Yi-Chia Kung1, ChangWei Wesley Wu2,3, Pei-Jung Tsai4, Chia-Wei Lee5, Chun-Yi Zac Lo6, and Ching-Po Lin1
1Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan, 2Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan, 3Research Center of Brain and Consciousness, Shuang-Ho Hospital, New Taipei, Taiwan, 4Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States, 5Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan, 6Institute of Science and Technology for Brain-inspired Intelligence (ISTBI), Shanghai, China
Synopsis
Multiscale entropy (MSE) was used to disclose the mixture between
functional integration and segregation of brain circuits across NREM sleep. MSE
showed N0>N2 and N1>N2 in Scale 1, accompanied with N2>N1 and
N2>N3 in Scale 3. The scale-dependent entropy reflects distinct
aspects of information processing in the sleeping brain: brain tends to distribute
information distantly during the N2 stage and disintegrate both locally and
distantly at the N3 stage.
Introduction
A growing body of the literature indicated that
the neurophysiology changes across NREM sleep stages with the complexity
alteration. Multiscale entropy (MSE) analysis enables capturing multiple complexity
levels within each temporal signal across different time scales, which has been
applied to the functional MRI (fMRI) for differentiating patients with schizophrenia1.
Previous study indicated that MSE at different scales indicates different types
of local and distant neural communications2. In
this study, we applied the scale-dependent MSE to human fMRI data in sleep and attempted
to reveal the distinct brain communication patterns. We suggest the distinct
pattern of brain communication in NREM sleep could lead to the entropy change
and corresponding consciousness loss.Methods
The simultaneous
EEG-fMRI data were collected from 12 healthy men (age=22.9±2.5 y/o) using 3T
SIEMENS Trio MRI with a 32-channel MRI-compatible system (Brain Products,
Gilching, Germany) at National Yang-Ming University. The fMRI data were
acquired at most two hours long during sleep (TR/TE/FA=2500ms/30ms/80°,
FOV=220, matrix size=64x64, 35 slices with 3.4 mm thickness). T1-weighted image
(3d-MPRAGE, voxel size=1x1x1mm, TR/TE/TI/FA=1900ms/2.28ms/900ms/9°) and
resting-state fMRI were acquired before the sleep session, regarded as
wakefulness (N0). Recorded EEG data were analyzed offline for sleep scoring by
a licensed sleep technician to categorize sleep stages as Stage I (N1), Stage
II (N2), or Stage III (N3).
MSE analysis3 using a
coarse-graining procedure to estimate sample entropy in multiple time scales
(scale = 1-3 due to limited time samples). MSE
calculation could be summarized as follows: (a) coarse-grained time series was
constructed according to different scale factors; (b) the sample entropy was
quantified for each coarse-grained time series, and (c) the sample entropy
profile was examined over a range of scales. Results
MSE in Scale 1 and Scale 3 shows the significant
difference (Figure 1, repeated measure ANOVA, PFDR<.05) among sleep
stages. Precisely, entropy shows N0>N2 (P=.0001) and N1>N2 (P=.013)
in Scale 1, accompanied with N2>N1 (P=.006) and N2>N3 (P=.002)
in Scale 3. We
further examined the changes for the sleep-related networks including default mode
network, dorsal attention network, and cingulo-opercular network (Figure 2). Complexity
during N2 reduced in Scale 1 than wakefulness; while in Scale 3,
entropy in N2 was increased relative to N0 and N3. In
contrast to the wakefulness (N0), brain in N2 sleep moved toward a true
complexity pattern, where sample entropy decreased in shortest time scales while
increased in the largest scale factor.Discussion
Our
finding suggests that the changes in entropy that occur throughout the
sleep-wake cycle are strongly dependent on the time scale. Based
on the regulation of neural synchrony theory, scale-dependent neural complexity
is related to the information process that reflects the tendency of brain
communication. Coarse time scales reflect long-range interactions among distributed
neural populations, while fine time scales reflect interconnectivity across
local neural populations4. Compared to the
other NREM sleep stage, the highest entropy at coarse time scale during N2
sleep reflected the brain communication toward distributed interaction5. In contrast, the
lower complexity in N1 and N3 sleep presented decreased long-range
communication, in good agreements with the reduced physical long-distance
connections in NREM sleep6.Conclusion
The MSE approach for brain functional network provides new metric for
distinct information processing in NREM sleep. The scale-dependent
entropy suggests the mixture between functional integration and segregation of
a system’s output7. Brain communication
tends to integration at the distributed level during N2 sleep and in favor of
local integration during N1 and N3. Acknowledgements
This study
was supported in part by grants from Ministry of Science and Technology, Taiwan
(MOST 108-2321-B-010-010-MY2, MOST 108-2420-H-010-011, MOST108-2410-H-038-007) and National Health Research Institutes, Taiwan (NHRI-EX108-10611EI).References
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