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Project Title:  Advanced Algorithms for the Prediction of Adverse Cognitive and Behavioral Conditions in Space Reduce
Fiscal Year: FY 2021 
Division: Human Research 
Research Discipline/Element:
TRISH--TRISH 
Start Date: 01/01/2019  
End Date: 12/31/2020  
Task Last Updated: 07/22/2021 
Download report in PDF pdf
Principal Investigator/Affiliation:   Basner, Mathias  M.D., Ph.D. / University of Pennsylvania 
Address:  Department of Psychiatry, Division of Sleep and Chronobiology 
423 Service Dr, 1013 Blockley Hall 
Philadelphia , PA 19104-4209 
Email: basner@pennmedicine.upenn.edu 
Phone: 215-573-5866  
Congressional District:
Web:  
Organization Type: UNIVERSITY 
Organization Name: University of Pennsylvania 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Dinges, David  Ph.D. University of Pennsylvania 
Romoser, Amelia  Ph.D. KBR/NASA Johnson Space Center 
Shou, Haochang  Ph.D. University of Pennsylvania 
Stahn, Alexander  Ph.D. University of Pennsylvania 
Williams, Edward  Ph.D. NASA Johnson Space Center 
Project Information: Grant/Contract No. NNX16AO69A-T0403 
Responsible Center: TRISH 
Grant Monitor:  
Center Contact:   
Unique ID: 12175 
Solicitation / Funding Source: 2018 TRA BRASH1801: Translational Research Institute for Space Health (TRISH) Biomedical Research Advances for Space Health 
Grant/Contract No.: NNX16AO69A-T0403 
Project Type: GROUND 
Flight Program:  
TechPort: Yes 
No. of Post Docs:
No. of PhD Candidates:
No. of Master's Candidates:
No. of Bachelor's Candidates:
No. of PhD Degrees:
No. of Master's Degrees:
No. of Bachelor's Degrees:
Human Research Program Elements: None
Human Research Program Risks: None
Human Research Program Gaps: None
Task Description: This study utilizes Reaction Self-Test (RST) data collected in N=24 astronauts on 6-month International Space Station missions, arguably the largest cognitive dataset ever collected in spaceflight. The main objective is to additionally obtain data on key environmental stressors (i.e., CO2 levels, temperature, noise, and radiation) and combine them with RST data and other operational data. All data will be integrated in one carefully annotated database, which will be delivered to NASA at the end of the project and could be later amended and mined by other researchers. In addition, this project will develop an individualized dynamic prediction model that informs future Psychomotor Vigilance Test (PVT) performance based on environmental data, survey data, prior PVT administrations, and person-specific characteristics using state-of-the-art machine learning techniques such as functional concurrent regressions and neural networks for time series forecasting. At the end of the study, the team will deliver an algorithm to NASA that, for the first time, can predict adverse cognitive conditions in astronauts early and with an unprecedented precision.

Research Impact/Earth Benefits: This project develops a state-of-the-art predictive algorithm for adverse cognitive conditions. It is unique as it utilizes the arguably largest cognitive data set ever collected in spaceflight. It is innovative as it combines data on key environmental stressors prevalent in spaceflight with other contextual and time series data to predict cognitive performance. The fact that it will be possible for the first time to predict adverse cognitive conditions in astronauts early and with an unprecedented precision demonstrates the high impact of this proposal for both spaceflight and Earth.

Task Progress & Bibliography Information FY2021 
Task Progress: This proposal addresses the Human Research Program (HRP) Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders and several other critical HRP risks and gaps. This study utilizes Reaction Self-Test (RST) data collected by the Principal Investigator (PI) and his team in N=24 astronauts on 6-month International Space Station (ISS) missions, arguably the largest cognitive dataset ever collected in spaceflight. RST consists of a survey module and a 3-minute version of the Psychomotor Vigilance Test (PVT). Our main objective is to additionally obtain data on key environmental stressors (i.e., CO2 and O2 levels, temperature, noise, and radiation) and combine them with RST data and other operational data collected by the PI and his team (Specific Aim 1). All data will be integrated in one carefully annotated database, which will be delivered to NASA at the end of the project and could be later amended and mined by other researchers (Deliverable 1). We will then develop an individualized dynamic prediction model that informs future PVT performance based on environmental data, survey data, prior PVT administrations, and person-specific characteristics using state-of-the-art machine learning techniques such as functional concurrent regressions and neural networks for time series forecasting (Specific Aim 2). We will perform model selection and identify those variables that have the highest predictive value for PVT performance (Deliverable 2) and could preferentially be collected on future spaceflight missions to inform relevant changes in cognition and behavioral health. At the end of the study, we will deliver an algorithm to NASA that, for the first time, can predict adverse cognitive conditions in astronauts early and with an unprecedented precision (Deliverable 3). The predictive algorithms can be translated to several settings on Earth where high performing individuals have to sustain high levels of cognitive performance while facing several environmental or other challenges (e.g., US Navy personnel on submarines).

We obtained approval from the Lifetime Surveillance of Astronaut Health Board, the Institutional Review Board (IRB) of Johnson Space Center, European Space Agency (ESA), and Japan Aerospace Exploration Agency (JAXA), and the IRB of the University of Pennsylvania to use already collected RST data for the purposes of this project. N=7 non-US astronauts who had not provided broad consent for the re-use of their RST data were re-consented. These included 6 non-US astronauts, for which NASA previously had no process in place for re-consenting them. We also obtained key environmental variables from the ISS for the duration of the RST project (2009-2014): O2 and CO2 levels, noise levels, radiation levels, temperature levels. The temporal resolution of these data varies. We also extracted key demographic (prior days in space, prior missions, DOB, sex, nationality, space agency, military background, degree, marital status, number of children), and operational (number of ISS occupants, date of extravehicular activities (EVAs) or spacecraft dockings/undockings, sleep schedule)) information for the relevant time period. The four data streams (RST data, environmental data, demographic data, operational data) were combined in a data matrix. We have identified those variables that have the highest predictive value for PVT performance combining a number of variable selection methods: Past PVT performance Self-reported RST PC scores (Workload, Sleep Quality, Sleepiness, Physical Exhaustion, Mental Fatigue, Stress), Radiation, Temperature, O2 and CO2 levels, Sleep duration, predicted lapses based on sleep history, and Age. We investigated predictive properties of three classes of prediction models (Linear Mixed Effect Model, Functional Concurrent Regression Model, Random Forest Model) and an Ensemble Model that combines predictions from the previously mentioned three models. The ensemble model was found to provide the highest prediction accuracy. Finally, we built a Shiny App for visualizing the effects of several stressors on PVT performance, which can be a powerful tool for investigating the effects of individual stressors, their combination, and to generate hypothesis. This tool could be amended to include additional predictors and/or outcomes.

Bibliography: Description: (Last Updated: 02/19/2024) 

Show Cumulative Bibliography
 
 None in FY 2021
Project Title:  Advanced Algorithms for the Prediction of Adverse Cognitive and Behavioral Conditions in Space Reduce
Fiscal Year: FY 2020 
Division: Human Research 
Research Discipline/Element:
TRISH--TRISH 
Start Date: 01/01/2019  
End Date: 12/31/2020  
Task Last Updated: 05/29/2020 
Download report in PDF pdf
Principal Investigator/Affiliation:   Basner, Mathias  M.D., Ph.D. / University of Pennsylvania 
Address:  Department of Psychiatry, Division of Sleep and Chronobiology 
423 Service Dr, 1013 Blockley Hall 
Philadelphia , PA 19104-4209 
Email: basner@pennmedicine.upenn.edu 
Phone: 215-573-5866  
Congressional District:
Web:  
Organization Type: UNIVERSITY 
Organization Name: University of Pennsylvania 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Dinges, David  Ph.D. University of Pennsylvania 
Romoser, Amelia  Ph.D. Wyle Laboratories, Inc./NASA Johnson Space Center 
Shou, Haochang  Ph.D. University of Pennsylvania 
Stahn, Alexander  Ph.D. University of Pennsylvania 
Williams, Edward  Ph.D. NASA Johnson Space Center  
Project Information: Grant/Contract No. NNX16AO69A-T0403 
Responsible Center: TRISH 
Grant Monitor:  
Center Contact:   
Unique ID: 12175 
Solicitation / Funding Source: 2018 TRA BRASH1801: Translational Research Institute for Space Health (TRISH) Biomedical Research Advances for Space Health 
Grant/Contract No.: NNX16AO69A-T0403 
Project Type: GROUND 
Flight Program:  
TechPort: Yes 
No. of Post Docs:
No. of PhD Candidates:
No. of Master's Candidates:
No. of Bachelor's Candidates:
No. of PhD Degrees:
No. of Master's Degrees:
No. of Bachelor's Degrees:
Human Research Program Elements: None
Human Research Program Risks: None
Human Research Program Gaps: None
Task Description: This study utilizes Reaction Self-Test (RST) data collected in N=24 astronauts on 6-month International Space Station missions, arguably the largest cognitive dataset ever collected in spaceflight. The main objective is to additionally obtain data on key environmental stressors (i.e., CO2 levels, temperature, noise, and radiation) and combine them with RST data and other operational data. All data will be integrated in one carefully annotated database, which will be delivered to NASA at the end of the project and could be later amended and mined by other researchers. In addition, this project will develop an individualized dynamic prediction model that informs future Psychomotor Vigilance Test (PVT) performance based on environmental data, survey data, prior PVT administrations, and person-specific characteristics using state-of-the-art machine learning techniques such as functional concurrent regressions and neural networks for time series forecasting. At the end of the study, the team will deliver an algorithm to NASA that, for the first time, can predict adverse cognitive conditions in astronauts early and with an unprecedented precision.

Research Impact/Earth Benefits: This project develops a state-of-the-art predictive algorithm for adverse cognitive conditions. It is unique as it utilizes the arguably largest cognitive data set ever collected in spaceflight. It is innovative as it combines data on key environmental stressors prevalent in spaceflight with other contextual and time series data to predict cognitive performance. The fact that it will be possible for the first time to predict adverse cognitive conditions in astronauts early and with an unprecedented precision demonstrates the high impact of this proposal for both spaceflight and Earth.

Task Progress & Bibliography Information FY2020 
Task Progress: [Ed. note May 2020: Report submitted by TRISH to Task Book in March 2020; covers reporting as of November 2019.]

This proposal addresses the Human Research Program (HRP) Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders and several other critical HRP risks and gaps. This study utilizes Reaction Self-Test (RST) data collected by the Principal Investigator (PI) and his team in N=24 astronauts on 6-month International Space Station (ISS) missions, arguably the largest cognitive dataset ever collected in spaceflight. RST consists of a survey module and a 3-minute version of the Psychomotor Vigilance Test (PVT). Our main objective is to additionally obtain data on key environmental stressors (i.e., CO2 levels, temperature, noise, and radiation) and combine them with RST data and other operational data collected by the PI and his team (Specific Aim 1). All data will be integrated in one carefully annotated database, which will be delivered to NASA at the end of the project and could be later amended and mined by other researchers (Deliverable 1). We will then develop an individualized dynamic prediction model that informs future PVT performance based on environmental data, survey data, prior PVT administrations, and person-specific characteristics using state-of-the-art machine learning techniques such as functional concurrent regressions and neural networks for time series forecasting (Specific Aim 2). We will perform model selection and identify those variables that have the highest predictive value for PVT performance (Deliverable 2) and could preferentially be collected on future spaceflight missions to inform relevant changes in cognition and behavioral health. At the end of the study, we will deliver an algorithm to NASA that, for the first time, can predict adverse cognitive conditions in astronauts early and with an unprecedented precision (Deliverable 3). The predictive algorithms can be translated to several settings on Earth where high performing individuals have to sustain high levels of cognitive performance while facing several environmental or other challenges (e.g., US Navy personnel on submarines).

During the first year of the project, we obtained approval from the Lifetime Surveillance of Astronaut Health Board, the Institutional Review Board (IRB) of Johnson Space Center, and the IRB of the University of Pennsylvania to use already collected RST data for the purposes of this project. N=7 non-United States astronauts need to be re-consented, and Life Sciences Data Archive (LSDA) is in the process obtaining these consents. We also obtained key environmental variables from the ISS for the duration of the RST project (2009-2014): O2 and CO2 levels, noise levels, radiation levels, temperature levels. The temporal resolution of these data varies. We also extracted key demographic and operational information for the relevant time period. The four data streams (RST data, environmental data, demographic data, operational data) were combined in a data matrix. We are currently exploring simple associations between the different predictors and cognitive performance, and are cleaning the database from any erroneous values in the process. We have also begun identifying variables with high predictive value for Psychomotor Vigilance Test (PVT) performance.

