dorsal/arxiv
View SchemaAnalysis of Dynamic Brain Imaging Data
| Authors | P. P. Mitra, B. Pesaran |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0309028 |
| URL | https://arxiv.org/abs/q-bio/0309028 |
| DOI | 10.1016/S0006-3495(99)77236-X |
Abstract
Modern imaging techniques for probing brain function, including functional Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques of analysis and visualization of such imaging data, in order to separate the signal from the noise, as well as to characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: `noise' characterization and suppression, and `signal' characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for non-stationarity in the data. Of particular note are (a) the development of a decomposition technique (`space-frequency singular value decomposition') that is shown to be a useful means of characterizing the image data, and (b) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.
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"abstract": "Modern imaging techniques for probing brain function, including functional\nMagnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging,\nand magnetoencephalography, generate large data sets with complex content. In\nthis paper we develop appropriate techniques of analysis and visualization of\nsuch imaging data, in order to separate the signal from the noise, as well as\nto characterize the signal. The techniques developed fall into the general\ncategory of multivariate time series analysis, and in particular we extensively\nuse the multitaper framework of spectral analysis. We develop specific\nprotocols for the analysis of fMRI, optical imaging and MEG data, and\nillustrate the techniques by applications to real data sets generated by these\nimaging modalities. In general, the analysis protocols involve two distinct\nstages: `noise\u0027 characterization and suppression, and `signal\u0027 characterization\nand visualization. An important general conclusion of our study is the utility\nof a frequency-based representation, with short, moving analysis windows to\naccount for non-stationarity in the data. Of particular note are (a) the\ndevelopment of a decomposition technique (`space-frequency singular value\ndecomposition\u0027) that is shown to be a useful means of characterizing the image\ndata, and (b) the development of an algorithm, based on multitaper methods, for\nthe removal of approximately periodic physiological artifacts arising from\ncardiac and respiratory sources.",
"arxiv_id": "q-bio/0309028",
"authors": [
"P. P. Mitra",
"B. Pesaran"
],
"categories": [
"q-bio.NC",
"q-bio.QM"
],
"doi": "10.1016/S0006-3495(99)77236-X",
"title": "Analysis of Dynamic Brain Imaging Data",
"url": "https://arxiv.org/abs/q-bio/0309028"
},
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