Sleep experimental techniques

Development novel approaches to investigate human sleep. We focused on some non-invasive neuroimaging techniques, i.e., resting-state EEG, resting-state fMRI and simultaneous EEG-fMRI. Our recent studies include the following:
(1) A systematic review and meta-analysis on EEG spectral analysis in insomnia disorder (Zhao et al., 2021 Sleep Medicine Reviews);
(2) Distinct neural responses of morningness and eveningness chronotype revealed by resting-state fMRI(Wang et al., 2022 CNS Neuroscience and Therapeutics);
(3) A Simultaneous EEG-fMRI Study on Sleep Inertia(Chen et al., 2020 Hum Brain Mapp).

Sleep, emotion and memory

We investigate the sleep stage-related basic emotional operations and hihg-level socio-emotion. Research focuses on the role of different sleep stages (and its loss) in human emotional function. Our recent studies include the following:
(1) Control intrusive thoughts during motivated forgetting needs activation of anterior cingulate cortex(Garcia et al., 2022 J Neuroscience);
(2) Negative bias of mood after sleep deprivation (Liu et al., 2014 J Sleep Research);
(3) Emotion memory consolidation of fear conditioning in resting state (Feng et al., 2014 Social Cognitive and Affective Neuroscience).

Sleep intervention and promotion

We have investigated the brain network consequences of sleep disorders such as insomnia, sleep apnea and aging. Our recent papers include the following:
(1) Prediction on treatment improvement in depression with resting state connectivity (Long et al., 2020 Journal of Affective Disorders);
(2) Spa Therapy Improves Sleep Quality: Evidence from Questionnaire and Actigraphy (Yao et al., 2022 Brain-Apparatus Communication);
(3) Use of machine learning in predicting the efficacy of repetitive transcranial magnetic stimulation on treating depression (Chen et al., 2022 Journal of Psychiatric Research).

Recent Publications and Abstract

Jan 2019: Electrophysiological Signatures of the Resting-state fMRI Global Signal
The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined.
New methods
We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects.
We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the gamma band (30-45?Hz) and a negative correlation in the low-frequency band (4-20?Hz). Comparison with existing method(s). Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity.

These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.

Xiaoli Huang, Zhiliang Long, Xu Lei. Electrophysiological Signatures of the Resting-state fMRI Global Signal: A Simultaneous EEG-fMRI Study. Journal of Neuroscience Methods. In Press. DOI: 10.1016/j.jneumeth.2018.09.017.  BLOG discussion

Jan 2015: Competition between FPN and DMN supports social working memory and empathy
Abstract: An extensive body of literature has indicated that there is increased activity in the frontoparietal control network (FPC) and decreased activity in the default mode network (DMN) during working memory (WM) tasks. The FPC and DMN operate in a competitive relationship during tasks requiring externally-directed attention. However, the association between this FPC-DMN competition and performance in social WM tasks has rarely been reported in previous studies. To investigate this question, we measured FPC-DMN connectivity during resting-state and two emotional face recognition WM tasks using the 2-back paradigm. Thirty-four individuals were instructed to perform the tasks based on either the expression (EMO) or the identity (ID) of the same set of face stimuli. Consistent with previous studies, an increased anti-correlation between the FPC and DMN was observed during both tasks relative to the resting-state. Specifically, this anti-correlation during the EMO task was stronger than during the ID task, as the former has a higher social load. Intriguingly, individual differences in self-reported empathy were significantly correlated with the FPC-DMN anti-correlation in the EMO task. These results indicate that the top-down signals from the FPC suppress the DMN to support social WM and empathy.

