Brain Imaging and Behavior DOI 10.1007/s11682-015-9490-5
ORIGINAL RESEARCH
Increased interhemispheric resting-state functional connectivity after sleep deprivation: a resting-state fMRI study Yuanqiang Zhu 1 & Zhiyan Feng 1 & Junling Xu 2 & Chang Fu 2 & Jinbo Sun 1 & Xuejuan Yang 1 & Dapeng Shi 2 & Wei Qin 1
# Springer Science+Business Media New York 2015
Abstract Several functional imaging studies have investigated the regional effects of sleep deprivation (SD) on impaired brain function; however, potential changes in the functional interactions between the cerebral hemispheres after SD are not well understood. In this study, we used a recently validated approach, voxel-mirrored homotopic connectivity (VMHC), to directly examine the changes in interhemispheric homotopic resting-state functional connectivity (RSFC) after SD. Resting-state functional MRI (fMRI) was performed in 28 participants both after rest wakefulness (RW) and a total night of SD. An interhemispheric RSFC map was obtained by calculating the Pearson correlation (Fisher Z transformed) between each pair of homotopic voxel time series for each subject in each condition. The between-condition differences in interhemispheric RSFC were then examined at global and voxelwise levels separately. Significantly increased global VMHC was found after sleep deprivation; specifically, a significant increase in VMHC was found in specific brain regions, including the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus. No regions showed significantly reduced VMHC after sleep deprivation. Further analysis indicates that these findings did not depend on the various sizes of smoothing kernels that were adopted in the preprocessing steps and that the differences in these regions were still significant with or without global signal regression. Our data suggest that the increased VMHC
* Junling Xu [email protected] 1
Sleep and Neuroimage Group, School of Life Sciences and Technology, Xidian University, Xi’an, China
2
Department of Nuclear Medicine, People’s Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
might reflect the compensatory involvement of bilateral brain areas, especially the bilateral thalamus, to prevent cognitive performance deterioration when sleep pressure is elevated after sleep deprivation. Our findings provide preliminary evidence of interhemispheric correlation changes after SD and contribute to a better understanding of the neural mechanisms of SD. Keywords Sleep deprivation . Voxel mirrored homotopic connectivity . Resting-state functional connectivity . Thalamus . FMRI
Introduction A single night of total sleep deprivation (SD) can result in diminished attention and cognitive performance deterioration, contributing to disastrous outcomes (Boonstra et al. 2007; Durmer and Dinges 2005). A number of neuroimaging studies have examined the neuroanatomical correlates of impaired performance during SD (Chee and Chuah 2008; Chee and Tan 2010; Chee et al. 2010). However, these investigations have focused on the regional effects of SD on executive function, attention and short-term memory (Chee and Choo 2004; Chee et al. 2010). Recent works have emphasized the role of disrupted brain connectivity (both functional and structural) on delineating the deleterious effects of SD: Functional brain connectivity within the default mode network and its anti-correlations with the attention networks were found reduced together with decreased task performance (De Havas et al. 2012); functional brain connectivity in the resting state was also found to be a predictor of cognitive resilience in the face of SD (Yeo et al. 2015). Structural brain connectivity was found to be closely associated with performance vulnerability after SD as well (Rocklage et al. 2009). In the aforementioned
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structural brain connectivity study, less efficient connectivity in white matter tracts (characterized by lower fractional anisotropy), particularly in the corpus callosum, was associated with lower accuracy of a visual-motor control (VMC) task after SD. As the corpus callosum is the major white-matter tract connecting the left and right cerebral hemispheres, the observed associations suggested that interhemispheric connectivity is important for cognitive control after SD. Therefore, functional interhemispheric connectivity might also be affected after SD. Supporting this, electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies have demonstrated asymmetric interhemispheric EEG coherence (Achermann et al. 2001; Kattler et al. 1994) and altered bilateral task-related activation after SD (Chee and Choo 2004; Chee et al. 2010). Resting-state functional fMRI has provided a way to quantify directly the interhemispheric functional connectivities (Kelly et al. 2011). Temporal coherence between activity in one voxel and its homotopic voxel in the opposite hemisphere is one of the most fundamental characteristics of the brain’s intrinsic functional architecture. Alterations in interhemispheric resting-state functional connectivity (RSFC) have been found in our previous studies, suggesting that interhemispheric correlations are a sensitive indicator and may be of great importance in brain function (Yuan et al. 2012; Zhou et al. 2013). To our knowledge, no studies on the interhemispheric RSFC changes after SD have been published. Here, we directly examined the changes in interhemispheric RSFC after SD using a recently validated approach, voxel-mirrored homotopic connectivity (VMHC) (Zhou et al. 2013; Zuo et al. 2010). which quantifies the RSFC between each voxel in one hemisphere and its mirrored counterpart in the other hemisphere. A recent meta-analysis study has indicated that bilateral thalamic activation is increased compared to reduced frontal-parietal activation following SD (Ma et al. 2015). The study suggested that the increased bilateral thalamic activation after SD might reflect increased effort to compensate for dysfunction of the frontal-parietal attentional network after sleep loss. Therefore in this study, we compared VMHC between condition of SD and condition of resting wakefulness (RW) and hypothesized that the VMHC in multiple regions, particularly in the thalamus, was increased after SD.
Methods Subjects Thirty participants were recruited from a group of college students. The participants were carefully screened to ensure that they met the inclusion and exclusion criteria. The
inclusion criteria were the following: (1) 18–35 years of age and (2) right-handed. The exclusion criteria were the following: (1) a history of alcohol or drug abuse, (2) a present or past history of any psychiatric or neurologic disorders, (3) a history of sleep disorders, (4) an extreme morning or extreme evening type as assessed by a questionnaire (Horne and Ostberg 1975). and (5) work that required shift hours. To ensure normal sleep–wake patterns, participants were monitored for 3 consecutive days before study participation, using a wrist Actiwatch (a wristwatch movement sensor sensitive to wake–sleep states, Philips Respironics, USA) and via subjective sleep logs. These measurements validated that participants had habitual good sleep (7:45 ± 1:22 h each night, rising at ∼7:46 ± 1:13 A.M). All subjects provided written informed consent after the experimental procedures were fully explained. All research procedures were approved by the People’s Hospital of Zhengzhou University Subcommittee on Human Studies and were conducted in accordance with the Declaration of Helsinki. One of the 30 subjects quit after the total sleep deprivation session; another subject was excluded because of excessive movement (displacement/rotation >1.0 mm/degrees) during the SD session. Therefore, 28 right-handed, healthy graduates (14 males; mean ± SD age, 22.1 ± 1.6 years old; range, 19– 26 years old) successfully completed this study. All participants declared that they did not smoke or consume any stimulants, medications, alcohol or caffeine for at least 24 h prior to the scanning. Study procedure The participants made three visits to the laboratory. During the first visit, all participants were briefed about the study protocol and practiced the experimental tasks. Effects of SD on the performances of these tasks have been illustrated in our previous study (Xu et al. 2015). One week later, the subjects came to the laboratory either for a RW session or a SD session. The session order was randomized in a cross-over fashion, and there was a ~ 1-week interval to minimize possible residual effects of total sleep deprivation on cognition (Van Dongen et al. 2003). The participants reported to the laboratory at 6:00 A. M. in the RW condition. For the SD session, research assistants remained with the sleep-deprived volunteers throughout the night to prevent them from falling asleep. Every hour from 8:00 PM to 6:00 AM, the participants completed 10 min of the psychomotor vigilance task (Lim and Dinges 2008). For the remaining time, they were allowed to engage in non-strenuous activities, such as reading or watching videos. No food was given after midnight. The resting-state functional images were obtained at the same time for the RW and SD sessions (7:00 AM). The high-resolution T1 scans used in these analyses were performed during the sleep deprivation session.
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Data acquisition The imaging data were collected using a 3-Tesla MRI system (EXCITE, General Electric, Milwaukee, Wisconsin) at the Department of Nuclear Medicine of the People’s Hospital of Zhengzhou University, Zhengzhou, China. A standard birdcage head coil was used, along with restraining foam pads, to minimize head motion and to diminish scanner noise. The resting-state functional images were obtained using a gradient-echo planar imaging sequence with the following parameters: TR = 2000 ms, TE = 30 ms, FOV = 240 mm × 240 mm, data matrix = 64 × 64, slices = 33 and total 210 volumes. The subjects were instructed to keep their eyes closed, not to think about anything, and to stay awake during the entire session. After the scan, the subjects were asked whether they were awake in the previous session; all subjects confirmed that they were awake. The participants were also instructed to fulfill the Stanford Sleepiness Scale (SSS) by choosing one from seven statements that best described their current state of alertness after the scan. The Zung Self-Rating Anxiety Scale (SAS), Zung Self-Rating Depression Scale (SDS) and self-reports for the descriptors vigorous, restless and irritable were also used to quantify their behavioral states at the end of the scan. A high-resolution T1-weighted image was also acquired during the SD session with a volumetric three-dimensional spoiled gradient recall sequence with the following parameters: FOV = 256 × 256 mm2, TR = 8.2 ms, TE = 3.2 ms, matrix =128 × 128, slice thickness = 1 mm, and 140 slices in the axial plane. Functional image preprocessing Preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF3.0 Advanced edition) (Chao-Gan and Yu-Feng 2010), which synthesizes procedures in statistical parametric mapping (SPM8, http://www. fil.ion.ucl.ac.uk/spm) and the Resting-State Functional MR imaging toolkit (REST; http://www.restfmri.net) (Song et al. 2011). The first 10 volumes were discarded to eliminate the non-equilibrium effects of magnetization and allow the participants to acclimate to the EPI scanning environment. The remaining 200 images were corrected for the acquisition delay between slices and then were realigned to the first volume. One subject in the SD session exceeded our rigorous motion threshold of less than 1 mm spatial displacement in any direction and was excluded in the following analysis. Individual T1-weighted images were co-registered to the mean of the realigned EPI images. The transformed T1 images were then segmented into gray matter, white matter, and cerebrospinal fluid. The diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) tool was used to compute the transformation from individual space to MNI
space. The resting-state fMRI images were normalized in a resolution of 3 × 3 × 3 mm3 using the same transformation information as the T1 images, then smoothed with a 6-mm full-width at half-maximum isotropic Gaussian kernel. Previous study has shown that the micro-head motions can impact the resting-state fMRI measures such as VMHC. Therefore, higher-level Friston-24 model was used to regress head motion effects out of the realigned data (the 24 parameters include 6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items). We further calculated the mean frame-wise displacement (FD) as a measure of the micro-head motion of each subject. To further reduce the effects of confounding factors, the average signals arising from the ventricles and white matter were removed from the data with the linear regression. Linear trend removal and temporal bandpass filtering (0.01– 0.08 Hz) were then performed. The data preprocessing pipeline was illustrated in Fig. 1. Interhemispheric RSFC and statistical analysis For VMHC analysis, a left-right hemisphere symmetric brain template was generated from the 28 subjects to minimize the effects of geometric differences between the two hemispheres. First, a mean normalized T1 image was generated by averaging the 28 spatially normalized T1 images. Next, the group-specific symmetrical template was obtained by averaging the mean normalized T1 image and its left–right mirrored version. Then each individual T1 image that had been normalized to MNI space was co-registered nonlinearly to this group-specific symmetric template. The same transformation was then applied to the resting-state functional images. Pearson correlations (Fisher Z transformed) between each pair of homotopic voxel (with opposite MNI space x coordinate) time series were computed for each subject to generate VMHC maps (Zhou et al. 2013). The global VMHC of each subject was calculated by averaging VMHC values across all brain voxels within a unilateral hemispheric gray matter mask (there is only one correlation for each pair of homotopic voxels). Group comparisons of global VMHC were performed using a paired two-tailed t-test between the RW and SD conditions. A difference was considered to be significant when p < 0.05. With regard to the regional group differences in VMHC, a general liner model analysis was performed to investigate the difference in VMHC maps in a voxel-by-voxel manner between the RW and SD conditions using the SPM8 software package while accounting for the confounding effects of Jenkinson’s mean FD by including this term as a regressor as recommended in a previous study although no significance difference was found between two conditions on mean FD values (p = 0.16). A difference was considered to be significant when p < 0.05, FWE (family-wise error) was corrected, and a minimum cluster size of 3 voxels was present.
Brain Imaging and Behavior Fig. 1 Processing pipeline used to construct symmetrical hemispheric maps. `Preprocessed functional images (realigned and slice-time corrected) were normalized into Montreal Neurological Institute (MNI) space using the same transformed information got from T1 images. The functional images were then smoothed, detrended and filtered. Before calculating VMHC, the resulting functional images were firstly registered nonlinearly to a group-specific symmetric brain template generated by individual normalized T1 images
As different spatial smoothing kernel had an impact on the VMHC results indicated by a previous study (Zuo et al. 2010). various sizes of the kernel (FWHM of 4 and 8 mm) were used, and the statistical analysis was reperformed. Whole brain signal regression can introduce negative correlations between brain regions and has become a controversial issue in resting-state fMRI preprocessing (Fox et al. 2009). and therefore, we also repeated our analysis with global signal regression and tested whether the results remained. As noted above, all the statistical analysis are based on regression of the signals of ventricular and WM, while global signal regression was only performed as an ancillary method. To investigate the correlation between the VMHC values and the behavioral measures, we computed Pearson correlation coefficients between the behavioral measures changes (RW-SD) and altered VMHC values within the group differences (RW-SD) when 6 mm FWHM was used in the preprocessing step without global signal regression.
Results
Scale were significantly increased after TSD; self-reports for vigor were significantly decreased after SD; whereas self-reports for restless, irritable, SAS and SDS did not differ in SD and RW conditions. Group VMHC and the spatial distribution of homotopic connectivity The mean global VMHC in the SD condition is significantly higher than that in the RW condition (RW, 0.75 ± 0.13; SD, Table 1
Group effects of TSD on behavioral measures
Behavioral measures
RW
TSD
p value
Stanford sleepiness scale Vigor Restless Irritable SAS SDS
2.85 ± 0.86 6.67 ± 1.95 1.52 ± 1.66 2.16 ± 2.27 30.87 ± 7.45 32.6 ± 5.96
4.04 ± 1.04 4.45 ± 2.68 2.35 ± 2.25 2.7 ± 2.93 30.62 ± 6.09 32.52 ± 7.47
p < 0.0001 p < 0.0005 NS NS NS NS
Values represent mean ± SEM (n = 28).
Effects of sleep deprivation on behavioral measures are summarized in Table 1, Self-reports for the Stanford Sleepiness
RW rest wakefulness, SD sleep deprivation, SAS Self-Rating Anxiety Scale, SDS Zung Self-Rating Depression Scale.
Brain Imaging and Behavior Fig. 2 Spatial distribution of VMHC for the RW and SD conditions. The color bar shows the Fisher Z-transformed correlation. The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: VMHC: voxel-mirrored homotopic connectivity; RW: resting wakefulness; SD: sleep deprivation; L, left
0.83 ± 0.08; p = 0.003). However, the spatial distribution of homotopic connectivity showed broad similarities between the two conditions (Fig. 2). The gray matter showed larger VMHC than the white matter, and the regions closer to the midline, such as the parietal and occipital lobes and the cerebellum, appeared to have higher VMHC. However, a higher interhemispheric RSFC was not uniquely observed in brain areas closer to the midline. For example, brain areas such as sensorimotor, insula, and putamen were observed to have high FC values despite being off the midline.
by increase in regional VMHC, we also calculated the mean global VMHC using the same mask but with the specific regions found above excluded. We found that the background VMHC was also increased after SD (p = 0.005). In addition, we also found the whole brain signal amplitude (defined as standard deviation values of the whole brain signal) was increased after SD, with 4.9 in RW and 7.9 in SD (p < 0.0001). Effects of smooth kernel and global signal regression on the results
Following sleep deprivation, VMHC was significantly increased in multiple regions including the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus (Fig. 3, Table 2). The effective size and peak coordinates of significant clusters are summarized in Table 1. No region exhibited significantly decreased VMHC after SD. We also evaluated the VMHC differences between SD and normal conditions including gender as a covariate in the design matrix. However, the results remained same and no gender effect on the VMHC differences was found. Therefore, gender was not included as a covariate in the later analysis. To investigate whether the increase in global VMHC after SD was driven by increase in total background VMHC or just
To address the smoothing effect on the results, we re-performed the statistical analysis using different smooth kernels (FWHM of 4 and 8 mm). The results indicated that the overall pattern of findings did not depend on the level of spatial smoothing, and the differences in the aforementioned regions were still significant when various sizes of smoothing kernels were adopted in the preprocessing step (Fig. 4). We also repeated our analysis with global signal regression, and the findings revealed good consistency with the results without global signal regression, although some minor differences were detected (Fig. 5). VMHC in the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus remained significantly increased after SD (p < 0.05, FWE corrected); no regions showed significantly reduced VMHC after SD. However, the number of voxels that
Fig. 3 Axial views of significant changes in VMHC between the RW and SD conditions (RW > SD, FWE-corrected, p < 0.05). There were no regions where VMHC was greater for the RW than the SD condition.
The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: VMHC: voxel-mirrored homotopic connectivity; RW: resting wakefulness; SD: sleep deprivation; L, left
Regional differences between RW and SD conditions
Brain Imaging and Behavior Table 2 Peak coordinates of significant brain regions in which interhemispheric resting-state functional connectivity was increased after sleep deprivation
Regions
Number of voxels
Peak Coordinates (talairach) x
y
t-value z
Thalamus
140
±36
37
28
8.2
Paracentral Lobule
136
±33
35
43
8.9
Supplementary Motor Area
97
±27
36
44
9.0
Postcentral Gyrus Lingual Gyrus
62 20
±8 ±39
40 26
35 23
8.3 7.8
Results was set at p < 0.05, FWE corrected
showed significant differences within the thalamus increased after global signal regression, and the location of significant changes in the lingual gyrus changed after global signal regression. Correlation behavioral measures changes and VMHC changes No significant correlation was observed between VMHC values and behavioral measures (SSS, SAS, SDS and self-reports for vigor, restless, irritable) in any brain regions with altered VMHC values (p > 0.05).
paired t-test with FD as a covariate was used to compare the VMHC differences between the RW condition and the SD condition. The results were shown in Fig. 6a and basically replicated the findings from the full sample. For the leave-one-out sample validation method, we left one subject out the sample and performed the same group comparisons based on the remaining sample (i.e. 27 RW vs. 27 SD), and this led to total 28 paired t-test images. For each voxel, the reproducibility of the VMHC results was calculated as the number of test where this voxel exhibited significant group differences across the total 28 tests. As expected, Fig. 6b indicated highly reproducible patterns of VMHC increases after sleep deprivation.
Reproducibility The sample size in this work is relatively small, and thus the reproducibility of the findings needs to be assessed. Therefore, a split-half validation method and a leave-one-out sample validation method were adopted (Li et al. 2015). For the split-half sample validation method, 14 subjects were randomly selected from the sample (FWHM is 6 mm without GSR) and a Fig. 4 Axial views of significant changes in VMHC between the RW and SD conditions (RW > SD, FWE-corrected, p < 0.05) with various sizes of smoothing that were adopted in the reprocessing step. The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: FWHM, full width at half-maximum; L, left
Discussion In this study, we found a significantly increased global VMHC after sleep deprivation using resting-state fMRI. More specifically, a significant increase in VMHC was found in specific brain regions, including the thalamus, paracentral lobule,
Brain Imaging and Behavior Fig. 5 Effects of global signal regression on the changes in VMHC between the RW and SD conditions (RW > TSD, FWE-corrected, p < 0.05). The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: L, left
supplementary motor area, postcentral gyrus and lingual gyrus. No regions showed significantly reduced VMHC after sleep deprivation. Further analysis indicated that these findings did not depend on the strategies that were adopted in the preprocessing steps, such as the various sizes of the smoothing kernels or the global signal regression. To our knowledge, this is the first evidence of altered interhemispheric RSFC after sleep deprivation. The VMHC approach shows high test-retest reliability, and has been applied in normal aging, psychiatric and neurological diseases (Zuo and Xing 2014). Various sizes of kernels (FWHM of 4, 6 and 8 mm) were used in our analysis to investigate the effects of spatial smoothing on the VMHC results. However, consistent with our previous findings (Zhou et al. 2013). similar results were obtained suggesting that the overall patterns of spatial distribution might depend partly on the level of spatial smoothing (Zuo et al. 2010). but Fig. 6 Reproducibility of VMHC findings. a Split-half sample validation. 14 subjects were randomly selected from the sample and a paired t-test with FD as a covariate was used to compare the VMHC differences between the RW condition and the SD condition. b Leave-oneout sample validation. The group comparisons based upon the permutated samples (i.e., 27 RW vs. 27 SD) for total 28 times. For each voxel, the color indicates number of tests where this voxel exhibited significant group differences across the total 28 tests
the between-group differences had little dependence on the level of spatial smoothing. Given the controversy of the global signal in the preprocessed resting state fMRI data (Fox et al. 2009). we analyzed our data both with and without global signal regression. Interestingly, the VMHC differences after sleep deprivation were similar. Therefore, the selection of global signal regression had little impact on this current study. Altered interactions between hemispheres after sleep deprivation have been observed in several brain imaging studies. For example, EEG slow wave activity (SWA) enhancement was more pronounced in the left hemisphere but not in the right hemisphere after SD (Kattler et al. 1994). Activation in response to object-selective attention tasks after SD showed left hemisphere dominant activation (Chee et al. 2010). Amplified brain reactivity in response to pleasure-evoking stimuli were also more pronounced in the left hemisphere after
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SD (Gujar et al. 2011). However, the focus of previous studies was mainly on regional brain activity alterations after SD. Inter-regional correlations in spontaneous activity, which are also an important feature of the brain’s functional architecture, have not been examined directly after SD. The spatial distribution of interhemispheric RSFC observed in this study was consistent with that observed in other studies. Importantly, brain areas those that were not closer to midline such as sensorimotor, insula, and putamen also appeared to have greater interhemispheric correlation. Robust homotopic RSFC between these areas may reflect the contributions of subcortical hubs rather than the contributions of direct callosal connections for interhemispheric communication (Zuo et al. 2010). Furthermore, increased VMHC in the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus was found after sleep deprivation. This is reasonable because these regions are among the vulnerable brain areas that showed altered cerebral metabolism after sleep deprivation (Thomas et al. 2000; Wu et al. 2006). The greatest increase in VMHC was found in the thalamus, a region that is thought to facilitate and regulate communication in the neocortical areas and has a substantial role in regulating states of wakefulness and sleep (Steriade and Llinás 1988). Sleep-wake mechanisms modulating arousal derive primarily from subcortical structures, including the thalamus and brainstem areas (Chee et al. 2008). The increased VMHC in the thalamus may contribute to maintaining cognitive performance when arousal is low after sleep deprivation. This is consistent with previous studies that found increased bilateral thalamus activation during a working memory task and elevated thalamic activation during non-lapse periods contrasted with lapse periods after sleep deprivation (Chee and Choo 2004; Chee et al. 2008). Furthermore, thalamic activation was also found to be inversely correlated with parietal and prefrontal activation during the attention task after SD (Ma et al. 2015). The increased thalamic activation observed in previous studies and the increased intra-thalamic connectivity found in our study might reflect the compensatory involvement of the bilateral thalamus to prevent cognitive performance deterioration when sleep pressure is elevated after sleep deprivation. However, it should be noted that the VMHC results found in our study have limited extent in thalamus, appearing to be specific to mediodorsal regions rather than more globally; the elevated thalamic activation found in the previous study was lateralized and ipsilateral to increased activation in left dorsal-lateral prefrontal cortex, rather than exhibiting an increased connectivity to its homologue in the opposite hemisphere (Chee and Choo 2004). and what’s more, no significant correlations were found between the altered VMHC values and altered behavioral measures between RW and SD condition. These observations might indicate that the compensatory adaptation of increased VMHC after sleep deprivation might function in a somewhat more sophisticated way
and future work will be needed to characterize in detail the effects of increased functional interhemispheric interactions after sleep deprivation on brain activity during task and resting baselines. There are several limitations to this study. First, the brain is asymmetrical. However, we attempted to resolve this issue by smoothing the functional data and using a symmetric template. Second, the sample size in this study was relatively small; thus, the findings should be replicated in a larger sample. Third, we did not measure white matter diffusivity or bilateral gray matter volume; therefore, we could not exclude the influence of structural abnormalities to VMHC. Compliance with ethical standards Funding This study was financially supported by National Basic Research Program of China under Grant Nos. 2015CB856403, 2014CB543203 and 2012CB518501, the National Natural Science Foundation of China under Grant Nos. 81271644, 81471811, 81471738, 61401346, 81271534, 81160452 and 31200837, and the Fundamental Research Funds for the Central Universities. Conflict of interest All authors have indicated no financial conflicts of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the People’s Hospital of Zhengzhou University Subcommittee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study. Disclosure statement This was not an industry supported study. The authors have indicated no financial conflicts of interest.
