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. 2016 Feb 15:127:324-332.
doi: 10.1016/j.neuroimage.2015.12.028. Epub 2015 Dec 19.

The brain functional connectome is robustly altered by lack of sleep

Tobias Kaufmann  1 , Torbjørn Elvsåshagen  2 , Dag Alnæs  3 , Nathalia Zak  4 , Per Ø Pedersen  5 , Linn B Norbom  6 , Sophia H Quraishi  7 , Enzo Tagliazucchi  8 , Helmut Laufs  8 , Atle Bjørnerud  9 , Ulrik F Malt  10 , Ole A Andreassen  11 , Evangelos Roussos  12 , Eugene P Duff  12 , Stephen M Smith  12 , Inge R Groote  13 , Lars T Westlye  14
Affiliations

The brain functional connectome is robustly altered by lack of sleep

Tobias Kaufmann et al. Neuroimage. .

Abstract

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group)-a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome.

Keywords: Circadian variability; Machine learning; Sleep deprivation; fMRI-based connectivity.

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Conflict of interest statement

Conflict of Interest

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1. Sleep deprivation results in higher reaction time variability and increased reaction times.
TP1: first morning, TP2: evening, TP3: second morning. (A) Coefficient of variation in reaction time. (B) Mean reaction times (C) Self-reported subjective sleepiness obtained from the Karolinska Sleepiness Scale (Akerstedt and Gillberg, 1990).
Figure 2
Figure 2. Functional connectivity based classification of morning, evening and sleep deprivation scanning sessions.
The figure illustrates confusion matrices from various classification tasks. Significance was assessed based on permutation testing across 10000 iterations, each randomly permuting assignment to the three time points within subjects. (A) Within-group classifications of the three time points. (B) Classification of the three time points with classifiers trained on data from one group and tested on data from the other group. (C) Classification of TP3 with classifiers trained on TP1 and TP2. This allows to assess if TP3 is more similar to TP1 or TP2. (D) Classification of TP1 and TP2 in the merged sample (N=60, morning-to-evening variability, independent of sleep deprivation). SI-Figure 4 depicts similar classification patterns for networks based on partial correlations with automatic optimization of regularization strength on the single subject level.
Figure 3
Figure 3. Sleep deprivation altered brain connectivity in a range of functional network units.
The figure illustrates the 17 edges showing a significant group x time interaction effect in connectivity (due to deprivation at TP3), grouped by their functional sub-networks. The width of a link in each circular plot reflects the strength of the group x time interaction effect (ηpartial2 effect size). Each circular plot highlights the significant connections of one sub-network only, comprising default mode (red), dorsal attention (green), frontal (yellow), visual (blue), SMA (magenta), auditory/speech (purple), amygdala/hippocampus (orange) and cerebellum (cyan). The spatial maps of the 19 involved nodes (independent components) are shown on top. For surface-maps of all components, see SI-Figure 1. TP1: first morning, TP2: evening, TP3: second morning.
Figure 4
Figure 4. Scatter plots of connectivity strength between TP1 and TP3 show a drift in connectivity strength following sleep deprivation.
The figure presents the 17 edges showing a group x time interaction (corresponding to figure 3). Colour indicates connectivity at TP2 and the white line indicates the diagonal. TP1: first morning, TP2: evening, TP3: second morning.
Figure 5
Figure 5. Temporal connectivity profiles of the two visual-DAN edges show a remarkable overlap with the profiles of attentional vigilance and subjective sleepiness (Figure 1).
Connectivity strength of IC 1-8 was significantly associated with IIV-RT and KSS scores. Connectivity strength of IC 3-9 was significantly correlated with mean RT in the ANT task (performed outside of scanner). TP1: first morning, TP2: evening, TP3: second morning.
Figure 6
Figure 6. Prediction of wakefulness and sleep stages (N1, N2 and N3) based on the connectivity profiles of the continuous fMRI data showed increased sleep probability at TP3 in the deprived group.
We used the classifier from Tagliazucchi and Laufs (2014).
Figure 7
Figure 7. Correlation of the t-statistics reflecting morning-to-evening variability (TP1 vs. TP2) to t-statistics reflecting evening-to-next-morning variability (TP2 vs. TP3) indicated the impact of sleep on diurnal connectivity alterations (sign flip of the regression slope).
Least square regression lines are depicted in red. Colour indicates t-statistics for TP1 vs. TP3. TP1: first morning, TP2: evening, TP3: second morning
See this image and copyright information in PMC

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