Analysis of Dynamic Brain Imaging Data

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.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files. Originally submitted to the neuro-sys archive which was never publicly announced (was 9804003

Cancer stem cells: a reality, a myth, a fuzzy concept or a misnomer? An analysis.

The concept of cancer stem cells (CSC) embodies two aspects: the stem cell as the initial target of the oncogenic process and the existence of two populations of cells in cancers: the CSC and derived cells. The second is discussed in this review. CSC are defined as cells having three properties: a selectively endowed tumorigenic capacity, an ability to recreate the full repertoire of cancer cells of the parent tumor and the expression of a distinctive repertoire of surface biomarkers. In operational terms, the CSC are among all cancer cells those able to initiate a xenotransplant. Other explicit or implicit assumptions exist, including the concept of CSC as a single unique infrequent population of cells. To avoid such assumptions, we propose to use the operational term tumor-propagating cells (TPC); indeed, the cells that initiate transplants did not initiate the cancer. The experimental evidence supporting the explicit definition is analyzed. Cancers indeed contain a fraction of cells mainly responsible for the tumor development. However, there is evidence that these cells do not represent one homogenous population. Moreover, there is no evidence that the derived cells result from an asymmetric, qualitative and irreversible process. A more general model is proposed of which the CSC model could be one extreme case. We propose that the TPC are multiple evolutionary selected cancer cells with the most competitive properties [maintained by (epi-)genetic mechanisms], at least partially reversible, quantitative rather than qualitative and resulting from a stochastic rather than deterministic process.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe