Moore Lucille A, Hermosillo Robert J M, Feczko Eric, Moser Julia, Koirala Sanju, Allen Madeleine C, Buss Claudia, Conan Greg, Juliano Anthony C, Marr Mollie, Miranda-Dominguez Oscar, Mooney Michael, Myers Michael, Rasmussen Jerod, Rogers Cynthia E, Smyser Christopher D, Snider Kathy, Sylvester Chad, Thomas Elina, Fair Damien A, Graham Alice M
Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States.
Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States.
Imaging Neurosci (Camb). 2024 May 10;2:1-20. doi: 10.1162/imag_a_00165. eCollection 2024 May 1.
The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.
功能性脑系统精确的网络拓扑结构具有高度个体特异性，且在发育的关键时期会发生显著变化。研究这些个体差异需要大量高质量的静息态数据，但在婴儿早期很难获取。我们使用模板匹配方法，生成了一组婴儿网络模板，作为在两个独立的新生儿数据集中进行个体化功能性静息态网络映射的先验信息，这两个数据集扩展采集了静息态功能磁共振成像（fMRI）数据。我们发现，模板匹配能够在个体婴儿中检测到所有主要的成人静息态网络，并且这些静息态网络图谱的拓扑结构具有个体特异性。有趣的是，对于长达25分钟的静息态数据，个体内部网络图谱相似度并没有达到平稳状态，这表明获得稳定或高精度网络图谱所需的婴儿数据量和/或质量高于成人。这些发现是朝着婴儿个性化精准功能性脑图谱迈出的关键一步，为静息态fMRI的临床应用开辟了新途径，并有可能对早期功能连接模式如何与后续行为表型和健康结果相关进行强有力的预测。
