We also explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation such as TMS treatment for depression. (8/8)

Using data from the #HCPD study we find that network topography is fairly well-preserved from 8 to 21 years old. (7/8)

Using this technique you can identify regions where networks overlap, which we call integration zones, that are similar to hubs that others have identified. (6/8)

We developed a procedure called OMNI mapping that allows multiple networks to overlap. We quantified the number of overlapping networks in an individual, and for matched groups of ~3,000 subjects each to identify integration zones. (5/8)

We show that region sets derived from probabilistic maps not only demonstrate higher within-network connectivity compared to conventional region sets, but also demonstrate higher brain-behavior reliability than standard whole-brain parcellations (4/8).

Leveraging large independent groups (n=~3000 each, balanced for demographics) from the #ABCDstudy, we demonstrate that the probabilistic maps show high between-group reliability (3/8).

While the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. Using multiple community detection, we identified reliable individualized network maps in the cortex and subcortical structures (2/8).

Our paper “A Precision Functional Atlas of Personalized Network Topography and Probabilities” is out now in Nature Neuroscience! The MIDB Precision Brain Atlas is a collection of individualized neural network maps from n~10,000 and probabilistic atlases (1/8) rdcu.be/dCvuC