References Achermann, P., Finelli, L. A., & Borbély, A. A. (2001). Unihemispheric enhancement of delta power in human frontal sleep EEG by prolonged wakefulness. Brain Research, 913, 220–223. Boonstra, T., Stins, J., Daffertshofer, A., & Beek, P. (2007). Effects of sleep deprivation on neural functioning: an integrative review. Cellular and Molecular Life Sciences, 64, 934–946. Chao-Gan, Y., Yu-Feng, Z., 2010. DPARSF: a MATLAB toolbox for Bpipeline^ data analysis of resting-state fMRI. Frontiers in systems neuroscience, 4, 13. Chee, M. W., & Choo, W. C. (2004). Functional imaging of working memory after 24 hr of total sleep deprivation. The Journal of Neuroscience., 24, 4560–4567. Chee, M. W., & Chuah, L. Y. (2008). Functional neuroimaging insights into how sleep and sleep deprivation affect memory and cognition. Current Opinion in Neurology, 21, 417–423. Chee, M. W., & Tan, J. C. (2010). Lapsing when sleep deprived: neural activation characteristics of resistant and vulnerable individuals. NeuroImage, 51, 835–843.
Brain Imaging and Behavior Chee, M. W., Tan, J. C., Zheng, H., Parimal, S., Weissman, D. H., Zagorodnov, V., et al. (2008). Lapsing during sleep deprivation is associated with distributed changes in brain activation. The Journal of Neuroscience., 28, 5519–5528. Chee, M. W., Tan, J. C., Parimal, S., & Zagorodnov, V. (2010). Sleep deprivation and its effects on object-selective attention. NeuroImage, 49, 1903–1910. De Havas, J. A., Parimal, S., Soon, C. S., & Chee, M. W. (2012). Sleep deprivation reduces default mode network connectivity and anticorrelation during rest and task performance. NeuroImage, 59, 1745–1751. Durmer, J.S., Dinges, D.F., 2005. Neurocognitive consequences of sleep deprivation. In Seminars in neurology, 25, 117–129. Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology., 101, 3270–3283. Gujar, N., Yoo, S.-S., Hu, P., & Walker, M. P. (2011). Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences. The Journal of Neuroscience., 31, 4466–4474. Horne, J. A., & Ostberg, O. (1975). A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. International Journal of Chronobiology, 4, 97–110. Kattler, H., Dijk, D. J., & Borbély, A. A. (1994). Effect of unilateral somatosensory stimulation prior to sleep on the sleep EEG in humans. Journal of Sleep Research, 3, 159–164. Kelly, C., Zuo, X.-N., Gotimer, K., Cox, C. L., Lynch, L., Brock, D., et al. (2011). Reduced interhemispheric resting state functional connectivity in cocaine addiction. Biological Psychiatry, 69, 684–692. Li, H. J., Xu, Y., Zhang, K. R., Hoptman, M. J., & Zuo, X. N. (2015). Homotopic connectivity in drug-naïve, first-episode, early-onset schizophrenia. Journal of Child Psychology and Psychiatry, 56, 432–443. Lim, J., & Dinges, D. F. (2008). Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences, 1129, 305–322. Ma, N., Dinges, D. F., Basner, M., & Rao, H. (2015). How acute total sleep loss affects the attending brain: a meta-analysis of neuroimaging studies. Sleep, 38, 233–240. Rocklage, M., Williams, V., Pacheco, J., & Schnyer, D. M. (2009). White matter differences predict cognitive vulnerability to sleep deprivation. Sleep, 32, 1100.
Song, X.-W., Dong, Z.-Y., Long, X.-Y., Li, S.-F., Zuo, X.-N., Zhu, C.-Z., et al. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PloS One, 6, e25031. Steriade, M., & Llinás, R. R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiological Reviews, 68, 649–742. Thomas, M., Sing, H., Belenky, G., Holcomb, H., Mayberg, H., Dannals, R., et al. (2000). Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. Journal of Sleep Research, 9, 335–352. Van Dongen, H. P., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The cumulative cost of additional wakefulness: doseresponse effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26, 117–126. Wu, J. C., Gillin, J. C., Buchsbaum, M. S., Chen, P., Keator, D. B., Wu, N. K., et al. (2006). Frontal lobe metabolic decreases with sleep deprivation not totally reversed by recovery sleep. Neuropsychopharmacology, 31, 2783–2792. Xu, J., Zhu, Y., Fu, C., Sun, J., Li, H., Yang, X., et al. (2015). Frontal metabolic activity contributes to individual differences in vulnerability toward total sleep deprivation-induced changes in cognitive function. Journal of sleep research. Yeo, B. T., Tandi, J., & Chee, M. W. (2015). Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. NeuroImage, 111, 147–158. Yuan, K., Qin, W., Liu, P., Zhao, L., Yu, D., Zhao, L., et al. (2012). Reduced fractional anisotropy of corpus callosum modulates interhemispheric resting state functional connectivity in migraine patients without aura. PloS One, 7, e45476. Zhou, G., Liu, P., Zeng, F., Yuan, K., Yu, D., Deneen, K. M., et al. (2013). Increased interhemispheric resting-state functional connectivity in functional dyspepsia: a pilot study. NMR in Biomedicine, 26, 410–415. Zuo, X.-N., & Xing, X.-X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neuroscience and Biobehavioral Reviews, 45, 100–118. Zuo, X.-N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D. S., Bangaru, S., et al. (2010). Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. The Journal of Neuroscience., 30, 15034–15043.
