Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Chinese Institute for Brain Research, Beijing, China.
Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania.
Biol Psychiatry. 2022 Dec 15;92(12):973-983. doi: 10.1016/j.biopsych.2022.05.014. Epub 2022 May 18.
The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth.
The authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology.
Personalized functional network topography significantly predicted unseen individuals' major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p < .001), which drove prediction of the 4 correlated dimensions.
These results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.
大规模功能脑网络的空间布局在个体之间存在差异,在与广泛的精神障碍有关的联合皮层中尤为多变。然而,功能拓扑的这种变化是否与青年精神病理学的主要维度有关尚不清楚。
作者研究了 790 名年龄在 8 至 23 岁的年轻人,他们作为费城神经发育队列的一部分进行了 27 分钟的高质量功能磁共振成像数据。使用确认相关特质因子分析对 112 项症状进行分析,得出了四个相关维度,使用确认因子分析得出了一个整体精神病理学因子和四个正交维度。使用空间正则化非负矩阵分解为每个参与者确定 17 个个体特定的功能网络。采用具有半分割交叉验证的偏最小二乘回归来评估个性化功能网络拓扑结构在多大程度上编码精神病理学的主要维度。
个性化功能网络拓扑结构显著预测了未见过的个体的主要精神病理学维度,包括恐惧、精神病、外化和焦虑痛苦。联合网络代表性降低是预测所有四个维度的最重要特征之一。进一步的分析表明,个性化功能网络拓扑结构预测了整体精神病理学(r = 0.16,置换检验 p <.001),这驱动了对四个相关维度的预测。
这些结果表明,联合网络中功能网络拓扑的个体差异与青年整体精神病理学有关。这些结果强调了考虑功能神经解剖学对于精神病学中的个性化诊断和治疗的重要性。