A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography

地图集(解剖学) 概率逻辑 计算机科学 人工智能 人口 非负矩阵分解 匹配(统计) 功能磁共振成像 模式识别(心理学) 集合(抽象数据类型) 大脑定位 地图学 机器学习 矩阵分解 地理 数学 心理学 统计 神经科学 生物 古生物学 人口学 特征向量 社会学 物理 程序设计语言 量子力学
作者
Robert Hermosillo,Lucille A. Moore,Eric Fezcko,Ally Dworetsky,Adam Pines,Greg Conan,Michael A. Mooney,Anita Randolph,Babatunde Adeyemo,Eric Earl,Anders Perrone,Cristian Morales Carrasco,Johnny Uriarte-Lopez,Kathy Snider,Olivia Doyle,Michaela Cordova,Bonnie J. Nagel,Sarah W. Feldstein Ewing,Theodore D. Satterthwaite,Nico U.F. Dosenbach
出处
期刊: [Cold Spring Harbor Laboratory]
被引量:8
标识
DOI:10.1101/2022.01.12.475422
摘要

SUMMARY The brain is organized into a broad set of functional neural networks. These networks and their various characteristics have been described and scrutinized through in vivo resting state functional magnetic resonance imaging (rs-fMRI). While the basic properties of networks are generally similar between healthy individuals, there is vast variability in the precise topography across the population. These individual differences are often lost in population studies due to population averaging which assumes topographical uniformity. We leveraged precision brain mapping methods to establish a new open-source, method-flexible set of precision functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision network maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), an overlapping template matching method for identifying integration zones, as well as an unsupervised community detection algorithm (Infomap). From these individualized maps we also generated probabilistic network maps and integration zones for two demographically-matched groups of n∼3000 each. We demonstrate high reproducibility between groups (Pearson’s r >0.999) and between methods (r=0.96), revealing both regions of high invariance and high variability. Compared to using parcellations based on groups averages, the MIDB Precision Brain Atlas allowed us to derive a set of brain regions that are largely invariant in network topography across populations, which provides more reproducible statistical maps of executive function in brain-wide associations. We also explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision Brain Atlas is expandable to alternative datasets and methods and is provided open-source with an online web interface to encourage the scientific community to experiment with probabilistic atlases and individual-specific topographies to more precisely relate network phenomenon to functional organization of the human brain.
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