Objective: Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health. Methods: We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n=1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance. Results: Our model was able to predict age in the independent test samples, with a model performance of r=0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN. Discussion: Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

中文翻译：

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使用fMRI网络耦合技术预测年轻人的年龄以及与精神病症状的关联

目的：磁共振成像（MRI）表明，在各种常见的脑部疾病中，估计的大脑年龄与按年龄排序的年龄不同。脑龄估计可能有助于研究脑成熟和完整性的模式，有助于阐明这些异质性条件下的脑机制。在这里，我们检查了两个大样本儿童和青少年的功能性大脑年龄及其与心理健康的关系。方法：我们使用了来自费城神经发育队列（PNC; n = 1126，年龄范围8-22岁）的静止状态fMRI数据来估计大脑网络之间的功能连接，并将其用作预测大脑年龄的特征。我们在健康大脑网络样本（HBN）中将预测模型应用于1387名个体（年龄介于8-22岁之间）。此外，我们使用交叉验证框架估算了PNC中的大脑年龄。接下来，我们测试了大脑年龄差距与心理病理学和认知表现各个方面之间的关联。结果：我们的模型能够预测独立测试样品的年龄，HBN测试集的模型性能为r = 0.54，支持了样品和扫描仪之间功能连接方式的一致性。线性模型揭示了PNC中脑年龄差距与精神病理学之间的显着相关性，其中估计脑年龄较低的个体的总体症状负担较高。这些关联未在HBN中复制。讨论：我们的发现支持基于基于功能磁共振成像的连通性对脑年龄预测的使用。在需要进一步扩展和验证的同时，