The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

N. Williams; A. Ojanperä; F. Siebenhühner; B. Toselli; S. Palva; G. Arnulfo; S. Kaski; J. M. Palva

doi:10.1016/j.neuroimage.2023.120318

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

N. Williams^*
, A. Ojanperä
, F. Siebenhühner
, B. Toselli
, S. Palva
, G. Arnulfo
, S. Kaski
, J. M. Palva

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

6 Sitaatiot (Scopus)

97 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Artikkeli	120318
Sivut	1-22
Sivumäärä	22
Julkaisu	NeuroImage
Vuosikerta	279
DOI - pysyväislinkit	https://doi.org/10.1016/j.neuroimage.2023.120318
Tila	Julkaistu - 1 lokak. 2023
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

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-IT project.

Pääsy asiakirjaan

10.1016/j.neuroimage.2023.120318Lisenssi: CC BY-NC-ND

The influence of inter-regional delays in generating large-scale brain networks of phase synchronizationFinal published version, 8,84 MBLisenssi: CC BY-NC-ND

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Kaski, S. (Vastuullinen johtaja), Bhat, A. (Projektin jäsen), Trinh, T. (Projektin jäsen), Scherting, B. (Projektin jäsen), Siren, J. (Projektin jäsen), Gadd, C. (Projektin jäsen), Hegde, P. (Projektin jäsen), Chauhan, R. (Projektin jäsen), Jain, A. (Projektin jäsen), Jälkö, J. (Projektin jäsen), Kortia, A. (Projektin jäsen), Tran, A. (Projektin jäsen) & Shen, Z. (Projektin jäsen)
01/01/2019 → 31/08/2021
Projekti: Academy of Finland: Other research funding
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen johtaja) & Filstroff, L. (Projektin jäsen)
01/01/2016 → 31/08/2021
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
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title = "The influence of inter-regional delays in generating large-scale brain networks of phase synchronization",

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.",

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",

author = "N. Williams and A. Ojanper{\"a} and F. Siebenh{\"u}hner and B. Toselli and S. Palva and G. Arnulfo and S. Kaski and Palva, \{J. M.\}",

note = "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-IT project. Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = oct,

day = "1",

doi = "10.1016/j.neuroimage.2023.120318",

language = "English",

volume = "279",

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journal = "NeuroImage",

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TY - JOUR

T1 - The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

AU - Williams, N.

AU - Ojanperä, A.

AU - Siebenhühner, F.

AU - Toselli, B.

AU - Palva, S.

AU - Arnulfo, G.

AU - Kaski, S.

AU - Palva, J. M.

N1 - 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-IT project. Publisher Copyright: © 2023 The Authors

PY - 2023/10/1

Y1 - 2023/10/1

N2 - 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.

AB - 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.

KW - Approximate Bayesian Computation (ABC)

KW - Axonal conduction delays

KW - Bayesian optimization for Likelihood-Free Inference (BOLFI)

KW - Biophysical Network Models (BNMs)

KW - Magnetoencephalography (MEG) resting-state

KW - Phase synchronization

UR - https://www.scopus.com/pages/publications/85168553543

U2 - 10.1016/j.neuroimage.2023.120318

DO - 10.1016/j.neuroimage.2023.120318

M3 - Article

C2 - 37572765

AN - SCOPUS:85168553543

SN - 1053-8119

VL - 279

SP - 1

EP - 22

JO - NeuroImage

JF - NeuroImage

M1 - 120318

ER -

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

Abstrakti

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Pääsy asiakirjaan

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Interaktiivinen koneoppiminen useista biodatalähteistä

Interaktiivinen koneoppiminen useista biodatalähteistä

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