Edited by: Mark Lovell, University of Pittsburgh Medical Center, USA
Reviewed by: Frank M. Webbe, Florida Institute of Technology, USA; Gregory Gruener, Loyola University, USA; Luke C. Henry, University of Pittsburgh, USA
This article was submitted to Sports Neurology, a section of the journal Frontiers in Neurology.
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Glider flying is a unique skill that requires pilots to control an aircraft at high speeds in three dimensions and amidst frequent full-body rotations. In the present study, we investigated the neural correlates of flying a glider using voxel-based morphometry. The comparison between gray matter densities of 15 glider pilots and a control group of 15 non-pilots exhibited significant gray matter density increases in left ventral premotor cortex, anterior cingulate cortex, and the supplementary eye field. We posit that the identified regions might be associated with cognitive and motor processes related to flying, such as joystick control, visuo-vestibular interaction, and oculomotor control.
In order to keep up with the demands of a changing environment, our brains adapt quickly and efficiently. This is best demonstrated by gray and white matter structure changes in brain regions associated with practicing specific motor or cognitive skills. Such findings have been reported by many cross-sectional studies done over the past decade comparing trained experts to non-experts. Now termed as experience-dependent structural plasticity, the process is thought to be active throughout our lives (
Flying a glider is a unique skill as human beings are not naturally suited for operation in a non-terrestrial environment. Very little is known about how the brain of a glider pilot adapts to the needs of being in the air, which are very different from other land-based motor skills. Glider flying involves operation in three dimensions, at considerably variable velocities, altitudes, and g-forces. To avoid motion sickness, pilots must habituate to the unusual visual–vestibular interaction resulting from full-body rotation within a thermal column. Glider pilots have to simultaneously integrate multiple streams of sensory information from visual, vestibular, and kinesthetic systems to form a mental construct of their position and orientation to control the glider. Specifically, pilots use a joystick with one hand to control the roll and pitch of the glider, foot pedals to control the yaw, and a dive brake with the other hand to increase the drag during landing. Co-ordination of all four degrees of freedom is required to be able to fly and land a glider properly. Apart from precise sensorimotor control, flying demands high levels of cognitive control, as pilots have to continuously monitor their performance based on multimodal sensory feedback mechanisms. Moreover, the process has to be predictive, has a low margin of error, and often pilots have to resolve conflicting information coming in from different senses. These factors make flying a very interesting skill to study from a neuroscience perspective and investigating the neural correlates of flying has the potential to throw light on many brain processes involved in motor control, multisensory integration, and cognitive control.
Recently, a few studies from our group reported functional activation patterns as subjects tried to fly an aircraft in a flight simulator inside a MRI scanner (
Previous studies that have looked at physiological differences between pilots and non-pilots, point toward vestibular habituation and adaptation of the vestibulo-ocular reflex (VOR) in pilots (
In the present study, we wanted to investigate the structural correlates of flying a glider by analyzing gray matter differences between glider pilots and non-pilots using VBM. Unlike the previous study done to detect changes in of white matter structure between fighter pilots and non-pilots (
All subjects gave written informed consent for experimental procedures approved by the ATR Human Subject Review Committee in accordance with the principles expressed in the Declaration of Helsinki.
Thirty right-handed subjects participated in this study. The handedness of the subjects was determined using a questionnaire based on Edinburgh Handedness Inventory (
High-resolution anatomical scans were acquired with T1 weighting (TE = 3.06 ms, TR = 2.25 s, matrix size = 256 × 256, voxel size = 1 mm × 1 mm × 1 mm) were acquired on a Siemens Trio 3 T scanner at the ATR Brain Activity Imaging Center.
