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Autoregressive modeling of fMRI time series: state space approaches and the general linear model

from Part I - State space methods for neural data

Published online by Cambridge University Press:  05 October 2015

By
A. Galka ,
M. Siniatchkin ,
U. Stephani ,
K. Groening ,
S. Wolff ,
J. Bosch-Bayard  and
T. Ozaki
Edited by
Zhe Chen
A. Galka
Affiliation:
University of Kiel
M. Siniatchkin
Affiliation:
University of Frankfurt
U. Stephani
Affiliation:
University of Kiel
K. Groening
Affiliation:
University of Kiel
S. Wolff
Affiliation:
University of Kiel
J. Bosch-Bayard
Affiliation:
Cuban Neuroscience Center
T. Ozaki
Affiliation:
Tohoku University
Zhe Chen
Affiliation:
New York University
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Summary

Introduction

In this chapter we discuss predictive modeling of series time obtained by functional magnetic resonance imaging (fMRI), representing an important case of spatiotemporal data. Following its development in the early 1990s, fMRI has become a well established approach to investigating brain activity in vivo (Huettel et al. 2004), providing temporally and spatially resolved recordings of the “blood oxygen level dependent” (BOLD) signal of neural tissue. fMRI time series consist of a temporal sequence of scans of the brain and the surrounding space (discretized into voxels), such that the resulting data sets may be stored as vector time series.

The practical work with fMRI time series poses considerable challenges in many aspects, including the huge dimensionality of the data, which usually is recorded from several 10 of voxels, and the plethora of artifacts and contaminations disturbing the data (Strother 2006). Further difficulties arise from the low temporal sampling frequency, typically well below 1 Hz, and the indirect relationship between the BOLD signal and the underlying neural processes.

Currently available approaches to fMRI time series analysis may be broadly classified into three groups:

  1. • exploratory methods, such as cluster analysis (Goutte et al. 1999), principal component analysis (PCA) (Anderson et al. 1999) and independent component analysis (ICA) (McKeown 2000);

  2. • massively univariate (voxel-wise) regression methods, implemented in software packages such as statistical parametric mapping (SPM) (Friston et al. 1994), the FMRIB software library (FSL) (Smith et al. 2004), or the analysis of functional Neuroimages (AFNI) package (Cox 1996);

  3. • generative dynamic models, based on specific assumptions regarding the properties of the underlying neural masses and the biophysical processes which produce the experimental data; as examples we mention dynamic causal modeling (DCM) (Friston et al. 2003) and the hemodynamic state space model (SSM) of Riera et al. (2004a).

Among these three groups of methods, the third may be interpreted as an example of predictive modeling, while for the second group this is possible only in a very limited sense, and essentially impossible for the first group.

Type
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Advanced State Space Methods for Neural and Clinical Data , pp. 79 - 113
Publisher: Cambridge University Press
Print publication year: 2015

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