ORIGINAL RESEARCH
Increased interhemispheric resting-state functional connectivity after sleep deprivation: a resting-state fMRI study Yuanqiang Zhu 1 & Zhiyan Feng 1 & Junling Xu 2 & Chang Fu 2 & Jinbo Sun 1 & Xuejuan Yang 1 & Dapeng Shi 2 & Wei Qin 1
# Springer Science+Business Media New York 2015
Abstract Several functional imaging studies have investigated the regional effects of sleep deprivation (SD) on impaired brain function; however, potential changes in the functional interactions between the cerebral hemispheres after SD are not well understood. In this study, we used a recently validated approach, voxel-mirrored homotopic connectivity (VMHC), to directly examine the changes in interhemispheric homotopic resting-state functional connectivity (RSFC) after SD. Resting-state functional MRI (fMRI) was performed in 28 participants both after rest wakefulness (RW) and a total night of SD. An interhemispheric RSFC map was obtained by calculating the Pearson correlation (Fisher Z transformed) between each pair of homotopic voxel time series for each subject in each condition. The between-condition differences in interhemispheric RSFC were then examined at global and voxelwise levels separately. Significantly increased global VMHC was found after sleep deprivation; specifically, a significant increase in VMHC was found in specific brain regions, including the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus. No regions showed significantly reduced VMHC after sleep deprivation. Further analysis indicates that these findings did not depend on the various sizes of smoothing kernels that were adopted in the preprocessing steps and that the differences in these regions were still significant with or without global signal regression. Our data suggest that the increased VMHC
* Junling Xu [email protected] 1
Sleep and Neuroimage Group, School of Life Sciences and Technology, Xidian University, Xi’an, China
2
Department of Nuclear Medicine, People’s Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
might reflect the compensatory involvement of bilateral brain areas, especially the bilateral thalamus, to prevent cognitive performance deterioration when sleep pressure is elevated after sleep deprivation. Our findings provide preliminary evidence of interhemispheric correlation changes after SD and contribute to a better understanding of the neural mechanisms of SD. Keywords Sleep deprivation . Voxel mirrored homotopic connectivity . Resting-state functional connectivity . Thalamus . FMRI
Introduction A single night of total sleep deprivation (SD) can result in diminished attention and cognitive performance deterioration, contributing to disastrous outcomes (Boonstra et al. 2007; Durmer and Dinges 2005). A number of neuroimaging studies have examined the neuroanatomical correlates of impaired performance during SD (Chee and Chuah 2008; Chee and Tan 2010; Chee et al. 2010). However, these investigations have focused on the regional effects of SD on executive function, attention and short-term memory (Chee and Choo 2004; Chee et al. 2010). Recent works have emphasized the role of disrupted brain connectivity (both functional and structural) on delineating the deleterious effects of SD: Functional brain connectivity within the default mode network and its anti-correlations with the attention networks were found reduced together with decreased task performance (De Havas et al. 2012); functional brain connectivity in the resting state was also found to be a predictor of cognitive resilience in the face of SD (Yeo et al. 2015). Structural brain connectivity was found to be closely associated with performance vulnerability after SD as well (Rocklage et al. 2009). In the aforementioned
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structural brain connectivity study, less efficient connectivity in white matter tracts (characterized by lower fractional anisotropy), particularly in the corpus callosum, was associated with lower accuracy of a visual-motor control (VMC) task after SD. As the corpus callosum is the major white-matter tract connecting the left and right cerebral hemispheres, the observed associations suggested that interhemispheric connectivity is important for cognitive control after SD. Therefore, functional interhemispheric connectivity might also be affected after SD. Supporting this, electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies have demonstrated asymmetric interhemispheric EEG coherence (Achermann et al. 2001; Kattler et al. 1994) and altered bilateral task-related activation after SD (Chee and Choo 2004; Chee et al. 2010). Resting-state functional fMRI has provided a way to quantify directly the interhemispheric functional connectivities (Kelly et al. 2011). Temporal coherence between activity in one voxel and its homotopic voxel in the opposite hemisphere is one of the most fundamental characteristics of the brain’s intrinsic functional architecture. Alterations in interhemispheric resting-state functional connectivity (RSFC) have been found in our previous studies, suggesting that interhemispheric correlations are a sensitive indicator and may be of great importance in brain function (Yuan et al. 2012; Zhou et al. 2013). To our knowledge, no studies on the interhemispheric RSFC changes after SD have been published. Here, we directly examined the changes in interhemispheric RSFC after SD using a recently validated approach, voxel-mirrored homotopic connectivity (VMHC) (Zhou et al. 2013; Zuo et al. 2010). which quantifies the RSFC between each voxel in one hemisphere and its mirrored counterpart in the other hemisphere. A recent meta-analysis study has indicated that bilateral thalamic activation is increased compared to reduced frontal-parietal activation following SD (Ma et al. 2015). The study suggested that the increased bilateral thalamic activation after SD might reflect increased effort to compensate for dysfunction of the frontal-parietal attentional network after sleep loss. Therefore in this study, we compared VMHC between condition of SD and condition of resting wakefulness (RW) and hypothesized that the VMHC in multiple regions, particularly in the thalamus, was increased after SD.
Methods Subjects Thirty participants were recruited from a group of college students. The participants were carefully screened to ensure that they met the inclusion and exclusion criteria. The
inclusion criteria were the following: (1) 18–35 years of age and (2) right-handed. The exclusion criteria were the following: (1) a history of alcohol or drug abuse, (2) a present or past history of any psychiatric or neurologic disorders, (3) a history of sleep disorders, (4) an extreme morning or extreme evening type as assessed by a questionnaire (Horne and Ostberg 1975). and (5) work that required shift hours. To ensure normal sleep–wake patterns, participants were monitored for 3 consecutive days before study participation, using a wrist Actiwatch (a wristwatch movement sensor sensitive to wake–sleep states, Philips Respironics, USA) and via subjective sleep logs. These measurements validated that participants had habitual good sleep (7:45 ± 1:22 h each night, rising at ∼7:46 ± 1:13 A.M). All subjects provided written informed consent after the experimental procedures were fully explained. All research procedures were approved by the People’s Hospital of Zhengzhou University Subcommittee on Human Studies and were conducted in accordance with the Declaration of Helsinki. One of the 30 subjects quit after the total sleep deprivation session; another subject was excluded because of excessive movement (displacement/rotation >1.0 mm/degrees) during the SD session. Therefore, 28 right-handed, healthy graduates (14 males; mean ± SD age, 22.1 ± 1.6 years old; range, 19– 26 years old) successfully completed this study. All participants declared that they did not smoke or consume any stimulants, medications, alcohol or caffeine for at least 24 h prior to the scanning. Study procedure The participants made three visits to the laboratory. During the first visit, all participants were briefed about the study protocol and practiced the experimental tasks. Effects of SD on the performances of these tasks have been illustrated in our previous study (Xu et al. 2015). One week later, the subjects came to the laboratory either for a RW session or a SD session. The session order was randomized in a cross-over fashion, and there was a ~ 1-week interval to minimize possible residual effects of total sleep deprivation on cognition (Van Dongen et al. 2003). The participants reported to the laboratory at 6:00 A. M. in the RW condition. For the SD session, research assistants remained with the sleep-deprived volunteers throughout the night to prevent them from falling asleep. Every hour from 8:00 PM to 6:00 AM, the participants completed 10 min of the psychomotor vigilance task (Lim and Dinges 2008). For the remaining time, they were allowed to engage in non-strenuous activities, such as reading or watching videos. No food was given after midnight. The resting-state functional images were obtained at the same time for the RW and SD sessions (7:00 AM). The high-resolution T1 scans used in these analyses were performed during the sleep deprivation session.
