We demonstrate the prevalence of sleep in two different resting-state data sets: (1) “Frankfurt data set,” EEG-fMRI of 71 subjects instructed to lie still in the scanner bore with eyes closed and scanned for 52 min with experiments starting at 7:00 p.m., and (2) a large cohort of subjects scanned by different research groups throughout the world (termed “Connectome data set”).

The EEGs of the Frankfurt data set were sleep staged according to AASM rules classifying each 30 s epoch of the experiment as either awake or as any one of the three stages of NREM sleep: N1 (light sleep), N2, and N3 (slow wave or deep sleep). Figure 1 summarizes the results. The number of continuously awake subjects (subjects in uninterrupted wakefulness) quickly decreased after the resting-state scanning sequence started ( Figure 1 A)—of note, 5% of the subjects were already asleep at the time of the fMRI scan start. After 10 min had elapsed (a typical duration of a resting-state experiment), one in two subjects had undergone a transition into light sleep at least once. The probability of finding an awake subject decreased rapidly within the first 5 min of the experiment and reached a minimum (p = 0.42) ∼20 min into the experiment before then slowly increasing again ( Figure 1 B). After 4 min of the experiment, one-third of the subjects were asleep, and ∼50% were asleep after 10 min. Following the natural course of sleep, early in the scan, N1 sleep was predominant with a peak at ∼10 min (p ≈ 0.5). After this point, the probability of finding a subject in N2 sleep rapidly increased to a maximum at ∼20 min (p ≈ 0.3). While subjects did not reach N3 sleep during the first 15 min, this changed later in the experiment, with a peak in the N3 sleep probability at ∼30 min (p≈ 0.2). Overall, subjects were asleep 40% of the scanning time ( Figure 1 C).

(C) Total time spent by the subjects in wakefulness and all NREM sleep stages. Throughout ∼40% of the experiment, subjects were asleep. The vertical dashed lines indicate that 10 min have elapsed since the experiment started.

Our data features were correlation matrices over short periods of time, pooled over both time and subjects—where each data feature was equipped with a sleep stage label, based upon EEG. We note the possible classifier outputs as SVM(i = 1,2,3,4), starting from wakefulness (i = 1) to N3 sleep (i = 4), and the AASM-scored (based on the EEG data) sleep stages as EEG(i = 1,2,3,4) (analogous enumeration). Thus, the confusion matrix C(i.e., the matrix containing the accuracy of all pairwise classification outcomes) can be expressed as C= P(SVM|EEG). In Figure 3 A, all matrix elements of Care plotted, showing that classification accuracy was highest for wakefulness and lowest for N1 sleep, but in all cases above the level of chance. The probability of correct detection given a certain classifier output can be obtained—via Bayes’ rule—from the inverse conditional probabilities P(EEG|SVM), plotted in Figure 3 B. Highest confidence in the SVM classifier output corresponds to wakefulness and N3 sleep, with lower probabilities for N1 and N2 sleep. Notably, in the case of misclassifications of the SVM scoring compared to the “true” gold standard EEG scoring, the classifier was much more likely to score wakefulness in the case of “true” sleep than to classify any epoch as sleep in the case of “true” wakefulness. In other words, sleep classification in the Connectome data set was conservative, i.e., wakefulness—if at all—was overestimated. We excluded the possibility of the classifier detecting a temporal trend other than sleep depth (see Figure S6 available online).

(B) Probability of NREM sleep stages, as detected by EEG-based AASM scoring rules, given different SVM outputs, P(EEG i |SVM j ). Insets: results of the binary wakefulness versus sleep classification are shown (i.e., with all sleep stages pooled together). Bars representing correct classifications are colored in red (diagonal), representing misclassifications toward less sleep depth in green and toward higher sleep depth in blue. In all panels, light blue dashed lines indicate the chance accuracy level.

The 55 subjects in the Frankfurt data who fell asleep during the scan were split into two groups: a training set (30 subjects) and a testing set (25 subjects). Based on AASM rules (), the training set was further subdivided into wakefulness and the three NREM sleep stages. Using nonoverlapping 2 min windows, functional connectivity was computed in the training data between mean BOLD signals from all regions in the automated anatomical labeling (AAL) template (). Only those connections yielding significant differences between pairs of sleep stages (p < 0.05, Student’s t test, Bonferroni corrected) were kept to train the multiclass SVM classifier. These connections are shown in Figure 2 . The most remarkable feature of these patterns is the surge of increased functional connectivity during N1 and N2 sleep (compared to wakefulness) and the general connectivity breakdown observed during N3 sleep (compared to wakefulness and all other NREM sleep stages). After optimal parameter selection via 5-fold cross-validation, the performance of the classifier was evaluated in the testing data set using a 2 min sliding window to compute functional connectivity between AAL regions, obtaining an overall accuracy of 75% (given a chance accuracy of 25%).

