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\(\delta \)-MAPS: From fMRI Data to Functional Brain Networks

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Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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Abstract

We propose \(\delta \)-MAPS, a spatio-temporal data analysis method that identifies functionally distinct, possibly overlapping, spatially contiguous regions in the brain, referred to as “domains”, and infers the functional (i.e., correlation-based) connections between them. The proposed network inference method examines the statistical significance of each lagged cross-correlation between two domains, infers a range of lag values for each edge, and assigns a weight to each edge based on the covariance of the signal of the two domains. We illustrate the application of \(\delta \)-MAPS on cortical resting-state fMRI data.

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Notes

  1. 1.

    A heuristic to infer \(\delta \) is proposed in [13].

  2. 2.

    The definition of a domain’s signal may vary per application. For example, in climate data it makes sense to define it as the cumulative anomaly, normalized by the size of each grid cell, over the domain’s scope [13].

  3. 3.

    We have experimented with other pruning thresholds between 20%–50% and the results are very similar at the first two hierarchy levels.

  4. 4.

    Grid cells are referred to as voxels in the fMRI literature.

  5. 5.

    MELODIC ICA has an option to automatically estimate the number of ICs to return. Choosing this option yielded approximately 200–250 components in each scan. Activations were much lower than the ones shown in Fig. 3(G,H) both in strength and spatial extent. We could not identify RSNs similar to those shown here.

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Author information

Authors and Affiliations

  1. School of Computer Science, Georgia Tech, Atlanta, GA, USA

    Ilias Fountalis & Constantine Dovrolis

  2. School of Computational Science and Engr, Georgia Tech, Atlanta, GA, USA

    Bistra Dilkina

  3. Department of Biomedical Engineering, Georgia Tech and Emory, Atlanta, GA, USA

    Shella Keilholz

Authors
  1. Ilias Fountalis
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  2. Constantine Dovrolis
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  3. Bistra Dilkina
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  4. Shella Keilholz
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Corresponding author

Correspondence to Ilias Fountalis .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Fountalis, I., Dovrolis, C., Dilkina, B., Keilholz, S. (2018). \(\delta \)-MAPS: From fMRI Data to Functional Brain Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_100

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_100

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