Graph Theoretical Analysis of BOLD Functional Connectivity during Human Sleep without EEG Monitoring

Abstract

BackgroundFunctional brain networks of human have been revealed to have small-world properties by both analyzing electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) time series.Methods & ResultsIn our study, by using graph theoretical analysis, we attempted to investigate the changes of paralimbic-limbic cortex between wake and sleep states. Ten healthy young people were recruited to our experiment. Data from 2 subjects were excluded for the reason that they had not fallen asleep during the experiment. For each subject, blood oxygen level dependency (BOLD) images were acquired to analyze brain network, and peripheral pulse signals were obtained continuously to identify if the subject was in sleep periods. Results of fMRI showed that brain networks exhibited stronger small-world characteristics during sleep state as compared to wake state, which was in consistent with previous studies using EEG synchronization. Moreover, we observed that compared with wake state, paralimbic-limbic cortex had less connectivity with neocortical system and centrencephalic structure in sleep.ConclusionsIn conclusion, this is the first study, to our knowledge, has observed that small-world properties of brain functional networks altered when human sleeps without EEG synchronization. Moreover, we speculate that paralimbic-limbic cortex organization owns an efficient defense mechanism responsible for suppressing the external environment interference when humans sleep, which is consistent with the hypothesis that the paralimbic-limbic cortex may be functionally disconnected from brain regions which directly mediate their interactions with the external environment. Our findings also provide a reasonable explanation why stable sleep exhibits homeostasis which is far less susceptible to outside world.

Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

“…Pulse oximetry is easier to implement and less affected by MR gradient artifact than ECG. While the smooth pulse waveform might offer less precision for peak detection, it has been shown to produce comparable HRV values as ECG (Iyriboz et al, 1991 ) and used to derive HRV values during rs-fMRI (Lv et al, 2015 ; Guo et al, 2016 ). Therefore, pulse oximetry might be used for sleep detection instead of ECG, which could be formally tested in the future studies.…”

“…Nevertheless, it is hard for subjects to fall asleep with EEG scalp on. Lv et al identified sleep state using HRV derived from peripheral pulse signals, and observed consistent brain network properties compared to those derived from EEG based studies (Lv et al, 2015 ). Moreover, HRV measures are widely used, solely or combined with other physiological signal measures, as features in machine learning models to predict and detect the fatigue and sleepiness of drivers.…”

Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders

“…In our study, we measured the effective connectivity determined by the Granger causality only at the level of the scalp, which may limit the interpretations of our results. Otherwise, although we only analysed data from a relatively small group of healthy individuals, the samples included in this study were at least similar or even larger than those of most other studies investigating the SWN organization during sleep [2,4,33,55,57,69,75,[82][83][84][85][86][87], which should allow an adequate interpretation of our results. However, in order to confirm the results highlighted in our study, it seems important to carry out replication studies on samples at least similar to those in our study.…”

Overall Population Generalities, Sex Differences, and Individual Differences in Sleep Electroencephalography Functional Connectivity

“…Basic research on different stages of normal sleep is important for understanding sleep function and how sleep affects mental states. Functional connectivity (FC) in human brain networks, which objectively describes the communication between different brain regions, has also been introduced to the field of sleep research [5]- [7]. Some studies have described the FC during normal sleep [8]- [10]; however, they focused on the generalities of overall normal groups and seldom mentioned differences between groups with different characteristics (such as different sex) or among individuals.…”