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Data acquisition The imaging data were collected using a 3-Tesla MRI system (EXCITE, General Electric, Milwaukee, Wisconsin) at the Department of Nuclear Medicine of the People’s Hospital of Zhengzhou University, Zhengzhou, China. A standard birdcage head coil was used, along with restraining foam pads, to minimize head motion and to diminish scanner noise. The resting-state functional images were obtained using a gradient-echo planar imaging sequence with the following parameters: TR = 2000 ms, TE = 30 ms, FOV = 240 mm × 240 mm, data matrix = 64 × 64, slices = 33 and total 210 volumes. The subjects were instructed to keep their eyes closed, not to think about anything, and to stay awake during the entire session. After the scan, the subjects were asked whether they were awake in the previous session; all subjects confirmed that they were awake. The participants were also instructed to fulfill the Stanford Sleepiness Scale (SSS) by choosing one from seven statements that best described their current state of alertness after the scan. The Zung Self-Rating Anxiety Scale (SAS), Zung Self-Rating Depression Scale (SDS) and self-reports for the descriptors vigorous, restless and irritable were also used to quantify their behavioral states at the end of the scan. A high-resolution T1-weighted image was also acquired during the SD session with a volumetric three-dimensional spoiled gradient recall sequence with the following parameters: FOV = 256 × 256 mm2, TR = 8.2 ms, TE = 3.2 ms, matrix =128 × 128, slice thickness = 1 mm, and 140 slices in the axial plane. Functional image preprocessing Preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF3.0 Advanced edition) (Chao-Gan and Yu-Feng 2010), which synthesizes procedures in statistical parametric mapping (SPM8, http://www. fil.ion.ucl.ac.uk/spm) and the Resting-State Functional MR imaging toolkit (REST; http://www.restfmri.net) (Song et al. 2011). The first 10 volumes were discarded to eliminate the non-equilibrium effects of magnetization and allow the participants to acclimate to the EPI scanning environment. The remaining 200 images were corrected for the acquisition delay between slices and then were realigned to the first volume. One subject in the SD session exceeded our rigorous motion threshold of less than 1 mm spatial displacement in any direction and was excluded in the following analysis. Individual T1-weighted images were co-registered to the mean of the realigned EPI images. The transformed T1 images were then segmented into gray matter, white matter, and cerebrospinal fluid. The diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) tool was used to compute the transformation from individual space to MNI
space. The resting-state fMRI images were normalized in a resolution of 3 × 3 × 3 mm3 using the same transformation information as the T1 images, then smoothed with a 6-mm full-width at half-maximum isotropic Gaussian kernel. Previous study has shown that the micro-head motions can impact the resting-state fMRI measures such as VMHC. Therefore, higher-level Friston-24 model was used to regress head motion effects out of the realigned data (the 24 parameters include 6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items). We further calculated the mean frame-wise displacement (FD) as a measure of the micro-head motion of each subject. To further reduce the effects of confounding factors, the average signals arising from the ventricles and white matter were removed from the data with the linear regression. Linear trend removal and temporal bandpass filtering (0.01– 0.08 Hz) were then performed. The data preprocessing pipeline was illustrated in Fig. 1. Interhemispheric RSFC and statistical analysis For VMHC analysis, a left-right hemisphere symmetric brain template was generated from the 28 subjects to minimize the effects of geometric differences between the two hemispheres. First, a mean normalized T1 image was generated by averaging the 28 spatially normalized T1 images. Next, the group-specific symmetrical template was obtained by averaging the mean normalized T1 image and its left–right mirrored version. Then each individual T1 image that had been normalized to MNI space was co-registered nonlinearly to this group-specific symmetric template. The same transformation was then applied to the resting-state functional images. Pearson correlations (Fisher Z transformed) between each pair of homotopic voxel (with opposite MNI space x coordinate) time series were computed for each subject to generate VMHC maps (Zhou et al. 2013). The global VMHC of each subject was calculated by averaging VMHC values across all brain voxels within a unilateral hemispheric gray matter mask (there is only one correlation for each pair of homotopic voxels). Group comparisons of global VMHC were performed using a paired two-tailed t-test between the RW and SD conditions. A difference was considered to be significant when p < 0.05. With regard to the regional group differences in VMHC, a general liner model analysis was performed to investigate the difference in VMHC maps in a voxel-by-voxel manner between the RW and SD conditions using the SPM8 software package while accounting for the confounding effects of Jenkinson’s mean FD by including this term as a regressor as recommended in a previous study although no significance difference was found between two conditions on mean FD values (p = 0.16). A difference was considered to be significant when p < 0.05, FWE (family-wise error) was corrected, and a minimum cluster size of 3 voxels was present.
Brain Imaging and Behavior Fig. 1 Processing pipeline used to construct symmetrical hemispheric maps. `Preprocessed functional images (realigned and slice-time corrected) were normalized into Montreal Neurological Institute (MNI) space using the same transformed information got from T1 images. The functional images were then smoothed, detrended and filtered. Before calculating VMHC, the resulting functional images were firstly registered nonlinearly to a group-specific symmetric brain template generated by individual normalized T1 images
As different spatial smoothing kernel had an impact on the VMHC results indicated by a previous study (Zuo et al. 2010). various sizes of the kernel (FWHM of 4 and 8 mm) were used, and the statistical analysis was reperformed. Whole brain signal regression can introduce negative correlations between brain regions and has become a controversial issue in resting-state fMRI preprocessing (Fox et al. 2009). and therefore, we also repeated our analysis with global signal regression and tested whether the results remained. As noted above, all the statistical analysis are based on regression of the signals of ventricular and WM, while global signal regression was only performed as an ancillary method. To investigate the correlation between the VMHC values and the behavioral measures, we computed Pearson correlation coefficients between the behavioral measures changes (RW-SD) and altered VMHC values within the group differences (RW-SD) when 6 mm FWHM was used in the preprocessing step without global signal regression.
Results
Scale were significantly increased after TSD; self-reports for vigor were significantly decreased after SD; whereas self-reports for restless, irritable, SAS and SDS did not differ in SD and RW conditions. Group VMHC and the spatial distribution of homotopic connectivity The mean global VMHC in the SD condition is significantly higher than that in the RW condition (RW, 0.75 ± 0.13; SD, Table 1
Group effects of TSD on behavioral measures
Behavioral measures
RW
TSD
p value
Stanford sleepiness scale Vigor Restless Irritable SAS SDS
2.85 ± 0.86 6.67 ± 1.95 1.52 ± 1.66 2.16 ± 2.27 30.87 ± 7.45 32.6 ± 5.96
4.04 ± 1.04 4.45 ± 2.68 2.35 ± 2.25 2.7 ± 2.93 30.62 ± 6.09 32.52 ± 7.47
p < 0.0001 p < 0.0005 NS NS NS NS
Values represent mean ± SEM (n = 28).
Effects of sleep deprivation on behavioral measures are summarized in Table 1, Self-reports for the Stanford Sleepiness
RW rest wakefulness, SD sleep deprivation, SAS Self-Rating Anxiety Scale, SDS Zung Self-Rating Depression Scale.
