Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-25T23:12:58.108Z Has data issue: false hasContentIssue false
  • English
  • Français

Frequency-dependent alterations of global signal topography in patients with major depressive disorder

Published online by Cambridge University Press:  16 February 2024

Chengxiao Yang ,
Bharat Biswal ,
Qian Cui ,
Xiujuan Jing ,
Yujia Ao  and
Yifeng Wang
Chengxiao Yang
Affiliation:
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
Bharat Biswal
Affiliation:
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Qian Cui
Affiliation:
School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
Xiujuan Jing
Affiliation:
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
Yujia Ao
Affiliation:
Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
Yifeng Wang*
Affiliation:
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
*
Corresponding author: Yifeng Wang; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Background

Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear.

Methods

We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed.

Results

The GS fluctuations were reduced in patients with MDD across the full frequency range (0–0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728–0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group.

Conclusion

The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.

Keywords

frequency-dependenceglobal signalglobal topographymajor depressive disorderresting-state fMRI
Type
Original Article
Information
Psychological Medicine , Volume 54 , Issue 9 , July 2024 , pp. 2152 - 2161
Check for updates
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

Major depressive disorder (MDD) is one of the most prevalent mental disorders in the world and is characterized by negative mood, cognitive dysfunction, and decreased interest (Collaborators, Reference Collaborators2022). Given its adverse effects on individuals and society, a great deal of research is anticipated to develop effective intervention programs by providing insight into its brain mechanisms. Current research, usually using the functional magnetic resonance imaging (fMRI) technique, suggests that MDD is likely to be associated with the alteration of large-scale networks rather than specific brain regions (Hamilton, Farmer, Fogelman, & Gotlib, Reference Hamilton, Farmer, Fogelman and Gotlib2015; Stern, Reference Stern2022; Thiebaut de Schotten & Forkel, Reference Thiebaut de Schotten and Forkel2022). Contrary to the spatial dimension, the time scale or frequency is a major determinant of brain functions, which is usually impaired in mental disorders including MDD (Buzsáki, Reference Buzsáki2006; He et al., Reference He, Cui, Zheng, Duan, Pang, Gao and Chen2016; Qiao, Wang, & Wang, Reference Qiao, Wang and Wang2022b). Converging evidence suggests that MDD is a brain-wide spatiotemporal disorder (Guo et al., Reference Guo, Liu, Dai, Jiang, Zhang, Yu and Xiao2013; Sheng et al., Reference Sheng, Shen, Qin, Zhang, Jiang, Li and Wang2018; Wang et al., Reference Wang, Kong, Li, Su, Zeng, Zhang and Si2016).

The global signal (GS) of fMRI time series is the grand average of signals across all gray matter voxels, reflecting the overall state of brain activity (Ao, Ouyang, Yang, & Wang, Reference Ao, Ouyang, Yang and Wang2021). Researchers have long debated whether to regress out GS in fMRI studies due to its extensive influence on the functional connectivity (FC) among all brain networks (Murphy & Fox, Reference Murphy and Fox2017) and its particular significance for physiological and pathological states (Scalabrini et al., Reference Scalabrini, Vai, Poletti, Damiani, Mucci, Colombo and Northoff2020). The regression of GS usually induces negative FCs by modulating the phase of local signals (Anderson et al., Reference Anderson, Druzgal, Lopez-Larson, Jeong, Desai and Yurgelun-Todd2011; Gutierrez-Barragan, Basson, Panzeri, & Gozzi, Reference Gutierrez-Barragan, Basson, Panzeri and Gozzi2019; Zhang et al., Reference Zhang, Magioncalda, Huang, Tan, Hu, Hu and Northoff2019). The GS facilitates the synchronization of functional systems not solely through signal interactions, but also by fluctuated arousal (Orban, Kong, Li, Chee, & Yeo, Reference Orban, Kong, Li, Chee and Yeo2020; Raut et al., Reference Raut, Snyder, Mitra, Yellin, Fujii, Malach and Raichle2021) or glucose metabolism (Thompson et al., Reference Thompson, Riedl, Grimmer, Drzezga, Herman and Hyder2016). Therefore, the examination of GS and its correlation (GSCORR) with local signals presents a promising avenue for reconciling the disparate findings in various networks that are affected by MDD. Additionally, this investigation may uncover novel mechanisms or biomarkers that can aid in the treatment of MDD.

Altered GS and GSCORR have been found in multiple mental disorders, such as schizophrenia (SCZ) (Wang et al., Reference Wang, Liao, Han, Li, Wang, Zhang and Chen2021; Yang et al., Reference Yang, Murray, Glasser, Pearlson, Krystal, Schleifer and Anticevic2017; Yang et al., Reference Yang, Murray, Repovs, Cole, Savic, Glasser and Pearlson2014), bipolar disorder (BD) (Zhang et al., Reference Zhang, Magioncalda, Huang, Tan, Hu, Hu and Northoff2019), and MDD (Han et al. Reference Han, Wang, He, Sheng, Zou, Li and Chen2019; Scalabrini et al. Reference Scalabrini, Vai, Poletti, Damiani, Mucci, Colombo and Northoff2020; Zhu et al. Reference Zhu, Cai, Yuan, Yue, Jiang, Chen and Yu2018). The SCZ patients often exhibit increased GS variability and GSCORR, whereas the BD patients have increased and decreased GSCORRs in different regions and mental states. For MDD patients, an increased, decreased, and unchanged GS and GSCORR in different regions or states have also been reported. These findings suggest that GS and GSCORR may serve as disease-specific biomarkers. However, their alterations in MDD have not reached a consensus.

In terms of the temporal dimension, the GS has a unique temporal structure that evolves throughout the lifespan (Ao et al., Reference Ao, Kou, Yang, Wang, Huang, Jing and Chen2022). The interaction between GS and local signals is also constrained by time scales (Wang et al., Reference Wang, Yang, Li, Ao, Jiang, Cui and Jing2023). The temporal characteristics of GS and its interaction with local signals have been frequently observed to be disturbed by mental disorders. For instance, altered GSCORR has been found in adolescent-onset schizophrenia patients in slow-5 (0.01–0.027 Hz) but not slow-4 (0.027–0.073 Hz) (Wang et al., Reference Wang, Liao, Han, Li, Wang, Zhang and Chen2021). For MDD, decreased static and increased dynamic GSCORR were reported (Han et al., Reference Han, Wang, He, Sheng, Zou, Li and Chen2019). Frequency-dependent alterations in FC as well as local activity have also been demonstrated in various diseases (Ries et al., Reference Ries, Hollander, Glim, Meng, Sorg and Wohlschlager2019; Yang et al., Reference Yang, Zhang, Meng, Wohlschlager, Brandl, Di and Biswal2022; Yang et al., Reference Yang, Cui, Pang, Chen, Tang, Guo and Chen2021; Zhang et al., Reference Zhang, Zhu, Chen, Duan, Lu, Li and Chen2015). Frequency-dependent abnormalities in MDD tend to appear in slow-4 and slow-5 (Li, Qiu, Hu, & Luo, Reference Li, Qiu, Hu and Luo2022; Wang et al., Reference Wang, Kong, Li, Su, Zeng, Zhang and Si2016; Xue, Wang, Wang, Liu, & Qiu, Reference Xue, Wang, Wang, Liu and Qiu2016). These findings indicate that patients with MDD may exhibit a reduced GSCORR at particular frequencies.

In the current study, we investigated the altered frequency characteristics of GS variability and GSCORR in patients with MDD. To mitigate the effects of sampling and system biases, a multicenter resting-state fMRI dataset was utilized, resulting in a larger sample size and more generalizable results. Based on previous studies (Ries et al., Reference Ries, Hollander, Glim, Meng, Sorg and Wohlschlager2019; Wang et al., Reference Wang, Kong, Li, Su, Zeng, Zhang and Si2016), we hypothesized that patients with MDD would exhibit a frequency-dependent reduction of GS variability and GSCORR.

