Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA.
Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA.
Brain Connect. 2022 Sep;12(7):617-628. doi: 10.1089/brain.2021.0079. Epub 2021 Nov 22.
While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity spatial scales is unknown. We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.
尽管功能连接性已得到广泛研究，但对不同空间尺度下的功能连接性的研究却很少。同样，功能连接性与空间尺度之间的关系也尚不清楚。我们提出了一种基于独立成分分析（ICA）的方法，以捕获多个模型阶（成分数量）的信息，并评估模型内和模型间的功能网络连接（FNC）。我们通过研究来自精神分裂症（SZ）个体和健康对照（HC）的静息态功能磁共振成像（rsfMRI）数据的研究中的组间差异来评估该方法。使用基于支持向量机的分类评估了多空间尺度上 FNC 的预测能力。除了在多模型阶和单模型阶上都具有一致的预测模式外，还在多模型阶和模型阶之间的交互中看到了独特的预测信息。我们观察到，在模型阶 25 到 50 之间的 FNC 在 HC 和 SZ 之间保持了最高的预测信息。结果突出了躯体运动和视觉域在模型内和模型间的预测能力，与其他功能域相比具有更高的预测能力。此外，皮质下-躯体运动、颞部-躯体运动和颞部-皮质下的 FNC 在预测 SZ 方面具有相对较高的权重。总之，多模型阶 ICA 为研究 FNC 提供了一种更全面的方法，产生了有意义和有趣的结果，这些结果可适用于未来的研究。我们共享了来自这项工作的不同模型阶的空间模板，为社区提供了一个参考，可在基于回归或完全自动化（空间约束）的 ICA 方法中加以利用。