Fei Xin, Xu Lei. Competition between frontoparietal control and default networks supports social working memory and empathy. Social Cognitive and Affective Neuroscience. Aug 2015 10(8): PDF file BLOG discussion

Jan 2014: Alcohol intoxication enhanced the theta rhythm and DMN-DAN anti-correlation
Abstract: Functional magnetic imaging (fMRI) studies showed that resting state activity in the healthy brain is organized into multiple large-scale networks encompassing distant regions. A key finding of resting state fMRI studies is the anti-correlation typically observed between the dorsal attention network (DAN) and the default mode network (DMN), which--during task performance--are activated and deactivated, respectively. Previous studies have suggested that alcohol administration modulates the balance of activation/deactivation in brain networks, as well as it induces significant changes in oscillatory activity measured by electroencephalography (EEG). However, our knowledge of alcohol-induced changes in band-limited EEG power and their potential link with the functional interactions between DAN and DMN is still very limited. Here we address this issue, examining the neuronal effects of alcohol administration during resting state by using simultaneous EEG-fMRI. Our findings show increased EEG power in the theta frequency band (4-8 Hz) after administration of alcohol compared to placebo, which was prominent over the frontal cortex.

More interestingly, increased frontal tonic EEG activity in this band was associated with greater anti-correlation between the DAN and the frontal component of the DMN. Furthermore, EEG theta power and DAN-DMN anti-correlation were relatively greater in subjects who reported a feeling of euphoria after alcohol administration, which may result from a diminished inhibition exerted by the prefrontal cortex. Overall, our findings suggest that slow brain rhythms are responsible for dynamic functional interactions between brain networks. They also confirm the applicability and potential usefulness of EEG-fMRI for central nervous system drug research.

Xu Lei, Yulin Wang, Hong Yuan, Dante Mantini. Neuronal oscillations and functional interactions between resting state networks: effects of alcohol intoxication. Human Brain Mapping. Jul 2014 35(7): PDF file

May 2012: Long memory of DMN predict personality trait
Abstract: Resting-state functional Magnetic Resonance Imaging (rsfMRI) is a powerful tool to investigate neurological and psychiatric diseases. Recently, the evidences linking the scaling properties of resting-state activity and the personality have been accumulated. However, it remains unknown whether personality is associated with the scale-free dynamics of default mode network (DMN) – the most widely studied network in the rsfMRI literatures. To investigate this question, we estimated the Hurst exponent, quantifying long memory of a time-series, in DMN of rsfMRI in 20 healthy individuals. The Hurst exponent in DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with the extraversion score of the revised Eysenck Personality Questionnaire. Specifically, longer memory in DMN corresponded to lower extraversion. We provide evidence for an association between individual differences in personality and scaling dynamics in DMN, whose alteration has been previously linked with introspective cognition. This association might arise from the efficient in online information processing. Our results suggest that personality trait may be reflected by the scaling property of resting-state networks.

1. Xu Lei, Zhiying Zhao, Hong Chen. Extraversion is Encoded by Scale-free Dynamics of Default Mode Network. NeuroImage. Jul 2013 74: 52–57. (Cover Article) download PDF file BLOG discussion

Jul 2011: Utilize fMRI coherent networks as prior in EEG source imaging
Abstract: The brain exhibits temporally coherent networks (TCNs) involving numerous cortical and sub-cortical regions both during the rest state and during the performance of cognitive tasks. TCNs represent the interactions between different brain areas, and understanding such networks may facilitate electroencephalography (EEG) source estimation. We propose a new method for examining TCNs using scalp EEG in conjunction with data obtained by functional magnetic resonance imaging (fMRI). In this approach, termed NEtwork based SOurce Imaging (NESOI), multiple TCNs derived from fMRI with independent component analysis (ICA) are used as the covariance priors of the EEG source reconstruction using Parametric Empirical Bayesian (PEB). In contrast to previous applications of PEB in EEG source imaging with smoothness or sparseness priors, TCNs play a fundamental role among the priors used by NESOI. NESOI achieves an efficient integration of the high temporal resolution EEG and TCN derived from the high spatial resolution fMRI.

Using synthetic and real data, we directly compared the performance of NESOI with other distributed source inversion methods, with and without the use of fMRI priors. Our results indicated that NESOI is a potentially useful approach for EEG source imaging.

Xu Lei, Peng Xu, Cheng Luo, Jinping Zhao, Dong Zhou, Dezhong Yao. fMRI Functional Networks for EEG Source Imaging. Hum Brain Mapp. Jul 2011 32(7):1141-1160. (Cover Article)download PDF file BLOG discussion

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