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

Abstract

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.

Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging

“…This notion is supported by a recent study (Moore et al, 2023) looking at individual specific functional connectivity networks in newborn infants. Even though infant networks were clearly individual specific and contained all major networks detected in adults, the split-half reliability analysis showed no plateau in stability of network solutions with amounts of data that are known to produce stable solutions in adolescents and adults (Hermosillo et al, 2022; Moore et al, 2023; Sylvester et al, 2022).…”

“…Furthermore, recent evidence revealed distinct patterns of the expansion of the salience network in individuals with major depression (Lynch et al, 2023), supporting the idea that PFM can provide important information for designing targeted neuromodulation treatments (Elbau et al, 2023). Functional brain organization in adults is as individual as a fingerprint (Miranda-Dominguez et al, 2014) with areas of high and low probability of network overlap between persons (Hermosillo et al, 2022). Even during childhood and adolescence, personalized network topography is providing insights into the associations of brain development, cognition, age (Cui et al, 2020), and psychopathology (Cui et al, 2022).…”

Causally probing the role of the human hippocampus in fear discrimination: A precision functional mapping guided TMS study in individuals with post-traumatic stress symptoms

“…This approach was motivated by ndings showing that TMS propagates within functional networks (48-50) and that such networks are highly variable across individuals (51-53). To generate individual precision functional network maps, we used a template matching method (34,51,54) (see Supplementary Materials) that assigned voxels to one of 17 functional brain networks.…”

Framewise multi-echo distortion correction for superior functional MRI

“…Patient-(clinical) and individual-specific (research) precision functional mapping (PFM) [30] are specific applications of RSFC and task fMRI where ME-fMRI and by extension MEDIC are most valuable. Averaging fMRI data across individuals blurs spatial boundaries, effectively smoothing the underlying data [16,[30][31][32][33][34][35][36][37]. Therefore, group-averaging partially obscures the greater spatial precision obtainable with ME-fMRI and MEDIC.…”