Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

EVALUATING STATIONARITY VIA CHANGE-POINT ALTERNATIVES WITH APPLICATIONS TO FMRI DATA

John A. D. Aston and Claudia Kirch
The Annals of Applied Statistics
Vol. 6, No. 4 (December 2012), pp. 1906-1948
Stable URL: http://www.jstor.org/stable/41713500
Page Count: 43
  • Read Online (Free)
  • Download ($19.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
EVALUATING STATIONARITY VIA CHANGE-POINT ALTERNATIVES WITH APPLICATIONS TO FMRI DATA
Preview not available

Abstract

Functional magnetic resonance imaging (fMRI) is now a well-established technique for studying the brain. However, in many situations, such as when data are acquired in a resting state, it is difficult to know whether the data are truly stationary or if level shifts have occurred. To this end, change-point detection in sequences of functional data is examined where the functional observations are dependent and where the distributions of change-points from multiple subjects are required. Of particular interest is the case where the change-point is an epidemic change—a change occurs and then the observations return to baseline at a later time. The case where the covariance can be decomposed as a tensor product is considered with particular attention to the power analysis for detection. This is of interest in the application to fMRI, where the estimation of a full covariance structure for the three-dimensional image is not computationally feasible. Using the developed methods, a large study of resting state fMRI data is conducted to determine whether the subjects undertaking the resting scan have nonstationarities present in their time courses. It is found that a sizeable proportion of the subjects studied are not stationary. The change-point distribution for those subjects is empirically determined, as well as its theoretical properties examined.

Page Thumbnails

  • Thumbnail: Page 
1906
    1906
  • Thumbnail: Page 
1907
    1907
  • Thumbnail: Page 
1908
    1908
  • Thumbnail: Page 
1909
    1909
  • Thumbnail: Page 
1910
    1910
  • Thumbnail: Page 
1911
    1911
  • Thumbnail: Page 
1912
    1912
  • Thumbnail: Page 
1913
    1913
  • Thumbnail: Page 
1914
    1914
  • Thumbnail: Page 
1915
    1915
  • Thumbnail: Page 
1916
    1916
  • Thumbnail: Page 
1917
    1917
  • Thumbnail: Page 
1918
    1918
  • Thumbnail: Page 
1919
    1919
  • Thumbnail: Page 
1920
    1920
  • Thumbnail: Page 
1921
    1921
  • Thumbnail: Page 
1922
    1922
  • Thumbnail: Page 
1923
    1923
  • Thumbnail: Page 
1924
    1924
  • Thumbnail: Page 
1925
    1925
  • Thumbnail: Page 
1926
    1926
  • Thumbnail: Page 
1927
    1927
  • Thumbnail: Page 
1928
    1928
  • Thumbnail: Page 
1929
    1929
  • Thumbnail: Page 
1930
    1930
  • Thumbnail: Page 
1931
    1931
  • Thumbnail: Page 
1932
    1932
  • Thumbnail: Page 
1933
    1933
  • Thumbnail: Page 
1934
    1934
  • Thumbnail: Page 
1935
    1935
  • Thumbnail: Page 
1936
    1936
  • Thumbnail: Page 
1937
    1937
  • Thumbnail: Page 
1938
    1938
  • Thumbnail: Page 
1939
    1939
  • Thumbnail: Page 
1940
    1940
  • Thumbnail: Page 
1941
    1941
  • Thumbnail: Page 
1942
    1942
  • Thumbnail: Page 
1943
    1943
  • Thumbnail: Page 
1944
    1944
  • Thumbnail: Page 
1945
    1945
  • Thumbnail: Page 
1946
    1946
  • Thumbnail: Page 
1947
    1947
  • Thumbnail: Page 
1948
    1948