Materials and methods

Participants

All subjects in this study were obtained from the Strategic Research Program for Brain Sciences (SRPBS) (Tanaka, Reference Tanaka2020), which included 255 MDD patients and 524 healthy controls (HCs) from six sites (Tanaka et al., Reference Tanaka, Yamashita, Yahata, Itahashi, Lisi, Yamada and Imamizu2021). The severity of depression was assessed according to the Beck Depression Inventory, Second Edition (BDI-II). The written informed consent was obtained from each participant.

Imaging acquisition and preprocessing

All resting-state fMRI data were collected on 3.0-T MRI scanners, but from six locations, including Hiroshima University Hospital (HUH), Hiroshima Rehabilitation Center (HRC), Hiroshima Kajikawa Hospital (HKH), Center of Innovation at Hiroshima University (COI), Kyoto University TimTrio (KUT), and University of Tokyo Hospital (UTO). The participants were instructed to look at the fixation point, relax, stay awake, but not think about anything in particular, and not move their bodies. The details of scanner parameters for the six centers are shown in Table 1.

Table 1. Imaging protocols for resting-state fMRI

Abbreviations: FoV, field of view; TR, repetition time; TE, echo time; HUH, Hiroshima University Hospital; HRC, Hiroshima Rehabilitation Center; HKH, Hiroshima Kajikawa Hospital; COI, Center of Innovation at Hiroshima University; KUT, Kyoto University TimTrio; UTO, University of Tokyo Hospital. Data link: https://doi.org/10.7303/syn22317081.

Resting-state fMRI image processing was performed using the DPARSF toolbox (http://www.restfmri.net) (Yan & Zang, Reference Yan and Zang2010). Preprocessing steps consisted of removing the first ten time points, slice-timing, realignment, coregistration, normalization to the Montreal Neurological Institute (MNI) space, spatial smoothing with an isotropic Gaussian kernel of 6 mm full-width at half-maximum, and nuisance regression. The linear trend, white matter, cerebrospinal fluid, and 24 rigid body motion parameters were regressed out in the last step. Subjects whose head motion of rotation >2.0̊ or translation >2.0 mm were excluded. The mean frame-wise displacement (FD) of each subject was further calculated to screen out participants whose mean FD was outside the range of the overall group mean plus three standard deviations (s.d.) (Power, Barnes, Snyder, Schlaggar, & Petersen, Reference Power, Barnes, Snyder, Schlaggar and Petersen2012). Participants without BDI-II scores were also not included in the analysis. To minimize potential bias in clinical diagnoses resulting from differences in centers, we excluded HC with BDI-II scores exceeding 16 and MDD subjects with scores below 16. Finally, Given the acknowledged correlation between depression and gender, we meticulously screened the HC individuals to ensure that the gender ratio closely resembled that of the MDD group. There remained 171 MDD patients and 165 HCs, whose demographic information is shown in Table 2.

Table 2. Demographic and clinical variables

a Chi-squared test.

b Two-sample t test.

Abbreviations: MDD, major depressive disorder; HC, healthy control; BDI-II, Beck Depression Inventory- II.

For structural scans, we segmented the whole brain into gray matter (GM), white matter, and cerebrospinal fluid maps using the CAT12 toolbox (http://www.neuro.uni-jena.de/cat/) of SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). Then we normalized the GM images to MNI space (voxel size 1.5 × 1.5 × 1.5 mm3) and smoothed them using a Gaussian kernel (4 mm full width at half maximum). The total gray matter volume (GMV) was obtained by summing the normalized, smoothed GM maps and used for subsequent analysis.

GSsd and GSpower

The GS of each subject at each time point was the mean signal of all gray matter voxels constrained by the binary Human Brainnetome Atlas (Fan et al., Reference Fan, Li, Zhuo, Zhang, Wang, Chen and Jiang2016; Wang et al., Reference Wang, Yang, Li, Ao, Jiang, Cui and Jing2023). We measured the GS variability in the entire frequency range with two indices: GSsd and GSpower. The GSsd was the time standard deviation value of the GS series, and the power spectrum of GS (GSpower) was obtained by transforming the GS time series into frequency domain using the Welch method with a Hamming window (window length = 16 TR, overlap = 50%) (Baria, Baliki, Parrish, & Apkarian, Reference Baria, Baliki, Parrish and Apkarian2011). Since the TR varies from site to site, we opted to limit the frequency range from 0.0012 to 0.1852 Hz based on the sampling theory, which ensures consistency in the bandwidth across sites (Proakis & Manolakis, Reference Proakis and Manolakis1988).

We regressed out age, gender, and site to control for demographic and environmental factors, as well as the total GMV to eliminate the effect of anatomical difference. The normalized residuals were used for subsequent statistical analysis. Two-sample t tests were performed to detect whether there were any group differences in GSsd and GSpower.

GSCORR

Aiming to alleviate any potential limitations of the slow5–slow2 filtering method based on electrophysiological signals for fMRI signals (Buzsaki & Draguhn, Reference Buzsaki and Draguhn2004), we applied the k-means clustering analysis to classify frequency bands. The t-value of the two-sample t test for GSpower in each frequency bin was used to distinguish frequency-specific effects of GSCORR. The optimal clustering number was determined as three using the Davies Bouldin evaluation (Tibshirani, Walther, & Hastie, Reference Tibshirani, Walther and Hastie2001). For each frequency band, we divided the gray matter into 246 regions-of-interest (ROI) using the Human Brainnetome Atlas and obtained the signal of each ROI by averaging all voxel signals in that region. The GSCORR was obtained by calculating the Pearson's correlation between GS and each ROI signal. R values were then transformed to z-scores using the Fisher's Z transformation (Fisher, Reference Fisher1915; Just, Cherkassky, Keller, Kana, & Minshew, Reference Just, Cherkassky, Keller, Kana and Minshew2007). Normalized residuals of GSCORR after regressing out age, gender, site, and total GMV were used for subsequent statistical analysis. At each band, two-sample t tests were used to detect differences between the two groups. Furthermore, we have employed commonly used slow 5–slow 3 and 0.01–0.08 Hz filtering methods to complement our findings.

Correlation analysis

If there were significant differences in GSsd, GSpower, and GSCORR between MDD patients and HCs, their associations with clinical symptoms (BDI-II scores) were further evaluated by Pearson's correlation analysis for each group.

Multiple comparison corrections were performed for both t test and correlation analyses (q < 0.05, FDR corrected) (Wilks, Reference Wilks2006).

Control analysis

In our data analysis, we considered the influence of age, gender, site, and total GMV, and we included them as covariates in our assessment of GS. To delve further into these variables, we conducted separate analyses exploring their associations with GS. For the MDD and HC groups, we used two-sample t tests to examine gender differences in GSsd, GSpower, and GSCORR. Additionally, Pearson's correlation coefficients were used to weigh age/total GMV correlations for both groups' GSsd, GSpower, and GSCORR. To enhance the robustness of our utilization of multicenter data, we conducted similar analysis steps at each collection site. Subsequently, in order to mitigate the impact of time series duration on GSCORR due to variability in collection times across the multicenters, we performed a separate analysis utilizing data collected from the COI site. The analysis was conducted with two different time lengths: one encompassing all time points from the site (230 in total), and the other using a subset of 110 time points. Finally, in light of the debate surrounding the information contained in the GS (Liu, Nalci, & Falahpour, Reference Liu, Nalci and Falahpour2017; Power, Plitt, Laumann, & Martin, Reference Power, Plitt, Laumann and Martin2017), we have taken outcomes derived from the GS regression analyses, which use the same rigorous data analysis process, to supplement our findings. The outcomes of control analysis can be found in the Supplementary Material.

Results

Demographics and clinical variables

Table 2 shows the demographic and clinical variable data for all subjects. The two groups exhibit no significant difference in terms of gender (χ2 = 0.03, p = 0.85, df = 334), hand (χ2 = 0.96, p = 0.33, df = 334), and age (t = 0.68, p = 0.50, df = 334). Likewise, the disparity in age distribution shows no statistical significance. The BDI-II scores were significantly higher in the MDD group (t = 34.00, p < 0.0001, df = 334).

Reduced GSsd in patients with MDD and its correlation with clinical scores

As shown in Fig. 1A, the GSsd was significantly reduced in patients with MDD compared with the HCs (t = −4.015, p < 0.0001, df = 334). Neither the MDD group (r = 0.044, p = 0.824) nor the HC group (r = 0.017, p = 0.566) exhibited a significant correlation between GSsd and BDI-II scores (see Fig. 1B). The findings of the subsamples COI (including 230 time points and 110 time points), HKH, HRC, and HUH were shown in online Supplementary Figures S1 – S5 consistently demonstrated lower GSsd in MDD compared to HC. Additionally, there was no significant correlation found between GSsd and BDI-II scores.

Figure 1. Altered GSsd in patients with MDD. (a). The violin figure shows the distribution of GSsd in HC and MDD groups, respectively. (b). The Pearson correlation between GSsd and BDI-II scores.

Reduced GSpower in patients with MDD and its correlation with clinical scores

The GSpower was significantly lower in the MDD group than in the HC group at all frequency points (Fig. 2A). The correlation between GSpower and BDI-II scores appeared positive in MDD and negative in HC (see Fig. 2B). Similar patterns were observed in the subsamples, with lower GSpower observed in MDD. Furthermore, the MDD showed a positive correlation between GSpower and BDI-II scores, whereas the HC group showed a negative trend (online Supplementary Figures S6 – S10).

Figure 2. Altered GSpower in patients with MDD. (a). The GSpower was reduced in patients with MDD at the full frequency range. FDR correction, q < 0.05. Red and blue shadows represent standard errors. (b). The Pearson's correlation between GSpower and BDI-II scores in the two groups.

Frequency-dependent alteration of GSCORR in patients with MDD and its correlation with clinical scores

Based on the outcomes of the clustering process, we segregated the preprocessed image data into three distinct frequency bands: low frequency (LF: 0.0012–0.0728 Hz), medium frequency (MF: 0.0728–0.1383 Hz), and high frequency (HF: 0.1383–0.1852 Hz). The GSCORR was comparable between the two groups at the LF, but it was significantly reduced in almost the entire brain in the MDD group at MF band. Additionally, it was reduced in the default mode network (DMN: including the medial frontal cortex, medial temporal lobe, parahippocampal gyrus, lateral temporal lobe, and parietal lobe), salience network (SN: including the dorsal anterior cingulate cortex, anterior insula, amygdala, and thalamus), and sensorimotor network (SMN: including the precentral gyrus, postcentral gyrus, superior occipital gyrus, posterior inferior temporal gyrus, posterior insula, and superior temporal gyrus) at the HF (see Fig. 3A).

Figure 3. Frequency-dependent alteration of GSCORR in patients with MDD. (a). Reduced GSCORR in MDD patients at MF and HF bands, respectively. (b). The Pearson's correlation between GSCORR and BDI-II scores in the MDD and HC groups at MF and HF band, respectively. MF, medium frequency; HF, high frequency.

At the LF, there was no significant correlation between GSCORR and BDI-II score. However, at MF and HF bands, the correlations between GSCORR and clinical score were mainly observed in specific brain regions such as the middle frontal gyrus, inferior temporal gyrus, and occipital lobe. This observation, as depicted in Fig. 3B and Table 3, suggests that the brain-symptom association is expressed in different functional systems in patients and HCs. The results of subsamples COI, HKH, HRC and HUH are shown in online Supplementary Figures S11 – S15. The predominant differences in GSCORR were primarily observed at MF band, exhibiting a significant correlation with BDI-II scores in the frontal gyrus and occipital lobe. As a complement to our GSpower intergroup difference-based filtering approach, results with traditional frequency division of 0.01–0.08 Hz revealed that the GSCORR was comparable between the two groups and not significantly correlated with the BDI-II scores. However, applying slow5–slow3 filtering revealed a significant reduction in GSCORR in the MDD group at slow 3 band, which was further correlated with BDI-II scores. The MDD group showed a significant positive correlation between GSCORR and scores in the middle frontal gyrus, while the HC group showed a significant negative correlation between GSCORR and scores in the middle occipital gyrus and inferior temporal gyrus. These results were illustrated in online Supplementary Figures S16 and S17. Finally, results of the GS regression as a control analysis provided valuable insights. When controlling for GS, the significant difference in GSCORR between the MDD and HC groups at MF and HF bands were no longer observed. Additionally, GSCORR was not found to be significantly correlated with BDI-II scores (online Supplementary Figure 18).

Table 3. Brain areas showing significant correlations between GSCORR and clinical scores

Relationships between GS and gender, age, and total GMV

For the gender effect, there was no significant difference (p > 0.05, FDR) in GSsd, GSpower, and GSCORR between males and females in either the MDD or the HC group (also see online Supplementary Figure S19). Regarding the age effect, GSsd and GSpower were negatively correlated with age in both the MDD and HC groups. Furthermore, we observed distinct patterns of significant correlation between GSCORR and age in the two groups, as seen in online Supplementary Figure S20. Lastly, our analysis of the relationship between total GMV and GS revealed that GSsd and GSCORR did not have a significant correlation with total GMV in either group. By contrast, GSpower in the MDD group showed a significant positive correlation with GMV, as shown in online Supplementary Figure S21.

Discussion

Using a multicenter resting-state fMRI dataset, we explored the altered variability of GS and GSCORR in MDD patients. These indicators were systematically reduced in MDD patients, especially at the medium frequency. As the brain signal variability (BSV) is indicative of the amount of kinetic energy that the brain can utilize to shift between various potential states (Garrett, McIntosh, & Grady, Reference Garrett, McIntosh and Grady2014; Wang et al., Reference Wang, Ao, Yang, Liu, Ouyang, Jing and Chen2020), and the GSCORR reflects a global reconciliation for multiple functional systems (Zhang and Northoff, Reference Zhang and Northoff2022), the reduced variability of GS and its interaction with local signals may indicate an impairment in the coordination among various functional systems in patients with MDD. Fortunately, the patients may invoke the association cortex to cope with the increase in depression, as shown by a positive correlation between GS indicators and depressive scores, primarily in the association cortex. In addition, we investigated a subset of data gathered from singular site and discovered that the outcomes from these centers paralleled the combined outcomes from multicenter effectively, validating the trustworthiness of utilizing data from multi-site. Moreover, the findings were consistent between the complete (230 time points) and the reduced (110 time points) subsets of data acquired from the COI site, indicating that the scanning duration minimally impacts GS. In addition to validating our findings through classical filtering methods and GS regression, we observed that gender, age, and total GMV show significant associations with GS. These variables should be controlled when assessing the clinical implications of GS.

Reduced GS variability in patients with MDD

We employed the GSsd and GSpower to measure variabilities in the GS (Tolkunov, Rubin, & Mujica-Parodi, Reference Tolkunov, Rubin and Mujica-Parodi2010). The BSV has been demonstrated to have a close relationship with brain functions and psychiatric disorders (Garrett et al., Reference Garrett, Samanez-Larkin, MacDonald, Lindenberger, McIntosh and Grady2013; Li et al., Reference Li, Wang, Ye, Chen, Huang, Cui and Chen2019; Månsson et al., Reference Månsson, Waschke, Manzouri, Furmark, Fischer and Garrett2022). The GS variability, as an overall response of BSV, has been demonstrated to be negatively correlated to alertness or arousal (Falahpour, Wong, & Liu, Reference Falahpour, Wong and Liu2016; Wong, Olafsson, Tal, & Liu, Reference Wong, Olafsson, Tal and Liu2013; Zhang & Northoff, Reference Zhang and Northoff2022). It decreases with the intake of caffeine (Wong et al., Reference Wong, Olafsson, Tal and Liu2013) and increases during sleep deprivation (Nilsonne et al., Reference Nilsonne, Tamm, Schwarz, Almeida, Fischer, Kecklund and Akerstedt2017). Yang et al., found higher GS variability in patients with SCZ but not in those with BD (Yang et al., Reference Yang, Murray, Repovs, Cole, Savic, Glasser and Pearlson2014). This difference may be due to variations in patients’ alertness, which is impaired in patients with SCZ but not in those with BD (Elias et al., Reference Elias, Miskowiak, Vale, Kohler, Kjaerstad, Stubbs and Carvalho2017; Klein, Shekels, McGuire, & Sponheim, Reference Klein, Shekels, McGuire and Sponheim2020). Since the only confirmed role of GS is its inverse correlation with alertness, we hypothesized that the reduced GS variability in the MDD group may have responded to their abnormally increased arousal (Hegerl & Hensch, Reference Hegerl and Hensch2014; Hegerl, Wilk, Olbrich, Schoenknecht, & Sander, Reference Hegerl, Wilk, Olbrich, Schoenknecht and Sander2012; Schmidt et al., Reference Schmidt, Pschiebl, Sander, Kirkby, Thormann, Minkwitz and Himmerich2016). Evidence from sleep deprivation therapy in MDD patients supports this hypothesis. For instance, it has been found that depression symptoms were alleviated, and GS variability increased in MDD patients by sleep deprivation (Benedetti & Colombo, Reference Benedetti and Colombo2011; Nilsonne et al., Reference Nilsonne, Tamm, Schwarz, Almeida, Fischer, Kecklund and Akerstedt2017; Wolf et al., Reference Wolf, Kuhn, Normann, Mainberger, Maier, Maywald and Nissen2016). These findings seem to suggest that MDD patients have lower GS variability and higher arousal, whereas sleep deprivation resulted in increased GS variability and reduced arousal, alleviating depression.

Reduced GSCORR in patients with MDD

The GSCORR has an inherent structure that is marked by a decline in r values from sensorimotor areas to association areas (Zhang, Huang, Tumati, and Northoff, Reference Zhang, Huang, Tumati and Northoff2020). Such intrinsic structure is thought to reflect the sensorimotor-to-transmodal heterogeneity of neurodevelopmental order, functional connectivity, and gene expression (Huntenburg, Bazin, & Margulies, Reference Huntenburg, Bazin and Margulies2018). As suggested by the dual-layer model (Zhang and Northoff, Reference Zhang and Northoff2022), the GS regulates arousal while the GSCORR coordinates different forms of cognition. Therefore, the reduced GSCORR in patients with MDD is plausibly linked to abnormalities in cognitive functions.

The GSCORR has been found to be impaired in patients with psychiatric disorders (Han et al., Reference Han, Wang, He, Sheng, Zou, Li and Chen2019; Yang et al., Reference Yang, Murray, Glasser, Pearlson, Krystal, Schleifer and Anticevic2017; Zhang et al., Reference Zhang, Magioncalda, Huang, Tan, Hu, Hu and Northoff2019) as well as in epileptic patients (Li et al., Reference Li, Wang, Wang, Zhang, Zou, Wang and Chen2021), usually associated with particular symptoms or cognitive/emotional functions. In patients with MDD, the reduction of GSCORR in almost the whole brain at the medium frequency and in both sensorimotor areas (e.g. SMN) and association areas (e.g. DMN and SN) at the high frequency suggests the systematic weakening of global-local interaction (Wang et al., Reference Wang, Yang, Li, Ao, Jiang, Cui and Jing2023). This may indicate that MDD is in an abnormal cognitive/emotional state compared to HCs. This is in line with the global disorder hypothesis, which suggests the over-processing of bottom-up negative information from the sensory system and the failure of top-down negative emotion suppression from the higher-order system in patients with MDD (Xia et al., Reference Xia, Liu, Mechelli, Sun, Ma, Wang and He2022). Coincidentally, Northoff et al. (Northoff, Wiebking, Feinberg, and Panksepp, Reference Northoff, Wiebking, Feinberg and Panksepp2011) proposed a ‘resting-state hypothesis’ for MDD, which posits that MDD is a widespread brain disorder that affects various functional brain networks. This hypothesis was supported by the findings that patients with MDD display abnormalities in the DMN, SN, central executive network, affective network, and the limbic system, which involve most areas of the brain (Dichter, Gibbs, & Smoski, Reference Dichter, Gibbs and Smoski2015; Mulders, van Eijndhoven, Schene, Beckmann, & Tendolkar, Reference Mulders, van Eijndhoven, Schene, Beckmann and Tendolkar2015; Wang, Hermens, Hickie, & Lagopoulos, Reference Wang, Hermens, Hickie and Lagopoulos2012). The current results support these findings from the global-local interaction viewpoint. Earlier studies have observed both an increased and a decreased GS presentation in MDD (Han et al., Reference Han, Wang, He, Sheng, Zou, Li and Chen2019; Keskin, Eker, Gonul, & Northoff, Reference Keskin, Eker, Gonul and Northoff2023; Scalabrini et al., Reference Scalabrini, Vai, Poletti, Damiani, Mucci, Colombo and Northoff2020). Our investigation has yielded partially consistent and partially contradictory results, which may be attributed to the different approaches in signal frequency manipulation. Specifically, the previous study employed classical filtering within the 0.01–0.08 Hz frequency range, whereas we utilized a distinct frequency band (0.0728–0.1383 Hz). This discrepancy in frequency bands may imply that the signal frequencies serve distinct functional roles (Buzsaki & Draguhn, Reference Buzsaki and Draguhn2004; Knyazev, Reference Knyazev2007), which will be further explored in the forthcoming section. Alternatively, the inconsistencies across studies may be attributed to variations in the inclusion of covariates. Prior studies have discovered a correlation between GS and age (Ao et al., Reference Ao, Yang, Drewes, Jiang, Huang, Jing and Wang2023; Nomi et al., Reference Nomi, Bzdok, Li, Bolt, Chang, Kornfeld and Uddin2022) and a significantly smaller total GMV in the MDD group (Lai & Wu, Reference Lai and Wu2014; Tae, Reference Tae2015), which aligns with our findings. Consequently, we conducted the regression analysis with these variables as covariates to ensure the accuracy and reliability of our results.

Frequency-dependent functional alteration in patients with MDD

Does the widespread decline of GSCORR result from the GS? A recent study found that altered intra- and inter-network connections of the DMN in patients with MDD were primarily contributed by the GS, highlighting the coordination of GS on various functional systems (Scalabrini et al., Reference Scalabrini, Vai, Poletti, Damiani, Mucci, Colombo and Northoff2020). We have demonstrated in a previous study that the global–local interaction is dominated by the causal impact of the GS on local signals (i.e. functional separation) at higher frequency bands, whereas by the effect of local signals on the GS (e.g. functional integration) at lower frequency bands (Wang et al., Reference Wang, Yang, Li, Ao, Jiang, Cui and Jing2023). The declined GSCORR in patients with MDD, mainly at higher frequency bands, is thus possibly determined by the reduced GS.

The brain is a complex system that encompasses multi-scale spatiotemporal structures. It seems that different frequencies are associated with different spatial organizations of brain activity (Qiao et al., Reference Qiao, Li, Wang, Wang, Li, Lu and Wang2022a), which further support specific cognitive processes (Siegel, Donner, & Engel, Reference Siegel, Donner and Engel2012). As a result, the frequency-dependent effect may serve as a valuable window for probing the pathological mechanism of mental disorders. Frequency-dependent alterations of brain activity have been extensively documented in MDD (Fingelkurts et al., Reference Fingelkurts, Fingelkurts, Rytsälä, Suominen, Isometsä and Kähkönen2007; He et al., Reference He, Cui, Zheng, Duan, Pang, Gao and Chen2016; Ries et al., Reference Ries, Hollander, Glim, Meng, Sorg and Wohlschlager2019), as well as in other mental disorders (Newson & Thiagarajan, Reference Newson and Thiagarajan2019; Qiao et al., Reference Qiao, Li, Wang, Wang, Li, Lu and Wang2022a; Yang et al., Reference Yang, Cui, Pang, Chen, Tang, Guo and Chen2021), indicating that the pathological alteration of brain activity occurs at particular time scales. However, the association between altered spatiotemporal structures and impaired cognitions are largely unknown.

Here we attempt to explain the frequency-dependent decline of GSCORR within the dual-layer model of GS. First, the reduction of GS fluctuation in patients with MDD is associated with their hyper-alertness (see section 4.1). Second, the declined GSCORR is primarily attributed to the reduction in GS (see above). The decreased GSCORR throughout the brain, therefore, implies the modulation of slow oscillation of arousal on the entire brain (Raut et al., Reference Raut, Snyder, Mitra, Yellin, Fujii, Malach and Raichle2021). As heightened alertness is known to cause impaired cognitive control and irritable emotions (Baldi & Bucherelli, Reference Baldi and Bucherelli2005; Fredholm, Bättig, Holmén, Nehlig, & Zvartau, Reference Fredholm, Bättig, Holmén, Nehlig and Zvartau1999; Peifer, Schulz, Schächinger, Baumann, & Antoni, Reference Peifer, Schulz, Schächinger, Baumann and Antoni2014), it is not surprising that patients suffering from MDD experience impairment in multiple cognitive and emotional domains.

Clinical relevance of GS indicators

The results showed that the correlations between GSpower/GSCORR and clinical scores were positive in the MDD group, whereas the correlations were mainly negative, although not significant, in the HCs. Because the GS topography is primarily located in sensory-motor areas (Ao et al., Reference Ao, Ouyang, Yang and Wang2021), enhanced connectivity in higher control areas (e.g. the DMN, SN) suggests that patients with MDD may invoke executive control functions in response to depressive symptoms. Similarly, Zhu et al. Reference Zhu, Cai, Yuan, Yue, Jiang, Chen and Yu2018) reported a positive correlation between the variable coefficient of GS and depressive score, while the former can predict the effect of antidepressants in MDD patients. On the contrary, the negative correlation in HCs is mainly located in the sensory and motor areas, suggesting that the bottom-up depression-related information affects normal people, but their GSCORR remains intact. Therefore, we argue that positive correlations between depressive scores and GSpower/GSCORR in the current study reflect a compensatory mechanism. That is, patients with more serious symptoms tend to enhance the GS and its influence on local activities to compensate for impaired brain functions, whereas HCs do not need such a compensatory mechanism (e.g. no correlation).

Limitations

We present here several disadvantages of the current study. First, the dataset used for the analysis does not contain the medication status and disease history, which limits the interpretation of the clinical relevance of GS. Second, respiratory and cardiac noises were not eliminated as they were not involved in the dataset. However, the GS topography may be insensitive to these noises (Yan, Yang, Colcombe, Zuo, and Milham, Reference Yan, Yang, Colcombe, Zuo and Milham2017), which suggests that our results are still reliable. Third, all participants were recruited from Japan, which may reflect an East Asian-specific result and cannot be generalized to other samples. Finally, in the retained sample, the female-to-male ratio of the MDD group differs from that of the control group. Considering that MDD is more prevalent in women (Albert, Reference Albert2015; Picco, Subramaniam, Abdin, Vaingankar, & Chong, Reference Picco, Subramaniam, Abdin, Vaingankar and Chong2017), further investigation is needed to understand the gender effect, which was regressed out in the current study.

Conclusion

Utilizing a multicenter dataset, we unveiled reduced GS fluctuations in MDD, suggesting a potential association with alertness. Furthermore, we noted a frequency-dependent reduction of GSCORR in MDD, which might indicate a weakened effect of GS on local activities through the arousal system in patients with MDD (Wang et al., Reference Wang, Yang, Li, Ao, Jiang, Cui and Jing2023), impairs the coordination among various functional systems. However, the patients may invoke higher executive control functions to compensate for these deficits, shedding light on the antidepressant treatment. These results strongly indicate that temporal and spatial variations of GS play a pivotal role in interpreting and understanding depression, as outlined in ‘Spatiotemporal Psychopathology’ (Northoff, Reference Northoff2016; Northoff and Hirjak, Reference Northoff and Hirjak2022).

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291724000254

Author contributions

Chengxiao Yang: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing. Bharat Biswal: Methodology, Writing – review & editing. Qian Cui: Writing – review & editing, Funding acquisition. Xiujuan Jing: Writing – review & editing. Yujia Ao: Writing – review & editing, Yingfeng Wang: Conceptualization, Methodology, Writing – review & editing, Funding acquisition.

Funding statement

This work was supported by the National Science Foundation of China (62177035, 82172059).

Competing interests

The authors declare no conflict of interests.

References

Albert, P. R. (2015). Why is depression more prevalent in women? Journal of Psychiatry & Neuroscience, 40(4), 219. doi:10.1503/jpn.150205CrossRefGoogle ScholarPubMed
Anderson, J. S., Druzgal, T. J., Lopez-Larson, M., Jeong, E. K., Desai, K., & Yurgelun-Todd, D. (2011). Network anticorrelations, global regression, and phase-shifted soft tissue correction. Human Brain Mapping, 32(6), 919–934. doi:10.1002/hbm.21079CrossRefGoogle ScholarPubMed
Ao, Y., Kou, J., Yang, C., Wang, Y., Huang, L., Jing, X., … Chen, J. (2022). The temporal dedifferentiation of global brain signal fluctuations during human brain ageing. Scientific reports, 12(1), 3616. doi:10.1038/s41598-022-07578-6CrossRefGoogle ScholarPubMed
Ao, Y., Ouyang, Y., Yang, C., & Wang, Y. (2021). Global signal topography of the human brain: A novel framework of functional connectivity for psychological and pathological investigations. Frontiers in Human Neuroscience, 15, 644892. doi:10.3389/fnhum.2021.644892CrossRefGoogle Scholar
Ao, Y., Yang, C., Drewes, J., Jiang, M., Huang, L., Jing, X., … Wang, Y. (2023). Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan. Human Brain Mapping, 44(17), 5906–5918. doi:10.1002/hbm.26484CrossRefGoogle ScholarPubMed
Baldi, E., & Bucherelli, C. (2005). The inverted “u-shaped” dose-effect relationships in learning and memory: Modulation of arousal and consolidation. Nonlinearity in Biology, Toxicology, Medicine, 3(1), nonlin-003. doi:10.2201/nonlin.003.01.002CrossRefGoogle ScholarPubMed
Baria, A. T., Baliki, M. N., Parrish, T., & Apkarian, A. V. (2011). Anatomical and functional assemblies of brain BOLD oscillations. Journal of Neuroscience, 31(21), 7910–7919. doi:10.1523/JNEUROSCI.1296-11.2011CrossRefGoogle ScholarPubMed
Benedetti, F., & Colombo, C. (2011). Sleep deprivation in mood disorders. Neuropsychobiology, 64(3), 141–151. doi:10.1159/000328947CrossRefGoogle ScholarPubMed
Buzsáki, G. (2006). Rhythms of the brain. New York: Oxford University Press.10.1093/acprof:oso/9780195301069.001.0001CrossRefGoogle Scholar
Buzsaki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science (New York, N.Y.), 304(5679), 1926–1929. doi:10.1126/science.1099745CrossRefGoogle ScholarPubMed
Collaborators, G. B. D. M. D. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet. Psychiatry, 9(2), 137–150. doi:10.1016/S2215-0366(21)00395-3Google Scholar
Dichter, G. S., Gibbs, D., & Smoski, M. J. (2015). A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. Journal of Affective Disorders, 172, 8–17. doi:10.1016/j.jad.2014.09.028CrossRefGoogle ScholarPubMed
Elias, L. R., Miskowiak, K. W., Vale, A. M., Kohler, C. A., Kjaerstad, H. L., Stubbs, B., … Carvalho, A. F. (2017). Cognitive impairment in euthymic pediatric bipolar disorder: A systematic review and meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 56(4), 286–296. doi:10.1016/j.jaac.2017.01.008CrossRefGoogle ScholarPubMed
Falahpour, M., Wong, C. W., & Liu, T. T. (2016). The resting state fMRI global signal is negatively correlated with time-varying EEG vigilance. Paper presented at the Proceedings of the 24th Annual Meeting of the ISMRM.Google Scholar
Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., … Jiang, T. (2016). The human brainnetome atlas: A New brain atlas based on connectional architecture. Cerebral Cortex (New York, N.Y.: 1991), 26(8), 3508–3526. doi:10.1093/cercor/bhw157CrossRefGoogle Scholar
Fingelkurts, A. A., Fingelkurts, A. A., Rytsälä, H., Suominen, K., Isometsä, E., & Kähkönen, S. (2007). Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Human Brain Mapping, 28(3), 247–261. doi:10.1002/hbm.20275CrossRefGoogle ScholarPubMed
Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10(4), 507–521. doi:10.2307/2331838Google Scholar
Fredholm, B. B., Bättig, K., Holmén, J., Nehlig, A., & Zvartau, E. E. (1999). Actions of caffeine in the brain with special reference to factors that contribute to its widespread use. Pharmacological Reviews, 51(1), 83–133.Google ScholarPubMed
Garrett, D. D., McIntosh, A. R., & Grady, C. L. (2014). Brain signal variability is parametrically modifiable. Cerebral cortex, 24(11), 2931–2940. doi:10.1093/cercor/bht150CrossRefGoogle ScholarPubMed
Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W. S., Lindenberger, U., McIntosh, A. R., & Grady, C. L. (2013). Moment-to-moment brain signal variability: A next frontier in human brain mapping? Neuroscience & Biobehavioral Reviews, 37(4), 610–624. doi:10.1016/j.neubiorev.2013.02.015CrossRefGoogle ScholarPubMed
Guo, W., Liu, F., Dai, Y., Jiang, M., Zhang, J., Yu, L., … Xiao, C. (2013). Decreased interhemispheric resting-state functional connectivity in first-episode, drug-naive major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 41, 24–29. doi:10.1016/j.pnpbp.2012.11.003CrossRefGoogle ScholarPubMed
Gutierrez-Barragan, D., Basson, M. A., Panzeri, S., & Gozzi, A. (2019). Infraslow state fluctuations govern spontaneous fMRI network dynamics. Current Biology, 29(14), 2295–2306 e2295. doi:10.1016/j.cub.2019.06.017CrossRefGoogle ScholarPubMed
Hamilton, J. P., Farmer, M., Fogelman, P., & Gotlib, I. H. (2015). Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biological Psychiatry, 78(4), 224–230. doi:10.1016/j.biopsych.2015.02.020CrossRefGoogle ScholarPubMed
Han, S., Wang, X., He, Z., Sheng, W., Zou, Q., Li, L., … Chen, H. (2019). Decreased static and increased dynamic global signal topography in major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 94, 109665. doi:10.1016/j.pnpbp.2019.109665CrossRefGoogle ScholarPubMed
He, Z., Cui, Q., Zheng, J., Duan, X., Pang, Y., Gao, Q., … Chen, H. (2016). Frequency-specific alterations in functional connectivity in treatment-resistant and-sensitive major depressive disorder. Journal of Psychiatric Research, 82, 30–39. doi:10.1016/j.jpsychires.2016.07.011CrossRefGoogle ScholarPubMed
Hegerl, U., & Hensch, T. (2014). The vigilance regulation model of affective disorders and ADHD. Neuroscience & Biobehavioral Reviews, 44, 45–57. doi:10.1016/j.neubiorev.2012.10.008CrossRefGoogle Scholar
Hegerl, U., Wilk, K., Olbrich, S., Schoenknecht, P., & Sander, C. (2012). Hyperstable regulation of vigilance in patients with major depressive disorder. The World Journal of Biological Psychiatry, 13(6), 436–446. doi:10.3109/15622975.2011.579164CrossRefGoogle ScholarPubMed
Huntenburg, J. M., Bazin, P.-L., & Margulies, D. S. (2018). Large-scale gradients in human cortical organization. Trends in Cognitive Sciences, 22(1), 21–31. doi:10.1016/j.tics.2017.11.002CrossRefGoogle ScholarPubMed
Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., & Minshew, N. J. (2007). Functional and anatomical cortical underconnectivity in autism: Evidence from an FMRI study of an executive function task and corpus callosum morphometry. Cerebral Cortex, 17(4), 951–961. doi:10.1093/cercor/bhl006CrossRefGoogle ScholarPubMed
Keskin, K., Eker, M. C., Gonul, A. S., & Northoff, G. (2023). Abnormal global signal topography of self modulates emotion dysregulation in major depressive disorder. Translational Psychiatry, 13(1), 107. doi:10.1038/s41398-023-02398-2CrossRefGoogle ScholarPubMed
Klein, S. D., Shekels, L. L., McGuire, K. A., & Sponheim, S. R. (2020). Neural anomalies during vigilance in schizophrenia: Diagnostic specificity and genetic associations. NeuroImage: Clinical, 28, 102414. doi:10.1016/j.nicl.2020.102414CrossRefGoogle ScholarPubMed
Knyazev, G. G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience & Biobehavioral Reviews, 31(3), 377–395. doi:10.1016/j.neubiorev.2006.10.004CrossRefGoogle ScholarPubMed
Lai, C. H., & Wu, Y. T. (2014). Frontal-insula gray matter deficits in first-episode medication-naive patients with major depressive disorder. Journal of Affective Disorders, 160, 74–79. doi:10.1016/j.jad.2013.12.036CrossRefGoogle ScholarPubMed
Li, L., Wang, Y., Ye, L., Chen, W., Huang, X., Cui, Q., … Chen, H. (2019). Altered brain signal variability in patients with generalized anxiety disorder. Frontiers in Psychiatry, 10, 84. doi:10.3389/fpsyt.2019.00084CrossRefGoogle ScholarPubMed
Li, R., Wang, H., Wang, L., Zhang, L., Zou, T., Wang, X., … Chen, H. (2021). Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy. Human Brain Mapping, 42(2), 412–426. doi:10.1002/hbm.25231CrossRefGoogle ScholarPubMed
Li, X. K., Qiu, H. T., Hu, J., & Luo, Q. H. (2022). Changes in the amplitude of low-frequency fluctuations in specific frequency bands in major depressive disorder after electroconvulsive therapy. World Journal of Psychiatry, 12(5), 708. doi:10.5498/wjp.v12.i5.708CrossRefGoogle ScholarPubMed
Liu, T. T., Nalci, A., & Falahpour, M. (2017). The global signal in fMRI: Nuisance or information? Neuroimage, 150, 213–229. doi:10.1016/j.neuroimage.2017.02.036CrossRefGoogle ScholarPubMed
Månsson, K. N. T., Waschke, L., Manzouri, A., Furmark, T., Fischer, H., & Garrett, D. D. (2022). Moment-to-moment brain signal variability reliably predicts psychiatric treatment outcome. Biological Psychiatry, 91(7), 658–666. doi:10.1016/j.biopsych.2021.09.026CrossRefGoogle ScholarPubMed
Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F., & Tendolkar, I. (2015). Resting-state functional connectivity in major depressive disorder: A review. Neuroscience & Biobehavioral Reviews, 56, 330–344. doi:10.1016/j.neubiorev.2015.07.014CrossRefGoogle ScholarPubMed
Murphy, K., & Fox, M. D. (2017). Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage, 154, 169–173. doi:10.1016/j.neuroimage.2016.11.052CrossRefGoogle ScholarPubMed
Newson, J. J., & Thiagarajan, T. C. (2019). EEG Frequency bands in psychiatric disorders: A review of resting state studies. Frontiers in Human Neuroscience, 12, 521. doi:10.3389/fnhum.2018.00521CrossRefGoogle ScholarPubMed
Nilsonne, G., Tamm, S., Schwarz, J., Almeida, R., Fischer, H., Kecklund, G., … Akerstedt, T. (2017). Intrinsic brain connectivity after partial sleep deprivation in young and older adults: Results from the Stockholm sleepy brain study. Scientific Reports, 7(1), 9422. doi:10.1038/s41598-017-09744-7CrossRefGoogle Scholar
Nomi, J. S., Bzdok, D., Li, J., Bolt, T., Chang, C., Kornfeld, S., … Uddin, L. Q. (2022). Global fMRI signal topography differs systematically across the lifespan. bioRxiv, 2022.2007. 2027.501804. doi:10.1101/2022.07.27.501804.Google Scholar
Northoff, G. (2016). How do resting state changes in depression translate into psychopathological symptoms? From ‘Spatiotemporal correspondence'to ‘Spatiotemporal Psychopathology’. Current Opinion in Psychiatry, 29(1), 18–24. doi:10.1097/YCO.0000000000000222CrossRefGoogle Scholar
Northoff, G., & Hirjak, D. (2022). Spatiotemporal psychopathology–An integrated brain-mind approach and catatonia. Schizophrenia Research, 263, 153–159. doi:10.1016/j.jad.2015.05.007Google Scholar
Northoff, G., Wiebking, C., Feinberg, T., & Panksepp, J. (2011). The ‘resting-state hypothesis’ of major depressive disorder—A translational subcortical–cortical framework for a system disorder. Neuroscience & Biobehavioral Reviews, 35(9), 1929–1945. doi:10.1016/j.neubiorev.2010.12.007CrossRefGoogle ScholarPubMed
Orban, C., Kong, R., Li, J., Chee, M. W. L., & Yeo, B. T. T. (2020). Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity. PLoS Biology, 18(2), e3000602. doi:10.1371/journal.pbio.3000602CrossRefGoogle Scholar
Peifer, C., Schulz, A., Schächinger, H., Baumann, N., & Antoni, C. H. (2014). The relation of flow-experience and physiological arousal under stress—can u shape it? Journal of Experimental Social Psychology, 53, 62–69. doi:10.1016/j.jesp.2014.01.009CrossRefGoogle Scholar
Picco, L., Subramaniam, M., Abdin, E., Vaingankar, J. A., & Chong, S. A. (2017). Gender differences in major depressive disorder: Findings from the Singapore Mental Health Study. Singapore Medical Journal, 58(11), 649–655. doi:10.11622/smedj.2016144CrossRefGoogle ScholarPubMed
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142–2154. doi:10.1016/j.neuroimage.2011.10.018CrossRefGoogle ScholarPubMed
Power, J. D., Plitt, M., Laumann, T. O., & Martin, A. (2017). Sources and implications of whole-brain fMRI signals in humans. Neuroimage, 146, 609–625. doi:10.1016/j.neuroimage.2016.09.038CrossRefGoogle ScholarPubMed
Proakis, J. G., & Manolakis, D. G. (1988). Introduction to digital signal processing: Prentice Hall Professional Technical Reference.Google Scholar
Qiao, J., Li, X., Wang, Y., Wang, Y., Li, G., Lu, P., … Wang, S. (2022a). The infraslow frequency oscillatory transcranial direct current stimulation over the left dorsolateral prefrontal cortex enhances sustained attention. Frontiers in Aging Neuroscience, 14, 879006. doi:10.3389/fnagi.2022.879006CrossRefGoogle ScholarPubMed
Qiao, J., Wang, Y., & Wang, S. (2022b). Natural frequencies of neural activities and cognitions may serve as precise targets of rhythmic interventions to the aging brain. Frontiers in Aging Neuroscience, 14, 988193. doi:10.3389/fnagi.2022.988193CrossRefGoogle Scholar
Raut, R. V., Snyder, A. Z., Mitra, A., Yellin, D., Fujii, N., Malach, R., & Raichle, M. E. (2021). Global waves synchronize the brain's functional systems with fluctuating arousal. Science Advances, 7(30), eabf2709. doi:10.1126/sciadv.abq3851CrossRefGoogle ScholarPubMed
Ries, A., Hollander, M., Glim, S., Meng, C., Sorg, C., & Wohlschlager, A. (2019). Frequency-dependent spatial distribution of functional hubs in the human brain and alterations in major depressive disorder. Frontiers in Human Neuroscience, 13, 146. doi:10.3389/fnhum.2019.00146CrossRefGoogle ScholarPubMed
Scalabrini, A., Vai, B., Poletti, S., Damiani, S., Mucci, C., Colombo, C., … Northoff, G. (2020). All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder. Neuropsychopharmacology, 45(12), 2058–2069. doi:10.1038/s41386-020-0785-xCrossRefGoogle Scholar
Schmidt, F. M., Pschiebl, A., Sander, C., Kirkby, K. C., Thormann, J., Minkwitz, J., … Himmerich, H. (2016). Impact of serum cytokine levels on EEG-measured arousal regulation in patients with major depressive disorder and healthy controls. Neuropsychobiology, 73(1), 1–9. doi:10.1159/000441190CrossRefGoogle ScholarPubMed
Sheng, J., Shen, Y., Qin, Y., Zhang, L., Jiang, B., Li, Y., … Wang, J. (2018). Spatiotemporal, metabolic, and therapeutic characterization of altered functional connectivity in major depressive disorder. Human Brain Mapping, 39(5), 1957–1971. doi:10.1002/hbm.23976CrossRefGoogle ScholarPubMed
Siegel, M., Donner, T. H., & Engel, A. K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience, 13(2), 121–134. doi:10.1038/nrn3137CrossRefGoogle ScholarPubMed
Stern, P. (2022). No neuron is an island. Science (New York, N.Y.), 378, 486–487. doi:10.1126/science.adf4275CrossRefGoogle ScholarPubMed
Tae, W.-S. (2015). Regional gray matter volume reduction associated with major depressive disorder: A voxel-based morphometry. Investigative Magnetic Resonance Imaging, 19(1), 10–18. doi:10.13104/imri.2015.19.1.10CrossRefGoogle Scholar
Tanaka, S. C. (2020). SRPBS Multi-disorder MRI dataset (unrestricted). Synapse (New York, N.Y.). doi:10.7303/syn22317081Google Scholar
Tanaka, S. C., Yamashita, A., Yahata, N., Itahashi, T., Lisi, G., Yamada, T., … Imamizu, H. (2021). A multi-site, multi-disorder resting-state magnetic resonance image database. Scientific Data, 8(1), 227. doi:10.1038/s41597-021-01004-8CrossRefGoogle ScholarPubMed
Thiebaut de Schotten, M., & Forkel, S. J. (2022). The emergent properties of the connected brain. Science (New York, N.Y.), 378(6619), 505–510. doi:10.1126/science.abq2591CrossRefGoogle ScholarPubMed
Thompson, G. J., Riedl, V., Grimmer, T., Drzezga, A., Herman, P., & Hyder, F. (2016). The whole-brain “global” signal from resting state fMRI as a potential biomarker of quantitative state changes in glucose metabolism. Brain Connectivity, 6(6), 435–447. doi:10.1089/brain.2015.0394CrossRefGoogle ScholarPubMed
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411–423. doi:10.1111/1467-9868.00293CrossRefGoogle Scholar
Tolkunov, D., Rubin, D., & Mujica-Parodi, L. (2010). Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: Adapting methods optimized for characterizing autonomic dysregulation to neural dynamic time series. Neuroimage, 50(1), 72–80. doi:10.1016/j.neuroimage.2009.12.021CrossRefGoogle ScholarPubMed
Wang, L., Hermens, D. F., Hickie, I. B., & Lagopoulos, J. (2012). A systematic review of resting-state functional-MRI studies in major depression. Journal of Affective Disorders, 142(1–3), 6–12. doi:10.1016/j.jad.2012.04.013CrossRefGoogle ScholarPubMed
Wang, L., Kong, Q., Li, K., Su, Y., Zeng, Y., Zhang, Q., … Si, T. (2016). Frequency-dependent changes in amplitude of low-frequency oscillations in depression: A resting-state fMRI study. Neuroscience Letters, 614, 105–111. doi:10.1016/j.neulet.2016.01.012CrossRefGoogle ScholarPubMed
Wang, X., Liao, W., Han, S., Li, J., Wang, Y., Zhang, Y., … Chen, H. (2021). Frequency-specific altered global signal topography in drug-naive first-episode patients with adolescent-onset schizophrenia. Brain Imaging and Behavior, 15(4), 1876–1885. doi:10.1007/s11682-020-00381-9CrossRefGoogle ScholarPubMed
Wang, Y., Ao, Y., Yang, Q., Liu, Y., Ouyang, Y., Jing, X., … Chen, H. (2020). Spatial variability of low frequency brain signal differentiates brain states. PLoS One, 15(11), e0242330. doi:10.1371/journal.pone.0242330CrossRefGoogle ScholarPubMed
Wang, Y., Yang, C., Li, G., Ao, Y., Jiang, M., Cui, Q., … Jing, X. (2023). Frequency-dependent effective connections between local signals and the global brain signal during resting-state. Cognitive Neurodynamics, 17(2), 555–560. doi:10.1007/s11571-022-09831-0CrossRefGoogle ScholarPubMed
Wilks, D. S. (2006). On “field significance” and the false discovery rate. Journal of Applied Meteorology and Climatology, 45(9), 1181–1189. doi:10.1175/Jam2404.1CrossRefGoogle Scholar
Wolf, E., Kuhn, M., Normann, C., Mainberger, F., Maier, J. G., Maywald, S., … Nissen, C. (2016). Synaptic plasticity model of therapeutic sleep deprivation in major depression. Sleep Medicine Reviews, 30, 53–62. doi:10.1016/j.smrv.2015.11.003CrossRefGoogle ScholarPubMed
Wong, C. W., Olafsson, V., Tal, O., & Liu, T. T. (2013). The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures. Neuroimage, 83, 983–990. doi:10.1016/j.neuroimage.2013.07.057CrossRefGoogle ScholarPubMed
Xia, M., Liu, J., Mechelli, A., Sun, X., Ma, Q., Wang, X., … He, Y. (2022). Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes. Molecular Psychiatry, 27(3), 1384–1393. doi:10.1038/s41380-022-01519-5CrossRefGoogle ScholarPubMed
Xue, S., Wang, X., Wang, W., Liu, J., & Qiu, J. (2016). Frequency-dependent alterations in regional homogeneity in major depression. Behavioural Brain Research, 306, 13–19. doi:10.1016/j.bbr.2016.03.012CrossRefGoogle ScholarPubMed
Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for” pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 1377. doi:10.3389/fnsys.2010.00013Google Scholar
Yan, C. G., Yang, Z., Colcombe, S. J., Zuo, X. N., & Milham, M. P. (2017). Concordance among indices of intrinsic brain function: Insights from inter-individual variation and temporal dynamics. Science Bulletin, 62(23), 1572–1584. doi:10.1016/j.scib.2017.09.015CrossRefGoogle ScholarPubMed
Yang, G. J., Murray, J. D., Glasser, M., Pearlson, G. D., Krystal, J. H., Schleifer, C., … Anticevic, A. (2017). Altered global signal topography in schizophrenia. Cerebral Cortex (New York, N.Y.: 1991), 27(11), 5156–5169. doi:10.1093/cercor/bhw297Google ScholarPubMed
Yang, G. J., Murray, J. D., Repovs, G., Cole, M. W., Savic, A., Glasser, M. F., … Pearlson, G. D. (2014). Altered global brain signal in schizophrenia. Proceedings of the National Academy of Sciences, 111(20), 7438–7443. doi:10.1073/pnas.1405289111CrossRefGoogle ScholarPubMed
Yang, H., Zhang, H., Meng, C., Wohlschlager, A., Brandl, F., Di, X., … Biswal, B. (2022). Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study. Human Brain Mapping, 43(12), 3792–3808. doi:10.1002/hbm.25884CrossRefGoogle ScholarPubMed
Yang, Y., Cui, Q., Pang, Y., Chen, Y., Tang, Q., Guo, X., … Chen, H. (2021). Frequency-specific alteration of functional connectivity density in bipolar disorder depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 104, 110026. doi:10.1016/j.pnpbp.2020.110026CrossRefGoogle ScholarPubMed
Zhang, J., Huang, Z., Tumati, S., & Northoff, G. (2020). Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. PLoS Biology, 18(7), e3000733. doi:10.1371/journal.pbio.3000733CrossRefGoogle ScholarPubMed
Zhang, J., Magioncalda, P., Huang, Z., Tan, Z., Hu, X., Hu, Z., … Northoff, G. (2019). Altered global signal topography and Its different regional localization in motor cortex and hippocampus in mania and depression. Schizophrenia Bulletin, 45(4), 902–910. doi:10.1093/schbul/sby138CrossRefGoogle ScholarPubMed
Zhang, J., & Northoff, G. (2022). Beyond noise to function: Reframing the global brain activity and its dynamic topography. Communications Biology, 5(1), 1350. doi:10.1038/s42003-022-04297-6CrossRefGoogle ScholarPubMed
Zhang, Y., Zhu, C., Chen, H., Duan, X., Lu, F., Li, M., … Chen, H. (2015). Frequency-dependent alterations in the amplitude of low-frequency fluctuations in social anxiety disorder. Journal of Affective Disorders, 174, 329–335. doi:10.1016/j.jad.2014.12.001CrossRefGoogle ScholarPubMed
Zhu, J., Cai, H., Yuan, Y., Yue, Y., Jiang, D., Chen, C., … Yu, Y. (2018). Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder. Brain Imaging and Behavior, 12, 1768–1774. doi:10.1007/s11682-018-9845-9CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Imaging protocols for resting-state fMRI

Figure 1

Table 2. Demographic and clinical variables

Figure 2

Figure 1. Altered GSsd in patients with MDD. (a). The violin figure shows the distribution of GSsd in HC and MDD groups, respectively. (b). The Pearson correlation between GSsd and BDI-II scores.

Figure 3

Figure 2. Altered GSpower in patients with MDD. (a). The GSpower was reduced in patients with MDD at the full frequency range. FDR correction, q < 0.05. Red and blue shadows represent standard errors. (b). The Pearson's correlation between GSpower and BDI-II scores in the two groups.

Figure 4

Figure 3. Frequency-dependent alteration of GSCORR in patients with MDD. (a). Reduced GSCORR in MDD patients at MF and HF bands, respectively. (b). The Pearson's correlation between GSCORR and BDI-II scores in the MDD and HC groups at MF and HF band, respectively. MF, medium frequency; HF, high frequency.

Figure 5

Table 3. Brain areas showing significant correlations between GSCORR and clinical scores

Supplementary material: File

Yang et al. supplementary material

Yang et al. supplementary material
Download Yang et al. supplementary material(File)
File 8.2 MB