Study design

The current study is a retrospective follow-up study. T2-weighted MRI scans of 27 astronauts were obtained from the NASA Lifetime Surveillance of Astronaut Health. Because of the retrospective nature of this study, not all MR images were collected using the same protocol.

Participants

We included data from 27 astronauts of whom 13 completed a space shuttle mission (~2 weeks) and 14 completed a mission to the International Space Station (ISS) (~6 months). Demographic information is presented in Table 1. The astronauts’ age ranged from ~40 to ~60 years (mean = 48.0, sd = 3.6), while their mission duration ranged from 12 to less than 200 days. Prior spaceflight experience in these astronauts ranged from 0 (two astronauts) to >300 days. The MRI and neurosensory data were collected for mission-related medical monitoring. All astronauts in this study provided written informed consent for this retrospective analysis.

Table 1 Demographics Full size table

Balance control

Balance control was measured using the Sensory Organization Tests (SOTs) provided by the EquiTest System platform (NeuroCom, Clackamas, OR).21 Out of 27 astronauts there were 21 subjects with complete SOT assessment at both pre and postflight assessments where the postflight measurement was collected within the first two days postflight. We report data from all trials that were conducted with sway-referenced support surface intended to disrupt somatosensory feedback and with eyes closed (SOT5); this test reflects how well vestibular input could be utilized to maintain balance.

Image acquisition

Within the group of 27 astronauts either (1) low resolution (n = 10) or (2) high-resolution (n = 17) pre- and postflight image pairs were acquired:

1) Low-resolution scans were either sagittal (n = 8) or axial (n = 2) acquisitions. In both cases MRI was performed on a 3T Philips Intera MRI scanner with an 8-channel head coil. Sagittal T2-weighted sensitivity encoding (SENSE) images had the following parameters (TR = 6.1 s, TE = 80 ms, flip angle = 90°, number of signal averages (NSA) = 1, field of view (FOV) = 240 × 240 mm, slice thickness = 3.0 mm (no slice gap), 50 sagittal slices, matrix = 512 × 512, and voxel size = 3.00 × 0.47 × 0.47 = 0.66 mm3). Identical parameters were used for pre- and postflight data collection, except for one out of these ten subjects for whom post data was collected with a slightly larger FOV (i.e., 256 × 256 mm) resulting in an in-plane voxel size of 0.50 × 0.50 mm. Axial T2-weighted SENSE images had the following parameters (TR = 3.6 s, TE = 80 ms, flip angle = 90°, NSA = 2, FOV = 240 × 240 mm, slice thickness = 4.0 mm (1mm slice gap), 32 axial slices, matrix = 512 × 512, and voxel size = 0.47 × 0.47 × 5.00 = 1.10 mm3). 2) All high-resolution T2 scans were obtained using a 3T SIEMENS Verio scanner applying a sagittal 3D TSE (turbo spin echo) SPACE (sampling perfection with application optimized contrasts by using different flip angle evolutions) sequence with the following scan parameters: parameters (TR = 3.2 s, TE = 409 ms, flip angle = 120°, NSA = 1, FOV = 250 × 250 mm, slice thickness = 1.0 mm (no slice gap), 176 sagittal slices, matrix = 512 × 512, and voxel size = 1.00 × 0.49 × 0.49 = 0.24 mm3).

Preflight scans were collected at a median of 194 days before launch (range = 18–627). Postflight scans were collected at a median of 6 days after return (range = 1–20).

Image processing

Longitudinal voxel-based morphometry and region of interest (ROI) analyses (see below, under “Sensorimotor Regions of Interest”) were used to detect significant changes in brain gray matter volume from preflight to postflight. Voxel-based morphometry involves voxel-wise comparison of probabilistic gray matter maps that have been transformed to the same stereotactic space.22,23,24 The following software packages were used for image processing: Advanced Normalization Tools (ANTs) version 1.9.x, FMRIB Software Library (FSL) version 5.0.8, Statistical Parametric Mapping (SPM) 8 v6313, SPM 12 v6470, and MATLAB 8.3.0.532 (R2014a).

Preprocessing

Image intensity non-uniformity correction was applied to all T2 images within a subject specific brain mask using N4ITK with a shrink factor of 2 and 80, 60, and 40 iterations at each level of resolution.25 The brain masks were created using FSL’s Brain Extraction Tool26 with robust brain center estimation.

Segmentation

Bias field corrected images were segmented into 6 probabilistic tissue classes (i.e., gray matter, white matter, cerebrospinal fluid, bone, fat, and air) using unified segmentation with a sampling distance of 1 under SPM 12.27,28 The unified segmentation algorithm uses a prior probability map per tissue class and voxel intensity to attribute the a posteriori probability of each voxel belonging to a tissue class. Bias regularization was set to ten and the bias full width at half maximum (FWHM) cutoff was set to 150 because of our initial non-uniformity correction. Segmentation quality of the high-resolution and low-resolution images (see Supplementary Fig. 1) was satisfactory.

Normalization

High-resolution images (i.e., 0.49 × 0.49 × 1.00 mm) were down sampled to 0.98 × 0.98 × 1.00 mm to reduce memory costs and speed up the normalization process.29 We used a stepwise approach to transform the preflight and postflight gray matter maps of each subject into MNI space. This method first registers all bias field corrected T2 images to an initial template using six degrees of freedom. Transformation parameters were stored in the header of the image to avoid rounding. Separate initial template images were created for the high-resolution image pairs and the low-resolution image pairs to fit their initial resolutions. These templates were constructed by rigid body registration of the T2 MNI ICBM152 nonlinear symmetric image30 to the average of the non-transformed data in our sample that were acquired using a sagittal sequence (i.e., eight out of ten astronauts for the low-resolution images and 17 astronauts for the high-resolution image). An initial subject specific template was created from each co-registered pair of preflight and postflight scans by averaging the two images and subsequently smoothing the resulting image with a Gaussian kernel of 1 mm. For further normalization steps we selected ANTs because it has proven to be superior in normalization than other algorithms and offers readily available scripts to create templates and combine warp fields.31 From ANTs, we used buildtemplateparallel with the initial subject specific template as reference image with probability mapping as similarity metric and symmetric normalization as transformation model to create a final subject specific template. After that, we calculated the warp from the single subject template to the T2 MNI ICBM152 nonlinear symmetric image (2009a)30 using ANTs with cross correlation as similarity metric and symmetric normalization as transformation model. For each subject, for each time point the non-linear warp field from subject space to the subject specific template and the non-linear warp field from the subject specific template to the MNI template were combined into one flow field. From this flow field we obtained the Jacobian determinant image using ANTs’ CreateJacobianDeterminantImage. The Jacobian determinant encodes local expansion/shrinkage for each voxel in the image. The gray matter map of each subject, for each time point was then warped to MNI space using the combined affine and non-linear transformations that we obtained from brining the subject specific and time point specific T2 image into MNI space. Subsequently, these normalized images were modulated by multiplying them with their Jacobian determinant image to preserve the amount of gray matter volume that was present in the untransformed image. Finally, the modulated warped images were smoothed with a Gaussian kernel of 8 mm FWHM to increase the signal to noise ratio.

Sensorimotor regions of interest

A spherical ROI around MNI coordinate = 42, −24, 18 with a diameter of 5 mm (33 mm3) was used to mask out gray matter volume of the smoothed modulated gray matter images in MNI space as a proxy of the right vestibular cortex. This voxel coordinate represents the center of operculum parietale 2 (i.e., the homolog of the parietoinsular vestibular cortex in nonhuman primates).32, 33 Volumes of the precentral gyrus, postcentral gyrus, and paracentral gyrus were also obtained per subject per time point by masking out the gray matter volume in these regions from the smoothed modulated gray matter images in MNI space. The masks were obtained from the T1 MNI ICBM152 using Freesurfer.34 Finally, we tested for changes in global tissue volume of gray matter, white matter, and cerebrospinal fluid.

Analysis

Voxel-wise nonparametric sign-flip and permutation based one-sample t-tests on pre-to-post GM difference maps with 15,000 random permutations and threshold-free cluster enhancement (TFCE)35 implemented in FSL’s randomize36 were used to test (1) if there were local gray matter increases or decreases as a function of spaceflight, (2) if there was a difference in GM changes between astronauts who completed a shuttle mission versus an ISS mission, and (3) if focal GM volume obtained at the preflight scan was associated with previous flight experience in days (i.e., a cross-sectional analysis). Non-parametric permutation tests with 15,000 random permutations and TFCE were used to analyze the association between local changes in GM volume and changes in balance control. Analysis of this association was conducted at the whole brain level as well as restricted to those locations in which we observed significant changes in GM volume from preflight to postflight and was performed in the whole group as well as stratified for mission type (ISS versus Shuttle). Because we only included the non-linear transformation in the Jacobian determinant image and not the affine transformations, the GM maps were already scaled for head size and subsequent adjustment for head size was unnecessary. All voxel-wise analyses were adjusted for multiple comparisons by applying a family-wise error correction (p < 0.1).

Changes in regional volume and balance control were analyzed using linear-mixed models with subject as random intercept and restricted maximum likelihood (REML) as maximum likelihood estimation because REML is less sensitive to small sample bias than traditional maximum likelihood estimation.37 Regional volumes were obtained from gray matter maps that were scaled for head size and thereby adjusted for total intracranial volume. Any significant changes in regional GM volume were correlated with changes in balance performance using Spearman’s rank correlation test. Alpha levels were set at 0.05 for all analyses. Stata SE was used for all analyses (StataCorp. 2013. Stata Statistical Software: Release 13.1. College Station, TX: StataCorp LP).