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Robust Methods for Detecting Spontaneous Activations in fMRI Data

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Studies in Neural Data Science (START UP RESEARCH 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 257))

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Abstract

Functional magnetic resonance imaging (fMRI) is a technique for measuring brain activity. The outcomes of fMRI measurements are complex data that can be interpreted as multivariate time series, recorded at different brain locations, usually across subjects. The literature has been mainly concerned with task-based fMRI analysis, which focuses on the response to controlled exogenous stimuli. Nevertheless, resting state fMRI (RfMRI) analysis, dealing with spontaneous brain activity, is considered the key to understand the neuronal organisation of the brain. The aim of this paper is to identify spontaneous neural activations and to estimate the brain response function in RfMRI data, called Hemodynamic Response Function (HRF). To this purpose, we apply an existing method based on a normality assumption for the data generating process and we consider a novel, more general method, based on robust filtering. Finally, we compare the neural activations and HRF estimates for two specific patients.

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Acknowledgements

We thank two anonymous referees for their insightful comments and Federico Crescenzi, Michele Peruzzi and Alexios Polymeropoulos for constructive discussions at the Certosa di Pontignano, Bologna and Milano during the initial stages of the current work. We would like to thank Antonio Canale, Daniele Durante, Lucia Paci and Bruno Scarpa for bringing us together and providing us with the challenging dataset analysed in the paper. These data are provided by Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who graciously pre-processed the raw DTI and R-fMRI imaging data available at http://fcon_1000.projects.nitrc.org/indi/CoRR/html/nki_1.html, using the pipelines ndmg and C-PAC. We would also like to thank all the participants of the StartUp Research event held at the Certosa di Pontignano on June 25-27, 2017, for the stimulating and nice discussions.

Author information

Authors and Affiliations

  1. MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy

    Francesca Gasperoni

  2. Department of Statistics, University of Bologna, Bologna, Italy

    Alessandra Luati

Authors
  1. Francesca Gasperoni
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  2. Alessandra Luati
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Corresponding author

Correspondence to Francesca Gasperoni .

Editor information

Editors and Affiliations

  1. Department of Statistical Sciences, University of Padova, Padua, Italy

    Antonio Canale

  2. Department of Decision Sciences, Bocconi University, Milan, Italy

    Daniele Durante

  3. Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy

    Lucia Paci

  4. Department of Statistical Sciences, University of Padova, Padua, Italy

    Bruno Scarpa

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Gasperoni, F., Luati, A. (2018). Robust Methods for Detecting Spontaneous Activations in fMRI Data. In: Canale, A., Durante, D., Paci, L., Scarpa, B. (eds) Studies in Neural Data Science. START UP RESEARCH 2017. Springer Proceedings in Mathematics & Statistics, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-00039-4_6

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