Robust Methods for Detecting Spontaneous Activations in fMRI Data
- 354 Downloads
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.
KeywordsBOLD signal Heavy tails HRF estimation Resting state Robust filtering Spatial dependence
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.
- 6.Castruccio, S., Ombao, H., Genton, M. G.: A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data. Biometrics (2018)Google Scholar
- 7.Catania, L., Billé, A.G.: Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances. J. Appl. Econom. (2017)Google Scholar
- 8.Choi, S.S., Cha, S.H., Tappert, C.C.: A survey of binary similarity and distance measures. J. Syst. Cybern. Inf. 8, 43–48 (2010)Google Scholar
- 19.Harvey, A.C.: Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press (2013)Google Scholar
- 25.Poldrack, R.A., Mumford, J.A., Nichols, T.E.: Handbook of Functional MRI data Analysis. Cambridge University Press (2011)Google Scholar