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MRI/Optical Publications

The accuracy of near infrared spectroscopy and imaging
during focal changes in cerebral hemodynamics.

David A. Boas, Tom Gaudette, Gary Strangman, Xuefeng Cheng, John J. A. Marota, Joseph B. Mandeville
NeuroImage 13: 76-90, 2000.

Abstract

Near infrared spectroscopy (NIRS) can detect changes in the concentrations of oxy-hemoglobin ([HbO]) and deoxy-hemoglobin ([Hb]) in tissue based upon differential absorption at multiple wavelengths. The common analysis of NIRS data uses the modified Beer-Lambert law, which is an empirical formulation that assumes global concentration changes. We used simulations to examine the errors that result when this analysis is applied to focal hemodynamic changes, and we performed simultaneous NIRS measurements during a motor task in adult human and a neonate to evaluate the dependence of the measured changes on detector probe geometry. For both simulations and in vivo measurements, the wide range of NIRS results was compared to an imaging analysis, diffuse optical tomography (DOT). The results demonstrate that relative changes in [HbO] and [Hb] cannon, in general, be quantified with NIRS> In contrast to that method, DOT analysis was shown to accurately quantify simulated changes in chromophore concentrations. These results and the general principles sugest that DOT can accurately measure changes in [Hb] and [HbO], but NIRS cannot accurately determine even relative focal changes in these chromophore concentrations. FOr the standard NIRS analysis to become more accurate for focal changes, it must account for the position of the focal change relative to the source and detector as well as the wavelength dependent optical properties of the medium.



Spike Train Statistics Publications

Detecting synchronous cell assemblies with
limited data and overlapping assemblies.

Gary Strangman
Neural Computation 9: 51-76, 1997.

Abstract

Two statistical methods--cross correlation (Moore et al. 1966) and gravity clustering (Gerstein et al. 1985)--were evaluated for their ability to detect synchronous cell assemblies from simulated spike train data. The two methods were first analyzed for their temporal sensitivity to synchronous cell assemblies. The presented approach places a lower bound on the amount of recording time to detect significant pairwise correlations, but the gravity method exhibited less variance in the recording time. The precise length of recording depends on the consistency with which a neuron fires synchronously with the assembly, but was independent of the assembly firing rate. Next, the statistical methods were tested with respect to their ability to differentiate two distinct assemblies that overlapped in time and space. Both statistics could adequately differentiate two overlapping synchronous assemblies. For cross correlation, this ability deterriorates when considering three or more simultaneously active, overlapping assemblies, whereas the gravity method should be more flexible in this regard. The work demonstrates the difficulty of detecting assembly phenomena from simultaneous neuronal recordings. Other statistical methods and the detection of other types of assemblies are also discussed.

Searching for cell assemblies:
How many electrodes do I need?

Gary Strangman
Journal of Computational Neuroscience 3 (2): 111-124, 1996.

Abstract

Two methods were derived to estimate the probability of recording cell assemblies using multiple simultaneous electrode recordings. The derivations are independent of the definition of a cell assembly, and require only a statistic for evaluating cell assembly membership from spike train data. The resulting equations are functions of 1) the size of the search area, 2) the smallest expected assembly size, 3) the number of recorded neurons, and 4) the predicted spatial distribution of assembly neurons. The equations can be used to estimate the following three quantities. First, the equations directly calculate the probability of detecting i or more cells of an hypothesized assembly. Second, by making several such calculations, one can estimate when sufficient sampling has been performed to claim, at any desired confidence level, that a posited type of cell assembly does not exist. Third, the probability of detecting one out of several active assemblies can be calculated, given assumptions about assembly-assembly interactions.