Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps
Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps
复制标题
来自功能磁共振成像的大数据驱动的大脑分区：队列异质性对功能连接图的影响
DOI：
10.1109/embc46164.2021.9630267
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Stamoulis, Catherine
中科院分区：
文献类型：
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作者：
Brooks, Skylar J;Parks, Sean M;Stamoulis, Catherine
正在进行的大规模人脑研究正在从数千个个体中生成复杂的神经成像数据，这些数据可以用来获得数据驱动的、解剖学上准确的大脑切片。然而，尽管这些数据有希望和许多优点，但它们是高度异质性的，这一特性可能会影响模板的解剖准确性和泛化，但却很少受到关注。使用多种相似性的措施和阈值的方法，本研究调查的拓扑内部和个体间的变化restingstate（RS）功能边缘图（通常用于大脑parcellation），估计从rs-fMRI连接在n = 5878青少年大脑认知发展（ABCD）研究的儿童。从这个初步调查的结果表明，选择一个主题与队列为基础的阈值估计边缘地图连接矩阵不会显着影响地图拓扑结构。而相似性度量的选择以及相似性与边缘图稀疏性之间的非线性关系对地图分类和宗地地图集的生成有着重要的影响。多级分类揭示了多个聚类，其具有潜在的复杂映射到生物学变量上，而不仅仅是简单的人口统计学。临床相关性-病例对照神经影像学研究应使用领域特异性（例如，人口统计学特定的）地图集，用于分割大脑，以提高队列比较的准确性和严谨性。为了推广，这样的地图集需要从大型数据集派生出来，这些数据集本质上是异构的。在5878名儿童（年龄~9-10岁）的队列中，本研究系统地评估了边缘图的异质性和相似性的影响，边缘图来自rs-fMRI连接，通常用于生成包裹图谱。
Ongoing large-scale human brain studies are generating complex neuroimaging data from thousands of individuals that can be leveraged to derive data-driven, anatomically accurate brain parcellations. However, despite their promise and many strengths, these data are highly heterogeneous, a characteristic that may affect the anatomical accuracy and generalization of the template but has received relatively little attention. Using multiple similarity measures and thresholding approaches, this study investigated the topological intra- and inter-individual variability of restingstate (rs) functional edge maps (often used for brain parcellation), estimated from rs-fMRI connectivity in n = 5878 children from the Adolescent Brain Cognitive Development (ABCD) study. Findings from this initial investigation indicate that choosing a subject- vs cohort-based threshold for estimating edge maps from connectivity matrices does not significantly impact the map topology. In contrast, the choice of similarity measure and non-linear relationship between similarity and edge map sparsity may have a significant impact on map classification and the generation of parcellation atlases. Multi-level classification revealed multiple clusters with a potentially complex mapping onto biological variables beyond simple demographics.Clinical Relevance— Case-control neuroimaging studies should use domain-specific (e.g., demographics-specific) atlases for parcellating the brain, to improve accuracy and rigor of cohort comparisons. To be generalizable, such atlases need to be derived from large datasets, which are inherently heterogeneous. In a cohort of 5878 children (age ~9-10 years), this study systematically assessed the impact of heterogeneity and similarity of edge maps, which are derived from rs-fMRI connectivity and typically used to generate parcellation atlases.