NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.
NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.
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DOI：
10.1162/netn_a_00229
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发表时间：
2022-06
影响因子：
4.7
通讯作者：
Saggar, Manish
中科院分区：
文献类型：
--
作者：
Geniesse, Caleb;Chowdhury, Samir;Saggar, Manish
为了获得更好的转化结果，研究人员和临床医生都需要新的工具来将复杂的神经成像数据提取为简单但行为相关的表示。最近，映射方法从拓扑数据分析（TDA）已成功地应用于非侵入性的人类神经影像数据，以表征整个动态景观的全脑配置在个人层面上，而不需要任何时空平均在一开始。尽管取得了令人鼓舞的结果，但最初将Mapper应用于神经成像数据受到以下因素的限制：（1）需要降维;（2）缺乏有效探索巨大参数空间的生物学启发式方法。在这里，我们提出了一种新的计算框架映射专门为神经影像数据，消除了限制，降低了计算成本与降维和参数探索。我们还介绍了新的元分析方法，以更好地锚映射器生成的表示神经解剖学和行为。我们新的NeuMapper框架是使用多个fMRI数据集开发和验证的，参与者参与了模拟“持续”认知的连续多任务实验。展望未来，我们希望我们的框架将帮助研究人员推动精神病神经影像学的界限，在单个参与者层面上跨联盟规模的数据集产生见解。现代神经成像有望改变我们对人类大脑功能的理解，以及我们诊断和治疗精神疾病的方式。然而，这一承诺取决于计算工具的发展，用于将复杂的高维神经影像数据提取为可以在研究或临床环境中探索的简单表示。拓扑数据分析（TDA）的映射方法可以用来生成这样的表示。在这里，我们介绍了几个改进的底层算法，以帮助高维神经影像数据的可扩展性和参数选择。我们还提供了新的分析工具，用于从生成的表示中注释和提取神经生物学和行为见解。我们希望这个新的框架将有助于促进精确神经成像在临床环境中的转化应用。
For better translational outcomes, researchers and clinicians alike demand novel tools to distill complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper—designed specifically for neuroimaging data—that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic “ongoing” cognition. Looking forward, we hope our framework will help researchers push the boundaries of psychiatric neuroimaging toward generating insights at the single-participant level across consortium-size datasets. Modern neuroimaging promises to transform how we understand human brain function, as well as how we diagnose and treat mental disorders. However, this promise hinges on the development of computational tools for distilling complex, high-dimensional neuroimaging data into simple representations that can be explored in research or clinical settings. The Mapper approach from topological data analysis (TDA) can be used to generate such representations. Here, we introduce several improvements to the underlying algorithm to aid scalability and parameter selection for high-dimensional neuroimaging data. We also provide new analytical tools for annotating and extracting neurobiological and behavioral insights from the generated representations. We hope this new framework will help facilitate translational applications of precision neuroimaging in clinical settings.
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影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
通讯作者：
Varoquaux G
影响因子：
3
作者：
Bayá AE;Granitto PM
通讯作者：
Granitto PM
影响因子：
5.7
作者：
Diedrichsen, Joern;Balsters, Joshua H.;Ramnani, Narender
通讯作者：
Ramnani, Narender
影响因子：
6.2
作者：
Bonneel, Nicolas;van de Panne, Michiel;Heidrich, Wolfgang
通讯作者：
Heidrich, Wolfgang
影响因子：
16.2
作者：
Calhoun, Vince D.;Miller, Robyn;Pearlson, Godfrey;Adali, Tulay
通讯作者：
Adali, Tulay