Brain Imaging and Behavior Fig. 2 Spatial distribution of VMHC for the RW and SD conditions. The color bar shows the Fisher Z-transformed correlation. The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: VMHC: voxel-mirrored homotopic connectivity; RW: resting wakefulness; SD: sleep deprivation; L, left
0.83 ± 0.08; p = 0.003). However, the spatial distribution of homotopic connectivity showed broad similarities between the two conditions (Fig. 2). The gray matter showed larger VMHC than the white matter, and the regions closer to the midline, such as the parietal and occipital lobes and the cerebellum, appeared to have higher VMHC. However, a higher interhemispheric RSFC was not uniquely observed in brain areas closer to the midline. For example, brain areas such as sensorimotor, insula, and putamen were observed to have high FC values despite being off the midline.
by increase in regional VMHC, we also calculated the mean global VMHC using the same mask but with the specific regions found above excluded. We found that the background VMHC was also increased after SD (p = 0.005). In addition, we also found the whole brain signal amplitude (defined as standard deviation values of the whole brain signal) was increased after SD, with 4.9 in RW and 7.9 in SD (p < 0.0001). Effects of smooth kernel and global signal regression on the results
Following sleep deprivation, VMHC was significantly increased in multiple regions including the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus (Fig. 3, Table 2). The effective size and peak coordinates of significant clusters are summarized in Table 1. No region exhibited significantly decreased VMHC after SD. We also evaluated the VMHC differences between SD and normal conditions including gender as a covariate in the design matrix. However, the results remained same and no gender effect on the VMHC differences was found. Therefore, gender was not included as a covariate in the later analysis. To investigate whether the increase in global VMHC after SD was driven by increase in total background VMHC or just
To address the smoothing effect on the results, we re-performed the statistical analysis using different smooth kernels (FWHM of 4 and 8 mm). The results indicated that the overall pattern of findings did not depend on the level of spatial smoothing, and the differences in the aforementioned regions were still significant when various sizes of smoothing kernels were adopted in the preprocessing step (Fig. 4). We also repeated our analysis with global signal regression, and the findings revealed good consistency with the results without global signal regression, although some minor differences were detected (Fig. 5). VMHC in the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus remained significantly increased after SD (p < 0.05, FWE corrected); no regions showed significantly reduced VMHC after SD. However, the number of voxels that
Fig. 3 Axial views of significant changes in VMHC between the RW and SD conditions (RW > SD, FWE-corrected, p < 0.05). There were no regions where VMHC was greater for the RW than the SD condition.
The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: VMHC: voxel-mirrored homotopic connectivity; RW: resting wakefulness; SD: sleep deprivation; L, left
Regional differences between RW and SD conditions
Brain Imaging and Behavior Table 2 Peak coordinates of significant brain regions in which interhemispheric resting-state functional connectivity was increased after sleep deprivation
Regions
Number of voxels
Peak Coordinates (talairach) x
y
t-value z
Thalamus
140
±36
37
28
8.2
Paracentral Lobule
136
±33
35
43
8.9
Supplementary Motor Area
97
±27
36
44
9.0
Postcentral Gyrus Lingual Gyrus
62 20
±8 ±39
40 26
35 23
8.3 7.8
Results was set at p < 0.05, FWE corrected
showed significant differences within the thalamus increased after global signal regression, and the location of significant changes in the lingual gyrus changed after global signal regression. Correlation behavioral measures changes and VMHC changes No significant correlation was observed between VMHC values and behavioral measures (SSS, SAS, SDS and self-reports for vigor, restless, irritable) in any brain regions with altered VMHC values (p > 0.05).
paired t-test with FD as a covariate was used to compare the VMHC differences between the RW condition and the SD condition. The results were shown in Fig. 6a and basically replicated the findings from the full sample. For the leave-one-out sample validation method, we left one subject out the sample and performed the same group comparisons based on the remaining sample (i.e. 27 RW vs. 27 SD), and this led to total 28 paired t-test images. For each voxel, the reproducibility of the VMHC results was calculated as the number of test where this voxel exhibited significant group differences across the total 28 tests. As expected, Fig. 6b indicated highly reproducible patterns of VMHC increases after sleep deprivation.
Reproducibility The sample size in this work is relatively small, and thus the reproducibility of the findings needs to be assessed. Therefore, a split-half validation method and a leave-one-out sample validation method were adopted (Li et al. 2015). For the split-half sample validation method, 14 subjects were randomly selected from the sample (FWHM is 6 mm without GSR) and a Fig. 4 Axial views of significant changes in VMHC between the RW and SD conditions (RW > SD, FWE-corrected, p < 0.05) with various sizes of smoothing that were adopted in the reprocessing step. The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: FWHM, full width at half-maximum; L, left
Discussion In this study, we found a significantly increased global VMHC after sleep deprivation using resting-state fMRI. More specifically, a significant increase in VMHC was found in specific brain regions, including the thalamus, paracentral lobule,
Brain Imaging and Behavior Fig. 5 Effects of global signal regression on the changes in VMHC between the RW and SD conditions (RW > TSD, FWE-corrected, p < 0.05). The numbers at the bottom indicated the MNI coordinates of the slices. Abbreviations: L, left
supplementary motor area, postcentral gyrus and lingual gyrus. No regions showed significantly reduced VMHC after sleep deprivation. Further analysis indicated that these findings did not depend on the strategies that were adopted in the preprocessing steps, such as the various sizes of the smoothing kernels or the global signal regression. To our knowledge, this is the first evidence of altered interhemispheric RSFC after sleep deprivation. The VMHC approach shows high test-retest reliability, and has been applied in normal aging, psychiatric and neurological diseases (Zuo and Xing 2014). Various sizes of kernels (FWHM of 4, 6 and 8 mm) were used in our analysis to investigate the effects of spatial smoothing on the VMHC results. However, consistent with our previous findings (Zhou et al. 2013). similar results were obtained suggesting that the overall patterns of spatial distribution might depend partly on the level of spatial smoothing (Zuo et al. 2010). but Fig. 6 Reproducibility of VMHC findings. a Split-half sample validation. 14 subjects were randomly selected from the sample and a paired t-test with FD as a covariate was used to compare the VMHC differences between the RW condition and the SD condition. b Leave-oneout sample validation. The group comparisons based upon the permutated samples (i.e., 27 RW vs. 27 SD) for total 28 times. For each voxel, the color indicates number of tests where this voxel exhibited significant group differences across the total 28 tests
the between-group differences had little dependence on the level of spatial smoothing. Given the controversy of the global signal in the preprocessed resting state fMRI data (Fox et al. 2009). we analyzed our data both with and without global signal regression. Interestingly, the VMHC differences after sleep deprivation were similar. Therefore, the selection of global signal regression had little impact on this current study. Altered interactions between hemispheres after sleep deprivation have been observed in several brain imaging studies. For example, EEG slow wave activity (SWA) enhancement was more pronounced in the left hemisphere but not in the right hemisphere after SD (Kattler et al. 1994). Activation in response to object-selective attention tasks after SD showed left hemisphere dominant activation (Chee et al. 2010). Amplified brain reactivity in response to pleasure-evoking stimuli were also more pronounced in the left hemisphere after
Brain Imaging and Behavior
SD (Gujar et al. 2011). However, the focus of previous studies was mainly on regional brain activity alterations after SD. Inter-regional correlations in spontaneous activity, which are also an important feature of the brain’s functional architecture, have not been examined directly after SD. The spatial distribution of interhemispheric RSFC observed in this study was consistent with that observed in other studies. Importantly, brain areas those that were not closer to midline such as sensorimotor, insula, and putamen also appeared to have greater interhemispheric correlation. Robust homotopic RSFC between these areas may reflect the contributions of subcortical hubs rather than the contributions of direct callosal connections for interhemispheric communication (Zuo et al. 2010). Furthermore, increased VMHC in the thalamus, paracentral lobule, supplementary motor area, postcentral gyrus and lingual gyrus was found after sleep deprivation. This is reasonable because these regions are among the vulnerable brain areas that showed altered cerebral metabolism after sleep deprivation (Thomas et al. 2000; Wu et al. 2006). The greatest increase in VMHC was found in the thalamus, a region that is thought to facilitate and regulate communication in the neocortical areas and has a substantial role in regulating states of wakefulness and sleep (Steriade and Llinás 1988). Sleep-wake mechanisms modulating arousal derive primarily from subcortical structures, including the thalamus and brainstem areas (Chee et al. 2008). The increased VMHC in the thalamus may contribute to maintaining cognitive performance when arousal is low after sleep deprivation. This is consistent with previous studies that found increased bilateral thalamus activation during a working memory task and elevated thalamic activation during non-lapse periods contrasted with lapse periods after sleep deprivation (Chee and Choo 2004; Chee et al. 2008). Furthermore, thalamic activation was also found to be inversely correlated with parietal and prefrontal activation during the attention task after SD (Ma et al. 2015). The increased thalamic activation observed in previous studies and the increased intra-thalamic connectivity found in our study might reflect the compensatory involvement of the bilateral thalamus to prevent cognitive performance deterioration when sleep pressure is elevated after sleep deprivation. However, it should be noted that the VMHC results found in our study have limited extent in thalamus, appearing to be specific to mediodorsal regions rather than more globally; the elevated thalamic activation found in the previous study was lateralized and ipsilateral to increased activation in left dorsal-lateral prefrontal cortex, rather than exhibiting an increased connectivity to its homologue in the opposite hemisphere (Chee and Choo 2004). and what’s more, no significant correlations were found between the altered VMHC values and altered behavioral measures between RW and SD condition. These observations might indicate that the compensatory adaptation of increased VMHC after sleep deprivation might function in a somewhat more sophisticated way
and future work will be needed to characterize in detail the effects of increased functional interhemispheric interactions after sleep deprivation on brain activity during task and resting baselines. There are several limitations to this study. First, the brain is asymmetrical. However, we attempted to resolve this issue by smoothing the functional data and using a symmetric template. Second, the sample size in this study was relatively small; thus, the findings should be replicated in a larger sample. Third, we did not measure white matter diffusivity or bilateral gray matter volume; therefore, we could not exclude the influence of structural abnormalities to VMHC. Compliance with ethical standards Funding This study was financially supported by National Basic Research Program of China under Grant Nos. 2015CB856403, 2014CB543203 and 2012CB518501, the National Natural Science Foundation of China under Grant Nos. 81271644, 81471811, 81471738, 61401346, 81271534, 81160452 and 31200837, and the Fundamental Research Funds for the Central Universities. Conflict of interest All authors have indicated no financial conflicts of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the People’s Hospital of Zhengzhou University Subcommittee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study. Disclosure statement This was not an industry supported study. The authors have indicated no financial conflicts of interest.
