Risk Assessment of Maladaptive Behaviors in Adolescents: Nutrition, Screen Time, Prenatal Exposure, Childhood Adversities - Adolescent Brain Cognitive Development Study.

Khushbu Agarwal, Peter Manza, Hugo A Tejeda, Amber B Courville, Nora D Volkow, Paule V Joseph
Author Information
  1. Khushbu Agarwal: Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland; National Institute of Nursing Research, Bethesda, Maryland.
  2. Peter Manza: Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland.
  3. Hugo A Tejeda: Unit on Neuromodulation and Synaptic Integration, National Institute of Mental Health, Bethesda, Maryland.
  4. Amber B Courville: National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
  5. Nora D Volkow: Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland. Electronic address: nvolkow@nida.nih.gov.
  6. Paule V Joseph: Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland; National Institute of Nursing Research, Bethesda, Maryland. Electronic address: paule.joseph@nih.gov.

Abstract

PURPOSE: We aimed to identify significant contributing factors to the risk of maladaptive behaviors, such as alcohol use disorder or obesity, in children. To achieve this, we utilized the extensive adolescent brain cognitive development data set, which encompasses a wide range of environmental, social, and nutritional factors.
METHODS: We divided our sample into equal sets (test, validation; n = 3,415 each). On exploratory factor analysis, six factor domains were identified as most significant (fat/sugar intake, screen time, and prenatal alcohol exposure, parental aggressiveness, hyperactivity, family violence, parental education, and family income) and used to stratify the children into low- (n = 975), medium- (n = 967), high- (n = 977) risk groups. Regression models were used to analyze the relationship between identified risk groups, and differences in reward sensitivity, and behavioral problems at 2-year follow-up.
RESULTS: The functional magnetic resonance imaging analyses showed reduced activation in several brain regions during reward or loss anticipation in high/medium-risk (vs. low-risk) children on a monetary incentive delay task. High-risk children exhibited heightened middle frontal cortex activity when receiving large rewards. They also displayed increased impulsive and motivated reward-seeking behaviors, along with behavioral problems. These findings replicated in our validation set, and a negative correlation between middle frontal cortexthickness and impulsivity behavior was observed in high-risk children.
DISCUSSION: Our findings show altered reward function and increased impulsiveness in high-risk adolescents. This study has implications for early risk identification and the development of prevention strategies for maladaptive behaviors in children, particularly those at high risk.

Keywords

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Grants

  1. U24 DA041147/NIDA NIH HHS
  2. U01 DA041120/NIDA NIH HHS
  3. U24 DA041123/NIDA NIH HHS
  4. U01 DA041022/NIDA NIH HHS
  5. LIG OD033822/NIH HHS
  6. ZIA AA000136/Intramural NIH HHS
  7. ZIA AA000137/Intramural NIH HHS
  8. U01 DA041025/NIDA NIH HHS
  9. U01 DA041089/NIDA NIH HHS
  10. U01 DA041106/NIDA NIH HHS
  11. U01 DA041117/NIDA NIH HHS
  12. ZIA NR000035/Intramural NIH HHS
  13. U01 DA041028/NIDA NIH HHS
  14. ZIA AA000135/Intramural NIH HHS

MeSH Term

Humans
Female
Adolescent
Screen Time
Male
Pregnancy
Prenatal Exposure Delayed Effects
Risk Assessment
Magnetic Resonance Imaging
Brain
Adverse Childhood Experiences
Adolescent Behavior
Child
Cognition
Nutritional Status
Risk Factors
Reward

Word Cloud

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