Clustering Patterns of Structural and Functional Neuroimaging Markers to Examine Individual Variability in Healthy Populations.

在神经影像学和认知神经科学文献中，人们越来越认识到个体变异的重要性：聚集或平均的群体反应不能很好地映射到个体反应。这种个体差异性已被证明是描述广泛的基本人脑功能的实质性障碍。变异性一直是精神病学和其他临床研究中的一个特殊挑战，在这些研究中，由于对一般生物学异质性的描述不佳，疾病生物学标志物的寻找受到阻碍。我们的研究计划的目的是确定稳定的，可靠的，和表型有用的健康个体群体具有相似的功能和结构的大脑特征。这将增强我们对一般人群中变异性的理解，并验证用于临床样本后期翻译的工具。我们建议通过应用和开发先进的数据驱动方法来实现这一点，以在健康对照人群中识别具有相似大脑结构和功能模式的个体的子组或集群。我们将使用大型多模态神经成像数据集（包括公开可用的和本地生成的），并检查大脑中的功能和结构连接。我们将比较一系列统计方法来识别子组（例如分层聚类，k均值聚类，谱聚类和相似性网络融合）。将通过置换自举方法评估聚类解决方案的稳定性/可靠性，其中将对数据进行重复二次采样，并评估聚类分配的稳定性。然后，我们将使用多变量偏最小二乘法询问子组之间的脑行为关系，这将允许识别整个样本中常见的或特定个体集群中特有的脑行为关系。这项工作有可能促进我们对普通人群大脑结构和功能异质性的理解，以及这与行为的关系。这将作为一个模板的方法，以后可以应用于临床人群。一旦我们建立了可靠的方法来识别个体的重要亚组，或识别个体在维度上变化的神经成像度量沿着，这些方法就可以转变为临床样本，并作为临床群体中数据驱动的生物分型的工具进行重新评估。

There is a growing appreciation of the importance of individual variability in neuroimaging and cognitive neuroscience literature: aggregated or averaged group responses do not map well on to individual responses. This Individual variability has proven a substantive obstacle to describing a wide range of basic human brain function. Variability has been a particular challenge in psychiatric and other clinical research where the search for biological markers of disease has been hampered by poor characterization of general biological heterogeneity. The objective of our research program is to identify stable, reliable, and phenotypically useful groups of healthy individuals with similar functional and structural brain characteristics. This will enhance our understanding of variability in the general population, as well as validate tools for later translation in clinical samples. We propose to do this by applying and developing advanced data driven approaches to identify sub-groups or clusters' of individuals with similar patterns of brain structure and function, within a healthy control population. We will use large multi-modal neuroimaging data sets (both publicly available and locally generated) and examine functional and structural connectivity in the brain. We will compare a range of statistical approaches to identify sub-groups (e.g. hierarchical clustering, k-means clustering, spectral clustering, and similarity network fusion). The stability/reliability of clustering solutions will be assessed via a permuted bootstrapping approach, in which the data will be repeated subsampled and the stability of cluster assignments assessed. We will then interrogate brain-behavior relationship across sub-groups using a multivariate partial least squares approach, which will allow for the identification of brain-behavior relationships which are common across entire samples or unique to specific clusters of individuals. The proposed work has the potential to advance our understanding heterogeneity of brain structure and function within the general population, and how this related to behavior. This will serve as a template for approaches that can later be applied to clinical populations. Once we have established reliable approaches for identifying important sub-groups of individuals, or neuroimaging metrics along which individuals vary dimensionally, these approaches can be transitioned into clinical samples and re-evaluated as tools for data-driven biotyping in clinical populations.