References Achermann, P., Finelli, L. A., & Borbély, A. A. (2001). Unihemispheric enhancement of delta power in human frontal sleep EEG by prolonged wakefulness. Brain Research, 913, 220–223. Boonstra, T., Stins, J., Daffertshofer, A., & Beek, P. (2007). Effects of sleep deprivation on neural functioning: an integrative review. Cellular and Molecular Life Sciences, 64, 934–946. Chao-Gan, Y., Yu-Feng, Z., 2010. DPARSF: a MATLAB toolbox for Bpipeline^ data analysis of resting-state fMRI. Frontiers in systems neuroscience, 4, 13. Chee, M. W., & Choo, W. C. (2004). Functional imaging of working memory after 24 hr of total sleep deprivation. The Journal of Neuroscience., 24, 4560–4567. Chee, M. W., & Chuah, L. Y. (2008). Functional neuroimaging insights into how sleep and sleep deprivation affect memory and cognition. Current Opinion in Neurology, 21, 417–423. Chee, M. W., & Tan, J. C. (2010). Lapsing when sleep deprived: neural activation characteristics of resistant and vulnerable individuals. NeuroImage, 51, 835–843.
Brain Imaging and Behavior Chee, M. W., Tan, J. C., Zheng, H., Parimal, S., Weissman, D. H., Zagorodnov, V., et al. (2008). Lapsing during sleep deprivation is associated with distributed changes in brain activation. The Journal of Neuroscience., 28, 5519–5528. Chee, M. W., Tan, J. C., Parimal, S., & Zagorodnov, V. (2010). Sleep deprivation and its effects on object-selective attention. NeuroImage, 49, 1903–1910. De Havas, J. A., Parimal, S., Soon, C. S., & Chee, M. W. (2012). Sleep deprivation reduces default mode network connectivity and anticorrelation during rest and task performance. NeuroImage, 59, 1745–1751. Durmer, J.S., Dinges, D.F., 2005. Neurocognitive consequences of sleep deprivation. In Seminars in neurology, 25, 117–129. Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology., 101, 3270–3283. Gujar, N., Yoo, S.-S., Hu, P., & Walker, M. P. (2011). Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences. The Journal of Neuroscience., 31, 4466–4474. Horne, J. A., & Ostberg, O. (1975). A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. International Journal of Chronobiology, 4, 97–110. Kattler, H., Dijk, D. J., & Borbély, A. A. (1994). Effect of unilateral somatosensory stimulation prior to sleep on the sleep EEG in humans. Journal of Sleep Research, 3, 159–164. Kelly, C., Zuo, X.-N., Gotimer, K., Cox, C. L., Lynch, L., Brock, D., et al. (2011). Reduced interhemispheric resting state functional connectivity in cocaine addiction. Biological Psychiatry, 69, 684–692. Li, H. J., Xu, Y., Zhang, K. R., Hoptman, M. J., & Zuo, X. N. (2015). Homotopic connectivity in drug-naïve, first-episode, early-onset schizophrenia. Journal of Child Psychology and Psychiatry, 56, 432–443. Lim, J., & Dinges, D. F. (2008). Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences, 1129, 305–322. Ma, N., Dinges, D. F., Basner, M., & Rao, H. (2015). How acute total sleep loss affects the attending brain: a meta-analysis of neuroimaging studies. Sleep, 38, 233–240. Rocklage, M., Williams, V., Pacheco, J., & Schnyer, D. M. (2009). White matter differences predict cognitive vulnerability to sleep deprivation. Sleep, 32, 1100.
Song, X.-W., Dong, Z.-Y., Long, X.-Y., Li, S.-F., Zuo, X.-N., Zhu, C.-Z., et al. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PloS One, 6, e25031. Steriade, M., & Llinás, R. R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiological Reviews, 68, 649–742. Thomas, M., Sing, H., Belenky, G., Holcomb, H., Mayberg, H., Dannals, R., et al. (2000). Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. Journal of Sleep Research, 9, 335–352. Van Dongen, H. P., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The cumulative cost of additional wakefulness: doseresponse effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26, 117–126. Wu, J. C., Gillin, J. C., Buchsbaum, M. S., Chen, P., Keator, D. B., Wu, N. K., et al. (2006). Frontal lobe metabolic decreases with sleep deprivation not totally reversed by recovery sleep. Neuropsychopharmacology, 31, 2783–2792. Xu, J., Zhu, Y., Fu, C., Sun, J., Li, H., Yang, X., et al. (2015). Frontal metabolic activity contributes to individual differences in vulnerability toward total sleep deprivation-induced changes in cognitive function. Journal of sleep research. Yeo, B. T., Tandi, J., & Chee, M. W. (2015). Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. NeuroImage, 111, 147–158. Yuan, K., Qin, W., Liu, P., Zhao, L., Yu, D., Zhao, L., et al. (2012). Reduced fractional anisotropy of corpus callosum modulates interhemispheric resting state functional connectivity in migraine patients without aura. PloS One, 7, e45476. Zhou, G., Liu, P., Zeng, F., Yuan, K., Yu, D., Deneen, K. M., et al. (2013). Increased interhemispheric resting-state functional connectivity in functional dyspepsia: a pilot study. NMR in Biomedicine, 26, 410–415. Zuo, X.-N., & Xing, X.-X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neuroscience and Biobehavioral Reviews, 45, 100–118. Zuo, X.-N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D. S., Bangaru, S., et al. (2010). Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. The Journal of Neuroscience., 30, 15034–15043.
