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Variability of Non-parametric HRF in Interconnectedness and Its Association in Deriving Resting State Network

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Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

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Abstract

Blood Oxygen Level-Dependent (BOLD) time course in functional magnetic resonance imaging (fMRI) is modeled as the response of the hemodynamic response function (HRF) excited by an activity-inducing signal. Variability of the HRF across the brain influences functional connectivity (FC) estimates and some approaches have been attempted to separate the HRF and activity-inducing signal from the observed BOLD signal as a blind separation problem. In this work, an approach based on homomorphic filtering is proposed to estimate a non-parametric representation of HRF in resting state fMRI. Voxel-wise and region-wise variations of correlation of the estimated HRF (both the parametric and non-parametric representation) are analyzed in different functional networks. Principal component analysis of the correlation matrix using the estimated HRF is used to analyze the interconnectedness. HRF shows higher variability for the non-parametric representation over the parametric representation. Further, the contribution of the estimated HRF is then studied in producing resting-state networks using the dictionary learning framework.

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Notes

  1. 1.

    https://openneuro.org/datasets/ds000030/versions/00016.

  2. 2.

    http://www.fil.ion.ucl.ac.uk/spm/.

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Authors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, 175005, HP, India

    Sukesh Kumar Das

  2. Indian Institute of Technology Bhilai, Bhilai, 492015, Chhattisgarh, India

    Anil K. Sao

  3. New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Pratik Jain & Bharat Biswal

Authors
  1. Sukesh Kumar Das
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  2. Pratik Jain
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  3. Anil K. Sao
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  4. Bharat Biswal
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Correspondence to Sukesh Kumar Das .

Editor information

Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

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Das, S.K., Jain, P., Sao, A.K., Biswal, B. (2023). Variability of Non-parametric HRF in Interconnectedness and Its Association in Deriving Resting State Network. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_21

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_21

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  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

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