NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway.
Kristiania University College, Oslo, Norway.
Neuroimage. 2022 Nov;263:119611. doi: 10.1016/j.neuroimage.2022.119611. Epub 2022 Sep 5.
Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.
精神障碍具有高度遗传性和多基因性，许多精神障碍的发病高峰在儿童晚期和青少年期，这是一个发生巨大变化的时期。尽管精神疾病的神经发育前因已被广泛承认，但针对青年人群队列的研究仍然很少，这阻碍了我们朝着早期认识这些疾病的方向前进。我们纳入了来自青少年大脑与认知发展研究的 7124 名儿童（9-11 岁），以绘制结构和弥散脑成像与精神障碍和教育程度常见遗传变异和多基因评分之间的关联图。我们使用主成分分析来推导成像成分，并计算其遗传率。然后，我们使用单变量模型和典型相关分析（CCA）评估成像成分与遗传和临床精神风险的关系。大多数成像成分具有中度遗传率。单变量模型显示，在这个年龄，多基因评分与大脑结构的关联证据有限，关联较小。CCA 揭示了两种显著的协变模式。第一种模式将更高的教育程度多基因评分与较少的外化问题和更大的表面积联系起来。第二种模式将更高的精神分裂症、双相情感障碍和自闭症谱系障碍的多基因评分与更高的全脑皮质厚度、穹窿和扣带回的白质体积更小、中枕叶表面积更大、外侧和内侧颞叶表面积更小联系起来。虽然交叉验证表明可推广性有限，但我们的结果强调了多元模型在更好地理解心理健康和大脑结构在儿童晚期的跨诊断和分布式关系方面的潜力。