Voxel-based morphometry is a method used to automatically analyze differences in local brain anatomy ( After checking raw images for artifacts and setting the origin to Anterior Commissure (AC), they were segmented into GM, WM, and CSF using unified segmentation ( All images were then warped to a study specific template created using the DARTEL registration algorithm ( To preserve the original GM volume, flow fields generated by DARTEL in the previous step were combined to generate Jacobian scaled GM images. Warped and Jacobian scaled images were transformed to Montreal Neurological Institute (MNI) space and smoothed by a Gaussian kernel of 8 mm FWHM. The smoothed images were then used for statistical analysis.
General linear model as implemented in SPM8 was used for all statistical analysis. Differences in GMD between the two groups were analyzed using one-way ANCOVA. Data were corrected for global brain volume by dividing each voxel by the total intracranial volume and age was added as a regressor of no interest. Voxelwise statistical parametric maps showing differences in GMD between pilot and non-pilot groups were generated by setting the voxel level threshold at
Statistical analysis showed that compared to non-pilots, pilots had significantly higher GMD in the left ventral premotor area (lPMv) and right anterior cingulate cortex (rACC) (Table
Area | Number of voxels | Peak co-ordinate ( |
Brodmann area |
---|---|---|---|
FDR corrected ( |
|||
Left PMCv | 63 | −58.5, 6, 3 | 6 |
Right ACC | 101 | 9, 42, 24 | 9 |
Uncorrected ( |
|||
Right SEF (SEF) | 71 | 6, −10, 54 | 6 |
Individual GMD values within the pilot group extracted from peak voxels of the two significant clusters showed no significant correlation (
To the best of our knowledge, our study is the first to demonstrate structural differences in the gray matter of glider pilots. We show that pilots have increased GMD in regions that can all be grouped under the premotor areas of the frontal lobe, regions that influence various kinds of motor output through projections to the primary cortex and spinal cord (
As per a recent parcelation of the lateral premotor cortex, our lPMv blob lies in the cluster corresponding to area F5 in macaque (
The rACC cluster is located in the anterior rostral cingulate zone (RCZa) (
The cluster found in the supplementary motor area can be localized to a specialized region called the supplementary eye field (SEF) (
Evidently, the brain regions found significant in the present study could be responsible for physiological and perceptual processes involved in flying, such as motor learning, vestibular habituation, and cognitive control. The lack of correlation between in-air flight experience and GMD of the brain structures found significant may have several reasons. The lack of a significant correlation may be explained by the fact that habituation is a fast process and by the time a pilot is good enough to fly a real glider on his own, his eyes and vestibular senses are already well habituated. An additional explanation may be that in-air flight experience in our study is not a sensitive measure of differences in individual skill. It may be the case that our sample size is not large enough to capture such small differences in skill-related experience that is thought to be reflected by greater GMD in specific cortical regions. It should be pointed out that the aforementioned study, which looked at trained fighter pilots also did not find any correlations between flying hours and white matter changes (
The results of our study show that glider pilots have increased GMD in ventral premotor cortex, anterior cingulate cortex, and supplementary eye field, which are associated with sensorimotor learning, visual–vestibular interaction, and oculomotor control, respectively. Further studies are needed to evaluate the degree to which performance of flight-related tasks can be predicted from GMD in these regions and the longitudinal pattern of the changes.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank fMRI and MEG technicians Yasuhiro Shimada, Ichiro Fujimoto, Hiroaki Mano, and Hironori Nishimoto at the Brain Activity Imaging center at ATR as well as Yuka Furukawa for assisting in running the experiments. For assistance in recruiting pilots for this experiment, we would like to thank Yasushi Morikawa, Erika Matsumoto, and the Ohno Glider Club. We would also like to thank Ben Seymour for his feedback on the initial drafts of the manuscript. This research was supported in part by a contract with the National Institute of Information and Communications Technology, Japan, entitled, “Multimodal integration for brain imaging measurements,” by KAKENHI, Grant-in-Aid for Scientific Research(C) (21500321), and by a contract with the Ministry of Internal Affairs and Communications entitled, “Novel and innovative R&D making use of brain structures.”