Jiang Lin, Peng Yueheng, He Runyang, Yang Qingqing, Yi Chanlin, Li Yuqin, Zhu Bin, Si Yajing, Zhang Tao, Biswal Bharat B, Yao Dezhong, Xiong Lan, Li Fali, Xu Peng
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China.
Research (Wash D C). 2023 Jun 8;6:0171. doi: 10.34133/research.0171. eCollection 2023.
Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain regions. Owing to lacking an effective approach to quantifying the covarying of structure and functional responses, how the structural-functional circuits interact and how genes encode the relationships, to deepen our knowledge of human cognition and disease, are still unclear. Here, we propose a multimodal covariance network (MCN) construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single individual. We further explored the potential association between brain-wide gene expression patterns and structural-functional covarying in individuals involved in a gambling task and individuals with major depression disorder (MDD), adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent cohorts. MCN analysis showed a replicable cortical structural-functional fine map in healthy individuals, and the expression of cognition- and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN differences. Further analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences. In contrast, changes in MCN of MDD patients were enriched for biological processes related to synapse function and neuroinflammation in astrocytes, microglia, and neurons, suggesting its promising application in developing targeted therapies for MDD patients. Collectively, these findings confirmed the correlations of MCN-related differences with brain-wide gene expression patterns, which captured genetically validated structural-functional differences at the cellular level in specific cognitive processes and psychiatric patients.
人类认知通常由空间分布的脑区中的内在结构和功能性神经共激活所支撑。由于缺乏一种有效的方法来量化结构和功能反应的共变，结构 - 功能回路如何相互作用以及基因如何编码这些关系，以加深我们对人类认知和疾病的理解，目前仍不清楚。在此，我们提出一种多模态协方差网络（MCN）构建方法，以捕捉单个个体的结构骨架和瞬态功能活动的区域间共变。我们进一步采用来自公开可用的人类脑转录组图谱和2个独立队列的多模态数据，探索了参与赌博任务的个体和患有重度抑郁症（MDD）的个体中全脑基因表达模式与结构 - 功能共变之间的潜在关联。MCN分析在健康个体中显示出可重复的皮质结构 - 功能精细图谱，并且发现与认知和疾病表型相关的基因表达在空间上与相应的MCN差异相关。对细胞类型特异性特征基因的进一步分析表明，兴奋性和抑制性神经元转录组变化可以解释与任务诱发的MCN差异的大部分观察到的相关性。相比之下，MDD患者的MCN变化在与星形胶质细胞、小胶质细胞和神经元中的突触功能和神经炎症相关的生物学过程中富集，这表明其在为MDD患者开发靶向治疗方面具有广阔的应用前景。总体而言，这些发现证实了MCN相关差异与全脑基因表达模式的相关性，这在特定认知过程和精神疾病患者的细胞水平上捕捉到了经过基因验证的结构 - 功能差异。
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