TY - JOUR
T1 - A multivariate, multitaper approach to detecting and estimating harmonic response in cortical optical imaging data
AU - Sornborger, A. T.
AU - Yokoo, T.
N1 - Funding Information:
The authors would like to thank Udi Kaplan, Larry Sirovich and Bruce Knight for helpful discussions and help in the imaging laboratory. We would also like to thank David Thomson and Partha Mitra for helpful discussions and the Neuroinformatics course at the Marine Biological Laboratories in Woods Hole for providing an environment conducive to thinking about neural data analysis. ATS was funded by a University of Georgia Engineering Grant from the University of Georgia Research Foundation and NIH grants EB005432 , MH085973 and NS070159 .
PY - 2012/1/15
Y1 - 2012/1/15
N2 - The efficiency and accuracy of cortical maps from optical imaging experiments have been improved using periodic stimulation protocols. The resulting data analysis requires the detection and estimation of periodic information in a multivariate dataset. To date, these analyses have relied on discrete Fourier transform (DFT) sinusoid estimates. Multitaper methods have become common statistical tools in the analysis of univariate time series that can give improved estimates. Here, we extend univariate multitaper harmonic analysis methods to the multivariate, imaging context. Given the hypothesis that a coherent oscillation across many pixels exists within a specified bandwidth, we investigate the problem of its detection and estimation in noisy data by constructing Hotelling's generalized T 2-test. We then extend the investigation of this problem in two contexts, that of standard canonical variate analysis (CVA) and that of generalized indicator function analysis (GIFA) which is often more robust in extracting a signal in spatially correlated noise. We provide detailed information on the fidelities of the mean estimates found with our methods and comparison with DFT estimates. Our results indicate that GIFA provides particularly good estimates of harmonic signals in spatially correlated noise and is useful for detecting small amplitude harmonic signals in applications such as biological imaging measurements where spatially correlated noise is common. We demonstrate the power of our methods with an optical imaging dataset of the periodic response to a periodically rotating oriented drifting grating stimulus experiment in cat visual cortex.
AB - The efficiency and accuracy of cortical maps from optical imaging experiments have been improved using periodic stimulation protocols. The resulting data analysis requires the detection and estimation of periodic information in a multivariate dataset. To date, these analyses have relied on discrete Fourier transform (DFT) sinusoid estimates. Multitaper methods have become common statistical tools in the analysis of univariate time series that can give improved estimates. Here, we extend univariate multitaper harmonic analysis methods to the multivariate, imaging context. Given the hypothesis that a coherent oscillation across many pixels exists within a specified bandwidth, we investigate the problem of its detection and estimation in noisy data by constructing Hotelling's generalized T 2-test. We then extend the investigation of this problem in two contexts, that of standard canonical variate analysis (CVA) and that of generalized indicator function analysis (GIFA) which is often more robust in extracting a signal in spatially correlated noise. We provide detailed information on the fidelities of the mean estimates found with our methods and comparison with DFT estimates. Our results indicate that GIFA provides particularly good estimates of harmonic signals in spatially correlated noise and is useful for detecting small amplitude harmonic signals in applications such as biological imaging measurements where spatially correlated noise is common. We demonstrate the power of our methods with an optical imaging dataset of the periodic response to a periodically rotating oriented drifting grating stimulus experiment in cat visual cortex.
KW - Cortical maps
KW - Multitaper harmonic analysis
KW - Multivariate statistics
KW - Optical imaging
KW - Primary visual cortex
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U2 - 10.1016/j.jneumeth.2011.09.018
DO - 10.1016/j.jneumeth.2011.09.018
M3 - Article
C2 - 21970814
AN - SCOPUS:81555205770
SN - 0165-0270
VL - 203
SP - 254
EP - 263
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -