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J Neurosci Methods. 2014 Apr 30;227:75-82. doi: 10.1016/j.jneumeth.2014.02.003. Epub 2014 Feb 11.

An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

Author information

1
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
2
Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA 15260, USA.
3
Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15261, USA.
4
Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA.
5
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA. Electronic address: esejdic@ieee.org.

Abstract

BACKGROUND:

Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks.

NEW METHOD:

In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity.

RESULTS:

Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition.

COMPARISON WITH EXISTING METHODS:

Most of the current literature does not examine stationarity prior to processing.

CONCLUSIONS:

The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data.

KEYWORDS:

Foot tapping; Functional magnetic resonance imaging; Reverse arrangement test; Stationarity; Time series

PMID:
24530436
PMCID:
PMC3987746
DOI:
10.1016/j.jneumeth.2014.02.003
[Indexed for MEDLINE]
Free PMC Article

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