Upper Airway Volume Predicts Brain Structure and Cognition in Adolescents.

Abstract

<b>Rationale:</b> One in 10 children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood. <b>Objectives:</b> We assessed the relationship between magnetic resonance imaging-derived upper airway volume and children's cognition and regional cortical gray matter volumes. <b>Methods:</b> We used 5-year data from the Adolescent Brain Cognitive Development study (<i>N</i> = 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. <b>Measurements and Main Results:</b> 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; <i>P</i> < 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; <i>P</i> < 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; <i>P</i> < 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. <b>Conclusions:</b> 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.