Characterization of Regional Differences in Resting-state FMRI with a Data-driven Network Model of Brain Dynamics

Overview

Journal Sci Adv

Specialties Biology
Science

Date 2023 Mar 17

PMID 36930710

Authors

Viktor Sip

Meysam Hashemi

Timo Dickscheid

Katrin Amunts

Spase Petkoski

Viktor Jirsa

Affiliations

Soon will be listed here.

Abstract

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. Recently, studies focused on the role of regional variance of model parameters. Such analyses however necessarily depend on the properties of preselected neural mass model. We introduce a method to infer from the functional data both the neural mass model representing the regional dynamics and the region- and subject-specific parameters while respecting the known network structure. We apply the method to human resting-state fMRI. We find that the underlying dynamics can be described as noisy fluctuations around a single fixed point. The method reliably discovers three regional parameters with clear and distinct role in the dynamics, one of which is strongly correlated with the first principal component of the gene expression spatial map. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications for understanding the brain dynamics during aging or neurodegeneration.

Citing Articles

Revealing excitation-inhibition imbalance in Alzheimer's disease using multiscale neural model inversion of resting-state functional MRI.

Li G, Hsu L, Wu Y, Bozoki A, Shih Y, Yap P Commun Med (Lond). 2025; 5(1):17.

PMID: 39814858 PMC: 11735810. DOI: 10.1038/s43856-025-00736-7.

Role of homeostatic plasticity in critical brain dynamics following focal stroke lesions.

Rocha R, Zorzi M, Corbetta M Sci Rep. 2024; 14(1):31631.

PMID: 39738232 PMC: 11685905. DOI: 10.1038/s41598-024-80196-6.

Simulated brain networks reflecting progression of Parkinson's disease.

Jung K, Eickhoff S, Caspers J, Popovych O Netw Neurosci. 2024; 8(4):1400-1420.

PMID: 39735513 PMC: 11675161. DOI: 10.1162/netn_a_00406.

Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model.

Ding C, Sun Y, Li K, Xie S, Yan H, Li P Transl Psychiatry. 2024; 14(1):496.

PMID: 39695106 PMC: 11655856. DOI: 10.1038/s41398-024-03200-7.

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.

Chang Y, Chen Y, Stealey H, Zhao Y, Lu H, Contreras-Hernandez E PLoS One. 2024; 19(12):e0314268.

PMID: 39630698 PMC: 11616886. DOI: 10.1371/journal.pone.0314268.

References

Brunton S, Proctor J, Kutz J . Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci U S A. 2016; 113(15):3932-7. PMC: 4839439. DOI: 10.1073/pnas.1517384113. View

Deco G, Ponce-Alvarez A, Mantini D, Luca Romani G, Hagmann P, Corbetta M . Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci. 2013; 33(27):11239-52. PMC: 3718368. DOI: 10.1523/JNEUROSCI.1091-13.2013. View

Demirtas M, Burt J, Helmer M, Ji J, Adkinson B, Glasser M . Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics. Neuron. 2019; 101(6):1181-1194.e13. PMC: 6447428. DOI: 10.1016/j.neuron.2019.01.017. View

Glasser M, Coalson T, Bijsterbosch J, Harrison S, Harms M, Anticevic A . Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. Neuroimage. 2018; 181:692-717. PMC: 6237431. DOI: 10.1016/j.neuroimage.2018.04.076. View

Singh M, Braver T, Cole M, Ching S . Estimation and validation of individualized dynamic brain models with resting state fMRI. Neuroimage. 2020; 221:117046. PMC: 7875185. DOI: 10.1016/j.neuroimage.2020.117046. View

Aquino K, Fulcher B, Parkes L, Sabaroedin K, Fornito A . Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. Neuroimage. 2020; 212:116614. DOI: 10.1016/j.neuroimage.2020.116614. View

Glasser M, Sotiropoulos S, Wilson J, Coalson T, Fischl B, Andersson J . The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013; 80:105-24. PMC: 3720813. DOI: 10.1016/j.neuroimage.2013.04.127. View

Desikan R, Segonne F, Fischl B, Quinn B, Dickerson B, Blacker D . An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006; 31(3):968-80. DOI: 10.1016/j.neuroimage.2006.01.021. View

Wang X . Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat Rev Neurosci. 2020; 21(3):169-178. PMC: 7334830. DOI: 10.1038/s41583-020-0262-x. View

10.

Van Essen D, Ugurbil K, Auerbach E, Barch D, Behrens T, Bucholz R . The Human Connectome Project: a data acquisition perspective. Neuroimage. 2012; 62(4):2222-31. PMC: 3606888. DOI: 10.1016/j.neuroimage.2012.02.018. View

11.

Piccinini J, Deco G, Kringelbach M, Laufs H, Sanz Perl Y, Tagliazucchi E . Data-driven discovery of canonical large-scale brain dynamics. Cereb Cortex Commun. 2022; 3(4):tgac045. PMC: 9721525. DOI: 10.1093/texcom/tgac045. View

12.

Kong X, Kong R, Orban C, Wang P, Zhang S, Anderson K . Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nat Commun. 2021; 12(1):6373. PMC: 8568904. DOI: 10.1038/s41467-021-26704-y. View

13.

Gilson M, Zamora-Lopez G, Pallares V, Adhikari M, Senden M, Tauste Campo A . Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Netw Neurosci. 2020; 4(2):338-373. PMC: 7286310. DOI: 10.1162/netn_a_00117. View

14.

van den Heuvel M, Scholtens L, Barrett L, Hilgetag C, de Reus M . Bridging Cytoarchitectonics and Connectomics in Human Cerebral Cortex. J Neurosci. 2015; 35(41):13943-8. PMC: 6608182. DOI: 10.1523/JNEUROSCI.2630-15.2015. View

15.

Deco G, Kringelbach M, Jirsa V, Ritter P . The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Sci Rep. 2017; 7(1):3095. PMC: 5465179. DOI: 10.1038/s41598-017-03073-5. View

16.

Gilson M, Kouvaris N, Deco G, Mangin J, Poupon C, Lefranc S . Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability. Neuroimage. 2019; 201:116007. DOI: 10.1016/j.neuroimage.2019.116007. View

17.

Power J, Plitt M, Gotts S, Kundu P, Voon V, Bandettini P . Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data. Proc Natl Acad Sci U S A. 2018; 115(9):E2105-E2114. PMC: 5834724. DOI: 10.1073/pnas.1720985115. View

18.

Deco G, Kringelbach M, Arnatkeviciute A, Oldham S, Sabaroedin K, Rogasch N . Dynamical consequences of regional heterogeneity in the brain's transcriptional landscape. Sci Adv. 2021; 7(29). PMC: 8279501. DOI: 10.1126/sciadv.abf4752. View

19.

Lurie D, Kessler D, Bassett D, Betzel R, Breakspear M, Kheilholz S . Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci. 2020; 4(1):30-69. PMC: 7006871. DOI: 10.1162/netn_a_00116. View

20.

Amunts K, Mohlberg H, Bludau S, Zilles K . Julich-Brain: A 3D probabilistic atlas of the human brain's cytoarchitecture. Science. 2020; 369(6506):988-992. DOI: 10.1126/science.abb4588. View