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Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks

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Image Analysis and Processing. ICIAP 2022 Workshops (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13373))

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Abstract

Resting state brain networks have reached a strong popularity in recent scientific endeavors due to their feasibility to characterize the metabolic mechanisms at the basis of neural control when the brain is not engaged in any task. The evaluation of these states, consisting in complex physiological processes employing a large amount of energy, is carried out from diagnostic images acquired through resting-state functional magnetic resonance (RS-fMRI) on different populations of subjects. In the present study, RS-fMRI signals from the WU-Minn HCP 1200 Subjects Data Release of the Human Connectome Project were studied with the aim of investigating the high order organizational structure of the brain function in resting conditions. Image data were post-processed through Independent Component Analysis to extract the so-called Intrinsic Component Networks, and a recently proposed framework for assessing high-order interactions in network data through the so-called O-Information measure was exploited. The framework allows an information-theoretic evaluation of pairwise and higher-order interactions, and was here extended to the analysis of vector variables, to allow investigating interactions among multiple Independent Component Networks (ICNs) each composed by several brain regions. Moreover, surrogate data analysis was used to validate statistically the detected pairwise and high-order networks. Our results indicate that RSNs are dominated by redundant interactions among ICN subnetworks, with levels of redundancy that increase monotonically with the order of the interactions analyzed. The ICNs mostly involved in the interactions of any order were the Default Mode and the Cognitive Control networks, suggesting a key role of these areas in mediating brain interactions during the resting state. Future works should assess the alterations of these patterns of functional brain connectivity during task-induced activity and in pathological states.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Palermo, Palermo, Italy

    Simone Valenti, Laura Sparacino, Riccardo Pernice & Luca Faes

  2. Ri.MED Foundation, Via Bandiera 11, Palermo, Italy

    Simone Valenti & Albert Comelli

  3. Department of Data Analysis, Ghent University, Ghent, Belgium

    Daniele Marinazzo

  4. Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada

    Hannes Almgren

Authors
  1. Simone Valenti
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  2. Laura Sparacino
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  3. Riccardo Pernice
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  4. Daniele Marinazzo
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  5. Hannes Almgren
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  6. Albert Comelli
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  7. Luca Faes
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Corresponding author

Correspondence to Albert Comelli .

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Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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Valenti, S. et al. (2022). Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_34

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  • DOI: https://doi.org/10.1007/978-3-031-13321-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13320-6

  • Online ISBN: 978-3-031-13321-3

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