Generative Models For Large-Scale Simulations Of Connectome Development
Generative Models For Large-Scale Simulations Of Connectome Development
复制标题
连接体发育大规模模拟的生成模型
DOI：
10.1109/icasspw59220.2023.10193544
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Stamoulis, Catherine
中科院分区：
文献类型：
--
作者：
Brooks, Skylar J;Stamoulis, Catherine
大脑区域之间的功能相互作用和解剖连接形成了连接组。其以图表形式的数学表示反映了固有的神经解剖学组织结构和区域（节点），这些结构和区域通过神经纤维束互连和/或功能上相互作用（边缘）。如果不了解连接组的真实拓扑，功能（定向或非定向）图代表信号相关性的估计，从中很难阐明潜在的机制和过程，例如发育和衰老或神经病理学。使用具有可控参数的合成图进行具有生物学意义的模拟可以补充真实的数据分析，并为连接组组织的潜在机制提供重要的见解。生成模型是非常有价值的工具，可用于创建具有已知拓扑特征的大型合成图数据集。然而，为了使这些图表有意义，模型参数的变化需要由真实数据驱动。本文提出了一种新颖的数据驱动方法，用于调整生成性 LancichinettiFortunato-Radicchi (LFR) 模型的参数，该方法使用根据历史上大型青少年大脑认知发展研究 (ABCD) 中早期青少年的静息态 fMRI 估计的连接组数据集 (n = 5566)。它还提出了一种应用程序，即使用 LFR 进行模拟，以生成代表神经成熟不同阶段的大脑的大型合成图数据集，并深入了解其拓扑组织的发育变化。
Functional interactions and anatomic connections between brain regions form the connectome. Its mathematical representation in terms of a graph reflects the inherent neuroanatomical organization into structures and regions (nodes) that are interconnected through neural fiber tracts and/or interact functionally (edges). Without knowledge of the ground truth topology of the connectome, functional (directional or nondirectional) graphs represent estimates of signal correlations, from which underlying mechanisms and processes, such as development and aging, or neuropathologies, are difficult to unravel. Biologically meaningful simulations using synthetic graphs with controllable parameters can complement real data analyses and provide critical insights into mechanisms underlying the organization of the connectome. Generative models can be highly valuable tools for creating large datasets of synthetic graphs with known topological characteristics. However, for these graphs to be meaningful, the variation of model parameters needs to be driven by real data. This paper presents a novel, data-driven approach for tuning the parameters of the generative LancichinettiFortunato-Radicchi (LFR) model, using a large dataset of connectomes (n = 5566) estimated from resting-state fMRI from early adolescents in the historically large Adolescent Brain Cognitive Development Study (ABCD). It also presents an application, i.e., simulations using the LFR, to generate large datasets of synthetic graphs representing brains at different stages of neural maturation, and gain insights into developmental changes in their topological organization.
影响因子：
4.6
作者：
Stillman PE;Wilson JD;Denny MJ;Desmarais BA;Bhamidi S;Cranmer SJ;Lu ZL
通讯作者：
Lu ZL