Whole-brain functional connectivity predicted by indirect structural connections

Rasmus Røge, Karen Marie Sandø Ambrosen, Kristoffer Jon Albers, Casper Tabassum Eriksen, Matthew George Liptrot, Mikkel Nørgaard Schmidt, Kristoffer Hougaard Madsen, Morten Mørup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved by taking indirect structural pathways into account. In particular, we find that the length of the shortest structural path between brain regions is a good predictor of functional connectivity in sparse networks (density less than one percent), and that this improvement comes from integrating indirect pathways comprising up to three steps. Our results support the existence of important indirect relationships between structure and function, extending beyond the immediate direct structural connections that are typically investigated.
Original languageEnglish
Title of host publicationProceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging
PublisherIEEE
Publication date2017
Pages4 pp.
DOIs
Publication statusPublished - 2017
Event2017 International Workshop on Pattern Recognition in Neuroimaging - University of Toronto, Toronto, Canada
Duration: 21 Jun 201723 Jun 2017

Conference

Conference2017 International Workshop on Pattern Recognition in Neuroimaging
LocationUniversity of Toronto
CountryCanada
CityToronto
Period21/06/201723/06/2017
Series2017 International Workshop on Pattern Recognition in Neuroimaging (prni)

Keywords

  • Medical magnetic resonance imaging and spectroscopy
  • Patient diagnostic methods and instrumentation
  • Biophysics of neurophysiological processes
  • Biomedical magnetic resonance imaging and spectroscopy
  • Correlation
  • Magnetic resonance imaging
  • Streaming media
  • Atmospheric measurements
  • Density measurement
  • Particle measurements

Cite this

Røge, R., Ambrosen, K. M. S., Albers, K. J., Eriksen, C. T., Liptrot, M. G., Schmidt, M. N., ... Mørup, M. (2017). Whole-brain functional connectivity predicted by indirect structural connections. In Proceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging (pp. 4 pp.). IEEE. 2017 International Workshop on Pattern Recognition in Neuroimaging (prni) https://doi.org/10.1109/PRNI.2017.7981496
Røge, Rasmus ; Ambrosen, Karen Marie Sandø ; Albers, Kristoffer Jon ; Eriksen, Casper Tabassum ; Liptrot, Matthew George ; Schmidt, Mikkel Nørgaard ; Madsen, Kristoffer Hougaard ; Mørup, Morten. / Whole-brain functional connectivity predicted by indirect structural connections. Proceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging . IEEE, 2017. pp. 4 pp. (2017 International Workshop on Pattern Recognition in Neuroimaging (prni)).
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abstract = "Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved by taking indirect structural pathways into account. In particular, we find that the length of the shortest structural path between brain regions is a good predictor of functional connectivity in sparse networks (density less than one percent), and that this improvement comes from integrating indirect pathways comprising up to three steps. Our results support the existence of important indirect relationships between structure and function, extending beyond the immediate direct structural connections that are typically investigated.",
keywords = "Medical magnetic resonance imaging and spectroscopy, Patient diagnostic methods and instrumentation, Biophysics of neurophysiological processes, Biomedical magnetic resonance imaging and spectroscopy, Correlation, Magnetic resonance imaging, Streaming media, Atmospheric measurements, Density measurement, Particle measurements",
author = "Rasmus R{\o}ge and Ambrosen, {Karen Marie Sand{\o}} and Albers, {Kristoffer Jon} and Eriksen, {Casper Tabassum} and Liptrot, {Matthew George} and Schmidt, {Mikkel N{\o}rgaard} and Madsen, {Kristoffer Hougaard} and Morten M{\o}rup",
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Røge, R, Ambrosen, KMS, Albers, KJ, Eriksen, CT, Liptrot, MG, Schmidt, MN, Madsen, KH & Mørup, M 2017, Whole-brain functional connectivity predicted by indirect structural connections. in Proceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging . IEEE, 2017 International Workshop on Pattern Recognition in Neuroimaging (prni), pp. 4 pp., 2017 International Workshop on Pattern Recognition in Neuroimaging, Toronto, Canada, 21/06/2017. https://doi.org/10.1109/PRNI.2017.7981496

Whole-brain functional connectivity predicted by indirect structural connections. / Røge, Rasmus; Ambrosen, Karen Marie Sandø; Albers, Kristoffer Jon; Eriksen, Casper Tabassum; Liptrot, Matthew George; Schmidt, Mikkel Nørgaard; Madsen, Kristoffer Hougaard; Mørup, Morten.

Proceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging . IEEE, 2017. p. 4 pp. (2017 International Workshop on Pattern Recognition in Neuroimaging (prni)).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AU - Ambrosen, Karen Marie Sandø

AU - Albers, Kristoffer Jon

AU - Eriksen, Casper Tabassum

AU - Liptrot, Matthew George

AU - Schmidt, Mikkel Nørgaard

AU - Madsen, Kristoffer Hougaard

AU - Mørup, Morten

PY - 2017

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N2 - Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved by taking indirect structural pathways into account. In particular, we find that the length of the shortest structural path between brain regions is a good predictor of functional connectivity in sparse networks (density less than one percent), and that this improvement comes from integrating indirect pathways comprising up to three steps. Our results support the existence of important indirect relationships between structure and function, extending beyond the immediate direct structural connections that are typically investigated.

AB - Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved by taking indirect structural pathways into account. In particular, we find that the length of the shortest structural path between brain regions is a good predictor of functional connectivity in sparse networks (density less than one percent), and that this improvement comes from integrating indirect pathways comprising up to three steps. Our results support the existence of important indirect relationships between structure and function, extending beyond the immediate direct structural connections that are typically investigated.

KW - Medical magnetic resonance imaging and spectroscopy

KW - Patient diagnostic methods and instrumentation

KW - Biophysics of neurophysiological processes

KW - Biomedical magnetic resonance imaging and spectroscopy

KW - Correlation

KW - Magnetic resonance imaging

KW - Streaming media

KW - Atmospheric measurements

KW - Density measurement

KW - Particle measurements

U2 - 10.1109/PRNI.2017.7981496

DO - 10.1109/PRNI.2017.7981496

M3 - Article in proceedings

SP - 4 pp.

BT - Proceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging

PB - IEEE

ER -

Røge R, Ambrosen KMS, Albers KJ, Eriksen CT, Liptrot MG, Schmidt MN et al. Whole-brain functional connectivity predicted by indirect structural connections. In Proceedings of 2017 International Workshop on Pattern Recognition in Neuroimaging . IEEE. 2017. p. 4 pp. (2017 International Workshop on Pattern Recognition in Neuroimaging (prni)). https://doi.org/10.1109/PRNI.2017.7981496