Keller Arielle S, Sun Kevin Y, Francisco Ashley, Robinson Heather, Beydler Emily, Bassett Dani S, Cieslak Matthew, Cui Zaixu, Davatzikos Christos, Fan Yong, Gardner Margaret, Kishton Rachel, Kornfield Sara L, Larsen Bart, Li Hongming, Linder Isabella, Pines Adam, Pritschet Laura, Raznahan Armin, Roalf David R, Seidlitz Jakob, Shafiei Golia, Shinohara Russell T, Wolf Daniel H, Alexander-Bloch Aaron, Satterthwaite Theodore D, Shanmugan Sheila
Department of Psychological Sciences, University of Connecticut, Storrs, CT, 06269, USA.
Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, 06269, USA.
bioRxiv. 2024 Sep 29:2024.09.26.615061. doi: 10.1101/2024.09.26.615061.
A key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex.
We aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks.
We leveraged person-specific atlases of functional brain networks defined using nonnegative matrix factorization in a sample of = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants' sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression.
Sex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants' PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons.
Sex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.
理解对女性心理健康有重大影响的精神疾病的关键一步是描绘从童年到青春期这一关键过渡期大脑组织性别特异性模式的出现。先前的研究表明,整个皮质功能脑网络空间组织的个体差异与精神病理学有关,并且存在系统性的性别差异。
我们旨在评估性别对个体特异性功能脑网络空间组织的影响。
我们利用来自青少年大脑认知发展研究的6437名青少年样本,通过非负矩阵分解定义了个体特异性功能脑图谱。在独立的发现和复制样本中,我们使用广义相加模型来揭示性别与个性化功能网络(PFN)的空间布局(“拓扑结构”)之间的关联。接下来,我们训练支持向量机根据PFN拓扑结构的多变量模式对参与者的性别进行分类。最后,我们利用来自艾伦人类大脑图谱的转录组数据来评估PFN拓扑结构中的性别差异与基因表达之间的空间相关性。
PFN拓扑结构中的性别差异在包括额顶叶、腹侧注意和默认模式网络在内的关联网络中最为显著。基于参与者PFN训练的机器学习模型能够高精度地对参与者的性别进行分类。PFN拓扑结构中性别差异最大的脑区富含X连锁基因以及在星形胶质细胞和兴奋性神经元中表达的基因。
PFN拓扑结构中的性别差异是显著的,在大规模青少年样本中具有可重复性,并且与X连锁基因的表达模式相关。这些结果表明,在从童年到青春期过渡时出现的抑郁和焦虑障碍中,女性偏向风险可能存在一个潜在因素。