Characterising the nature of mental health trajectories across adolescent development through the integration of genomic, biomarker, neuroimaging and

年轻人的精神健康状况导致45%的残疾年损失。心理健康障碍往往在这一年龄段发病，造成同时和长期的不平等，对病人、家庭和社会产生影响。因此，有必要确定早期干预和预防措施，以应对疾病及其下游后果。然而，并非所有现有的干预和治疗措施都有效。一个关键的挑战是，在这一时期的心理健康障碍变化很大，而很少有人知道具体的风险因素是如何支撑的时间，过程和严重程度较差的青年心理健康轨迹。更好地理解这些轨迹的机制（如遗传风险、表观遗传变化、生物标志物大脑变化、神经认知障碍和环境风险因素之间的相互作用）可以大大提高我们在正确的时间向正确的人提供成功干预和预防的能力。纵向研究是研究心理健康如何随时间变化以及为谁变化的重要工具。该项目将使用新的大型纵向数据集来研究遗传风险，表观遗传变化，环境风险因素，脑成像和神经认知数据以及生物标志物的组合如何预测儿童，青少年和成年早期的不同心理健康轨迹。包括的数据有：青少年大脑认知发展研究（ABCD; n=~ 11，900;年龄9-14岁），雅芳家长和儿童纵向研究（ALSPAC; n~ 15，600;年龄0-30岁）和Teds Twins研究（TEDS; n=~ 10，000; 0-25岁）。这个项目将应用机器学习方法来模拟心理健康的异质心理健康轨迹，并确定具有对这些预测模型的影响最大。变量将包括基因组（例如，多基因风险评分），神经成像（例如，功能连接性），炎性（例如，血液生物标记）和环境数据（例如，创伤事件），这些数据在这些纵向数据集中的一系列时间点收集。机器学习提供了一种强大的计算方法，可以将显示多重共线性的联合收割机特征组合起来，并根据数据分布和预测变量对组进行分类。建立心理健康结果的预测模型将通过对患者群体进行分层来促进精确精神病学领域的发展。因此，可以在正确的时间为正确的人提取正确的治疗方法-这是精准医疗的关键。

Poorer mental health in young people contributes to 45% of all years lost because of disability. Mental health disorders will often onset during this age, resulting in concurrent and long-lasting inequalities with implications for the patient, family and society. Thus, there is an essential need to identify early interventions and preventions that combat both the disorder and downstream consequences.However, not all current interventions and treatments work. One key challenge is that mental health disorders during this period change considerably, and little is known about how specific risk factors underpin the timing, course and severity of poorer youth mental health trajectories. Greater understanding of the mechanisms underpinning these trajectories (such as the interplay between genetic risk, epigenetic changes, biomarkers brain changes, neurocognitive impairments and environmental risk factors) could greatly enhance our ability to deliver successful interventions and preventions to the right people at the right time.Longitudinal studies are an important tool for examining how mental health changes over time and for whom. This project will use newly available large longitudinal datasets to examine how a combination of genetic risk, epigenetic changes, environmental risk factors, brain imaging and neurocognitive data, and biomarkers predicts different mental health trajectories across childhood, adolescence and early adulthood. Data included are: the Adolescent Brain Cognitive Development study (ABCD; n=~11,900; age 9-14 years), the Avon Longitudinal Study of Parents and Children (ALSPAC; n~15,600; age 0-30 years) and the Teds Twins study (TEDS; n=~10,000; age 0-25).This project will apply machine learning approaches to model heterogenous mental health trajectories of mental health and identify variables that have the most influence on such prediction models. Variables will include genomic (e.g., polygenic risk scores), neuroimaging (e.g., functional connectivity), inflammatory (e.g., blood biomarkers) and environmental data (e.g, traumatic events) that has been collected at a series of time points within these longitudinal datasets. Machine learning provides a powerful computational approach that can combine features that display multicollinearity and classify groups based on the data distribution and predictive variables. Building predictive models of mental health outcomes will catalyse the field of precision psychiatry by stratifying patient groups. As a result, the right treatment can be distilled for the right person at the right time - the crux of precision medicine.