Title: | The influence of inter-regional delays in generating large-scale brain networks of phase synchronization |
Author(s): | Williams, N. ; Ojanperä, A. ; Siebenhühner, F. ; Toselli, B. ; Palva, S. ; Arnulfo, G. ; Kaski, S. ; Palva, J. M. |
Date: | 2023-10-01 |
Language: | en |
Pages: | 22 1-22 |
Department: | Department of Neuroscience and Biomedical Engineering Department of Computer Science University of Helsinki University of Genoa University of Glasgow Helsinki Institute for Information Technology (HIIT) Department of Neuroscience and Biomedical Engineering Department of Computer Science |
Series: | NeuroImage, Volume 279 |
ISSN: | 1053-8119 1095-9572 |
DOI-number: | 10.1016/j.neuroimage.2023.120318 |
Keywords: | Approximate Bayesian Computation (ABC), Axonal conduction delays, Bayesian optimization for Likelihood-Free Inference (BOLFI), Biophysical Network Models (BNMs), Magnetoencephalography (MEG) resting-state, Phase synchronization |
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Williams , N , Ojanperä , A , Siebenhühner , F , Toselli , B , Palva , S , Arnulfo , G , Kaski , S & Palva , J M 2023 , ' The influence of inter-regional delays in generating large-scale brain networks of phase synchronization ' , NeuroImage , vol. 279 , 120318 , pp. 1-22 . https://doi.org/10.1016/j.neuroimage.2023.120318 |
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Abstract:Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8–12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard “distance-dependent delays”, which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, “isochronous delays” and “mixed delays”. We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with “distance-dependent delays”, as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.
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Description:Funding Information: The authors are grateful to the reviewers of this manuscript for their thoughtful comments, addressing which has substantively improved the manuscript. Further, we acknowledge Finnish centre for Artificial Intelligence (FCAI), Academy of Finland ( NW: 321542 , SK: 292334 , 956 319264 , MP: 253130 , 256472 , 281414 , 296304 , 266745 , SP: 266402 , 266745 , 303933 , 957 325404 ), Department of Science & Technology (DST), India and Sigrid Juselius Foundation , for providing funding for this project. The authors are grateful to Prof. Sitabhra Sinha, Dr. Chandrasekhar Kuyyamudi, Dr. Ayush Bharti, Dr. Henri Pesonen, Dr. Michael Gutmann and Antti Karvanen for invaluable discussions, and to Alex Aushev, Anirudh Jain, Diego Mesquita and Sophie Wharrie for comments on manuscript drafts. Most of all, we are very grateful to Jarno Rantaharju, Thomas Pfau, Richard Darst and Enrico Glerean from Aalto Scientific Computing, for facilitating use of computational resources provided by the Aalto Science-ITproject. Publisher Copyright: © 2023 The Authors
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