Multiscale Modes of Functional Brain Connectivity

Overview

Journal bioRxiv

Date 2024 Jun 10

PMID 38854078

Authors

S Rezvan Farahibozorg

Samuel J Harrison

Janine D Bijsterbosch

Mark W Woolrich

Stephen M Smith

Affiliations

Soon will be listed here.

Abstract

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing -scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a paradigm shift in functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

References

Pervaiz U, Vidaurre D, Woolrich M, Smith S . Optimising network modelling methods for fMRI. Neuroimage. 2020; 211:116604. PMC: 7086233. DOI: 10.1016/j.neuroimage.2020.116604. View

Vidaurre D, Smith S, Woolrich M . Brain network dynamics are hierarchically organized in time. Proc Natl Acad Sci U S A. 2017; 114(48):12827-12832. PMC: 5715736. DOI: 10.1073/pnas.1705120114. View

Farahibozorg S, Bijsterbosch J, Gong W, Jbabdi S, Smith S, Harrison S . Hierarchical modelling of functional brain networks in population and individuals from big fMRI data. Neuroimage. 2021; 243:118513. PMC: 8526871. DOI: 10.1016/j.neuroimage.2021.118513. View

Smith S, Nichols T, Vidaurre D, Winkler A, Behrens T, Glasser M . A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015; 18(11):1565-7. PMC: 4625579. DOI: 10.1038/nn.4125. View

Alfaro-Almagro F, McCarthy P, Afyouni S, Andersson J, Bastiani M, Miller K . Confound modelling in UK Biobank brain imaging. Neuroimage. 2020; 224:117002. PMC: 7610719. DOI: 10.1016/j.neuroimage.2020.117002. View

Beckmann C, Smith S . Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004; 23(2):137-52. DOI: 10.1109/TMI.2003.822821. View

Jiang R, Calhoun V, Fan L, Zuo N, Jung R, Qi S . Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores. Cereb Cortex. 2019; 30(3):888-900. PMC: 7132922. DOI: 10.1093/cercor/bhz134. View

Friston K . Hierarchical models in the brain. PLoS Comput Biol. 2008; 4(11):e1000211. PMC: 2570625. DOI: 10.1371/journal.pcbi.1000211. View

Raichle M, MacLeod A, Snyder A, Powers W, Gusnard D, Shulman G . A default mode of brain function. Proc Natl Acad Sci U S A. 2001; 98(2):676-82. PMC: 14647. DOI: 10.1073/pnas.98.2.676. View

10.

Finn E, Shen X, Scheinost D, Rosenberg M, Huang J, Chun M . Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015; 18(11):1664-71. PMC: 5008686. DOI: 10.1038/nn.4135. View

11.

Calhoun V, Adali T, Pearlson G, Pekar J . A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp. 2001; 14(3):140-51. PMC: 6871952. DOI: 10.1002/hbm.1048. View

12.

Elliott L, Sharp K, Alfaro-Almagro F, Shi S, Miller K, Douaud G . Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018; 562(7726):210-216. PMC: 6786974. DOI: 10.1038/s41586-018-0571-7. View

13.

Glasser M, Coalson T, Robinson E, Hacker C, Harwell J, Yacoub E . A multi-modal parcellation of human cerebral cortex. Nature. 2016; 536(7615):171-178. PMC: 4990127. DOI: 10.1038/nature18933. View

14.

Buckner R, Vincent J . Unrest at rest: default activity and spontaneous network correlations. Neuroimage. 2007; 37(4):1091-6. DOI: 10.1016/j.neuroimage.2007.01.010. View

15.

McKeown M, Makeig S, Brown G, Jung T, Kindermann S, Bell A . Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp. 1998; 6(3):160-88. PMC: 6873377. View

16.

Woolrich M, Behrens T, Smith S . Constrained linear basis sets for HRF modelling using Variational Bayes. Neuroimage. 2004; 21(4):1748-61. DOI: 10.1016/j.neuroimage.2003.12.024. View

17.

Buschman T, Miller E . Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 2007; 315(5820):1860-2. DOI: 10.1126/science.1138071. View

18.

Alfaro-Almagro F, Jenkinson M, Bangerter N, Andersson J, Griffanti L, Douaud G . Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage. 2017; 166:400-424. PMC: 5770339. DOI: 10.1016/j.neuroimage.2017.10.034. View

19.

Calhoun V, Adali T . Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng. 2012; 5:60-73. PMC: 4433055. DOI: 10.1109/RBME.2012.2211076. View

20.

Haak K, Marquand A, Beckmann C . Connectopic mapping with resting-state fMRI. Neuroimage. 2017; 170:83-94. DOI: 10.1016/j.neuroimage.2017.06.075. View