Structural adaptive segmentation for statistical parametric mapping

NeuroImage

Volumn 52, Issue 2, 2010, Pages 515-523

  Polzehl, Jörg ^a   Voss, Henning U ^b   Tabelow, Karsten ^a  

^a WEIERSTRASS INSTITUTE FOR APPLIED ANALYSIS AND STOCHASTICS   (Germany)

^b WEILL CORNELL MEDICAL COLLEGE   (United States)

Author keywords

Functional MRI; Image enhancement; Multiscale testing; Structure adaptive smoothing

Indexed keywords

ADULT; ALGORITHM; ARTICLE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; IMAGE DISPLAY; IMAGE ENHANCEMENT; IMAGE PROCESSING; MALE; NEUROIMAGING; NOISE REDUCTION; PRIORITY JOURNAL; SIGNAL DETECTION; SIGNAL NOISE RATIO; STATISTICAL ANALYSIS; ARTIFACT; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; DATA BASE; DEVICES; HAND; IMAGE QUALITY; MOTOR ACTIVITY; NUCLEAR MAGNETIC RESONANCE IMAGING; PERCEPTION; PHYSIOLOGY; PROCEDURES; SIGNAL PROCESSING; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ARTIFACTS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; DATABASES AS TOPIC; HAND; HUMANS; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; MOTOR ACTIVITY; PERCEPTION; PHANTOMS, IMAGING; SIGNAL PROCESSING, COMPUTER-ASSISTED; STATISTICS AS TOPIC;

EID: 77953689705     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.04.241     Document Type: Article

References (24)

1. Adler R. On excursion sets, tube formulae, and maxima of random fields, (special invited paper). Ann. Appl. Probab. 2000, 10:1-74.
2. Chaudhuri P., Marron J.S. Scale space view of curve estimation. Ann. Statist. 2000, 28:408-428.
3. Cox R. Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomed. Res. 1996, 29:162.
4. Dümbgen L., Spokoiny V. Multiscale testing of qualitative hypotheses. Ann. Statist. 2001, 29:124-152.
5. Fisher R.A., Tippett L.H.C. Limiting forms of the frequency distribution of the largest or smallest member of a sample. Math. Proc. Cambridge Philosophical Society 1928, 24:180-190.
6. Statistical Parametric Mapping: The Analysis of Functional Brain Images 2007, Academic Press. K. Friston, J. Ashburner, S. Kiebel (Eds.).
7. Gnedenko B. Sur la distribution limite du terme maximum d'une serie aleatoiresur la distribution limite du terme maximum d'une serie aleatoire. Ann. Math. 1943, 44:423-453.
8. Harrison L., Penny W., Daunizeau J., Friston K. Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage 2008, 41:408-423.
9. Kriegeskorte N., Bandettini P. Analyzing for information, not activation, to exploit high-resolution fmri. NeuroImage 2007, 38:649-662.
10. Kriegeskorte N., Goebel R., Bandettini P. Information-based functional brain mapping. Proc. Natl. Acad. Sci. U.S.A. 2006, 103(10):3863-3868.
11. Lazar N.A. The Statistical Analysis of Functional MRI Data 2008, Springer, Statistics for Biology and Health.
12. Lu Y., Jiang T., Zang Y. Region growing method for the analysis of functional MRI data. NeuroImage 2003, 20:455-465.
13. Poline J., Mazoyer B. Enhanced detection in brain activation maps using a multifiltering approach. J. Cereb. Blood Flow Metab. 1994, 14:639-642.
14. Poline J.B., Mazoyer B.M. Analysis of individual brain activation maps using hierarchical description and multiscale detection 1994, IEEE Trans. Med, Imaging.
15. Polzehl J., Spokoiny V. Propagation-separation approach for local likelihood estimation. Probab. Theory and Relat. Fields 2006, 135:335-362.
16. Polzehl J., Tabelow K. fmri: A package for analyzing fmri data. RNews 2007, 7(2):13-17.
17. R Development Core Team R: A Language and Environment for Statistical Computing 2006, R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
18. Resnick S.I. Extreme Values, Regular Variation, and Point Processes 1987, Springer-Verlag.
19. Shorack G., Wellner J. Empirical Processes with Applications to Statistics 1986, Wiley, New York.
20. Tabelow K., Piëch V., Polzehl J., Voss H.U. High-resolution fmri: Overcoming the signal-to-noise problem. J. Neurosci. Meth. 2009, 178:357-365.
21. Tabelow K., Polzehl J., Voss H., Spokoiny V. Analyzing fmri experiments with structural adaptive smoothing procedures. NeuroImage 2006, 33:55-62.
22. 2, f and t fields. Adv. Appl. Probab. 1994, 26:13-42.
23. 2 random field, with an application to fmri. Adv. Appl. Probab. 2001, 33:773-793.
24. Worsley K. Detecting activation in fMRI data. Statist. Meth. Med. Res. 2003, 12:401-418.