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 probabilistic functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), as well as an unsupervised community detection algorithm (Infomap). We demonstrate that probabilistic network maps generated for two demographically-matched groups of n~3000 each were nearly identical, both between groups (Pearson 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 Atlases allowed us to derive a set of brain regions that are largely invariant in network topography and provide more reproducible statistical maps of executive function brain-wide associations. We explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision 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. Competing Interest Statement Author DF is a founder of Nous Imaging, Inc.,. Any potential conflict of interest has been reviewed and managed by the University of Minnesota. Authors OM, EE, DF, and AP are co-inventors of the FIRMM Technology #2198 (co-owned with WU), FIRMM: Real time monitoring and prediction of motion in MRI scans, exclusively licensed to Nous, Inc.) and any related research. Any potential conflict of interest has been reviewed and managed by the University of Minnesota. Footnotes * http://midbatlas.io/ * https://collection3165.readthedocs.io/en/stable/...

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

10.1101/2022.01.12.475422