Determining Which Regions of the Brain Are Active During Flight Simulation at Separate Timepoints During Training
NCT ID: NCT06606925
Last Updated: 2025-01-15
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
RECRUITING
Clinical Phase
NA
Total Enrollment
150 participants
Study Classification
INTERVENTIONAL
Study Start Date
2023-09-19
Study Completion Date
2026-09-18
Brief Summary
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The overall objective is to identify the cognitive circuits associated with military aviator performance by analyzing what anatomic regions of the brain are functionally "active" (neuronal circuit) while being performing virtual flight simulations, the Precision Instrument Control Task (PICT). The flight simulation test will be conducted at two separate timepoints while the subject is receiving a Functional Magnetic Resonance Imaging (fMRI) scan to evaluate which anatomic and functional brain function is associated with precise performance. By scanning at multiple time points we aim to quantify changes in functional and anatomic connectivity that occur throughout the course of training.
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Detailed Description
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Establishing, maintaining, and quantifying readiness in high performance individuals and populations, such as active-duty pilots, remains a significant challenge in the DoD. Proficiency in pilots, like other high-performance populations, consists of mastery of multiple tasks. Some tasks, such as G-straining relies upon known approaches to strengthen musculoskeletal endurance of the buttocks, quadriceps, and hamstrings with well-defined exercises to improve tolerance to high G-loads. Thus, if particular pilot or trainee is struggling with near G-induced loss of consciousness, clear training regimens can be prescribed to strengthen the necessary muscles to improve performance. However, cognitive tasks, such as precisely controlling an aircraft to maintain a specific trajectory, despite the presence of crosswinds and other perturbations, is more difficult to understand and train. Until several years ago, there was no clear approach in defining the neuronal circuit necessary for such tasks. Without that understanding of this cognitive circuitry, it is difficult if not impossible to prescribe targeted and efficient training modalities to strengthen its performance.
Neuroergonomics By detecting subtle changes in blood flow to different regions of a brain during a task, functional Magnetic Resonance Imaging (fMRI) can localize the most active regions of the brain at any point in time. This technology is advancing rapidly and for specified tasks is demonstrating remarkable consistency in multiple cortical regions of the brain employed during the same task, between different individuals. The multiple regions activated during a particular task are often referred to as "functionally connected." In addition, these functionally connected regions of the brain activated during a cognitive task share an analogy with the muscles activated to accomplish a physical task. Another MRI technology can quantify the connectivity through axons located in white matter (the wires in the brain) and measure the strength of the physical connection between different regions of the brain - termed structural connectivity. Interestingly, like muscles after prolonged training, the "strength" of connections between specific regions can show measurable increases with training and repetition.
However, because of the high magnetic fields existing within MRI machines, complex devices or video displays could not traditionally be used when scanning subjects. As a result, early tasks within scanners (termed paradigms) were often serial and not representative of the highly dynamic tasks of flying. But high-resolution display systems are available that are MRI compatible - which can generate high resolution and more immersive environments. Furthermore, increasingly sophisticated input devices have also been developed that are MRI compatible and now it is possible to include a realistic flight stick to control pitch, roll, and throttle of simulated airframes.
Thus, the field of neuroergonomics -- analyzing how the brain behaves during everyday operations in a more naturalistic way - can be applied to aviation. Recent fMRI work has started to identify the neurocircuitry involved in specific flying tasks such as aerial pursuit. Other work has identified regions of the brain activated during cognitive overload where subjects did not perceive audible alarms while flying in a simulator. Furthermore, specific brain regions appear activated during video feedback after performing a complex landing task. Thus, the brain regions active during aerial pursuit, cognitive overload, and feedback - all pertinent to aviation training - are beginning to be identified. However, given that much of this data has been collected from amateurs - not highly experienced military pilots - its applicability to highly trained military aviators needs to be tested.
Applying the techniques of neuroergonomics to improve military aviator performance will require two distinct steps: 1.) The neuroanatomic circuits associated with different aspects of high-performance aviation must be identified; and 2.) for each circuit, training paradigms will need to be identified to strengthen the neuroanatomic circuit of interest to track not only behavioral performance, but the neural correlates associated with enhanced performance.
PICT Task -- MRI flight Simulation Challenges
The Precision Instrument Control Task (PICT) flight simulator test is adapted from an existing human performance study, called "Wayfinding, Hypoxia, and Interceptive Performance in Pilots Executing Transitions" (WHIPPET), which is currently being conducted at the Brooks Research Altitude Chambers with the objective of measuring the piloting deterioration that results from moderate hypobaric hypoxia. The task generates quantitative metrics to assess the accuracy and swiftness with which a pilot can execute corrective control inputs while flying. The tasks will be adapted from their current psychophysical application for use in this neuroimaging application.
Both piloting tasks will be rendered using the commercially available application software called XPlane (Laminar Research, Inc., Columbia SC), which is a PC-based simulation suite that uses believable flight controls and dynamic aircraft models to present high-fidelity simulated sorties with the visual characteristics and demands of real flight. XPlane has been used in psychophysical investigations of the effect of environmental stressors on human performance in a cockpit environment, and to identify areas of brain activation during the execution of simulated aerial pursuit tasks. For this application, XPlane will be employed to present the aerodynamic characteristics of an F/A-18F. The visual interface will include a forward "out-the-window" display incorporating a generic head-up-display (HUD) with climb-dive ladder, horizon and heading indicators, and digital airspeed and vertical velocity indicators. The display will be presented in the fMRI scanner using stereogenic goggles called the Visual System HD (NordicNeuroLab, Bergen, Norway) display mounted in the scanner via the Siemens Vida 64-channel headcoil, as has been employed successfully to construct interactive virtual reality platforms for fMRI research applications. This apparatus will present the experimental visual interface in 1920 x 1200-pixel format via stereoscopic goggles, with each eye's array extending approximately 52 x 34 deg (horizontal x vertical) in field of view. This configuration should provide ample resolution and angular subtense to produce virtual presence in the simulated environment.
Specific Aims
1. Determine what portions of brain activity correlate with level of performance during flight simulation (PICT).
2. Determine the changes in brain activity that occur during two separate timepoints.
3. Determine what portions of brain anatomy correlates with level of performance during flight simulation (PICT).
4. Determine the changes in brain anatomy that occur during two separate timepoints.
Neuroergonomics By detecting subtle changes in blood flow to different regions of a brain during a task, functional Magnetic Resonance Imaging (fMRI) can localize the most active regions of the brain at any point in time. This technology is advancing rapidly and for specified tasks is demonstrating remarkable consistency in multiple cortical regions of the brain employed during the same task, between different individuals. The multiple regions activated during a particular task are often referred to as "functionally connected." In addition, these functionally connected regions of the brain activated during a cognitive task share an analogy with the muscles activated to accomplish a physical task. Another MRI technology can quantify the connectivity through axons located in white matter (the wires in the brain) and measure the strength of the physical connection between different regions of the brain - termed structural connectivity. Interestingly, like muscles after prolonged training, the "strength" of connections between specific regions can show measurable increases with training and repetition.
However, because of the high magnetic fields existing within MRI machines, complex devices or video displays could not traditionally be used when scanning subjects. As a result, early tasks within scanners (termed paradigms) were often serial and not representative of the highly dynamic tasks of flying. But high-resolution display systems are available that are MRI compatible - which can generate high resolution and more immersive environments. Furthermore, increasingly sophisticated input devices have also been developed that are MRI compatible and now it is possible to include a realistic flight stick to control pitch, roll, and throttle of simulated airframes.
Thus, the field of neuroergonomics -- analyzing how the brain behaves during everyday operations in a more naturalistic way - can be applied to aviation. Recent fMRI work has started to identify the neurocircuitry involved in specific flying tasks such as aerial pursuit. Other work has identified regions of the brain activated during cognitive overload where subjects did not perceive audible alarms while flying in a simulator. Furthermore, specific brain regions appear activated during video feedback after performing a complex landing task. Thus, the brain regions active during aerial pursuit, cognitive overload, and feedback - all pertinent to aviation training - are beginning to be identified. However, given that much of this data has been collected from amateurs - not highly experienced military pilots - its applicability to highly trained military aviators needs to be tested.
Applying the techniques of neuroergonomics to improve military aviator performance will require two distinct steps: 1.) The neuroanatomic circuits associated with different aspects of high-performance aviation must be identified; and 2.) for each circuit, training paradigms will need to be identified to strengthen the neuroanatomic circuit of interest to track not only behavioral performance, but the neural correlates associated with enhanced performance.
PICT Task -- MRI flight Simulation Challenges
The Precision Instrument Control Task (PICT) flight simulator test is adapted from an existing human performance study, called "Wayfinding, Hypoxia, and Interceptive Performance in Pilots Executing Transitions" (WHIPPET), which is currently being conducted at the Brooks Research Altitude Chambers with the objective of measuring the piloting deterioration that results from moderate hypobaric hypoxia. The task generates quantitative metrics to assess the accuracy and swiftness with which a pilot can execute corrective control inputs while flying. The tasks will be adapted from their current psychophysical application for use in this neuroimaging application.
Both piloting tasks will be rendered using the commercially available application software called XPlane (Laminar Research, Inc., Columbia SC), which is a PC-based simulation suite that uses believable flight controls and dynamic aircraft models to present high-fidelity simulated sorties with the visual characteristics and demands of real flight. XPlane has been used in psychophysical investigations of the effect of environmental stressors on human performance in a cockpit environment, and to identify areas of brain activation during the execution of simulated aerial pursuit tasks. For this application, XPlane will be employed to present the aerodynamic characteristics of an F/A-18F. The visual interface will include a forward "out-the-window" display incorporating a generic head-up-display (HUD) with climb-dive ladder, horizon and heading indicators, and digital airspeed and vertical velocity indicators. The display will be presented in the fMRI scanner using stereogenic goggles called the Visual System HD (NordicNeuroLab, Bergen, Norway) display mounted in the scanner via the Siemens Vida 64-channel headcoil, as has been employed successfully to construct interactive virtual reality platforms for fMRI research applications. This apparatus will present the experimental visual interface in 1920 x 1200-pixel format via stereoscopic goggles, with each eye's array extending approximately 52 x 34 deg (horizontal x vertical) in field of view. This configuration should provide ample resolution and angular subtense to produce virtual presence in the simulated environment.
Specific Aims
1. Determine what portions of brain activity correlate with level of performance during flight simulation (PICT).
2. Determine the changes in brain activity that occur during two separate timepoints.
3. Determine what portions of brain anatomy correlates with level of performance during flight simulation (PICT).
4. Determine the changes in brain anatomy that occur during two separate timepoints.
Conditions
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Cognitive Performance
Study Design
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Allocation Method
NA
Intervention Model
SINGLE_GROUP
This is a prospective observational study. Subjects will undergo functional and anatomic magnetic resonance imaging a two timepoints during pilot training. Performance in flight simulator will be correlated with fMRI measurements of axonal anatomy and brain activity.
Primary Study Purpose
DIAGNOSTIC
Blinding Strategy
NONE
Study Groups
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Functional (fMRI) and anatomic MRI imaging a two timepoints during pilot virtual reality simulation
Initial anatomic imaging and fMRI with virtual reality flight simulator scan with repeat testing performed at approximately 2 months (+/- 1 month) after initial scan.
Group Type
EXPERIMENTAL
fMRI with virtual reality flight simulator
Intervention Type
DIAGNOSTIC_TEST
During this scan, the subject will be wearing the stereogenic goggles called the Visual System HD (NordicNeuroLab) mounted in the scanner via a headcoil that can be adjusted to the subject's comfort using the control arm and completely cover the eyes to prevent light exposure and to clearly visualize eye movement during the flight simulation. The subject will be using a visual response system with customized grips to simulate a stick and throttle in a jet cockpit while visualizing the flight simulation (PICT) in the goggles.
Interventions
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fMRI with virtual reality flight simulator
During this scan, the subject will be wearing the stereogenic goggles called the Visual System HD (NordicNeuroLab) mounted in the scanner via a headcoil that can be adjusted to the subject's comfort using the control arm and completely cover the eyes to prevent light exposure and to clearly visualize eye movement during the flight simulation. The subject will be using a visual response system with customized grips to simulate a stick and throttle in a jet cockpit while visualizing the flight simulation (PICT) in the goggles.
Intervention Type
DIAGNOSTIC_TEST
Eligibility Criteria
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Inclusion Criteria
* Active Duty Military Pilots (Instructor Pilot Trainees or Remote Piloted Aircraft Trainees)
* Age 18-54 years
* Biological male or female
* Age 18-54 years
* Biological male or female
Exclusion Criteria
* Age \< 18 years
* Age \> 60 years
* Non-active-duty members
* History of recurrent migraine headaches requiring chronic suppressive medication or prescription drug intervention more frequently than once per year.
* History of head trauma or traumatic brain injury with any loss of consciousness or with confusion or amnesia of greater than five minutes.
* History of eye trauma related to a metallic object unless the presence of residual metal has been previously excluded by x-ray.
* Pregnancy
* History of significant neurological disease including cerebrovascular disease, demyelinating disease, or infections of the central nervous system (encephalitis, meningitis).
* History of medical conditions with potential neurological involvement such as obstructive sleep apnea, autoimmune disorders, etc.
* History of seizures since age six.
* Claustrophobia or intolerance of the MRI without medication.
* Any medical contraindication to MRI (ex: foreign bodies, non-MRI compatible pacemaker, metal devices).
* Age \> 60 years
* Non-active-duty members
* History of recurrent migraine headaches requiring chronic suppressive medication or prescription drug intervention more frequently than once per year.
* History of head trauma or traumatic brain injury with any loss of consciousness or with confusion or amnesia of greater than five minutes.
* History of eye trauma related to a metallic object unless the presence of residual metal has been previously excluded by x-ray.
* Pregnancy
* History of significant neurological disease including cerebrovascular disease, demyelinating disease, or infections of the central nervous system (encephalitis, meningitis).
* History of medical conditions with potential neurological involvement such as obstructive sleep apnea, autoimmune disorders, etc.
* History of seizures since age six.
* Claustrophobia or intolerance of the MRI without medication.
* Any medical contraindication to MRI (ex: foreign bodies, non-MRI compatible pacemaker, metal devices).
Minimum Eligible Age
18 Years
Maximum Eligible Age
54 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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59th Medical Wing
FED
Sponsor Role
collaborator
Uniformed Services University of the Health Sciences
FED
Sponsor Role
collaborator
The Geneva Foundation
OTHER
Sponsor Role
lead
Responsible Party
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Responsibility Role
SPONSOR
Principal Investigators
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Paul Sherman, MD
Role: PRINCIPAL_INVESTIGATOR
59th Medical Wing Science and Technology
Locations
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Joint Base San Antonio - Randolph & Lackland
San Antonio, Texas, United States
Site Status
RECRUITING
Countries
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United States
Central Contacts
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Facility Contacts
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References
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Grady CL, Rieck JR, Nichol D, Rodrigue KM, Kennedy KM. Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task-related fMRI data. Hum Brain Mapp. 2021 Jan;42(1):204-219. doi: 10.1002/hbm.25217. Epub 2020 Sep 30.
Reference Type
BACKGROUND
Beer J, Dart TS, Fischer J, Kisner J. Pulmonary Effects from a Simulated Long-Duration Mission in a Confined Cockpit. Aerosp Med Hum Perform. 2017 Oct 1;88(10):952-957. doi: 10.3357/AMHP.4854.2017.
Reference Type
BACKGROUND
Turner BO, Paul EJ, Miller MB, Barbey AK. Small sample sizes reduce the replicability of task-based fMRI studies. Commun Biol. 2018 Jun 7;1:62. doi: 10.1038/s42003-018-0073-z. eCollection 2018.
Reference Type
BACKGROUND
Li CX, Patel S, Zhang X. Evaluation of multi-shell diffusion MRI acquisition strategy on quantitative analysis using multi-compartment models. Quant Imaging Med Surg. 2020 Apr;10(4):824-834. doi: 10.21037/qims.2020.03.11.
Reference Type
BACKGROUND
Bhushan C, Haldar JP, Choi S, Joshi AA, Shattuck DW, Leahy RM. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. Neuroimage. 2015 Jul 15;115:269-80. doi: 10.1016/j.neuroimage.2015.03.050. Epub 2015 Mar 27.
Reference Type
BACKGROUND
Gonzalez-Castillo J, Panwar P, Buchanan LC, Caballero-Gaudes C, Handwerker DA, Jangraw DC, Zachariou V, Inati S, Roopchansingh V, Derbyshire JA, Bandettini PA. Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI. Neuroimage. 2016 Nov 1;141:452-468. doi: 10.1016/j.neuroimage.2016.07.049. Epub 2016 Jul 27.
Reference Type
BACKGROUND
Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI. Cell Rep. 2020 Dec 22;33(12):108540. doi: 10.1016/j.celrep.2020.108540.
