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. 2022 Jul 2;17(2):555–560. doi: 10.1007/s11571-022-09831-0

Frequency-dependent effective connections between local signals and the global brain signal during resting-state

Yifeng Wang 1,✉, Chengxiao Yang 1, Gen Li 1, Yujia Ao 1, Muliang Jiang 2, Qian Cui 3, Yajing Pang 4, Xiujuan Jing 5
PMCID: PMC10050607  PMID: 37007197

Abstract

The psychological and physiological meanings of resting-state global brain signal (GS) and GS topography have been well confirmed. However, the causal relationship between GS and local signals was largely unknown. Based on the Human Connectome Project dataset, we investigated the effective GS topography using the Granger causality (GC) method. In consistent with GS topography, both effective GS topographies from GS to local signals and from local signals to GS showed greater GC values in sensory and motor regions in most frequency bands, suggesting that the unimodal superiority is an intrinsic architecture of GS topography. However, the significant frequency effect for GC values from GS to local signals was primarily located in unimodal regions and dominated at slow 4 frequency band whereas that from local signals to GS was mainly located in transmodal regions and dominated at slow 6 frequency band, consisting with the opinion that the more integrated the function, the lower the frequency. These findings provided valuable insight for the frequency-dependent effective GS topography, improving the understanding of the underlying mechanism of GS topography.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09831-0.

Keywords: Frequency-specific, Global signal, Effective connection, Granger causality, fMRI

Introduction

The global signal (GS) of functional magnetic resonance imaging (fMRI), once thought of as noise (Murphy and Fox 2017), has been demonstrated to convey meaningful psychological and physiological information such as vigilance (Wong et al. 2013), behavioral traits (Li et al. 2019), brain states (Gutierrez-Barragan et al. 2019), and mental disorders (Scalabrini et al. 2020). The GS may be specifically regulated by the basal forebrain (Turchi et al. 2018). It, however, exerts significant impact on local neural activities and inter-regional connections or is impacted by local neural activities (Murphy and Fox 2017; Musch and Honey 2018). As more and more studies uncovered psychological and physiological significances of the GS, the relationship between GS and local signals is becoming a core issue of general concern.

The spatial representation of GS, known as GS topography, is primarily measured by temporal correlation, i.e., GSCORR. The strongest representation of GS is located in sensory and motor regions, which is called unimodal superiority (Ao et al. 2021). These unimodal areas have been found to be altered in various mental disorders (Zhang et al. 2019; Wang et al. 2021). However, the GSCORR cannot distinguish whether these effects are driven by the GS or local signals, leading to the urgent necessity to explore the causal relationship between the GS and local signals to deeply understand the underlying mechanism of GS topography as well as to provide effective biomarkers or targets for the treatment of mental disorders.

The direction of information flow depends on particular frequency (Zhang et al. 2020). For instance, delta and infra-slow waves propagate in opposite directions in the same circuit (Mitra et al. 2018). Because more integrative processes usually occupy lower frequencies (Buzsáki 2006), we assumed that the information transfer from local signals to the GS occupies lower frequency than that from the GS to local signals.

Here we used the Granger causality analysis (GCA) to investigate the effective connectivity (EC) between local signals and the GS at multiple frequency bands using an open dataset. The GCA is a popular method to investigate the influence of one system exerts over another and has been widely used to explore EC between different brain regions (Kim et al. 2017; Gao et al. 2021). According to the natural logarithm linear law of fMRI signal (Gong et al. 2021), frequency bands from slow 1 to slow 6 were used to study the frequency effect of EC between local signals and the GS. We expected the effective GS topography to be dominated by unimodal regions but at different frequency bands for the opposite directions.

Materials and methods

Data acquisition

The resting-state fMRI data was selected from the Human Connectome Project (HCP) 100 unrelated dataset (https://db.humanconnectome.org) with the respiratory, cardiac (Physio_log.txt), and head movement (Movement_Regressors_dt.txt) data available in all four runs. Eighty-two subjects met these standards. REST1 and REST2 were acquired on two different days. The HCP scanning protocol was approved by the local Institutional Review Board at Washington University in St. Louis. Informed consent was obtained from all subjects. All participants were scanned on a customized Siemens 3-T connectome-Skyra scanner. The imaging parameters were as follows: TR = 720 ms; TE = 33.1 ms; flip angle = 52°; 2 mm isotropic voxels (FOV = 208 × 180 mm; 72 slices); multiband factor = 8; echo spacing = 0.58 ms; bandwidth = 2290 Hz/px; volumes = 1200. Full details on the HCP dataset could be seen in Van Essen et al.’s article (Van Essen et al. 2013).

Data preprocessing

The HCP data with minimal preprocessing pipeline was adopted (Glasser et al. 2013). This pipeline included artifact removal, motion correction, and registration to standard space. Standard preprocessing procedure was further applied to the data according to our previous studies (Wang et al. 2018, 2020), including removal of the linear trend, removal of linear components related to the six motion parameters and their first derivatives, regression of respiratory and cardiac noises, the mean time courses of white matter and cerebrospinal fluid, smoothing with a full-width half-maximum (FWHM) of 6 mm. Respiratory and cardiac noises were down sampled to 1.39 Hz (1/720 ms) before noise regression. The preprocessing was conducted using the DPARSF 5.1 software (Yan and Zang 2010). After these operations, the data was blind de-convolved (https://www.nitrc.org/projects/rshrf/) to obtain neural level signals (Wu et al. 2013a, b; Wang et al. 2014, 2015). The time series were then band-pass filtered into six frequency bands with the DREAM software (Gong et al. 2021): slow 6 (0.007–0.012 Hz), slow 5 (0.012–0.030 Hz), slow 4 (0.030–0.082 Hz), slow 3 (0.082–0.223 Hz), slow 2 (0.223–0.607 Hz), and slow 1 (0.607–0.694 Hz). Finally, the functional images were segmented into 246 regions of interest (ROI) using the BN_Atlas_246_2 mm template as reported in previous studies (Fan et al. 2016; Wang et al. 2019a, b). For each subject, the ROI signal (ROIS) was obtained by averaging the fMRI time series of all voxels in that ROI, while the GS was computed by averaging the fMRI time series of all voxels in the template.

Granger causality analysis

We used the bivariate Granger causality to check the causal effect of GS on each ROIS (Granger 1969). In addition, we employed partially conditioned Granger causality to calculate the ECs from ROIS to GS to remove indirect interactions between each ROIS and GS (Marinazzo et al. 2012). The order of the auto-regression model for calculating GC is determined by a criterion such as Bayesian information Criterion (BIC). In this study, we make the BIC equal to 1 (Wu et al. 2013a, b). Homemade scripts on MathWorks 8.3 were used to conduct these analyses.

Statistical Analyses

For each run of each participant, the Granger causality maps (GCM) from the GS to ROIS (GS→ROIS) and from ROIS to the GS (ROIS→GS) were generated, respectively. The six levels (slow 1 to slow 6) repeated measures analysis of variance (ANOVA) was applied to each ROI to explore the frequency effect of GC values. The results of REST1-LR were shown in the next section whereas results of other runs were shown in the Supplementary Materials. All results were corrected using the family-wise error (FWE) method (p < 0.05/246) for multiple comparisons (Worsley et al. 1996).

Results

The distribution of effective GS topography

The ECs distributed unevenly among brain regions and frequency bands. The greatest ECs appeared in unimodal regions (i.e. visual, auditory, and sensorimotor areas) for GS→ROIS at all frequency bands. This pattern remained in slow 1-slow 5 but reversed to transmodal regions in slow 6 for ROIS→GS (see Fig. 1 and Figure S1-Figure S3). The greatest ECs appeared at slow 4 for GS→ROIS and at slow 6 for ROIS→GS, respectively. These findings suggest that the unimodal superiority reported by previous GS topography studies is primarily contributed by the influence of the GS on local signals.

Fig. 1.

Fig. 1

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The distribution of effective GS topography for REST1_LR

The frequency effect of effective GS topography

As shown in Fig. 2, the frequency effects were significant in almost all brain regions for both GS→ROIS and ROIS→GS. The greatest frequency effects appeared in unimodal regions for GS→ROIS and in transmodal regions for ROIS→GS, respectively. Regions with different F statistics were delineated with the line chart, expressing aforementioned spatial and frequency effects more clearly (see the bottom panel of Fig. 2 as well as Figs. S4–S6).

Fig. 2.

Fig. 2

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The frequency effect of effective GS topography revealed by ANOVA for REST1_LR (F > 4.96, p < 0.05, FWE-corrected). Upper: F value in each ROI. Lower: GC value in ROIs with different F values

Discussion

We investigated the effective GS topography in multiple frequency bands using the HCP data. Our findings mirrored the unimodal superiority of GS topography, supporting the idea that the GS topography is an intrinsic architecture of human brain function. The strongest frequency effect appeared in unimodal regions for GS→ROIS with the strongest ECs at slow 4. By contrast, the strongest frequency effect appeared in transmodal regions for ROIS→GS with the strongest ECs at slow 6. These results suggested that the unimodal superiority was determined more by the GS than by local signals, and supported the opinion that separation occupies higher frequency while integration occupies lower frequency.

The GS is a major contributor of GS topography

The unimodal superiority has been demonstrated to be the intrinsic architecture of GS topography (Ao et al. 2021), which is also true for the effective GS topography. The sensorimotor-to-transmodal heterogeneity, as an intrinsic gradient architecture of human brain function, is determined by neurodevelopmental order and gene expression (Huntenburg et al. 2018). The GS rather than local signals exerted more causally influences on the unimodal superiority, providing a new mechanism for the functional organization of human brain.

Functional integration and separation occupy different frequency bands

It has been suggested that the lower the frequency, the more integration the information processing (Buzsáki 2006). In line with this opinion, we found the largest scale of signal integration (ROIS→GS) at the lowest frequency (slow 6) and functional separation in the opposite direction (GS→ROIS) at a higher frequency (slow 4). Some studies have reported reliable functional connections in slow 6 with the HCP data (Wang et al. 2018, 2020). In slow 6, the amplitude of fMRI signals in the basal ganglia is positively correlated to the feedback in a sustained attention task (Zhang et al. 2015), while that in almost the whole brain decreased with age (Alcauter et al. 2015; Ao et al. 2022), suggesting that the slow 6 conveys certain physiological and psychological information. Recently, systematical dynamics of the human brain have been demonstrated to fluctuate mainly within slow 6, showing direct links between slow 6 and the GS (Gutierrez-Barragan et al. 2019, 2022). On the other hand, the slow 4 has the minimum artifact noises and the richest neural information (Glerean et al. 2012), which is usually associated with local connections or activities (Song et al. 2014; Wang et al. 2019a, b; Yang et al. 2021). Although our new findings are in line with these studies, some non-band limited methods (e.g., spectral GC) are warranted to examine the precise frequency information of effective GS topography due to the inconsistent frequency boundaries defined in various studies (Chen et al. 2006).

Conclusion

The unimodal superiority is an intrinsic architecture of GS topography which may be mainly driven by the GS. The GS causally influences local signals with the dominant frequency of slow 4 whereas local signals causally influence the GS with the dominant frequency of slow 6, exhibiting the frequency-dependent effective GS topographies.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15438 KB) (15.1MB, docx)

Authors' contributions

Y.F.W., M.L.J., Q.C., Y.J.P., X.J.J.: conceptualization; Y.F.W., C.X.Y., G.L., Y.J.A.: formal analysis; Y.F.W., Q.C., Y.J.P.: funding acquisition; Y.F.W., C.X.Y.: original draft; Y.F.W., C.X.Y., G.L., Y.J.A., M.L.J., Q.C., Y.J.P., X.J.J.: review & editing.

Funding

This work was supported by the National Natural Science Foundation of China (62177035, 82172059. 62103377).

Availability of data and material

The original data supporting the results of this research can be downloaded from https://db.humanconnectome.org.

Code availability

The code of this research can be obtained by requesting the corresponding author.

Declarations

Conflicts of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

The HCP scanning protocol was approved by the local Institutional Review Board at Washington University in St. Louis. Informed consent was obtained from all subjects.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 15438 KB) (15.1MB, docx)

Data Availability Statement

The original data supporting the results of this research can be downloaded from https://db.humanconnectome.org.

The code of this research can be obtained by requesting the corresponding author.

Articles from Cognitive Neurodynamics are provided here courtesy of Springer Science+Business Media B.V.

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