Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective

Abstract

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10^-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

Keywords: dynamic functional connectivity; psychomotor vigilance test; resting-state functional magnetic resonance imaging; sleep deprivation; temporal variability.

FIGURE 1

The analysis pipeline for computing the tree types of temporal variabilities. First, the whole brain was segmented into N ROIs according to a parcellation brain atlas such as the AAL3 template. Second, the mean time course of each ROI was extracted from the preprocessed BOLD signal. Third, the mean time series of all ROIs were segmented into M nonoverlapping windows. Forth, within the ith window, a N × N FC matrix F _i was generated. We extracted the kth column in F _i and denoted it as F _i,k . Then, we calculated the regional‐level temporal variability of the kth ROI ($V_{\text{region}}^k$). For the network‐level temporal variability, the N ROIs were assigned into nine functional subnetworks. For the network p, N _p ROIs were assigned into this network. Within the ith window, a N _p *(N _p  − 1)/2 FC matrix was obtained and reshaped as a 1D vector ($F_i^p$). Similar with the regional‐level temporal variability, we computed the intra‐network temporal variability of network p ($V_{\text{intra}-\text{network}}^p$). However, for the inter‐network variability ($V_{\text{inter}-\text{network}}^{p,q}$) between network p with N _p ROIs and network q with N _q ROIs, a N _p *N _q FC matrix between these two networks in the ith window was calculated and reshaped as a 1D vector (denoted as $F_i^{p,q}$). Finally, we calculated $V_{\text{inter}-\text{network}}^{p,q}$ according to the corresponding formula

FIGURE 2

Whole‐brain regional‐level temporal variability topography on AAL3 template after rested wakefulness and sleep deprivation. (a) After rested wakefulness (RW). The color scale represents regional‐level temporal variability. (b) After sleep deprivation (SD). The color scale represents regional‐level temporal variability. (c) Significant changes between RW and SD with FDR correction for multiple comparisons (p <.05). The color scale represents t value. The positive t values mean SD > RW; the negative t values mean SD < RW. R, right. L, left. These figures were constructed using the BrainNet Viewer (http://www.nitrc.org/projects/bnv/; Xia et al., 2013)

FIGURE 3

Significant changes in intra‐network temporal variability between RW and SD with the AAL3 atlas. Subjects showed increased intra‐network temporal variability in DMN and SMN and decreased in SUB and CN after SD with false discovery rate (FDR) correction (p <.05). CN, cerebellum; DMN, default mode network; RW, rested wakefulness; SD, sleep deprivation; SMN, sensorimotor network; SUB, subcortical network. ****p <.0001. ***p <.001. **p <.01

FIGURE 4

Significant changes in inter‐network temporal variability between RW and SD using the AAL3 atlas after FDR correction (p <.05). The color scale represents t value. The positive t values mean SD > RW; the negative t values mean SD < RW. Black asterisks (*) indicate the subnetwork pairs showing significantly increased inter‐network temporal variability after SD compared with RW. Red asterisks indicate the subnetwork pairs showing significantly decreased inter‐network temporal variability after SD compared with RW. ****p <.0001. ***p <.001. **p <.01. *p <.05. CN, cerebellum; DAN, dorsal attention network; DMN, default mode network; LN, limbic network; FDR, false discovery rate; FPN, frontal‐parietal network; SD, sleep deprivation; SMN, sensorimotor network; SUB, subcortical network; VAN, ventral attention network; VN, visual network; RW, rested wakefulness

FIGURE 5

The overlapped brain regions showing significant changes of regional‐level temporal variability using the AAL3 template and Shen‐268 functional atlas after SD. The color scale represents t value. The positive t values mean SD > RW; the negative t values mean SD < RW. R, right. L, left. RW, rested wakefulness; SD, sleep deprivation. This figure was constructed using the BrainNet Viewer (http://www.nitrc.org/projects/bnv/; Xia et al., 2013)

FIGURE 6

The common subnetwork pairs showing significant changes of inter‐network temporal variability after SD using the AAL3 atlas and Shen‐268 functional atlas. All of these subnetwork pairs showed increased inter‐network temporal variability after SD. CN, cerebellum; DAN, dorsal attention network; DMN, default mode network; FPN, frontal‐parietal network; LN, limbic network; SD, sleep deprivation; SMN, sensorimotor network; VAN, ventral attention network; VN, visual network. These figures were constructed using the BrainNet Viewer (http://www.nitrc.org/projects/bnv/; Xia et al., 2013)

FIGURE 7

The temporal variability is correlated with the performance of PVT in SD state. The 10% slow 1/RT showed significant negative correlation with the inter‐network temporal variability between VN and DMN in SD state after FDR correlation with the AAL3 atlas (β = −.42, p = 5.57 × 10⁻⁴) following the stepwise regression analysis. Considering the dimensional differences among temporal variability and performance measures, the 10% slow 1/RT and the inter‐network temporal variability between VN and DMN were normalized. The green solid line indicated the linear regression of the correlation. The green dotted line indicated the error bars. DMN, default mode network; FDR, false discovery rate; PVT, psychomotor vigilance test; RT, reaction time; SD, sleep deprivation; VN, visual network