Reference Type
BACKGROUND
Tan ET, Shih RY, Mitra J, Sprenger T, Hua Y, Bhushan C, Bernstein MA, McNab JA, DeMarco JK, Ho VB, Foo TKF. Oscillating diffusion-encoding with a high gradient-amplitude and high slew-rate head-only gradient for human brain imaging. Magn Reson Med. 2020 Aug;84(2):950-965. doi: 10.1002/mrm.28180. Epub 2020 Feb 3.
Reference Type
BACKGROUND
Tan ET, Hua Y, Fiveland EW, Vermilyea ME, Piel JE, Park KJ, Ho VB, Foo TKF. Peripheral nerve stimulation limits of a high amplitude and slew rate magnetic field gradient coil for neuroimaging. Magn Reson Med. 2020 Jan;83(1):352-366. doi: 10.1002/mrm.27909. Epub 2019 Aug 6.
Reference Type
BACKGROUND
Foo TKF, Tan ET, Vermilyea ME, Hua Y, Fiveland EW, Piel JE, Park K, Ricci J, Thompson PS, Graziani D, Conte G, Kagan A, Bai Y, Vasil C, Tarasek M, Yeo DTB, Snell F, Lee D, Dean A, DeMarco JK, Shih RY, Hood MN, Chae H, Ho VB. Highly efficient head-only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging. Magn Reson Med. 2020 Jun;83(6):2356-2369. doi: 10.1002/mrm.28087. Epub 2019 Nov 25.
Reference Type
BACKGROUND
Callan DE, Terzibas C, Cassel DB, Callan A, Kawato M, Sato MA. Differential activation of brain regions involved with error-feedback and imitation based motor simulation when observing self and an expert's actions in pilots and non-pilots on a complex glider landing task. Neuroimage. 2013 May 15;72:55-68. doi: 10.1016/j.neuroimage.2013.01.028. Epub 2013 Jan 26.
Reference Type
BACKGROUND
Callan DE, Gamez M, Cassel DB, Terzibas C, Callan A, Kawato M, Sato MA. Dynamic visuomotor transformation involved with remote flying of a plane utilizes the 'Mirror Neuron' system. PLoS One. 2012;7(4):e33873. doi: 10.1371/journal.pone.0033873. Epub 2012 Apr 20.
Reference Type
BACKGROUND
Durantin G, Dehais F, Gonthier N, Terzibas C, Callan DE. Neural signature of inattentional deafness. Hum Brain Mapp. 2017 Nov;38(11):5440-5455. doi: 10.1002/hbm.23735. Epub 2017 Jul 26.
Reference Type
BACKGROUND
Gougelet RJ, Terzibas C, Callan DE. Cerebellum, Basal Ganglia, and Cortex Mediate Performance of an Aerial Pursuit Task. Front Hum Neurosci. 2020 Feb 14;14:29. doi: 10.3389/fnhum.2020.00029. eCollection 2020.
Reference Type
BACKGROUND
Mehta RK, Parasuraman R. Neuroergonomics: a review of applications to physical and cognitive work. Front Hum Neurosci. 2013 Dec 23;7:889. doi: 10.3389/fnhum.2013.00889.
Reference Type
BACKGROUND
Cisek P, Kalaska JF. Neural mechanisms for interacting with a world full of action choices. Annu Rev Neurosci. 2010;33:269-98. doi: 10.1146/annurev.neuro.051508.135409.
Reference Type
BACKGROUND
Van de Putte E, De Baene W, Garcia-Penton L, Woumans E, Dijkgraaf A, Duyck W. Anatomical and functional changes in the brain after simultaneous interpreting training: A longitudinal study. Cortex. 2018 Feb;99:243-257. doi: 10.1016/j.cortex.2017.11.024. Epub 2017 Dec 12.
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BACKGROUND
DeYoe EA, Bandettini P, Neitz J, Miller D, Winans P. Functional magnetic resonance imaging (FMRI) of the human brain. J Neurosci Methods. 1994 Oct;54(2):171-87. doi: 10.1016/0165-0270(94)90191-0.
Reference Type
BACKGROUND
Tesch PA, Hjort H, Balldin UI. Effects of strength training on G tolerance. Aviat Space Environ Med. 1983 Aug;54(8):691-5.
Reference Type
BACKGROUND
Other Identifiers
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MW.65.R22
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
FWH20230088H
Identifier Type: -
Identifier Source: org_study_id
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