Bibliography: Description: (Last Updated: 02/19/2024) 

Show Cumulative Bibliography
 
 None in FY 2020
Project Title:  Advanced Algorithms for the Prediction of Adverse Cognitive and Behavioral Conditions in Space Reduce
Fiscal Year: FY 2019 
Division: Human Research 
Research Discipline/Element:
TRISH--TRISH 
Start Date: 01/01/2019  
End Date: 12/31/2020  
Task Last Updated: 02/04/2019 
Download report in PDF pdf
Principal Investigator/Affiliation:   Basner, Mathias  M.D., Ph.D. / University of Pennsylvania 
Address:  Department of Psychiatry, Division of Sleep and Chronobiology 
423 Service Dr, 1013 Blockley Hall 
Philadelphia , PA 19104-4209 
Email: basner@pennmedicine.upenn.edu 
Phone: 215-573-5866  
Congressional District:
Web:  
Organization Type: UNIVERSITY 
Organization Name: University of Pennsylvania 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Dinges, David  Ph.D. University of Pennsylvania 
Romoser, Amelia  Ph.D. Wyle Laboratories, Inc./NASA Johnson Space Center 
Shou, Haochang  Ph.D. University of Pennsylvania 
Stahn, Alexander  Ph.D. University of Pennsylvania 
Williams, Edward  Ph.D. NASA Johnson Space Center 
Project Information: Grant/Contract No. NNX16AO69A-T0403 
Responsible Center: TRISH 
Grant Monitor:  
Center Contact:   
Unique ID: 12175 
Solicitation / Funding Source: 2018 TRA BRASH1801: Translational Research Institute for Space Health (TRISH) Biomedical Research Advances for Space Health 
Grant/Contract No.: NNX16AO69A-T0403 
Project Type: GROUND 
Flight Program:  
TechPort: Yes 
No. of Post Docs:  
No. of PhD Candidates:  
No. of Master's Candidates:  
No. of Bachelor's Candidates:  
No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees:  
Human Research Program Elements: None
Human Research Program Risks: None
Human Research Program Gaps: None
Task Description: This proposal addresses the Human Research Program (HRP) Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders and several other critical HRP risks and gaps. This study utilizes Reaction Self-Test (RST) data collected by the Principal Investigator (PI) and his team in n=24 astronauts on 6-month International Space Station missions, arguably the largest cognitive dataset ever collected in spaceflight. RST consists of a survey module and a 3-minute version of the Psychomotor Vigilance Test (PVT). Our main objective is to additionally obtain data on key environmental stressors (i.e., carbon dioxide levels, temperature, noise, and radiation) and combine them with RST data and other operational data collected by the PI and his team (Specific Aim 1). All data will be integrated in one carefully annotated database, which will be delivered to NASA at the end of the project and could be later amended and mined by other researchers (Deliverable 1). We will then develop an individualized dynamic prediction model that informs future PVT performance based on environmental data, survey data, prior PVT administrations, and person-specific characteristics using state-of-the-art machine learning techniques such as functional concurrent regressions and neural networks for time series forecasting (Specific Aim 2). We will perform model selection and identify those variables that have the highest predictive value for PVT performance (Deliverable 2) and could preferentially be collected on future spaceflight missions to inform relevant changes in cognition and behavioral health. At the end of the study, we will deliver an algorithm to NASA that, for the first time, can predict adverse cognitive conditions in astronauts early and with an unprecedented precision (Deliverable 3). The predictive algorithms can be translated to several settings on Earth where high performing individuals have to sustain high levels of cognitive performance while facing several environmental or other challenges (e.g., US Navy personnel on submarines).

Research Impact/Earth Benefits:

Task Progress & Bibliography Information FY2019 
Task Progress: New project for FY2019.

Bibliography: Description: (Last Updated: 02/19/2024) 

Show Cumulative Bibliography
 
 None in FY 2019