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Eleven Russian cosmonauts and healthy age- and gender-matched volunteers (11 men not involved in the space program) took part in the study. At the time of the first exam the mean age of the participants was 45 years old (SD = 5) for the cosmonauts and 44 (SD = 6) for the control group. The study was approved as a part of the Brain DTI project by Committee of Biomedicine Ethics of the Institute of Biomedical Problems of the Russian Academy of Sciences and the Human Research Multilateral Review Board (HRMRB) according to the 18th World Medical Assembly of Helsinki, Finland, June 1964, amended by the 41st Assembly, Hong Kong, September 1989. All participants gave written informed consent for the study at enrollment.

In cosmonauts, subjective space motion sickness symptoms during space flight were assessed by a questionnaire first introduced at the MIR orbital station during the ‘ANKETA’ experiment (Kornilova, 1997) and since then used at the Institute of Biomedical Problems at the Russian Academy of Sciences. The cosmonauts were interviewed after the spaceflight before their vestibular tests on the first or 2nd day post-landing. Space motion sickness symptoms comprised complaints about orientation illusions, dizziness, poor coordination, difficulties in gaze fixation and tracing visual objects, nausea and vomiting (see the full list of questions in Supplementary Materials). The combination, intensity, and duration of these reactions were qualified according to the classification accepted in Russia (Kornilova et al., 2002, 2013). For the purpose of the present study, points were ascribed to each cosmonaut’s state after the flight: 0 corresponds to no complaints; 1 corresponds to moderately pronounced and rather short SMS syndrome; and 2 corresponds to complaints of pronounced and long lasting SMS syndrome with strong dizziness, vomiting, and discoordination. All control participants of the present study were ascribed zero points on this scale.

A 2 × 2 experimental design was used, with group (cosmonauts vs. controls) as a between-subject factor and session (post-flight vs. pre-flight) as a within-subject factor (repeated measures). In the control group, the first scanning session was treated as ‘preflight,’ and the second as ‘post-flight.’ According to the study design, only significant Group × Session effects were attributed to spaceflight.

Each cosmonaut was scanned before and after completing a long-term mission to the International Space Station between 2014 and 2017. For one cosmonaut, the data were aggregated across two separate space missions (and counted as the data from one subject). The control participants were scanned twice with comparable time intervals between the scans. A detailed description of demographics and timing data is presented in Table 1.

The pneumatic KORVIT system (VIT, Saint Petersburg, Russia and Center of Aviaspace Medicine, Moscow, Russia) was used for mechanical stimulation of the soles support zones. The device was initially developed in the Institute of Biomedical Problems at the Russian Academy of Sciences (Grigor’ev et al., 2004; Kozlovskaya et al., 2007; Layne and Forth, 2008) as a mean of compensation of support afferentation deficit and therefore as a countermeasure to microgravity-induced motor impairment. The main component of the system is a pair of plastic boots with inflatable chambers mounted into the boot soles under the metatarsal and the heel zone. This layout of the chambers ensures stimulation of the zones with the highest density of mechanoreceptors and therefore elicits maximal response in the tonic muscle system. The chambers are connected to an air compressor by plastic cables. All parts of the system except for the air compressor are MR safe; during the scanning session the boots and cables were located in the scanner room, while the air compressor was located and operated upon in the scanner console room. The setup is illustrated in Figure 1. In the present study, the gait-like simulation mode was used with alternating pressure of 40 kPa cyclically administered to four zones on participants’ feet (right heel, right toes, left heel, left toes) with the frequency equal to 75 steps per minute.

According to previous studies, this mode of support stimulation leads to an extensive activation of the sensorimotor cortex (SMC) that controls locomotion (Kremneva et al., 2013). It has also been shown that this paradigm does not reflect actual locomotion, because the person does not make any movements. However, it has been proven that the afferent impulses induced by rhythmic stimulation of the support zone of the sole at a frequency and load similar to those experienced during real walking play an important role in supraspinal control (Kozlovskaya et al., 2007). Moreover, these results are similar to the results of other research groups that used imaginary walking paradigms (Kremneva et al., 2013).

All participants were scanned with a 3T GE Discovery MR750 scanner equipped with a 16-channel head, neck and spine (HNS) array coil. The scanner was located at the Federal Center of Medicine and Rehabilitation in Moscow, Russia.

For each participant, 160 T2^*-weighted functional images were acquired in a single session of the gait-like plantar stimulation paradigm. Four extra volumes were scanned and automatically discarded by the scanner software prior to the acquisition of the functional data in order to achieve magnetic equilibrium. The gradient-echo echo-planar imaging (GRE EPI) pulse sequence was used with the following parameters: TR/TE/FA = 2000 ms/30 ms/77°, FoV = 192 mm × 192 mm × 126 mm, matrix size = 64 × 64 × 42, isotropic voxel size 3 mm. Each volume covered the whole brain with slices oriented parallel to the AC/PC line. The session lasted for approximately 5 min and was administered within the final part of the over 1 h long assessment that included both structural and functional scans (BRAINDTI project protocol). T1-weighted structural images were acquired in the first part of this program using the 3D fast spoiled gradient-echo (FSPGR) pulse sequence with an isotropic voxel size of 1 mm (TR/TE/FA = 7.9 ms/3.06 ms/12°).

During the plantar stimulation, a participant would lay in the scanner supine (head first) with KORVIT boots on his feet. He was instructed to keep his head still during the plantar stimulation. Extra foam padding was used to prevent excessive head motion elicited by the pulses of air pressure on the feet. The gait-like stimulation was administered in a block design with alternating 20-s blocks of two conditions: ‘stimulation’ and ‘rest.’ The stimulation cycle started with the rest period and was repeated eight times.

Data processing was performed with SPM 12 (Wellcome Institute of Cognitive Neurology¹) and GLMFlex2 software (Aaron Shultz²). Preprocessing included the following steps: slice timing correction; realignment; longitudinal spatial coregistration of structural images from the two scanning sessions; spatial coregistration of the mean structural image and functional images; segmentation of the average structural volume into six tissue volumes; normalization into Montreal Neurological Institute (MNI) space; and spatial smoothing of the functional images with a Gaussian kernel of 8 mm full width at half-maximum (FWHM). Six residual head motion parameters (three for translation and three for rotation) were extracted during the realignment step.

To reveal the task-based activation, data were modeled using the general linear model as implemented in SPM12 software. For each participant, the MR signal was modeled using the canonical hemodynamic response function (HRF) with temporal derivatives (Friston et al., 2007). Temporal derivatives of the HRF were included into the model to prevent possible artifactual differences between groups or conditions. This concern was raised because the influence of spaceflight on the neurovascular coupling and HRF parameters is not yet known, but some effect is highly plausible given the alterations in cardiovascular functioning in space (Zhu et al., 2015) and especially the alterations of brain hemodynamics (Kawai et al., 2003; Blaber et al., 2013; Taylor et al., 2013) due to fluid shifting to the upper body. The data were analyzed as a block design with one experimental (plantar stimulation) condition while the baseline condition was not explicitly modeled to avoid model redundancy. Six parameters describing the head motion throughout the experimental session were included into the model as nuisance regressors. T-test contrasts for the BOLD signal change evoked by the plantar stimulation were obtained from both HRF and HRF temporal derivative regressors and combined into a single image for further use in the second-level analysis with the ‘derivative boost’ method (Lindquist et al., 2009; Steffener et al., 2010) implemented in the spmup_hrf_boost script (Pernet, 2014). A 2 × 2 ANOVA with group (cosmonauts vs. healthy controls) as a between-subject factor and session (pre-flight vs. post-flight) as a within-subject factor was performed as a second-level analysis scheme with the GLMFlex2 software. The results were assessed with a FDR-corrected cluster-wise threshold of q = 0.05 based on an uncorrected voxel-wise threshold of p = 0.001. Lenient statistical thresholds (p < 0.05 uncorrected, k = 5) were additionally applied to the group activation map obtained from the data of all 22 participants (both experimental and control groups) in order to define ROIs and the sensorimotor system mask for subsequent connectivity analyses. Peak activation coordinates were labeled with either FSL Harvard-Oxford maximal likelihood cortical and subcortical atlases (Desikan et al., 2006) or the AAL atlas (Tzourio-Mazoyer et al., 2002) for the cerebellum.

CONN Functional Connectivity Toolbox v. 17a (Whitfield-Gabrieli and Nieto-Castanon, 2012³) was used for the task-based connectivity preprocessing and further statistical analysis. Several standard procedures in the Conn toolbox were applied to the data already preprocessed for the task-based activation analysis in order to account for the residual motion-induced artifacts and physiological noise (denoising). First, head motion artifact detection was performed with the Artifact Detection Toolbox (ART⁴). Medium level thresholds that result in rejecting 3% of the normative sample data were applied; images demonstrating scan-to-scan head motion of more than 0.9 mm or global mean intensity change of more than 5 SDs were considered outliers. Outliers were subsequently included as nuisance regressors into the denoising linear model along with the residual head motion parameters. Another set of nuissance regressors was introduced by the anatomical component-based noise correction technique (aCompCor; Behzadi et al., 2007). With this method, noise ROIs are defined within the white matter and CSF masks individually segmented for each participant. The signal from the noise ROIs is decomposed with a principal component analysis (PCA) and time courses of the resulting components are regressed out from the data. The main BOLD-signal effects of the plantar stimulation and rest blocks were also regressed out to restrict the analysis to within-condition connectivity alterations rather than global changes of the correlation evoked by the task onset or offset. The linear detrending term was also applied. Then a standard temporal high-pass filter with a cutoff of 0.008 Hz was applied on the time series in order to further restrict the analysis to signal fluctuations which characterize task-based fMRI BOLD frequency band.

Two approaches to the task-based functional connectivity analysis were taken: voxel-to-voxel (data-driven) and ROI-to-ROI (hypothesis-driven). Follow-up hypotheses-driven seed-to-voxel analyses were also used to aid interpretation of the results. The connectivity results were labeled with the Harvard-Oxford and AAL atlases as well as the activation data.

Firstly, voxel-to-voxel analysis was conducted to obtain the intrinsic connectivity contrast (ICC) values for each voxel in the whole brain. ICC was computed as a mean absolute value of the correlations of the time series for a given voxel with all other voxels included in the analysis (Martuzzi et al., 2011). The ICC values for each participant and condition (pre-flight and post-flight plantar stimulation and rest) were further used in the second-level ANCOVA with Group (cosmonauts vs. healthy controls) as a between-subject factor and session (post-flight vs. pre-flight) as a within-subject factor. Because of the imperfect cerebellum coverage in several scans, a concern was raised that the difference in the cerebellum ROIs between the preflight and post-flight sessions in the affected participants might also result in between-session differences in connectivity values. To account for this potential confound, the mean-centered difference in the total volume of the cerebellar network ROIs (defined with the Conn network atlas) for each participant was included in the analysis as a between-subject covariate. A Group × Session interaction was assessed with a FDR-corrected cluster-wise threshold of p = 0.05, q = 0.05 based on an uncorrected voxel-wise two-sided threshold of p = 0.001.

An exploratory ROI-to-ROI analysis was adopted to test for the possible effects of long-duration spaceflight. The set of ROI selected for the analysis covered the sensorimotor, visual, proprioceptive, and vestibular brain systems thus including all main sources of afference utilized by the motor control system.

First, we included eight clusters identified in the activation map obtained from the plantar stimulation versus rest contrasts in all participants (cosmonauts and controls) at the liberal statistical threshold, obtained as described above (Table 3). Then all clusters from the sensorimotor, cerebellar, and visual systems from the Conn Networks atlas were also included. Since the insula plays an important role in motor, vestibular and proprioceptive functions, we included the ROIs for the right and left anterior and posterior insula (outlines from Kelly et al., 2012 were used). The bilateral ROIs for the thalamus, which is believed to be a relay station for vestibular signals (Lopez and Blanke, 2011), and the putamen, which is believed to play a crucial role in proprioception (Goble et al., 2012), were taken from the Harvard-Oxford atlas. Other ROIs were constructed with the MarsBar toolbox (Brett et al., 2002) as spheres around coordinates taken from the literature. The selection of the vestibular ROI replicated that by Van Ombergen et al. (2017b), with a different outline of the thalamus ROI as the only exception. Besides the insula and thalamus, vestibular ROIs included the right parietal operculum area 2 (rOP2), which is currently believed to represent the human vestibular cortex; the precuneus; the inferior parietal lobule, which is believed to be a part of the multimodal vestibular cortex (Zu Eulenburg et al., 2012; Dieterich and Brandt, 2015); and the bilateral vestibular nuclei (Kirsch et al., 2016). Proprioception ROIs were selected on the basis of data presented by Goble et al. (2012) and, besides the putamen, included the inferior frontal gyrus (IFG) and the inferior parietal cortex (BA40) bilaterally. The initially selected set of ROIs was further cross-checked for intersections, and only one of the two ROIs were included if a significant overlap was found (for example, the spherical ROI for the rOP2 was almost entirely covered by the activation-based rOP ROI). Since only differential contrasts of connectivity across groups and conditions were further considered, the residual marginal ROI overlap was not taken into account. Characteristics of the 27 areas forming the resulting ROI set are presented in Table 2.

The signal from each ROI was extracted only from gray matter voxels of the unsmoothed functional volumes, in order to avoid any additional risk of contaminating the data with white matter or CSF signals or with signals from other ROIs. Then, the task modulation of the ROI-to-ROI functional connectivity was assessed for the ‘preflight’ and the ‘post-flight’ sessions with the individual general psychophysiological interaction model (gPPI; McLaren et al., 2012) for each participant. The resulting differential ROI-to-ROI connectivity values for the active plantar stimulation condition over the implicitly modeled baseline were further used in the second-level ANCOVA with group (cosmonauts vs. healthy controls) as a between-subject factor and session (pre-flight vs. post-flight) as a within-subject factor, and the mean-centered difference in the total volume of the cerebellar network ROIs as a covariate. The results were considered at the levels of (a) individual modified connection (two-sided t-test, analysis-level FDR-corrected, p_corr< 0.05); (b) ROI demonstrating a modified connectivity pattern; and (c) network (clusters of modified connections). At the latter two levels we used network-based statistics, taking into account the connection intensity (NBS; Zalesky et al., 2010) for the correction for multiple comparisons (FDR at the ROI level and FWE at the network level, p_corr< 0.05, cluster-defining threshold p < 0.05 uncorrected).

The NBS statistic utilizes the permutation test and may be understood as an analog of the topological correction for multiple comparisons in the connectivity domain (with individual connections in place of voxels and subnetworks as clusters of connections). Therefore the NBS statistics is more sensitive but less spatially specific than the more conventional analysis at the level of individual connection. Due to the nature of the NBS statistics, inference limitations apply: the subnetworks may only be discussed in their integrity; no individual connection change may be considered significant on the basis that it belongs to a significantly changing NBS network. As implemented in Conn, the ROI-level NBS analysis treats only connections originating from the ROI under consideration and involves an extra FDR correction for the number of ROIs.

To aid interpretation of the results, we performed a follow-up whole-brain seed-to-voxel analysis using as seeds the ROIs that demonstrated significant effects of Group × Scanning session interaction in the NBS ROI-to-ROI analysis (ROI level). While the NBS analysis does not allow for any inference regarding individual, pairwise connections between ROIs, the seed-to-voxel analysis aims at testing whether the observed connectivity alterations are diffuse in their nature or have any compact localizable addressees. We also performed a whole-brain seed-to-voxel analysis using as a seed the cluster revealed by the ICC analysis as sensitive to the cosmonaut vs. control, post- vs. pre-, task vs. baseline interaction.

In each seed-to-voxel analysis, we used the PPI connectivity data computed between the seed region and every brain voxel outside the seed region. These differential values were entered into the second-level ANCOVA, analogous to what was implemented for the ROI-to-ROI analysis: group (cosmonauts vs. healthy controls) as a between-subject factor, session (pre-flight vs. post-flight) as a within-subject factor, and the mean-centered difference in the total volume of the cerebellar network ROIs as a covariate. Seeds were tested one at a time.

Additionally, we conducted a set of second-level ANCOVAs, testing for possible correlations between the post-flight vs. pre-flight difference in connectivity of the seeds and the individual scores of space motion sickness severity (the mean-centered difference in the total volume of cerebellar network ROIs was also included as a covariate of no interest). Again, seeds were tested one at a time.

Besides the correction for multiple comparisons performed at the level of each resulting spatial map (cluster-wise topographic FDR correction; p < 0.05, q < 0.05; voxel-wise cluster-forming two-sided threshold p < 0.001 uncorrected), the statistical thresholds for the results were also corrected for the total number of seed-to-voxel analyses (p < 0.05/12 = 0.004).

There were no significant differences in the mean age of the groups at the first scanning session (see Table 1). No statistically significant differences were found between groups or sessions in the standard deviations of displacement or rotation across any of the six registered head motion parameters. The percentage of the scans classified by the ART toolbox as invalid varied from 0 to 8% per participant; the total of 1.22% scans were discarded as invalid for the entire dataset. No participant was excluded from the analysis due to excessive head motion. The scan-to-scan head motion before scrubbing averaged 0.15 mm, SD: 0.05 mm per participant with a mean maximum motion of 1.14 mm, SD: 1.28 mm. No effects of the group, session or interaction of these factors on the general voxel-to-voxel correlation (GCOR; Saad et al., 2013) were revealed.

A 2 × 2 ANOVA showed no effect of Group × Scanning session interaction on brain activation evoked by the plantar stimulation paradigm. The aggregated statistical map for both groups revealed an activation pattern that included the primary SMC, the supplementary motor cortex (SMA), extensive regions in operculum bilaterally, and, when applying low statistical thresholds, areas of the right insula and the temporal pole and of the anterior cerebellum (see Figure 2 and Table 3).

The whole-brain analysis revealed a cluster in the right posterior supramarginal gyrus (pSMG; 567 mm³; center of mass: x = 63, y = −37, z = 13; see Figure 3A) demonstrating the effect of a Group × Scanning session interaction for the ICC change during the task blocks over the baseline.

ROI-to-ROI analysis exploring the connectivity between the motor, somatosensory, visual, proprioceptive, cerebellar and vestibular brain systems, demonstrated significant post-flight alterations compared to the between-session differences observed in the control group.

Alterations were found at the levels of individual connection, ROI and subnetwork. The following individual connections showed modifications significant at the p < 0.05 threshold after analysis-level FDR-correction. Functional coupling between the right and left posterior insulae significantly increased in cosmonauts post-flight, while connections degraded between the posterior cerebellum and the primary visual cortex, and between the anterior cerebellum (activation cluster Cerebellum-03) and right parietal cortex (BA40).

In order to detect more subtle although less spatially specific changes, we computed ROI-level and network-level network-based statistics by intensity (NBS; Zalesky et al., 2010). The network-level NBS identified a subnetwork demonstrating effects of a Group × Scanning session interaction (at p < 0.05, FWE-corrected; see Figure 4). This subnetwork included five regions also showing significant connectivity modifications at the ROI level (at p < 0.05, FDR-corrected), namely: the vestibular nuclei (intensity = 22.88, size = 8), the right parietal cortex (intensity = 20.24, size = 6), the anterior part of the cerebellar network (Conn Networks atlas; intensity = 17.47, size = 7), the right posterior insula (intensity = 16.87, size = 5) and the left anterior insula (intensity = 16.47, size = 6). It is noteworthy that all suprathreshold connections of the vestibular nuclei, the right parietal cortex and the cerebellar network (anterior part) ROIs demonstrated a negative shift in PPI values (task vs. baseline differential connectivity) in cosmonauts post-flight vs. pre-flight. Connections comprising the altered networks of the affected ROIs are also presented in Figure 5 and Table 4.

Regions identified in the NBS ROI-to-ROI and ICC results as sites involved into connectivity alterations due to the spaceflight were used as seeds for the follow-up seed-to-voxel analyses performed according to the same scheme as in the main analysis (cosmonauts vs. controls, post-flight vs. pre-flight). This approach aimed to check whether the observed connectivity changes are diffuse in their nature or have any compact localizable addressees across the whole brain.

The second set of the seed-to-voxel analysis employed the same six seeds as described above (five ROIs from the NBS results, and the rpSMG cluster from the ICC results). Now the post-flight vs. pre-flight differences in PPI values characterizing the connectivity of these regions with the rest of the brain were tested for significant correlations with the individual SMS scores.

The results showed a positive correlation of the severity of space motion sickness with differential post-to-preflight, task-to-baseline connectivity between the rpSMG seed and the left insular, opercular, and frontal orbital cortices (Figure 3B and Table 5; cluster-defining threshold p < 0.001, cluster-level FDR correction with correction for the total number of seed-to-voxel follow-up analyses: p < 0.05/12 = 0.004).

The task-based fMRI activation pattern evoked by the KORVIT plantar stimulation system in our study included the primary SMC, the SMA, the SII cortex (operculum) bilaterally, and, at a liberal statistical threshold, cerebellar and insular areas. Such a pattern is typical for passive gait-like plantar stimulation (Dobkin et al., 2004; Golaszewski et al., 2006; Kremneva et al., 2013; Jaeger et al., 2014; Labriffe et al., 2017) and is characterized by reduced insular and cerebellar activation compared to active foot stimulation paradigms (Sahyoun et al., 2004). KORVIT gait-like stimulation was applied to the sole zones with the maximal density of mechanoreceptors, and we believe that through proper modeling of the support afference our fMRI paradigm evoked the neural circuits essential for human upright posture and normal bipedal locomotion (Kozlovskaya et al., 2007) which might be modified in microgravity and, therefore, were the target of our research. At the same time, it should be noted that KORVIT gait-like stimulation does not activate the neural representation of the tonic muscle system exclusively. This is due to the involvement of other skin receptors, the low spatial resolution of the fMRI, and, last but not the least, the complex principles of individual muscle representation in the SMC (Kakei et al., 1999), resulting in a substantial overlap of different muscle projections (Melgari et al., 2008). Therefore, we refer to the pattern of activation obtained in our study as the neural correlates of plantar stimulation rather than the neural correlates of support afference, or tonic muscle system.

The present study failed to reveal any spaceflight-related significant differences in brain activation evoked by the plantar stimulation. In the presence of substantial individual variability among the cosmonauts (reported also by Roberts et al., 2010 in a pilot HDBR study), this null result may indicate insufficient statistical power. However, it may also mean that the ‘core’ neural system associated with the reaction to support loading remains intact after microgravity exposure, or that it completely recovers by the time of the examination (9th day after landing). The latter interpretation is supported by a recent study by Yuan et al. (2018a), who found altered activation in the cerebellum, hippocampus, and visual areas elicited by an active foot tapping fMRI paradigm in a group of volunteers during 70-day HDBR, an effect which was not found a week after HDBR. Also, microgravity-induced changes within the human sensorimotor system are likely to be paradigm-dependent, as was illustrated by our earlier case study (Demertzi et al., 2016) that reported enhanced activation in the SMA during an imaginary tennis task, but not a task involving imagery navigation through a house, after spaceflight.

Our data-driven approach revealed altered ICC for the plantar stimulation over baseline within the right posterior SMG in cosmonauts after spaceflight in comparison to the control group. This part of the SMG belongs to the temporoparietal junction (TPJ) region which also contains a part of the angular gyrus and the most caudal portion of the superior temporal gyrus. The TPJ region in general is believed to play an important role in the processes of motor adaptation, multisensory integration (Grefkes and Fink, 2005) and bodily self-consciousness (Pfeiffer et al., 2014). Therefore our present finding resonates with the recent results by Van Ombergen et al. (2017d), who studied healthy participants exposed to acute alterations of gravity during a parabolic flight and found a decreased resting-state ICC in the right TPJ, specifically within the angular gyrus. Additionally, their results revealed increased connectivity of this region with the SMG bilaterally. The pSMG area is known to be involved in the processing of vestibular input (Bense et al., 2001), in the perception of being upright (Kheradmand et al., 2013), and in the visual perception of object motion according to the laws of gravity (Indovina et al., 2005); the angular gyrus is associated with coordination of sensory weighting and sensory realignment mechanisms during sensorimotor adaptation (Block et al., 2013). Interestingly, the two regions showing signs of structural connectivity disruption after the spaceflight that were found in a recent diffusion MRI study by Lee et al. (2019) also lie in the white matter below the right TPJ region next to the rpSMG. The involved white matter regions belong to the superior longitudinal fasciculus, the inferior longitudinal fasciculus, and the inferior fronto-occipital fasciculus, which are all important for connecting the parietal and frontal cortices and play a role in sensory integration (Lee et al., 2019).

While interpretations involving the functions listed above are appealing, they should be considered with caution because of the heterogeneity and polyfunctional nature of the TPJ (Lee and McCarthy, 2016; Schuwerk et al., 2017) and because of the lack of exact overlap between the coordinates reported in the discussed studies and the locus of our finding (except for the overlap with results by Indovina et al., 2005). The latter is especially important given the considerable spatial extension of both the TPJ and the SMG. Remarkably, the region in the right inferior parietal cortex (BA40) that in the present study was identified as a region showing not increasing but decreasing connectivity after the spaceflight with the ROI-to-ROI approach, is also located within the rSMG, although at a distance from the cluster revealed by the ICC approach.

Many researchers have found structural plasticity in the primary motor and somatosensory cortex after spaceflight and its analogs. One of the important findings in astronauts reported by Koppelmans et al. (2016) is a focal gray matter increase in the medial paracentral lobule, a region including the representation of the lower limbs within the primary SMC. Li et al. (2015) and Koppelmans et al. (2017b) have also found a volumetric increase of gray matter in the paracentral lobule after HDBR. Animal research has provided a detailed description of the ultrastractural plasticity in the primary motor and somatosensory cortices in rats during and after spaceflight (Belichenko and Krasnov, 1991; DeFelipe et al., 2002; Dyachkova, 2007).

Surprisingly, our data did not reveal any significant alterations in the connectivity of primary motor and somatosensory cortex ROIs. One possible reason for this may be that the gray matter increase reported by Koppelmans et al. (2016) is driven mostly by fluid shift mechanisms, i.e., by the complementary decrease in CSF volume in this region (Van Ombergen et al., 2018) rather than neuroplasticity.

For further clarification, we conducted a seed-to-voxel analysis of the PPI connectivity measures with two seeds in the paracentral lobules (left and right), adopted from T1 MNI ICBM152 and Freesurfer in order to replicate the ROI used by Koppelmans et al. (2016) in their VBM analysis. The results revealed decreased connectivity of the left (but not the right) paracentral lobule with the contralateral frontal pole and middle and inferior temporal gyri in cosmonauts vs. controls at post- vs. pre-flight (see Figure 6 and Table 6). This finding should be considered with caution due to the a posteriori character of the underlying analysis. It also does not rule out the idea that the post-flight alteration of gray matter volume is a mechanical effect. However, it shows that the major counterparts of the connectivity changes that engage the paracentral region lie beyond the set of areas chosen for the ROI-to-ROI analysis in the present study.

One possible explanation of this finding is that here we observed a correlate of the previously described phenomenon of an altered lower extremity functional asymmetry in cosmonauts. A change of the leading leg from right before launch to left during the flight was observed in about half of the cosmonauts, according to the data from support reaction registrations in a study of locomotion performance during long-duration spaceflight (Brykov et al., 2015). The plausibility of such an interpretation is increased given the evidence for inversion of other brain functional asymmetries due to support unloading. EEG recordings of presaccadic slow negative potentials (PSNPs) in a dry immersion model have shown that over the course of 7 days of simulated microgravity, the focus of presaccadic negativity shifted to the right hemisphere: the PSNP amplitude sharply decreased in the left and increased in the right hemisphere (Kirenskaya et al., 2006). The authors suggested that because of support unloading and a decrease in proprioceptive input, exposure to microgravity causes a corresponding decrease in the activity of the prefrontal and parietal cortices of the left hemisphere, initially involved in preparation and realization of motor responses. The activation of the right hemisphere in this case could be of compensatory character. The same logic seems to be applicable to our present results.

