Sleepiness Scale.
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Brain regions activated as a result of attending to houses as well as faces included the intraparietal sulcus (IPS) and inferior parietal lobes bilaterally (BA 40), left inferior frontal gyrus, right middle frontal gyrus, anterior cingulate cortex (
Brain regions showing significant activation in the conjunction of Attend House (AH) vs. baseline and Attend Face (AF) vs. baseline conditions (
Region | BA | Talairach coordinates | ||||
x | y | z | RW | |||
L intraparietal sulcus | 7/40 | −27 | −58 | 37 | 4.48 |
1.06 |
R intraparietal sulcus | 7/40 | 33 | −58 | 43 | 4.69 |
2.69 |
L superior frontal gyrus | 10 | −24 | 47 | 5 | 3.10 |
1.21 |
R superior frontal gyrus | 10 | 30 | 50 | 22 | 4.65 |
3.73 |
R middle frontal gyrus | 46 | 24 | 44 | −5 | 3.74 |
2.61 |
L inferior frontal gyrus | 13 | −36 | 11 | 4 | 4.97 |
4.01 |
Anterior cingulate cortex | 32 | −9 | 11 | 43 | 5.37 |
4.57 |
BA = Brodmann's area.
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Attending to houses elicited greater activation than ignoring houses in the left IPS (
Parameter estimates for each condition and state associated with the left inferior frontal gyrus (IFG), left intraparietal sulcus (IPS), anterior cingulate cortex (ACC), and left thalamus. Significant state-related differences were observed in the left IFG and IPS, but not in ACC or the thalamus.
After a normal night of sleep (RW), attending to houses resulted in greater activation in the PPA in both contrasts of interest AH vs. IH (
SD reduced activation in the left inferior frontal ROI (
Brain regions that showed a significant effect of state on activation in the Attend House (AH) vs. baseline contrast (
In the rested (RW) state, attention to houses (AH) resulted in significantly greater PPA activation compared to ignoring (IH) or observing (OH) houses. However, this attention biasing was lost during SD.
Whole-brain PPI analysis revealed significant connectivity between the seed voxels in the left IPS and the PPA bilaterally during RW (AH vs. IH:
Connectivity analysis was performed using seeds in the left intraparietal sulcus (IPS; Talairach co-ordinates: −27, −58, 37) and left inferior frontal regions (Talairach co-ordinates: −36, 11, 4) (seed regions represented by green squares). Each map represents the conjunction of regions showing significant PPI in the Attend House (AH) vs. Ignore House (IH) and AH vs. Observe House (OH) conditions (threshold
Seed region | Contrast | Talairach coordinates of PPA region showing PPI | ||||
x | y | z | RW | SD | ||
L parietal (−27,−58,37) | AH > IH | −33 | −44 | −8 | 4.77 |
1.52 |
AH > OH | −35 | −41 | −4 | 3.34 |
1.31 | |
L inferior frontal gyrus (−36,11,4) | AH > IH | −27 | −48 | −8 | 2.67 |
1.05 |
AH > OH | −24 | −46 | −6 | 3.31 |
0.48 |
Seeds for this analysis were in left parietal and left inferior frontal regions.
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Three key findings were of interest in the present study. First, we found that sleep deprivation attenuated connectivity between the IPS and the PPA when selective attention for houses was engaged, replicating our previous report
Although inter-individual differences in vulnerability to sleep deprivation
Although imaging studies can shed light on functional neuroanatomy, studies that focus their analysis on top-down control regions, which include prefrontal and parietal areas, typically do not decompose total activation into the relative contributions of component cognitive processes
In a related study
In order to reveal a state-related change in PPI, MR signal in the ‘target’ area has to show consistent trial-by-trial differences in co-variation of signal with that of the seed region involving both task and non-task related aspects of the signal. This represents a different aspect of how attention might modulate BOLD signal (as opposed to the more intuitive demonstration of selectivity in PPA activation as a function of attention).
In contrast to the related study
The presence of a valid cue significantly reduces response time in experiments evaluating spatial attention
Orienting recruits the parietal lobe
The availability of a valid cue may benefit behavior
In addition to the changes in PPI and in PPA activity modulation, sleep deprivation also resulted in significant reductions in activation across conditions in inferior frontal regions, IPS and ventral visual cortex. These state-related changes in activation are consistent with prior studies from our laboratory on visual short-term memory
We posit that in experiments where sustained attention is a major contributor to the behavioral effect, state-related changes in activation will correlate with the corresponding change in behavior
Using a novel imaging paradigm and an analysis strategy that focused on the ventral visual cortex, we were able to dissociate the brain activation changes that reflect how sleep deprivation influences selective attention from task-independent changes in brain activation that involve cognitive control and higher visual areas. For selective attention tasks, reductions in connectivity between cognitive control regions and relevant visual areas appear to be a consistent feature of neural activity following SD. Finally, the absence of a cue in the present paradigm could explain the loss of the biasing effect of attention on PPA activation in sleep-deprived persons.
Parameter estimates of activation for faces in areas associated with arousal and attention. Parameter estimates for each condition and state in the left inferior frontal gyrus (IFG), left intraparietal sulcus (IPS), left thalamus and anterior cingulate cortex (ACC) for the three conditions attend to face, ignore face, and observe face. Significant state-related differences were observed in the left IFG and IPS, but not in ACC or the thalamus, mirroring the results for the house conditions in
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Psychophysiological interaction related to the specific PPI contrasts and state. Connectivity analysis was performed using seeds in the left IPS (top panel; Talairach co-ordinates: −27, −58, 37) and left inferior frontal regions (bottom panel: Talairach co-ordinates: −36, 11, 4). Each map represents regions showing significant PPI in the AH vs. IH and AH vs. OH conditions (threshold p<.05) and in each state (RW, SD).
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The authors thank Grace Tang, Silma Sulaiman, Xiangyang Tang, Karren Chen, Michele Veldsman, Jingwei Lim and Annette Chen for their assistance in data collection and preprocessing and to Su Mei Lee for proof reading. We also acknowledge Dr. John Detre, Dr. Martha Farah, Dr. Geoffrey Aguirre and Dr. Hengyi Rao for their helpful comments on data design and analysis.