Adolescent Health Risk Behaviors: Convergent, Discriminant and Predictive Validity of Self-Report and Cognitive Measures

Demidenko, Michael I.; Huntley, Edward D.; Martz, Meghan E.; Keating, Daniel P.

doi:10.1007/s10964-019-01057-4

Cited by 29 publications

(29 citation statements)

References 75 publications

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“…AHRB consists of a nonprobability sample of 2,017 (Age Mean = 16.8, SD = 1.1; Female 56%) 10th‐ and 12th‐grade students recruited from nine public school districts across eight Southeastern Michigan counties, using a quota sampling method to enhance sample diversity. Phase I, described elsewhere (Demidenko et al., 2019), collected demographic, psychosocial, neurocognitive, and behavioral information across three waves of survey data collection. From Phase I of the study, a subsample of 115 adolescents, who were characterized as high and average/low risk, was recruited to participate in the neuroimaging phase of the study (elapsed time between Wave 1 and neuroimaging section (Months): M = 30.9 months SD = 5.0 months).…”

Section: Methodsmentioning

confidence: 99%

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior

et al. 2021

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Introduction Phenomena related to reward responsiveness have been extensively studied in their associations with substance use and socioemotional functioning. One important task in this literature is the Monetary Incentive Delay (MID) task. By cueing and delivering performance‐contingent reward, the MID task has been demonstrated to elicit robust activation of neural circuits involved in different phases of reward responsiveness. However, systematic evaluations of common MID task contrasts have been limited to between‐study comparisons of group‐level activation maps, limiting their ability to directly evaluate how researchers’ choice of contrasts impacts conclusions about individual differences in reward responsiveness or brain‐behavior associations. Methods In a sample of 104 participants (Age Mean = 19.3, SD = 1.3), we evaluate similarities and differences between contrasts in: group‐ and individual‐level activation maps using Jaccard's similarity index, region of interest (ROI) mean signal intensities using Pearson's r, and associations between ROI mean signal intensity and psychological measures using Bayesian correlation. Results Our findings demonstrate more similarities than differences between win and loss cues during the anticipation contrast, dissimilarity between some win anticipation contrasts, an apparent deactivation effect in the outcome phase, likely stemming from the blood oxygen level‐dependent undershoot, and behavioral associations that are less robust than previously reported. Conclusion Consistent with recent empirical findings, this work has practical implications for helping researchers interpret prior MID studies and make more informed a priori decisions about how their contrast choices may modify results.

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Section: Methodsmentioning

confidence: 99%

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior

et al. 2021

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“…While some use psychological characteristics such as sensation seeking (Bjork, Knutson, & Hommer, 2008), general measures of risky behaviors (Op de Macks et al, 2016;Saxbe, Piero, Immordino-Yang, Kaplan, & Margolin, 2015), substance use (Chung et al, 2015;Bjork et al, 2011) or likelihood of engaging in future risk (Galvan et al, 2007), several studies associate neural activation as a function of risk as measures by the task , such as driving (Cascio et al, 2015), probability or gambling tasks (Eshel, Nelson, Blair, Pine, & Ernst, 2007;Op de Macks et al, 2016;Qu et al, 2015;Telzer et al, 2015). These proxy-based measures of risky behavior may be inappropriate as laboratory tasks may require larger samples to capture small effects (Sherman et al, 2018) that have limited evidence for age-related differences de (Defoe, Dubas, Figner, & van Aken, 2015), and often serve as poor predictors of real-world risk behaviors in normative adolescent populations (Demidenko et al, 2019). Furthermore, if neurodevelopmental models are to use indicators of sensation seeking or reward sensitivity, it is important to recognize that these indicators vary in their association with different risky behaviors (Demidenko et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…11309). However, the use of proxy variables, such as cognitive task performance to identify risk behavior profiles as a dependent variable, may be problematic owing to their questionable construct validity (Demidenko, Huntley, Martz, & Keating, 2019). In addition, mixed results regarding adolescent neurodevelopment may reflect initial differences in the selection of parameters and locations in neuroimaging analyses rather than conflicting results.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, mixed results regarding adolescent neurodevelopment may reflect initial differences in the selection of parameters and locations in neuroimaging analyses rather than conflicting results. Specification and justification of these choices is thus paramount for efforts to accumulate evidence on these questions (Demidenko et al, 2019). Key differences in parameter decisions may invoke differing results, especially when the parameters are infrequently studied in the context of risk.…”

Section: Introductionmentioning

confidence: 99%

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Cortical and subcortical response to the anticipation of reward in high and average risk-taking adolescents.

Demidenko¹,

Huntley²,

Jahn³

et al. 2019

Preprint

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Since the first neurodevelopmental models that sought to explain the influx of risky behaviors during adolescence were proposed, there have been a number of revisions, variations and criticisms. Despite providing a strong multi-disciplinary heuristic to explain development of risk behavior, extant models have not isolated reliable neural systems that underlie risk behaviors in adolescence. To address this gap, we screened 2,017 adolescents from an ongoing longitudinal study assessing 15-health risk behaviors, targeting 104 adolescents characterized as high or average in risk-takers. Participants completed the Monetary Incentive Delay (MID) fMRI task, examining reward anticipation to “big win” versus “neutral”. We examined neural response variation associated with both early and longitudinal (multi-wave) risk classifications. Analyses included targeted examination of a priori regions of interest (ROIs), and exploratory non-parametric, whole-brain analyses. Hypothesis-driven ROI analysis revealed no significant differences between high- and average-risk profiles using either early or multi-wave risk classifications. Results of whole-brain analyses differed according to whether risk assessment was based on early assessment or multi-wave data. Despite significant mean-level task activation, results do not replicate prior neural substrates implicated in reward anticipation and adolescent risk taking. Further, these data indicate that whole-brain differences may depend on how risk-behavior profiles are defined.

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“…SI and SN youth did not differ on performance on the DD task, suggesting similar preference for immediate rewards under current task parameters. Unlike the BIS/BAS and DUSI-R which assess real-world preference and situationally-based behavior, the laboratory DD task (like WOF) may lack the sensitivity to detect group differences prior to initiation 140 . The absence of differences between SN and SI participants on the DD and WOF task performance, in the context of functional differences during WOF performance, lends further support to the notion that brain indices may be more sensitive to risk prior to the onset of SU, compared to behavioral measures.…”

Section: Wof Task: Behavior and Brainmentioning

confidence: 99%

Risk-related decision making evokes distinct brain activation patterns in reward evaluation regions in substance use naïve versus non-naïve adolescents

McQuaid

Darcey

Patterson

et al. 2020

Preprint

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Identifying brain and behavioral precursors to substance use (SU) may guide interventions that delay initiation in youth at risk for SU disorders (SUD). Heightened reward-sensitivity and risk-taking may confer risk for SUD. In a longitudinal, prospective study, we characterized behavioral and neural profiles associated with reward-sensitivity and risk-taking in substance-naïve adolescents, examining whether they differed as a function of SU initiation at 18- and 36-months follow-up.Adolescents (N=70; 11.1-14.0 years) completed a reward-related decision-making task (Wheel of Fortune (WOF)) while undergoing functional MRI. Measures of reward sensitivity (Behavioral Inhibition System-Behavioral Approach System; BIS-BAS), impulsive decision-making (delay discounting task), and SUD risk (Drug Use Screening Inventory, Revised (DUSI-R)) were collected at baseline. Baseline metrics were compared for youth who did (SI; n=27) and did not (SN; n=43) initiate SU at follow-up.While groups displayed similar discounting and risk taking behavior, SI youth showed more variable patterns of activation in left insular cortex during high-risk selections, and left anterior cingulate cortex in response to rewarded outcomes. SI participants scored higher on the DUSI-R and BAS subscales. Results suggest differences in brain regions critical in the development and experience of SUDs may precede SU and serve as a biomarker for SUD risk.

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Adolescent Health Risk Behaviors: Convergent, Discriminant and Predictive Validity of Self-Report and Cognitive Measures

Cited by 29 publications

References 75 publications

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior

Cortical and subcortical response to the anticipation of reward in high and average risk-taking adolescents.

Risk-related decision making evokes distinct brain activation patterns in reward evaluation regions in substance use naïve versus non-naïve adolescents

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