So far, the most unequivocal results advocating for microgravity-induced neuroplasticity in the sensorimotor system of the mammal brain have been obtained with animal models (Belichenko and Krasnov, 1991; DeFelipe et al., 2002; Dyachkova, 2007). As for the human data, even today microgravity-induced neuroplasticity is discussed in the literature only as a highly plausible theoretical speculation not sufficiently supported by empirical evidence (Koppelmans et al., 2017a; Van Ombergen et al., 2017a). The reason for this caution is not only a lack of data, but also the problems of inference. With non-invasive techniques, central neuroplasticity can hardly be disentangled from numerous low-level confounds arising from the upward displacement of the brain within the skull (Roberts et al., 2017), changes in CSF production and reabsorption (Nelson et al., 2014; Kramer et al., 2015), adaptations in cardiovascular functioning (Zhu et al., 2015) and brain hemodynamics (Kawai et al., 2003; Blaber et al., 2013; Taylor et al., 2013).

However, it is very unlikely that behavioral adaptation and motor learning in space travelers is acquired without any modifications in brain structure and function. Modern neuroscience has collected extensive evidence for experience-induced neuroplasticity in adults learning new skills, and such plasticity becomes visible with neuroimaging even in short periods of time (Dayan and Cohen, 2011; Sampaio-Baptista et al., 2018). This research may guide our understanding of spaceflight’s consequences and their reversible nature (Seidler et al., 2015).

Upon returning to Earth, the space crewmembers pass through a readaptation period since the new motor control strategies acquired in microgravity become maladaptive in the terrestrial settings. Transition to another state of the sensorimotor system takes time, and may be traced for up to 2 weeks at least (Mulavara et al., 2010). The time-course of the readaptation demonstrates substantial individual differences and may be affected by a wide range of factors, from genetic to behavioral (Seidler et al., 2015). Therefore, the signs of the neuroplasticity observed in the readaptation period should be considered not only as residual consequences of the long-term microgravity exposure, but as a mixture of effects induced by spaceflight and the subsequent readaptation to Earth. This view is supported by evidence from animal research which shows continuing ultrastructural modifications in the rat somatosensory cortex, especially in terms of the functional activity or degeneration of axonal terminals in the first hours upon reentry to the Earth’s gravity and even 14 days after landing (Dyachkova, 2007). With this consideration, the time period between landing and subsequent MRI examination, for example, the difference of 4 vs. 9 days discussed by Roberts et al. (2017) should be taken into account as an important variable in future research.

The results of the present study perfectly illustrate the inference problem arising from the mixture of adaptation and readaptation processes. Thus, the unchanged fMRI activation pattern elicited by the plantar stimulation may reflect either the preservation of the ‘core’ neural system associated with the reaction to support loading, or the fast and effective recovery of this system in the first days after spaceflight. Similarly, the observed modifications of the connectivity between different sensory inputs utilized by the motor system (vestibular, proprioceptive, visual) may reflect not only impairment in the default motor control connectivity due to the vestibular deprivation and biomechanic factors associated with microgravity, but also a dephasing of the motor control strategies adopted during spaceflight in favor of the neural implementation of ground-based locomotion.

Interestingly, the hypothesis that after a prolonged space flight the human brain features two co-existing neural networks supporting two modes of locomotion — one for 1 g-gravity and one for microgravity — could account for the fact that second-time flyers adapt more quickly and are less prone to microgravity-induced problems (Kozlovskaya et al., 2015). It also encourages a perspective on divergent signs between the task-based and resting-state connectivity alterations highlighted in the previous discussion (the right TPJ results in Van Ombergen et al., 2017d and in the present study; the insula connectivity results in Zhou et al., 2014; Demertzi et al., 2016 and in the present study). The task-based connectivity measures provide information about the current state of the functional brain organ accomplishing some motor activity such as locomotion. At the same time, it is widely believed that resting-state connectivity represents the background activity of large-scale neural networks that become active when triggered by certain stimulations or tasks (Heine et al., 2012). If so, the resting-state connectivity after a space flight provides amalgamated information about all co-existing motor networks, both the one presently used and the one adapted for microgravity conditions and unused in terrestrial settings. Therefore, a comparison of the connectivity data from these two sources may provide an important basis for future insights into the mechanisms of neuroplasticity within the motor system.

In microgravity, alterations in biomechanics such as modified relationships between the mass of a body part and the force required to move it call for a recalibration of the correspondence between sensory input and motor output (sensory realignment), which is believed to be the function of the cerebellum (Block and Bastian, 2012). At the same time, the otolith afferentation is substantially altered. Not only does it become unreliable, but the otherwise tight coupling of canal-otolith information gets lost, and a conflict in the input from different sensory modalities (vestibular, proprioceptive, and visual) is created (Kornilova and Kozlovskaya, 2003; Davis et al., 2008). The motion sickness syndrome is believed to be a consequence of such sensory mismatch (Reason and Brand, 1975; Schmäl, 2013) and is often experienced by space travelers and described as space motion sickness (Heer and Paloski, 2006; Kornilova et al., 2013).

This sensory conflict calls for a second type of sensorimotor adaptation that involves a recalibration of the relationship between several sensory modalities. Theoretically, this may be dealt with by reweighting the sensory inputs; that is, by downweighting the less reliable modality and prioritizing a more reliable one (Horak et al., 1990). The neural mechanisms of multisensory reweighting remain generally unclear. Reweighting has been studied predominantly in patients with postural pathologies and sensory loss, children and the elderly (e.g., Jeka et al., 2010; Polastri and Barela, 2013), but it has also been suggested as an important mechanism of motor adaptation in astronauts who demonstrate increased reliance on visual and tactile information during and after spaceflight (Reschke et al., 1998; Speers et al., 1998; Clément et al., 2001). Evidence for multisensory reweighting in astronauts has been collected on the basis of kinematic and tactile sensitivity research and subsequent modeling (Speers et al., 1998; Clément et al., 2001; Lowrey et al., 2014).

Given the substantial individual differences between space travelers in their susceptibility to space motion sickness (and therefore in the supposed degree of the proposed sensory reweighting) and the small size of our sample, it may be the case that pronounced neural correlates of sensory reweighting may be found in some but not all cosmonauts. Therefore we examined the role of individual differences and looked for possible correlations between the severity of space motion sickness symptoms and connectivity of the areas changing their connectivity pattern during plantar stimulation in cosmonauts post-flight as shown by the ROI-to-ROI and ICC approaches. The observed correlations suggest the potential importance of connection between the left insula and the right TPJ regions for the neural mechanisms of space motion sickness and sensory reweighting in microgravity. The greater the post- vs. pre-flight difference in connectivity between the two regions, the more pronounced were the symptoms of space motion sickness, which is consistent with the idea of a possible compensatory role of the insula and its connectivity, as discussed above.

According to the gravitational motor system theory (Kozlovskaya et al., 1988), keeping an upright stance in humans is the function of specialized tonic muscle system mainly comprised of the tonic motor units of the extensors. The support afference from the deep skin mechanoreceptors is considered to be the most important sensory input driving motor control in this system. Empirical evidence comes from both actual spaceflights and ground-based models showing that withdrawal of support entails a reflectory decline of transverse stiffness and a voluntary force of tonic extensor (postural) muscles, limiting their participation in locomotion and increasing the involvement of phasic muscle units (Kozlovskaya et al., 1988, 2007; Shigueva et al., 2015). The prevalence of the flexor over the extensor activity leads to the adoption of a quasi-embryonic flexor posture (Margaria, 1966; Bogdanov and Gurfinkel, 1976) and to a changed pattern of coordinated muscle recruitment (Roy et al., 1996). Support unloading has also been found to result in increased sensitivity of the vestibular system in different types of dry immersion in humans (Kreidich, 2009; Kornilova et al., 2016). Similar increased vestibular excitability has also been revealed in the actual microgravity in primates (Sirota et al., 1988) and in cats and a monkey in parabolic flight (Grigorian et al., 1995). These findings were in line with the hypothesis that the vestibular system normally experiences an inhibitory influence from the support afferent system and activates when the influence is removed (Kreidich, 2009). Such inhibitory modulation may be implemented through inhibitory connections projecting from the cerebellum onto the vestibular nuclei (Ito, 1972).

Our present results are in agreement with this theory. In space travelers we did not find evidence for alterations in brain activation elicited by the support afferentation (gait-like plantar stimulation). This is likely to be a result of the fast recovery of the system processing support afference in cosmonauts within the very first days upon their return to the Earth’s gravity. At the same time, evidence for the down-weighting of the vestibular input was still present on the 9th day upon return when most cosmonauts underwent their post-flight scan. Since in microgravity the support afferent system becomes effectively silent and therefore is not involved in sensory conflicts, we may suppose that it is also minimally involved in sensory re-weighting, unlike the vestibular system which produces massive but unreliable sensory signals during space flight. We may further suppose that the fast recovery of the support afferent system’s activity upon return to Earth is accompanied by the restoration of its inhibitory modulation on the vestibular system, which in turn slows down the recovery of the vestibular function. Testing this hypothesis may become a prospective direction of future behavioral and neuroimaging research.