Upper Airway Volume Predicts Brain Structure and Cognition in Adolescents

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Abstract Rationale One in 10 children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood. Objectives We assessed the relationship between magnetic resonance imaging–derived upper airway volume and children’s cognition and regional cortical gray matter volumes. Methods We used 5-year data from the Adolescent Brain Cognitive Development study (N = 11,875 children; 9–10 yr old at baseline). Upper airway volumes were derived using a deep learning model applied to 5,552,640 brain magnetic resonance imaging slices. The primary outcome was the Total Cognition Composite score from the NIH Toolbox (NIH-TB). Secondary outcomes included other NIH-TB measures and cortical gray matter volumes. Measurements and Main Results The habitual snoring group had significantly smaller airway volumes than nonsnorers (mean difference, 1.2 cm³; 95% confidence interval [CI], 1.0–1.4 cm³; P < 0.001). Deep learning–derived airway volume predicted the Total Cognition Composite score (estimated mean difference, 3.68 points; 95% CI, 2.41–4.96 points; P < 0.001) per one-unit increase in the natural log of airway volume (∼2.7-fold raw volume increase). This airway volume increase was also associated with an average 0.02-cm³ increase in right temporal pole volume (95% CI, 0.01–0.02 cm³; P < 0.001). Similar airway volume predicted most NIH-TB domain scores and multiple frontal and temporal gray matter volumes. These brain volumes mediated the relationship between airway volume and cognition. Conclusions We demonstrate a novel application of deep learning–based airway segmentation in a large pediatric cohort. Upper airway volume is a potential biomarker for cognitive outcomes in pediatric SDB, offers insights into neurobiological mechanisms, and informs future studies on risk stratification.

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