For each one of the six possible pairs (permutation) of sleep stages, significantly different (p < 0.05, one-tailed Student’s t test, Bonferroni corrected) functional connections are shown as a graph (or network; with node coordinates located at the center of mass of each AAL region) and in matrix form (with rows and columns corresponding to different AAL regions and each intersection representing a functional connection). Region codes and coordinates can be found in Table S1

Decoding Sleep in a Large Resting-State Data Set Revealed Regular Loss of Wakefulness

Biswal et al., 2010 Biswal B.B.

Mennes M.

Zuo X.N.

Gohel S.

Kelly C.

Smith S.M.

Beckmann C.F.

Adelstein J.S.

Buckner R.L.

Colcombe S.

et al. Toward discovery science of human brain function. Figure 4 Subjects in the Connectome Data Set Are More Likely to be Asleep as the Resting-State Experiment Progresses Show full caption (A) Probabilities of steady wakefulness, of finding an awake subject and a subject in one of each of all three NREM sleep stages, the total time spent in N1, N2, and N3 sleep and in all sleep stages combined. Results are for the Frankfurt data set. (B) Same plots as in (A), but for the Connectome data set (mean ± SEM, computed across all fMRI centers). (C) Total time spent in the different NREM sleep stages (left) and wakefulness probability as a function of time (right). Results are presented for the Frankfurt data set and for all individual fMRI centers contributing to the Connectome data set. An eye next to the fMRI center name indicates an experiment with eyes open, an eye plus a cross indicates eyes open and fixation, and “N/A” indicates lack of data. (D) Average sleeping time (normalized by scanning length) of subjects in the Connectome data set during the first and second half of the scan (individual data in gray and mean ± SEM in black). (E) Slope obtained from fitting a linear function in the wakefulness probability versus time function of each fMRI center. A negative slope indicates that the likelihood of finding an awake subject decreases with time. We then applied the classifier to the resting-state fMRI data of 1,147 subjects obtained from the 1000 Functional Connectomes Project (). Functional connectivity between all AAL regions and those corresponding to the connections presented in Figure 2 was computed based on 2 min sliding windows and served as input for the multiclass SVM classifier. Statistics summarizing the results are presented in Figure 4 A and Figure 4 B. The Connectome data set scanning sessions were shorter than those of the Frankfurt data set, hence, for ease of comparison, results are reported only for the first 5 min aside the results of the first 5 min of the EEG-based sleep-staged Frankfurt data set ( Figure 4 A). One-third of the subjects of the Connectome data set did not maintain steady wakefulness for longer than ∼3 min—compared to ∼4 min in the Frankfurt data set. In both data sets, the proportion of continuously awake subjects diminished over time, with a faster rate observed in the Connectome data set (8% of subjects per minute versus 15% per minute, obtained from the slope of the best linear fit during the first 5 min). Furthermore, in both data sets, the probability of finding an awake subject decreased monotonously with time, and the probability of finding a subject in N1 sleep monotonously increased with the latter being overall smaller for the Connectome data set. The probability of finding subjects in N2 sleep increased with elapsed time and was comparable for the two data sets. Finally, subjects from the Frankfurt data set did not enter N3 sleep at all within the first 5 min of the scanning session, whereas the SVM classifier detected N3 sleep in the Connectome data set. This probability did not show any monotonous trend over time and thus might have arisen from misclassifications. Finally, in both data sets wakefulness was the most prevalent sleep stage, followed by N1, N2, and N3 sleep.

−5, Student’s t test). In Figure 4 C, the probability of finding an awake subject as a function of time is shown, both for the Frankfurt data set and separately for every fMRI center forming the Connectome data set. Because this data set did not include EEG data, it was impossible to apply the gold standard polysomnography based on the AASM rules to detect the presence of sleep. However, if the SVM classifier correctly infers the sleep stage from fMRI functional connectivity data, then it can be expected that the probability of finding an asleep subject increases over time (and vice-versa for the probability of finding an awake subject). This heuristic was quantified in two ways. First, for each subject, the total amount of sleep (normalized by total scanning time) in the first and second half of the scanning session was computed (note that according to Figure 1 B this heuristic is only valid for the beginning of the experiment, i.e., during the first ≈20 min), revealing that subjects were more likely to be asleep during the second part of the experiment than the first (p = 0.0048, Student’s t test) ( Figure 4 D). Second, for each center, a linear function was fitted to the wakefulness probability as a function of time. A significant negative slope implies that, as the experiment progresses, it becomes more unlikely to find an awake subject. Results for all fMRI centers are presented in Figure 4 E, revealing that the majority (30 out of 38) of centers presented a negative slope. Across the whole sample, the mean slope values were significantly smaller than zero (p < 10, Student’s t test).