Reversible Brain Wavelet Entropy Following Partial Sleep Deprivation
Poster No:
2091
Submission Type:
Abstract Submission
Authors:
Siqi Cai1, Fan Yang1, Lixian Zou1, Lijuan Zhang1
Institutions:
1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong
First Author:
Co-Author(s):
Lixian Zou
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Shenzhen, Guangdong
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Shenzhen, Guangdong
Lijuan Zhang
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Shenzhen, Guangdong
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Shenzhen, Guangdong
Introduction:
Sleep deprivation (SD) is associated with a broad spectrum of risks on human health (Gohari et al., 2022). However, the effect of SD on brain activity remains unclear. In this study, we aimed to investigate the impact of partial SD (pSD) on the complexity of intrinsic brain activity based on wavelet entropy (WaveEp) analysis and resting-state BOLD fMRI (rs-fMRI).
Methods:
This study was approved by the local institutional review board. A total of 41 subjects with regularly scheduled night-shift works were recruited (34 females, aged 27±3 years). Inclusion criteria were (1) rotated through a regular night-shift calendar at least 1 month, (2) no history of sleep disorders, neurological and psychiatric diseases, (3) no history of neuropsychiatric or sleep-altering medications.
The MRI scan and 10-min psychomotor vigilance test (PVT) (Reifman et al., 2018) were successively conducted prior to the pSD (pre-pSD), immediately after the pSD (im-pSD), and 3 to 5 days post pSD (post-pSD), respectively. MRI data was acquired on a 3.0T MRI scanner (uMR 790, United Imaging Healthcare, Shanghai, China) equipped with a 32-channel phased array head coil. The rs-fMRI data was acquired using GE-EPI sequence (TR/TE 1000/30 ms, multi-band factor of 4, FA 62°, FOV 210×210 mm2, acquisition matrix 84×84, 60 slices with a thickness of 2.5 mm, 540 volumes).
Rs-fMRI data was preprocessed using the DPABI toolbox (Yan et al., 2016) with the following steps: removal of the first 10 volumes, slice timing and realignment correction, spatial normalization and smoothing, regression of nuisance variables, and detrend. The discrete wavelet transformation with the Daubechies-4 wavelet as the mother wavelet function was conducted to decompose the BOLD signal into 8 wavelet subbands. The lowest subband was excluded to avoid contamination of slow signal drifts. The WaveEp was calculated voxel-by-voxel according to the established method (Gupta et al., 2017). Z-standardization was performed across the entire brain to generate the individual WaveEp map. WaveEp of brain regions based on Brainnetome (BN) atlas were compared among three conditions (pre-, im-, and post-pSD) using one-way analysis of variance (ANOVA), followed by the Tukey's post hoc test for pairwise comparison. The significance level was set at P < 0.05 with false discovery rate (FDR) correction.
The MRI scan and 10-min psychomotor vigilance test (PVT) (Reifman et al., 2018) were successively conducted prior to the pSD (pre-pSD), immediately after the pSD (im-pSD), and 3 to 5 days post pSD (post-pSD), respectively. MRI data was acquired on a 3.0T MRI scanner (uMR 790, United Imaging Healthcare, Shanghai, China) equipped with a 32-channel phased array head coil. The rs-fMRI data was acquired using GE-EPI sequence (TR/TE 1000/30 ms, multi-band factor of 4, FA 62°, FOV 210×210 mm2, acquisition matrix 84×84, 60 slices with a thickness of 2.5 mm, 540 volumes).
Rs-fMRI data was preprocessed using the DPABI toolbox (Yan et al., 2016) with the following steps: removal of the first 10 volumes, slice timing and realignment correction, spatial normalization and smoothing, regression of nuisance variables, and detrend. The discrete wavelet transformation with the Daubechies-4 wavelet as the mother wavelet function was conducted to decompose the BOLD signal into 8 wavelet subbands. The lowest subband was excluded to avoid contamination of slow signal drifts. The WaveEp was calculated voxel-by-voxel according to the established method (Gupta et al., 2017). Z-standardization was performed across the entire brain to generate the individual WaveEp map. WaveEp of brain regions based on Brainnetome (BN) atlas were compared among three conditions (pre-, im-, and post-pSD) using one-way analysis of variance (ANOVA), followed by the Tukey's post hoc test for pairwise comparison. The significance level was set at P < 0.05 with false discovery rate (FDR) correction.
Results:
The mean PVT reaction time for pre-pSD, im-pSD and post-pSD was 301.43±36.54, 362.80±110.11, and 295.08±35.29 ms, respectively. PVT time of im-pSD was significantly increased compared with that of pre-pSD (P < 0.05). WaveEp of im-pSD was reduced as compared with that of pre- or post-pSD (P < 0.05, FDR correction) in the subregions of the default mode network (DMN) and frontal-parietal attention network (FPN) (Fig. 1). In contrast, WaveEp of im-pSD was significantly elevated in the rostral part of right lingual gyrus (BN196) and a subregion of right thalamus (BN232), suggesting a more uniform energy distribution across the signal subbands. The WaveEp of the subregions of left middle frontal gyrus (BN23) and inferior parietal gyrus (BN137) were negatively correlated with the mean PVT reaction time (P < 0.05) (Fig. 2).
·Figure 1. Brain regions of Brainnetome atlas with altered WaveEp following pSD
·Figure 2. Correlation analysis between WaveEp and the PVT mean reaction time
Conclusions:
Brain activity estimated by WaveEp is sensitive to cognitive and mental fatigue, psychiatric and sleep disorders (Geng et al., 2020). The WaveEp reduction of brain regions within DMN and FPN signifies a decline in the complexity of BOLD signals, which may link to the impaired sustained attention and mental fatigue following pSD (Wang et al., 2019). The WaveEp increase in the intrinsic functional activity of thalamus suggests the distinct role of thalamic functional plasticity in responding to sleep pressure (Chen et al.,2022). Nevertheless, both the PVT reaction and WaveEp levels recovered after several days of regular sleep. In conclusion, pSD has short-term and reversible impacts on brain WaveEp which is associated with individual psychomotor vigilance level.
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
FUNCTIONAL MRI
Sleep
Other - sleep deprivation，brain entropy
1|2Indicates the priority used for review
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Acknowledgement
This work was partially supported by Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0930000), National Natural Science Foundation of China (82341248), Shenzhen Science and Technology Programs (GJHZ20220913142812024), Postdoctoral Fellowship Program of CPSF (GZC20241847), and the Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences.