
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

73111
10.1038/s41598-024-73111-6
Article
Rapid reconfiguration of cortical networks after repeated exposure to visual-vestibular conflicts
Hua Anke 12
Wang Guozheng 34
Bai Jingyuan 1
Hao Zengming 5
Yang Yi 1
Luo Xin 1
Liu Jun 3
Meng Jun 6
Wang Jian pclabeeg@zju.edu.cn

17
1 https://ror.org/00a2xv884 grid.13402.34 0000 0004 1759 700X Department of Sports Science, Zhejiang University, Hangzhou, 310058 China
2 grid.411024.2 0000 0001 2175 4264 Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, 21201 USA
3 https://ror.org/00a2xv884 grid.13402.34 0000 0004 1759 700X College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310058 China
4 Taizhou Key Laboratory of Medical Devices and Advanced Materials, Research Institute of Zhejiang University-Taizhou,, Taizhou, 318000 China
5 https://ror.org/047bp1713 grid.440581.c 0000 0001 0372 1100 School of Sport And Physical Education, North University of China, Taiyuan, 030051 China
6 https://ror.org/00a2xv884 grid.13402.34 0000 0004 1759 700X College of Control Science and Engineering, Zhejiang University, Hangzhou, 310058 China
7 https://ror.org/00a2xv884 grid.13402.34 0000 0004 1759 700X Center for Psychological Science, Zhejiang University, Hangzhou, 310058 China
20 9 2024
20 9 2024
2024
14 2194327 2 2024
13 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Visual-vestibular conflicts can induce motion sickness and further postural instability. Visual-vestibular habituation is recommended to reduce the symptoms of motion sickness and improve postural stability with an altered multisensory reweighting progress. However, it is unclear how the human brain reweights multisensory information after repeated exposure to visual-vestibular conflicts. Therefore, we synchronized a rotating platform and a virtual scene to present visual-vestibular congruent (natural visual stimulation) and incongruent (conflicted visual stimulation) conditions and collected EEG and center of pressure (COP) data. We constructed the effective brain connectivity of region of interest (ROI) derived from source-space EEG in theta-band activity, and quantified the postural stability and the inflow and outflow of each ROI. We found repeated exposure to congruent and incongruent conditions both decreased COP path length and increased COP complexity. Besides, we found that repeated exposure to the incongruent environment decreased the inflow into visual cortex, suggesting the brain down-weighted the less reliable visual information for postural stability. In contrast, repeated exposure to the congruent environment increased the inflow into posterior parietal cortex and the outflow from visual cortex and S1, suggesting an increase in efficiency of multisensory integration. We concluded that repeated exposure to congruent and incongruent conditions both improved postural stability with different multisensory reweighting patterns as revealed by different dynamic changes of brain networks.

Keywords

Visual-vestibular conflict
Habituation
Brain connectivity
Multisensory reweighting
Postural control
Subject terms

Brain imaging
Rehabilitation
National Defense Foundation Strengthening Program Technology Field Fund Project of China2021-JCJQ-JJ-1029 Wang Jian issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Bipedal stance balance control in the complex and changing environment requires central nervous system (CNS) to integrate multiple information from the visual, vestibular and somatosensory systems to make rapid and appropriate postural adjustments1. Visual information is capable of providing movement information but is inherently ambiguous since the movement of external objects or self-movement can both cause similar visual stimuli2. In general, vestibular information can provide complementary information about head movements and changes in acceleration3 so that the CNS can integrate visual and vestibular information to provide a more accurate representation of self-motion4. However, the process of multisensory integration becomes particularly crucial when there is a conflict between visual and vestibular information. Such visual-vestibular conflicts can result in motion sickness5, and manifestations of motion sickness can further induce inappropriate neuromuscular responses and postural instability6.

Besides medications and cognitive behavioral psychotherapy, the vestibular rehabilitation therapy is generally recommended to reduce the symptoms of motion sickness7. The theoretical basis for the vestibular rehabilitation is sensory reweighting, and previous studies suggest that vestibular habituation can alter multisensory integration and improve postural stability8. However, it is still unclear how the human brain reweights the conflicting sensory information through repeated exposure to the visual-vestibular conflicting environments. Since recent studies used the connectivity of source-level EEG and provided evidence for sensory reweighting during postural control under conflicting environments9–11, we aimed to understand sensory reweighting after repeated exposure to visual-vestibular conflicts by investigating the connectivity patterns of the brain.

Our present study used partial directed coherence (PDC), a directional connectivity analysis tool12, to investigate the directed information flow after repeated exposure to visual-vestibular congruent and incongruent conditions, elicited by rotatory vestibular stimuli (rotation of the body) and rotatory visual stimuli (rotation of the visual scene). In EEG studies, PDC provides the directional information about the flow of activity between different brain regions in specific frequency bands13,14. This is valuable for understanding how the brain reweights conflicting sensory information for postural control. For example, our previous study used PDC as brain connectivity analysis and found different characteristics of brain network when exposed to physical-visual congruent and incongruent stimuli14. Here our present study further explored the dynamics of brain network when repeatedly exposed to visual-vestibular congruent and incongruent stimuli.

According to previous findings of dynamic reconfiguration of brain networks during visual conflicting environment or sensory perturbations10,14,15 and based on the sensory reweighting theory proposed to describe dynamic strategies of sensory integration during postural control16, we hypothesized that (1) repeated exposure to visual-vestibular incongruent condition would reduce the information flow of visual cortex to improve postural stability, as the visual information was less reliable; (2) repeated exposure to visual-vestibular congruent condition would increase the information flow of posterior parietal cortex to improve postural stability, as posterior parietal cortex has been suggested as a hub for multisensory integration under sensory congruent environment14,17.

Materials and methods

Participants

Thirty-two students participated in this study, and were randomly divided into two groups: congruent and incongruent groups. All participants had no prior experience with virtual reality. All participants possessed normal neurological and muscular functions, and either normal vision or vision corrected with lenses. Each participant provided written informed consent before the experiment. The present study was approved by the Ethics Committee of Zhejiang University Psychological Science Research Center, and the experimental methods were carried out in accordance with relevant guidelines and regulations.

We excluded the data from one participant in the incongruent group because of stepping during the experiment. Also, we excluded the data from three participants due to the severe body swaying during the experiment (one in incongruent group, two in congruent group). Therefore, 14 participants were included in the congruent group (mean age 22.93±2.28 years, height 172.21±8.80 cm, body mass 64.78±11.24 kg) and 13 participants were included in the incongruent group (mean age 24.85±2.57 years, height 169.35±7.05 cm, body mass 63.38±12.45 kg).

Procedure

In the present study, a rotating platform (All Controller, Nanjing) and a VR headset (HTC VIVE Pro2, Taiwan) were used to manipulate the vestibular and visual inputs, respectively. We used the Unity3D program to synchronously control the platform and the visual scene in the VR headset, presenting the congruent (i.e., natural visual stimulation) and the incongruent (i.e., conflicted visual stimulation) experimental conditions (Fig. 1A). As shown in Fig. 1A, the visual scene in the Unity3D program was based on the real experimental room with windows, curtains and doors.

Fig. 1 An illustration of stimulation protocol. (A) The visual scene in the VR headset was rotated counterclockwise in the congruent condition (i.e., natural visual stimulation) and clockwise in the incongruent condition (i.e., conflicted visual stimulation). (B) The direction of the rotating platform was clockwise in both congruent and incongruent conditions. Unity3D program was used to synchronize the control of the rotating platform and the visual scene in the VR headset.

Different from our previous study14, we aimed to directly stimulate the vestibular system, where semicircular canals can detect and code the angular acceleration information3. Thus, in our study, the rotating platform was accelerated at 4°/s2 for 10s to the right (clockwise) and kept rotating at 40°/s for 27s, and then decelerated at 4°/s2 for 10s (Fig. 1B) under both congruent and incongruent conditions. For the visual scene, there is no additional control for the visual scene in the congruent condition; thus, the visual scene rotated counterclockwise relative to the participant, which was congruent with the real-world experience (Fig. 1A). However, in the incongruent condition, the visual scene rotated clockwise relative to the participant with the same intensity, providing information in the opposite direction of the actual motion (Fig. 1A).

Participants were asked to cross arms on the chest and stand quietly in the standardized position of the feet (width: 14 cm; angle: 17 degree)18 on the platform for 30s (baseline), followed by 47s of the congruent or incongruent stimulation (rotation), and finally stand on the platform for 30s (recovery) (Fig. 1). Each participant was asked to complete five repeated tasks, either congruent or incongruent, with 5-min rest between each task. Participants were asked to complete the simulator sickness questionnaire and to answer the following questions immediately after each task: (1) which direction did you feel like you were rotating in just now (clockwise or counterclockwise)? and (2) did you feel any conflict between virtual scene and body motion (yes or no)?

Data collection

A Wii balance board (Nintendo, Japan) was placed in the center of the platform and the BrainBlox program software was used to collect COP data with a sample rate of 100 Hz19. The validity and reliability of the Wii balance board in collecting COP data have been confirmed20,21. A 32-channel EEG device (ANTNeuro, Netherlands) in the 10–20 standard regime was used to collect EEG data, with a sample rate of 1000 Hz. Throughout the experiment, the impedance of all electrodes stayed below 5k ohms.

Analysis of COP data

We used a custom script in MATLAB (R2021a, MathWorks, USA) to analyze the COP data. The raw COP data were filtered with a 0.2–20 Hz band-pass, 4th -order, zero-lag Butterworth filter22, and the mean of the filtered data was removed. As the semicircular canals within the vestibular system detect and code the angular acceleration information3 and our present study mainly focused on the visual-vestibular congruent or incongruent condition, then the COP data were divided into two phases: acceleration (0–10 s after platform start) and deceleration (37–47 s after platform start). Our present study calculated and compared the following COP measures from the 1st and 5th trial to examine the effect of trial. The outcome COP linear measure used in this study was the total path length, which was computed as shown in Eq. (1), where x(i) and y(i) are the COP trajectory in the medial-lateral (ML) and the anterior-posterior (AP) directions, respectively and N is the number of samples.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{C}\text{O}\text{P}\:\text{p}\text{a}\text{t}\text{h}\:\text{l}\text{e}\text{n}\text{g}\text{t}\text{h}={\sum\:}_{i=1}^{N}\sqrt{({x\left(i+1\right)-x\left(i\right))}^{2}+{(y\left(i+1\right)-y\left(i\right))}^{2}}$$\end{document}

The outcome COP nonlinear measure used in this study was the index of multiscale entropy (MSE) of COP time series in the AP and ML directions. MSE calculates the sample entropy over multiple timescales through a coarse-graining procedure. The coarse-grained series at the largest scale had 200 data points (i.e., 1000 points/5) in the present study, since the coarse-grained procedure divided COP time series into non-overlapping windows of length equaling a scale factor (τ), ranging from 1 to 5 in this study. The sample entropy measures the regularity of the time series, and is calculated as shown in the previous study23. The sample entropy of each coarse-grained time series was calculated by selecting m = 2 and r = 15% in the present study22. Finally, the MSE index was identified as the sum of sample entropy values over multiple time scales. A larger MSE index reflects larger complexity of COP time series. Previous studies suggested that higher complexity could be interpreted as improved flexibility and self-organization in postural control24,25.

Analysis of EEG data

We used a custom script in MATLAB to analyze the EEG data (R2021a, MathWorks, USA). The raw EEG data was first divided into two phases: acceleration (0–10 s after platform start) and deceleration (37–47 s after platform start). Then, the EEG data were processed based on a custom script in the EEGLAB toolbox26 (Fig. 2).

Fig. 2 An illustration of EEG processing flow. There are four major steps for the EEG preprocessing, including band-pass filter, removal of linear interference, artifact subspace reconstruction and independent component analysis. Then the sLORETA software package was used to transform sensor-space EEG into source activity, which is used to cluster int regions of interest (ROI). The partial directed coherence (PDC) was used to construct the effective connections at theta-band. Finally, the sum of inward and outward link weights of each ROI is computed as inflow and outflow, respectively.

First, the raw EEG data were filtered with a 1–48 Hz band-pass FIR filter27 and the 50 Hz line noise was removed using the Cleanline plug-in in EEGLAB. Furthermore, Artifact Subspace Reconstruction (ASR)28 was used to remove movement artifacts, as demonstrated in14,29. Finally, the Independent Component Analysis (ICA) was used to perform the decomposition with assistance from the ICLabel plug-in in EEGLAB, thus removing interfering signals like blinks, muscle artifacts, electrocardiogram activity and not homologous linear noise from EEG data30. The number of independent components removed at 1st and 5th congruent trials were 6.29 ± 1.53 and 5.86 ± 1.60, and at 1st and 5th incongruent trials were 6.54 ± 1.82 and 5.31 ± 1.49 (mean ± SD).

A standardized low-resolution EEG tomography (SLORETA) software package was used for source localization31. Previous studies showed that a widespread network of cortical areas is involved in multisensory integration and postural control (details in review articles32–34), such as prefrontal cortex, motor cortex, posterior parietal cortex and parieto-temporal areas. Also, there are several representations of vestibular information in the cortical areas, including the frontal eye field cortex and somatosensory cortex32. Thus, as demonstrated in14, the source-space EEG data was clustered into seven cortical regions as the regions of interest (ROIs) defined by the Brodmann atlas as follows: dorsolateral prefrontal cortex (DL-PFC), frontal eye field cortex (FEF), primary somatosensory (S1), motor cortex (MC), posterior parietal cortex (PPC), visual cortex (VC) and temporal-parietal junction (TPJ).

We used the PDC, a frequency domain extension of Granger Causality, to calculate the strength of directed information flow between ROIs and to construct the cortical effective connectivity12. Theta oscillations in human brain play an important role in sensorimotor integration35,36. Furthermore, in the context of postural control, theta-band activity increased when the balance task demand increased10,37, and theta-band connectivity plays a key role when facing sensorimotor perturbations to balance when standing and walking10. Thus, we used the HERMES toolbox to calculate PDC for theta (4–8 Hz) band38, as shown in Eq. (2). For PDC calculation, we set the time window to 5 s with 50% overlap, and used the Akaike information criterion to determine the model order p. The effective connectivity was visualized through the BrainNet Viewer Toolbox39. Finally, we calculated the sum of total inward and outward link weights for each ROI as inflow and outflow, respectively. The outcome connectivity measures used in this study were the total inflow and outflow of each ROI. Our present study compared the connectivity measures from the 1st and 5th trial to examine the effect of trial.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PDC_{{ij}} \left( f \right) = \frac{{\bar{a}_{{ij}} \left( f \right)}}{{~\sqrt {\bar{a}_{j}^{H} \left( f \right)\bar{a}_{j} } \left( f \right)}}$$\end{document}

PDCij(f) represents the PDC value from j to time series i at frequency f, and H represents conjugate transpose, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\stackrel{-}{a}}_{ij}\left(f\right)$$\end{document} is the coefficient of row i and column j in the frequency-domain transfer matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{a}\left(f\right)$$\end{document}.

Statistical analysis

Statistical analysis was performed using SPSS software (Version 24.0 for Mac). A mixed ANOVA was used to examine the effect of trial (i.e., trial 1st vs. trial 5th ) and the group (i.e., congruent and incongruent group) on the COP measures and the connectivity measures during acceleration and deceleration phases. A series of post-hoc multiple comparisons with Bonferroni correction was used. Also, a two-tailed Pearson correlation was used to determine whether the inflow and outflow of visual cortex is associated with the COP measures. Statistical significance was set at p < 0.05.

Results

Perception

Under the 1st and 5th congruent conditions, all 14 participants accurately perceived their clockwise rotation. Under the 1st incongruent condition, 3 out 13 participants perceived the sensory conflict and reported that they were rotating clockwise, while 8 out 13 participants did under the 5th incongruent condition.

COP measures

A two-way mixed ANOVA revealed a significant main effect of trial on the total path length and the MSE index of COP trajectory in the AP and ML directions of both acceleration and deceleration phases (Table S1). However, no significant main effect of group or interaction effect of trial × group was observed for COP measures. The post-hoc tests further found a significant reduction in the total path length, as well as a significant increase in the MSE index of COP trajectory in both AP and ML directions after repeated exposure to congruent condition (p < 0.05) and incongruent condition (p < 0.01) (Fig. 3).

Fig. 3 Changes of COP measures during acceleration (top) and deceleration (bottom) phases. Means and standard errors are shown. The asterisk (*) indicates a significant difference (***< 0.001, **< 0.01, *<0.05) between the 1st trial and the 5th trial. AP anterior-posterior, ML medial-lateral, MSE multiscale sample entropy.

Connectivity measures

Inflow of ROIs

In the case of the inflow of VC, there was a significant trial × group interaction and a significant main effect of group during deceleration. Also, there was a significant main effect of trial during acceleration (Table S2). The post-hoc tests further found that the inflow of VC was greater under the 1st incongruent condition compared with congruent condition (p < 0.05). After repeated exposure to the incongruent condition, the inflow of VC significantly reduced (p < 0.05), with no significant changes observed under the congruent condition (Figs. 4B and 5B).

In the case of the inflow of PPC, there was a significant trial × group interaction during acceleration. Also, there was a significant main effect of trial during deceleration (Table S2). The post-hoc tests further found that after repeated exposure to the congruent condition, the inflow of PPC significantly increased(p < 0.05), with no significant changes observed under the incongruent condition. The inflow of PPC was greater under the 5th congruent condition compared with incongruent condition(p < 0.05) (Figs. 4B and 5B).

In the case of the inflow of MC, there was a significant main effect of group during acceleration and deceleration (Table S2). The post-hoc tests further found that the inflow of MC was greater under the congruent condition compared with incongruent condition(p < 0.05) (Figs. 4B and 5B).

In the case of the inflow of TPJ, there was a significant main effect of trial during acceleration. The post-hoc tests further found that after repeated exposure to the congruent condition, the inflow of TPJ significantly increased(p < 0.05), with no significant changes observed under the incongruent condition during acceleration (Fig. 4B). However, no significant trial × group interaction or main effects of group or trial was observed during deceleration.

In the case of the inflow of FEF, there was a significant main effect of trial during deceleration (Table S2). The post-hoc tests further found that after repeated exposure to the incongruent condition, the inflow of FEF significantly increased(p < 0.01), with no significant changes observed under congruent condition (Fig. 5B). However, no significant trial × group interaction or main effects of group or trial was observed during acceleration.

In the case of the inflow of DL-PFC and S1, no significant trial × group interaction or main effects of group or trial was observed during acceleration or deceleration (Table S2).

Fig. 4 Changes of theta-band effective connectivity measures during acceleration phase. Figure 4A shows effective connectivity based on source-space EEG PDC in theta-band in congruent (left) and incongruent (right) conditions across time periods (1st and 5th trials) during acceleration. For better visualization, the connections are thresholded at edge weight from 0.1 to 0.4. Figure 4B and C show significant changes of inflow and outflow of ROIs, respectively. In each box plot, the box represents 1st and 3rd quartiles with the median value. The line connecting 1st and 5th trials represents the average value. The asterisk (*) and the sharp (#) indicate a significant difference (*< 0.05, #< 0.05) between training trials and groups, respectively. DL-PFC dorsolateral prefrontal cortex, FEF frontal eye field cortex, MC motor cortex,PPC posterior parietal cortex, TPJ temporal-parietal junction, VC visual cortex.

Outflow of ROIs

In the case of the outflow of VC, there was a significant main effect of trial during acceleration and there were significant main effects of trial and group during deceleration (Table S3). The post-hoc tests further found that after repeated exposure to congruent condition, the outflow of VC increased(p < 0.05), with no significant changes observed under the incongruent condition. Also, the outflow of VC was greater under the 5th congruent condition compared with incongruent condition during deceleration(p < 0.05) (Fig. 5C).

In the case of the outflow of S1, there was a significant main effect of trial during deceleration (Table S3). The post-hoc tests further found that after repeated exposure to the congruent and incongruent conditions, the inflow of S1 significantly increased(p < 0.05) (Fig. 5C). However, no significant trial × group interaction or main effects of group or trial was observed during acceleration.

In the case of the outflow of FEF, there was a significant trial × group interaction during acceleration (Table S3). The post-hoc tests further found that after repeated exposure to congruent condition, the outflow of FEF increased(p < 0.05) (Fig. 4C), with no significant changes observed under the incongruent condition. However, no significant trial × group interaction or main effects of group or trial was observed during deceleration.

In the case of the outflow of DL-PFC, there was a significant main effect of group during acceleration (Table S3). The post-hoc tests further found that the outflow of DL-PFC was greater under the 5th congruent condition compared with the incongruent condition(p < 0.01) (Fig. 4C). However, no significant trial × group interaction or main effects of group or trial was observed during deceleration.

In the case of the outflow of MC, PPC and TPJ, no significant trial × group interaction or main effects of group or trial was observed during acceleration or deceleration (Table S3).

Fig. 5 Changes of theta-band effective connectivity measures during deceleration phase. Figure 5A shows effective connectivity based on source-space EEG PDC in theta-band in congruent (left) and incongruent (right) conditions across time periods (1st and 5th trials) during deceleration. For better visualization, the connections are thresholded at edge weight from 0.1 to 0.4. Figure 5B and C show significant changes of inflow and outflow of ROIs, respectively. In each box plot, the box represents 1st and 3rd quartiles with the median value. The line connecting 1st and 5th trials represents the average value. The asterisk (*) and the sharp (#) indicate a significant difference (*<0.05, **<0.01, #<0.05, ##<0.01) between training trials and groups, respectively. FEF frontal eye field cortex, MC motor cortex, PPC posterior parietal cortex, S1 primary somatosensory, TPJ temporal-parietal junction, VC visual cortex.

Correlations between VC connectivity measures and COP measures

Pearson correlations revealed that the inflow of visual cortex was positively correlated with path length (r = 0.44, p = 0.001) and was negatively correlated with the MSE index in the AP direction (r=-0.36, p = 0.008) and ML direction (r=-0.41, p = 0.002) under the incongruent condition rather than the congruent condition (Fig. 6A). In contrast, the outflow of visual cortex was negatively correlated with path length (r=-0.29, p = 0.02) and was positively correlated with the MSE index in the ML direction (r = 0.42, p = 0.001) under the congruent condition rather than the incongruent condition (Fig. 6B). However, the MSE index in the AP direction was not significantly correlated with outflow of visual cortex under the congruent condition (r = 0.25, p = 0.06).

Fig. 6 Plots of the significant Pearson correlation between the COP outcome measures (in the x-axis) and the inflow or outflow of visual cortex (in the y-axis) under the incongruent (Fig. 6A) and congruent (Fig. 6B) conditions. r and p values indicate the Pearson correlation coefficients and the significance level, respectively.

Discussion

The present study demonstrated that repeated exposure to visual-vestibular congruent and incongruent environments could both improve postural stability and complexity with different rapid reconfiguration of effective brain connectivity.

We observed that total COP path length significantly decreased after repeated exposure to both visual-vestibular congruent and incongruent conditions (Fig. 3), as hypothesized, which is in agreement with previous studies40,41. Also, we observed that complexity of postural sway (i.e., the MSE index of COP time series) increased after repeated exposure to both congruent and incongruent conditions (Fig. 3). Previous studies suggested that higher complexity could be interpreted as improved flexibility and self-organization in postural control24,25. Regarding the nonlinear dynamics of postural control system, we construe that repeated exposure to complex environment can improve self-organization and efficiency in postural control. However, we did not observe the significant main effect of group on COP measures. This finding contradicts the commonly reported observation that sensory conflicting environments have a larger impact on reducing postural stability14,42. One possible explanation could be that the rotating platform was accelerated and decelerated at 4°/s2 and the bipedal standing task in this study was relatively simple, primarily relying on ankle strategy rather than hip or stepping strategy. Consequently, the COP measures, which serve as indicators of postural stability, did not exhibit significant differences between the visual-vestibular congruent and incongruent conditions. We could speculate that there might be complementary strategies of sensory integration which could explain the lack of differences in balance outcome measures.

In the present study, while repeated exposure to visual-vestibular congruent and incongruent conditions both improved postural stability without significant statistical differences, the reconstruction of EEG-based brain effective connectivity networks exhibited different characteristics between these two conditions after repeated exposure. Our results from connectivity measures revealed a significant decrease in inflow into the visual cortex after repeated exposure to the visual-vestibular incongruent condition during both acceleration and deceleration (Figs. 4B and 5B), rather than congruent condition, which validates our first hypothesis. This is in agreement with Peterson and Ferris10, who found visual rotations decreased connectivity between occipital and parietal areas. Such decreased connectivity between occipital and other cortical regions in our present study further suggests that repeated exposure to conflicting visual-vestibular environment enables the human brain to down-weight the less reliable visual information for determining one’s perceived direction and maintaining postural stability. As demonstrated by our current findings on self-motion perception, approximately 60% of participants perceived sensory conflict and reported a clockwise rotation under the 5th incongruent condition, compared to 23% under the 1st incongruent condition. These findings suggested that the visual information tended to dominate over vestibular information under the 1st incongruent environment for self-motion perception43,44. After repeated exposure to the incongruent condition, the domination of visual information decreased, as evidenced by our results on self-motion perception and decreased connectivity between visual cortex and other cortical regions.

Although visual information was less reliable for balance control under the incongruent condition in our experimental setting, the information flow from other regions into the visual cortex was larger under the 1st incongruent trial than the 1st congruent trial (Figs. 4B and 5B). Such larger inflow into the visual cortex under the incongruent environment could be interpreted as an indication of the top-down influence of the global on the visual component according to the global brain model45,46. Furthermore, the Pearson correlation found that the inflow into the visual cortex was negatively correlated with postural stability under the incongruent rather than congruent condition (Fig. 6A). These findings suggest that the increase of top-down influence on the visual cortex might be the compensatory strategy under the visual-vestibular conflict. However, repeated exposure to the incongruent condition significantly decreased the inflow into visual cortex (Figs. 4B and 5B). This results not only suggested that the brain probably down-weighted the less reliable visual information16, but also demonstrated that the compensatory top-down influence onto the visual component decreased with postural stability increased.

On the other hand, the brain effective connectivity network exhibited different characteristics after repeated exposure to visual-vestibular congruent condition. For instance, our results showed the inflow of posterior parietal cortex significantly increased during acceleration and deceleration (Figs. 4B and 5B), which validates our second hypothesis and suggests the improvement in efficiency of multisensory integration47 and in postural control32. This result is consistent with our previous findings14, where researchers asked participants to keep balance when exposed to physical-visual congruent and incongruent stimuli and used PDC as brain connectivity analysis; they found that the posterior parietal cortex as a hub of information flow during postural control under multisensory congruent environment. Different from the present experimental setting for the incongruent condition, visual information is reliable for balance control under the congruent condition. As shown in our brain connectivity results, repeated exposure to congruent condition increased the outflow from the visual cortex, which was not initially hypothesized (Fig. 5C). These results suggested that the brain probably up-weighted the visual information for increasing postural stability. Also, the Pearson correlation found that the outflow from the visual cortex was positively correlated with postural stability under congruent rather than incongruent condition (Fig. 6B). These results suggested that the greater integration of reliable visual information can be more advantageous for postural stability under the visual-vestibular congruent condition. Furthermore, our results demonstrated a significant increase in outflow of primary somatosensory after repeated exposure to both visual-vestibular congruent and incongruent conditions during deceleration (Fig. 5C). In the experimental setting of our study, somatosensory information was both reliable under the congruent and incongruent conditions for postural control. Thus, one possible explanation would be that the brain probably up-weighted the reliable somatosensory information after repeated exposure16.

Conclusion and limitation

Our findings provide evidence for how repeated exposure to visual-vestibular congruent and incongruent environments affects the sensory reweighting characteristic in the brain networks. By investigating cortical effective connectivity changes, we demonstrated that repeated exposure to congruent environments increased the information flow into the posterior parietal cortex and the information flow from the visual cortex for improving postural stability. In contrast, repeated exposure to visual incongruent environments decreased the information flow into visual cortex for improving postural stability. These observations further suggested that through rapidly reorganizing the brain networks, postural control system has the ability to flexibly adapt to different and complex environments with different sensory reweighting patterns for postural stability.

The limitation of our present study is that we used 32-channel EEG recordings to reconstruct the effective connectivity network. This might present challenges, such as spurious correlations, due to the limited spatial resolution, especially for the deeper brain regions48. Future studies should consider to use 64-channel EEG or fNIRS devices, with a higher spatial resolution to further validate our present results.

Supplementary Tables

Supplementary Tables.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-73111-6.

Author contributions

AK.H., GZ.W. and J.W. designed the research; AK.H., GZ.W., JY.B. and ZM.H. performed the experiments; ZM.H. and X.L. prepared the experimental platform; AK.H., GZ.W., J.L. and J.M. analyzed data; AK.H. and GZ.W. wrote the paper; AK.H., GZ.W. and Y.Y. prepared figures; J.W. revised the paper. All authors reviewed the manuscript.

Funding

This research was supported by the National Defense Foundation Strengthening Program Technology Field Fund Project of China (2021-JCJQ-JJ-1029) to Jian Wang.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Publisher’s note

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

These authors contributed equally: Anke Hua and Guozheng Wang
==== Refs
References

1. Massion J Postural control system Curr. Opin. Neurobiol. 1994 4 6 877 887 10.1016/0959-4388(94)90137-6 7888772
Massion, J. Postural control system. Curr. Opin. Neurobiol. 4 (6), 877–887 (1994).7888772
2. Fushiki H Kobayashi K Asai M Watanabe Y Influence of visually induced self-motion on postural stability Acta Otolaryngol. 2005 125 1 60 64 10.1080/00016480410015794 15799576
Fushiki, H., Kobayashi, K., Asai, M. & Watanabe, Y. Influence of visually induced self-motion on postural stability. Acta Otolaryngol. 125 (1), 60–64 (2005).15799576
3. Benson, A. Sensory functions and limitations of the vestibular system, in Perception and Control of self-motion, Psychology, 145–170. (2014).
4. Bronstein AM Multisensory integration in balance control Handb. Clin. Neurol. 2016 137 57 66 10.1016/B978-0-444-63437-5.00004-2 27638062
Bronstein, A. M. Multisensory integration in balance control. Handb. Clin. Neurol. 137, 57–66 (2016).27638062
5. Oman CM Motion sickness: a synthesis and evaluation of the sensory conflict theory Can. J. Physiol. Pharmacol. 1990 68 2 294 303 10.1139/y90-044 2178753
Oman, C. M. Motion sickness: a synthesis and evaluation of the sensory conflict theory. Can. J. Physiol. Pharmacol. 68 (2), 294–303 (1990).2178753
6. Dennison MS D’Zmura M Cybersickness without the wobble: experimental results speak against postural instability theory Appl. Ergon. 2017 58 215 223 10.1016/j.apergo.2016.06.014 27633216
Dennison, M. S. & D’Zmura, M. Cybersickness without the wobble: experimental results speak against postural instability theory. Appl. Ergon. 58, 215–223 (2017).27633216
7. Lacour M Bernard-Demanze L Interaction between vestibular compensation mechanisms and vestibular rehabilitation therapy: 10 recommendations for optimal functional recovery Front. Neurol. 2015 5 285 10.3389/fneur.2014.00285 25610424
Lacour, M. & Bernard-Demanze, L. Interaction between vestibular compensation mechanisms and vestibular rehabilitation therapy: 10 recommendations for optimal functional recovery. Front. Neurol. 5, 285 (2015).25610424
8. Appiah-Kubi KO Wright W Vestibular training promotes adaptation of multisensory integration in postural control Gait Posture 2019 73 215 220 10.1016/j.gaitpost.2019.07.197 31376748
Appiah-Kubi, K. O. & Wright, W. Vestibular training promotes adaptation of multisensory integration in postural control. Gait Posture 73, 215–220 (2019).31376748
9. Aubonnet R Brain network dynamics in the alpha band during a complex postural control task J. Neural Eng. 2023 20 2 026030 10.1088/1741-2552/acc2e9
Aubonnet, R. et al. Brain network dynamics in the alpha band during a complex postural control task. J. Neural Eng. 20 (2), 026030 (2023).
10. Peterson SM Ferris DP Group-level cortical and muscular connectivity during perturbations to walking and standing balance NeuroImage 2019 198 93 103 10.1016/j.neuroimage.2019.05.038 31112786
Peterson, S. M. & Ferris, D. P. Group-level cortical and muscular connectivity during perturbations to walking and standing balance. NeuroImage 198, 93–103 (2019).31112786
11. Shenoy Handiru V Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: a pilot study Hum. Brain. Mapp. 2021 42 14 4427 4447 10.1002/hbm.25554 34312933
Shenoy Handiru, V. et al. Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: a pilot study. Hum. Brain. Mapp. 42 (14), 4427–4447 (2021).34312933
12. Friston KJ Functional and effective connectivity: a review Brain Connect. 2011 1 1 13 36 10.1089/brain.2011.0008 22432952
Friston, K. J. Functional and effective connectivity: a review. Brain Connect. 1 (1), 13–36 (2011).22432952
13. Alanis-Espinosa, M. & Gutiérrez, D. Using the partial directed coherence to understand brain functional connectivity during movement imagery tasks, in International Conference on Brain Informatics, Springer, pp. 119–128. (2018).
14. Wang, G. et al. Dynamic changes of brain networks during standing balance control under visual conflict. Front. NeuroSci. 16, (2022).
15. Barollo F Cortical pathways during postural control: new insights from functional EEG source connectivity IEEE Trans. Neural Syst. Rehabil. Eng. 2022 30 72 84 10.1109/TNSRE.2022.3140888 34990367
Barollo, F. et al. Cortical pathways during postural control: new insights from functional EEG source connectivity. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 72–84 (2022).34990367
16. Peterka RJ Sensorimotor integration in human postural control J. Neurophysiol. 2002 88 3 1097 1118 10.1152/jn.2002.88.3.1097 12205132
Peterka, R. J. Sensorimotor integration in human postural control. J. Neurophysiol. 88 (3), 1097–1118. 10.1152/jn.2002.88.3.1097 (2002).12205132
17. Yang Y Liu S Chowdhury SA DeAngelis GC Angelaki DE Binocular disparity tuning and visual–vestibular congruency of multisensory neurons in macaque parietal cortex J. Neurosci. 2011 31 49 17905 17916 10.1523/JNEUROSCI.4032-11.2011 22159105
Yang, Y., Liu, S., Chowdhury, S. A., DeAngelis, G. C. & Angelaki, D. E. Binocular disparity tuning and visual–vestibular congruency of multisensory neurons in macaque parietal cortex. J. Neurosci. 31 (49), 17905–17916 (2011).22159105
18. McIlroy W Maki B Preferred placement of the feet during quiet stance: development of a standardized foot placement for balance testing Clin. Biomech. Elsevier Ltd 1997 12 1 66 70 10.1016/S0268-0033(96)00040-X
McIlroy, W. & Maki, B. Preferred placement of the feet during quiet stance: development of a standardized foot placement for balance testing. Clin. Biomech. Elsevier Ltd 12 (1), 66–70 (1997).
19. Cooper, J., Siegfried, K. & Ahmed, A. BrainBLoX: brain and biomechanics lab in a box software (Version 1.0)[Software]. (2014).
20. Clark RA Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance Gait Posture 2010 31 3 307 310 10.1016/j.gaitpost.2009.11.012 20005112
Clark, R. A. et al. Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance. Gait Posture 31 (3), 307–310 (2010).20005112
21. Clark RA Mentiplay BF Pua YH Bower KJ Reliability and validity of the Wii Balance Board for assessment of standing balance: a systematic review Gait Posture 2018 61 40 54 10.1016/j.gaitpost.2017.12.022 29304510
Clark, R. A., Mentiplay, B. F., Pua, Y. H. & Bower, K. J. Reliability and validity of the Wii Balance Board for assessment of standing balance: a systematic review. Gait Posture 61, 40–54 (2018).29304510
22. Zhou, J., Habtemariam, D., Iloputaife, I., Lipsitz, L. A. & Manor, B. The complexity of standing postural sway associates with future falls in community-dwelling older adults: the MOBILIZE Boston study. Sci. Rep. 7 (1). 10.1038/s41598-017-03422-4 (2017).
23. Richman, J. S. & Moorman, J. R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, 6, (2000). pp. H2039–H2049.
24. Busa MA van Emmerik RE Multiscale entropy: a tool for understanding the complexity of postural control J. Sport Health Sci. 2016 5 1 44 51 10.1016/j.jshs.2016.01.018 30356502
Busa, M. A. & van Emmerik, R. E. Multiscale entropy: a tool for understanding the complexity of postural control. J. Sport Health Sci. 5 (1), 44–51 (2016).30356502
25. Kędziorek, J. & B\lażkiewicz, M. Nonlinear measures to evaluate upright postural stability: A systematic review, Entropy, vol. 22, no. 12, p. 1357, (2020).
26. Delorme A Makeig S EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis J. Neurosci. Methods 2004 134 1 9 21 10.1016/j.jneumeth.2003.10.009 15102499
Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134 (1), 9–21 (2004).15102499
27. Artoni F Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking Neuroimage 2017 159 403 416 10.1016/j.neuroimage.2017.07.013 28782683
Artoni, F. et al. Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking. Neuroimage 159, 403–416 (2017).28782683
28. Mullen TR Real-time neuroimaging and cognitive monitoring using wearable dry EEG IEEE Trans. Biomed. Eng. 2015 62 11 2553 2567 10.1109/TBME.2015.2481482 26415149
Mullen, T. R. et al. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62 (11), 2553–2567 (2015).26415149
29. Chang CY Hsu SH Pion-Tonachini L Jung TP Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings IEEE Trans. Biomed. Eng. 2019 67 4 1114 1121 10.1109/TBME.2019.2930186 31329105
Chang, C. Y., Hsu, S. H., Pion-Tonachini, L. & Jung, T. P. Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings. IEEE Trans. Biomed. Eng. 67 (4), 1114–1121 (2019).31329105
30. Pion-Tonachini L Kreutz-Delgado K Makeig S ICLabel: an automated electroencephalographic independent component classifier, dataset, and website NeuroImage 2019 198 181 197 10.1016/j.neuroimage.2019.05.026 31103785
Pion-Tonachini, L., Kreutz-Delgado, K. & Makeig, S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. NeuroImage 198, 181–197 (2019).31103785
31. Pascual-Marqui, R. D. & Suppl, D. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, Methods Find Exp Clin Pharmacol, 24, no. 5–12, (2002).
32. Takakusaki, K. Functional neuroanatomy for posture and Gait Control. J. Mov. Disorders 10, 1, pp. 1–17, 2017, 10.14802/jmd.16062
33. Wittenberg E Thompson J Nam CS Franz JR Neuroimaging of human balance control: a systematic review Front. Hum. Neurosci. 2017 11 170 10.3389/fnhum.2017.00170 28443007
Wittenberg, E., Thompson, J., Nam, C. S. & Franz, J. R. Neuroimaging of human balance control: a systematic review. Front. Hum. Neurosci. 11, 170 (2017).28443007
34. Dijkstra BW Functional neuroimaging of human postural control: a systematic review with meta-analysis Neurosci. Biobehav. Rev. 2020 115 351 362 10.1016/j.neubiorev.2020.04.028 32407735
Dijkstra, B. W. et al. Functional neuroimaging of human postural control: a systematic review with meta-analysis. Neurosci. Biobehav. Rev. 115, 351–362. 10.1016/j.neubiorev.2020.04.028 (2020).32407735
35. Bland BH Oddie SD Theta band oscillation and synchrony in the hippocampal formation and associated structures: the case for its role in sensorimotor integration Behav. Brain. Res. 2001 127 1 2 10.1016/S0166-4328(01)00358-8
Bland, B. H. & Oddie, S. D. Theta band oscillation and synchrony in the hippocampal formation and associated structures: the case for its role in sensorimotor integration. Behav. Brain. Res. 127, 1–2 (2001).
36. Caplan JB Human þeta oscillations related to sensorimotor integration and spatial learning J. Neurosci. 2003 23 11 4726 4736 10.1523/JNEUROSCI.23-11-04726.2003 12805312
Caplan, J. B. et al. Human þeta oscillations related to sensorimotor integration and spatial learning. J. Neurosci. 23 (11), 4726–4736 (2003).12805312
37. Peterson, S. M. & Ferris, D. P. Differentiation in theta and beta electrocortical activity between visual and physical perturbations to walking and standing balance, eneuro, vol. 5, no. 4, (2018).
38. Niso G HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity Neuroinformatics 2013 11 405 434 10.1007/s12021-013-9186-1 23812847
Niso, G. et al. HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics 11, 405–434 (2013).23812847
39. Xia M Wang J He Y BrainNet Viewer: a network visualization tool for human brain connectomics PloS One 2013 8 7 e68910 10.1371/journal.pone.0068910 23861951
Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS ONE 8 (7), e68910 (2013).23861951
40. Oude Nijhuis, L. B. et al. Directional sensitivity of ‘first trial’ reactions in human balance control. J. Neurophysiol. 101 (6). 10.1152/jn.90945.2008 (2009).
41. Kim D Hwang JM The center of pressure and ankle muscle co-contraction in response to anterior-posterior perturbations PloS One 2018 13 11 e0207667 10.1371/journal.pone.0207667 30496202
Kim, D. & Hwang, J. M. The center of pressure and ankle muscle co-contraction in response to anterior-posterior perturbations. PloS ONE 13 (11), e0207667 (2018).30496202
42. Kabbaligere R Lee BC Layne CS Balancing sensory inputs: sensory reweighting of ankle proprioception and vision during a bipedal posture task Gait Posture 2017 52 244 250 10.1016/j.gaitpost.2016.12.009 27978501
Kabbaligere, R., Lee, B. C. & Layne, C. S. Balancing sensory inputs: sensory reweighting of ankle proprioception and vision during a bipedal posture task. Gait Posture 52, 244–250.10.1016/j.gaitpost.2016.12.009 (2017).27978501
43. Wright, W. G. Using virtual reality to induce cross-axis adaptation of postural control: implications for rehabilitation, in International Conference on Virtual Rehabilitation (ICVR), IEEE, 2013, pp. 289–294. (2013).
44. Wright, W. G., DiZio, P. & Lackner, J. R. Vertical linear self-motion perception during visual and inertial motion: more than weighted summation of sensory inputs. J. Vestib. Research: Equilib. Orientat. 15, 4, (2005).
45. Schlee, W. et al. A global brain model of tinnitus. Textbook Tinnitus, pp. 161–169, (2011).
46. Zobay O Palmer AR Hall DA Sereda M Adjamian P Source space estimation of oscillatory power and brain connectivity in tinnitus PloS One 2015 10 3 e0120123 10.1371/journal.pone.0120123 25799178
Zobay, O., Palmer, A. R., Hall, D. A., Sereda, M. & Adjamian, P. Source space estimation of oscillatory power and brain connectivity in tinnitus. PloS ONE 10 (3), e0120123 (2015).25799178
47. Fetsch, C. R., Deangelis, G. C. & Angelaki, D. E. Visual-vestibular cue integration for heading perception: applications of optimal cue integration theory. Eur. J. Neurosci. 31 (10). 10.1111/j.1460-9568.2010.07207.x (2010).
48. Liu Q Ganzetti M Wenderoth N Mantini D Detecting large-scale brain networks using EEG: impact of electrode density, head modeling and source localization Front. Neuroinformatics 2018 12 4 10.3389/fninf.2018.00004
Liu, Q., Ganzetti, M., Wenderoth, N. & Mantini, D. Detecting large-scale brain networks using EEG: impact of electrode density, head modeling and source localization. Front. Neuroinformatics 12, 4 (2018).
