On wakefulness fluctuations as a source of BOLD functional connectivity dynamics

Ariel Haimovici, Enzo Tagliazucchi, Pablo Balenzuela, Helmut Laufs, Ariel Haimovici, Enzo Tagliazucchi, Pablo Balenzuela, Helmut Laufs

Abstract

Human brain dynamics and functional connectivity fluctuate over a range of temporal scales in coordination with internal states and environmental demands. However, the neurobiological significance and consequences of functional connectivity dynamics during rest have not yet been established. We show that the coarse-grained clustering of whole-brain dynamic connectivity measured with magnetic resonance imaging reveals discrete patterns (dynamic connectivity states) associated with wakefulness and sleep. We validate this using EEG in healthy subjects and patients with narcolepsy and by matching our results with previous findings in a large collaborative database. We also show that drowsiness may account for previous reports of metastable connectivity states associated with different levels of functional integration. This implies that future studies of transient functional connectivity must independently monitor wakefulness. We conclude that a possible neurobiological significance of dynamic connectivity states, computed at a sufficiently coarse temporal scale, is that of fluctuations in wakefulness.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Definition of dynamic connectivity states. (A) We obtained correlation matrices for each window by computing the pairwise correlation coefficient between the blood oxygenation level-dependent (BOLD) signal time series for all 116 AAL template regions (example signals from two typical participants are presented in panel B). We applied k-means clustering to matrices from all subjects to divide them into dynamic connectivity states (in this example, arrows indicate time windows belonging to four different clusters, C1, C2, C3 and C4). (C) Assignment of each time window to a sleep stage following sleep scoring of the simultaneously acquired EEG data based on AASM scoring rules for two exemplary concatenated subjects. The time axis is presented in scan volumes, 1500 volumes correspond to 52 min of scanning (time of repetition: 2.08 s).
Figure 2
Figure 2
Clustering into four dynamic connectivity states tracks wakefulness and NREM sleep stages. (A) We matched occurrence probabilities over time of the identified (W, N1-N3; based on EEG; red lines) sleep labels with the assigned cluster IDs (CW, CN1, CN2, CN3; based on k-means clustering; black lines), the legends show the linear correlation coefficient r in each case. This allowed a bidirectionally unequivocal mapping of each cluster to one sleep stage. (B) Overlay of the average EEG spectra (PSD: power spectral density) obtained for the time windows identified with each sleep stage (black line) and the matched dynamic connectivity state (red line), respectively. (C) Comparison of the average fMRI correlation matrix obtained for the volumes identified with each sleep stage (left column) and with the matched clustered dynamic connectivity states (right column). (D) We confirmed the high visual similarity between both columns in panel C by computing the correlation between all pairs of correlation matrices, observing the highest values along the diagonal (confirming the best match). The explicit values of the correlation coefficient are shown along the diagonal. (E) Accuracy (defined as percentage of correctly labeled volumes taking EEG-based sleep scoring as the reference) as a function of the window length.
Figure 3
Figure 3
Validation using the wake only and the sleep data sets. (A) Probabilities of observing wakefulness (P(W)) and any NREM sleep stage (P(S)) in the data set containing sleep (“sleep data set”) based on the EEG scoring (left column, “EEG sleep”) and the matched clustered dynamic connectivity states (middle column, “Clustering sleep”) and respective probabilities for the clustered dynamic connectivity states in the wake only data set (right column, “Clustering wake”). For the latter data set, probabilities for the EEG-based scoring are not shown (P(W) equaling 1 and P(S) equaling zero by definition). All plots are mean ± SEM. (B) Distribution of accuracies obtained using a bootstrapping procedure with 10 randomly selected participants and 100 iterations, shown for the data set containing sleep epochs (red, “Sleep”), the wake only data set (blue, “Wake”) and the sleep data set with randomized sleep stage labels (black, “Random”).
Figure 4
Figure 4
Dynamic connectivity states allow the detection of wakefulness vs. N1 sleep with high accuracy during the first minutes of the scanning session. (A) Accuracy (EEG sleep scoring as the reference) for the detection of wakefulness vs. N1 sleep using clustered dynamic connectivity states (7 first minutes only) as a function of sample size (mean ± SD over 100 random subsets of 10 subjects for each sample size). The dashed line indicates the accuracy for the data with randomized sleep stage labels. (B) Probability of observing wakefulness based on EEG sleep scoring and using the matched connectivity state (red) as a function of time, for three sample sizes (10, 30 and 50 subjects, respectively; all plots are mean ± SEM). (C) Probability of observing the clustered dynamic connectivity state associated with wakefulness in 18 centres from the 1000 Functional Connectomes dataset (mean ± SEM). The inset shows three example centres exhibiting a trend of decreasing wakefulness probability.
Figure 5
Figure 5
Characterization of functional connectivity during wakefulness vs. NREM sleep. (A) Network modularity (extent of division of the network into sub-networks) for wakefulness, N1, N2 and N3 sleep (mean ± SEM, points are values for each participant, *p < 0.05, FDR corrected). (B) Correlation between structural connectivity (SC) and functional connectivity (FC) for wakefulness, N1, N3 and N3 sleep (mean ± SEM, points are values for each participant, *p < 0.05, FDR corrected). (C) Anatomical overlay of the AAL regions presenting decreased node strength (sum of weights attached to links belonging to a node) in N1, N2 and N3 sleep vs. wakefulness (p < 0.05, FDR corrected). (D) Differences in pairwise connectivity between wakefulness and N1, N2, N3 sleep. Resting state network abbreviations are as follows. VIS M: medial visual, VIS L: visual lateral, AUD: auditory, SM: sensorimotor, DMN: default mode, EC: executive control, DAN: dorsal attention, CER: cerebellar.
Figure 6
Figure 6
Clustered dynamic connectivity states allow the detection of NREM and REM sleep in a cohort of narcolepsy patients. (A) Probability of observing each sleep stage as detected using EEG (left column) and the matched fMRI-based clustered dynamic connectivity state (right column). (B) Average correlation matrices for each sleep stage (left column) and for the matched clustered dynamic connectivity states (right column). (C) The similarity between the left and right columns in panel B is confirmed by computing the correlation coefficient between the average correlation matrices observed during each sleep stage and those of each dynamic connectivity state (observing the highest values along the diagonal).

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