Variation in executive function relates to <scp>BMI</scp> increases in youth who were initially of a healthy weight in the <scp>ABCD</scp> Study

Adise, Shana; Ottino‐Gonzalez, Jonatan; Goedde, Lauren; Marshall, Andrew T.; Kan, Eric; Rhee, Kyung E.; Goran, Michael I.; Sowell, Elizabeth R.

doi:10.1002/oby.23811

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…These datasets have high inter-dataset heterogeneity, encompassing substantial variations in participant characteristics (age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens), imaging parameters (fMRI tasks and sequences), and behavioral measures. We used language abilities and function (EF) as two developmentally and clinically relevant phenotypes for prediction (Adise et al, 2023; Casey, 2023; Godfrey et al, 2022; Qi et al, 2021). We demonstrate that reproducible and generalizable brain-behavior associations using functional connectivity and connectome-based predictive modeling can be realized across diverse dataset features with sample sizes smaller than consortium-levels.…”

Section: Introductionmentioning

confidence: 99%

Brain-phenotype predictions can survive across diverse real-world data

Adkinson,

Rosenblatt,

Dadashkarimi

et al. 2024

Preprint

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Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

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Section: Introductionmentioning

confidence: 99%

Brain-phenotype predictions can survive across diverse real-world data

Adkinson,

Rosenblatt,

Dadashkarimi

et al. 2024

Preprint

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“…The article titled "Variation in executive functions relates to BMI increases in youth who were initially of a healthy weight in the ABCD study" by Adise and colleagues [1] addresses important questions about bidirectional associations between changes in body mass index (BMI) and cognition.…”

mentioning

confidence: 99%

A test score does not a construct make

Suchy

2023

Obesity

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Neurocognitive factors predicting BMI changes from adolescence to young adulthood

Reyes,

Peirano,

Gahagan

et al. 2024

Obesity

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ObjectiveThe objective of this study was to assess whether inhibitory task performance in adolescence could be prospectively related to weight gain in young adulthood. We proposed that this association would differ according to the BMI group in adolescence.MethodsA total of 318 adolescents performed the anti‐saccade task, and 530 completed the Stroop test. Accuracy and reaction time were assessed for each incentive type (neutral, loss, and reward) in the anti‐saccade task and for each trial type (control and incongruent trials) in the Stroop test. Changes in the BMI z score (∆BMI z score) from adolescence to young adulthood were calculated.ResultsThe relationship between the BMI z score and the anti‐saccade task accuracy showed an effect on the ∆BMI z score (β = −0.002, p < 0.05). The neutral and loss accuracies were related to ∆BMI z score in the groups with overweight (all β = −0.004, p = 0.05) and obesity (β = −0.006 and β = −0.005, p < 0.01). The interaction between adolescents' BMI z score with control (β = −0.312, p < 0.001) and incongruent (β = −0.384, p < 0.001) trial reaction times showed an effect on the ∆BMI z score. Control (β = 0.730, p = 0.036) and incongruent (β = 0.535, p = 0.033) trial reaction times were related to ∆BMI z score in the group with overweight.ConclusionsOur findings support the hypothesis that cognitive vulnerability could predict the BMI gain from adolescence to young adulthood.

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Variation in executive function relates to BMI increases in youth who were initially of a healthy weight in the ABCD Study

Cited by 6 publications

References 51 publications

Brain-phenotype predictions can survive across diverse real-world data

Brain-phenotype predictions can survive across diverse real-world data

A test score does not a construct make

Neurocognitive factors predicting BMI changes from adolescence to young adulthood

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