Adway Kanhere, Nithya Navarathna, Paul H Yi, Vishwa S Parekh, Jerrah Pickle, Christine C Cloak, Thomas Ernst, Linda Chang, Dongdong Li, Susan Redline, Amal Isaiah
{"title":"上呼吸道容量预测青少年大脑结构和认知能力。","authors":"Adway Kanhere, Nithya Navarathna, Paul H Yi, Vishwa S Parekh, Jerrah Pickle, Christine C Cloak, Thomas Ernst, Linda Chang, Dongdong Li, Susan Redline, Amal Isaiah","doi":"10.1164/rccm.202409-1748OC","DOIUrl":null,"url":null,"abstract":"<p><strong>Rationale: </strong>One in ten children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood.</p><p><strong>Objective: </strong>We assessed the relationship between magnetic resonance imaging (MRI)-derived upper airway volume and children's cognition and regional cortical gray matter volumes.</p><p><strong>Methods: </strong>We used five-year data from the Adolescent Brain Cognitive Development study (n=11,875 children, 9-10 years at baseline). Upper airway volumes were derived using a deep learning model applied to 5,552,640 brain MRI slices. The primary outcome was the Total Cognition Composite score from the National Institutes of Health Toolbox (NIH-TB). Secondary outcomes included other NIH-TB measures and cortical gray matter volumes.</p><p><strong>Results: </strong>The habitual snoring group had significantly smaller airway volumes than non-snorers (mean difference=1.2 cm<sup>3</sup>; 95% CI, 1.0-1.4 cm<sup>3</sup>; 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; 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 cm<sup>3</sup> increase in right temporal pole volume (95% CI, 0.01-0.02 cm<sup>3</sup>; 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.</p><p><strong>Conclusions: </strong>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. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).</p>","PeriodicalId":7664,"journal":{"name":"American journal of respiratory and critical care medicine","volume":" ","pages":""},"PeriodicalIF":19.4000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287761/pdf/","citationCount":"0","resultStr":"{\"title\":\"Upper Airway Volume Predicts Brain Structure and Cognition in Adolescents.\",\"authors\":\"Adway Kanhere, Nithya Navarathna, Paul H Yi, Vishwa S Parekh, Jerrah Pickle, Christine C Cloak, Thomas Ernst, Linda Chang, Dongdong Li, Susan Redline, Amal Isaiah\",\"doi\":\"10.1164/rccm.202409-1748OC\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Rationale: </strong>One in ten children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood.</p><p><strong>Objective: </strong>We assessed the relationship between magnetic resonance imaging (MRI)-derived upper airway volume and children's cognition and regional cortical gray matter volumes.</p><p><strong>Methods: </strong>We used five-year data from the Adolescent Brain Cognitive Development study (n=11,875 children, 9-10 years at baseline). Upper airway volumes were derived using a deep learning model applied to 5,552,640 brain MRI slices. The primary outcome was the Total Cognition Composite score from the National Institutes of Health Toolbox (NIH-TB). Secondary outcomes included other NIH-TB measures and cortical gray matter volumes.</p><p><strong>Results: </strong>The habitual snoring group had significantly smaller airway volumes than non-snorers (mean difference=1.2 cm<sup>3</sup>; 95% CI, 1.0-1.4 cm<sup>3</sup>; 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; 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 cm<sup>3</sup> increase in right temporal pole volume (95% CI, 0.01-0.02 cm<sup>3</sup>; 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.</p><p><strong>Conclusions: </strong>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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Upper Airway Volume Predicts Brain Structure and Cognition in Adolescents.
Rationale: One in ten children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood.
Objective: We assessed the relationship between magnetic resonance imaging (MRI)-derived upper airway volume and children's cognition and regional cortical gray matter volumes.
Methods: We used five-year data from the Adolescent Brain Cognitive Development study (n=11,875 children, 9-10 years at baseline). Upper airway volumes were derived using a deep learning model applied to 5,552,640 brain MRI slices. The primary outcome was the Total Cognition Composite score from the National Institutes of Health Toolbox (NIH-TB). Secondary outcomes included other NIH-TB measures and cortical gray matter volumes.
Results: The habitual snoring group had significantly smaller airway volumes than non-snorers (mean difference=1.2 cm3; 95% 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; 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. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
期刊介绍：
The American Journal of Respiratory and Critical Care Medicine focuses on human biology and disease, as well as animal studies that contribute to the understanding of pathophysiology and treatment of diseases that affect the respiratory system and critically ill patients. Papers that are solely or predominantly based in cell and molecular biology are published in the companion journal, the American Journal of Respiratory Cell and Molecular Biology. The Journal also seeks to publish clinical trials and outstanding review articles on areas of interest in several forms. The State-of-the-Art review is a treatise usually covering a broad field that brings bench research to the bedside. Shorter reviews are published as Critical Care Perspectives or Pulmonary Perspectives. These are generally focused on a more limited area and advance a concerted opinion about care for a specific process. Concise Clinical Reviews provide an evidence-based synthesis of the literature pertaining to topics of fundamental importance to the practice of pulmonary, critical care, and sleep medicine. Images providing advances or unusual contributions to the field are published as Images in Pulmonary, Critical Care, Sleep Medicine and the Sciences.
A recent trend and future direction of the Journal has been to include debates of a topical nature on issues of importance in pulmonary and critical care medicine and to the membership of the American Thoracic Society. Other recent changes have included encompassing works from the field of critical care medicine and the extension of the editorial governing of journal policy to colleagues outside of the United States of America. The focus and direction of the Journal is to establish an international forum for state-of-the-art respiratory and critical care medicine.
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