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                            Funding & Awards
 Pattern Classification of Neurocognitive and Socio-Emotional Developmental Factors Underlying Violent Outcomes in Adolescents
Note:
              This awardee has received supplemental funding. This award detail page includes information about both the original award and supplemental awards.
Award Information
Awardee
              GEORGETOWN UNIVERSITY (THE)
Award #
              2016-R2-CX-0019
Funding Category
              Competitive Discretionary
Location
                    WASHINGTON, DC
Congressional District
              98
Status
              Closed
Funding First Awarded
              2016
Total funding (to date)
              $150,000
Original Solicitation
              NIJ FY 16 Graduate Research Fellowship in Science, Technology, Engineering, and Mathematics
Description of original award (Fiscal Year 2016, $50,000)
As submitted by the proposer: The incidence rate of violent crimes follows a similar developmental trajectory as neurobehavioral metrics of risk taking, rewardsensitivity, inhibitory control and aggression; rapidly increasing following puberty and approaching a peak at ages 1719 before leveling off towards the middle of young adulthood Neuroscience research suggests the emergence of youth violence may be explained by an interaction between detrimental environmental factors and physiological changes during the critical period of adolescent development that predispose vulnerable youths to adverse outcomes. Specifically, family conflict, victimization and social adversity combined with violence exposure are hypothesized to impair brain development, promote aggression and increase risk for violent outcomes. The existence of a critical period for violent outcomes in adolescents provides an excellent opportunity for preventative measures. However, the impact of intervention programs is limited by a lack of knowledge about the interaction between environmental influences and the neurocognitive development of adolescents susceptible to violent outcomes. In this proposal, we will refine existing risk and outcome assessment metrics by applying machine learning to classify the reliability of environmental adversity scores (i.e., family conflict, exposure to violence, victimization and social adversity) in predicting developmental changes in brain activity and behavior that characterize violent and antisocial behavior in 135 preadolescents participating in an ongoing NIH youth development study from ages 11-17. The classifier will be validated by annual measurement of cognitive performance, brain activity and aggressive/antisocial behavior from 2016-2018 in an additional 78 adolescents (aged 12-15 at baseline) sampled from communities with the lowest average income and highest crimerates in D.C. Risk for violence, environmental adversity, aggression and violent outcomes will be measured with a repertoire of instruments commonly applied in youth violence prevention. Behavioral performance and functional magnetic resonance imaging (fMRI) will be used to track neurocognitive changes in inhibitory control, emotion and risk/reward processing tasks that explain variances in individual outcomes. A longitudinal support vector machine will be used to identify the combination of testing instruments that best predict and distinguish between violent outcomes on an individual basis . Refined metrics will be implemented in youth violence prevention practices via collaboration with the Pierce Prevention Research Center at Pennsylvania State University.
Note:  This project contains a research and/or development component, as defined in applicable law.
ca/ncf
Supplemental Award 1 (Fiscal Year 2017, $50,000)
As submitted by the proposer: The incidence rate of violent crimes follows a similar developmental trajectory as neurobehavioral metrics of risk taking, rewardsensitivity, inhibitory control and aggression; rapidly increasing following puberty and approaching a peak at ages 1719 before leveling off towards the middle of young adulthood Neuroscience research suggests the emergence of youth violence may be explained by an interaction between detrimental environmental factors and physiological changes during the critical period of adolescent development that predispose vulnerable youths to adverse outcomes. Specifically, family conflict, victimization and social adversity combined with violence exposure are hypothesized to impair brain development, promote aggression and increase risk for violent outcomes. The existence of a critical period for violent outcomes in adolescents provides an excellent opportunity for preventative measures. However, the impact of intervention programs is limited by a lack of knowledge about the interaction between environmental influences and the neurocognitive development of adolescents susceptible to violent outcomes. In this proposal, we will refine existing risk and outcome assessment metrics by applying machine learning to classify the reliability of environmental adversity scores (i.e., family conflict, exposure to violence, victimization and social adversity) in predicting developmental changes in brain activity and behavior that characterize violent and antisocial behavior in 135 preadolescents participating in an ongoing NIH youth development study from ages 11-17. The classifier will be validated by annual measurement of cognitive performance, brain activity and aggressive/antisocial behavior from 2016-2018 in an additional 78 adolescents (aged 12-15 at baseline) sampled from communities with the lowest average income and highest crimerates in D.C. Risk for violence, environmental adversity, aggression and violent outcomes will be measured with a repertoire of instruments commonly applied in youth violence prevention. Behavioral performance and functional magnetic resonance imaging (fMRI) will be used to track neurocognitive changes in inhibitory control, emotion and risk/reward processing tasks that explain variances in individual outcomes. A longitudinal support vector machine will be used to identify the combination of testing instruments that best predict and distinguish between violent outcomes on an individual basis . Refined metrics will be implemented in youth violence prevention practices via collaboration with the Pierce Prevention Research Center at Pennsylvania State University.
Note:  This project contains a research and/or development component, as defined in applicable law.
ca/ncf
    Supplemental Award 2 (Fiscal Year 2018, $50,000)
As submitted by the proposer: The incidence rate of violent crimes follows a similar developmental trajectory as neurobehavioral metrics of risk taking, rewardsensitivity, inhibitory control and aggression; rapidly increasing following puberty and approaching a peak at ages 1719 before leveling off towards the middle of young adulthood Neuroscience research suggests the emergence of youth violence may be explained by an interaction between detrimental environmental factors and physiological changes during the critical period of adolescent development that predispose vulnerable youths to adverse outcomes. Specifically, family conflict, victimization and social adversity combined with violence exposure are hypothesized to impair brain development, promote aggression and increase risk for violent outcomes. The existence of a critical period for violent outcomes in adolescents provides an excellent opportunity for preventative measures. However, the impact of intervention programs is limited by a lack of knowledge about the interaction between environmental influences and the neurocognitive development of adolescents susceptible to violent outcomes. In this proposal, we will refine existing risk and outcome assessment metrics by applying machine learning to classify the reliability of environmental adversity scores (i.e., family conflict, exposure to violence, victimization and social adversity) in predicting developmental changes in brain activity and behavior that characterize violent and antisocial behavior in 135 preadolescents participating in an ongoing NIH youth development study from ages 11-17. The classifier will be validated by annual measurement of cognitive performance, brain activity and aggressive/antisocial behavior from 2016-2018 in an additional 78 adolescents (aged 12-15 at baseline) sampled from communities with the lowest average income and highest crimerates in D.C. Risk for violence, environmental adversity, aggression and violent outcomes will be measured with a repertoire of instruments commonly applied in youth violence prevention. Behavioral performance and functional magnetic resonance imaging (fMRI) will be used to track neurocognitive changes in inhibitory control, emotion and risk/reward processing tasks that explain variances in individual outcomes. A longitudinal support vector machine will be used to identify the combination of testing instruments that best predict and distinguish between violent outcomes on an individual basis . Refined metrics will be implemented in youth violence prevention practices via collaboration with the Pierce Prevention Research Center at Pennsylvania State University.

Note:  This project contains a research and/or development component, as defined in applicable law.
ca/ncf


  
Grant-Funded Publications
        
                      Introducing an adolescent cognitive maturity index

  
Date Created: July 18, 2016
    
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