MEG sleep pattern dependence of auditory evoked fields in young infants.

We recorded magnetic auditory evoked responses (AERs) to brief 1 kHz tones in infants ranging in age from 1.5-8.5 weeks. Long continuous MEG recordings were collected with a 37-channel sensor and were classified into one of four patterns: low-amplitude irregular, high-amplitude slow, mixed, and REM-like sleep. The AERs were characterized by a monophasic deflection with nominal latency 250 msec, followed by a broader peak with opposite polarity and approximate latency 600 msec. No strong dependences on MEG sleep pattern were observed. The latency of the 250 msec component decreased with age, but the 600 msec component showed variable latency. The signal topography of both components was compatible with a source in the auditory cortex. We conclude that sleep state does not appreciably confound recording of the AER in young infants.

Maximum likelihood dipole fitting in spatially colored noise.

We evaluated a maximum likelihood dipole-fitting algorithm for somatosensory evoked field (SEF) MEG data in the presence of spatially colored noise. The method exploits the temporal multiepoch structure of the evoked response data to estimate the spatial noise covariance matrix from the section of data being fit, which eliminates the stationarity assumption implicit in prestimulus based whitening approaches. The performance of the method, including its effectiveness in comparison to other localization techniques (dipole fitting, LCMV and MUSIC) was evaluated using the bootstrap technique. Synthetic data results demonstrated robustness of the algorithm in the presence of relatively high levels of noise when traditional dipole fitting algorithms fail. Application of the algorithm to adult somatosensory MEG data showed that while it is not advantageous for high SNR data, it definitely provides improved performance (measured by the spread of localizations) as the data sample size decreases.

[Problems in estimating the dimensions of strange attractors in the analysis of biophysical data]. / Problemy otsenki razmernostei strannykh attraktorov pri analize dannykh biofizicheskikh éksperimentav.

Basic algorithms of calculating the dimensions of strange attractors from experimental data are considered. A special emphasis is placed on difficulties arising when the methods of solution of the reverse nonlinear dynamic problem are used in the analysis of the behavior of biological systems. These difficulties are associated with a poor convergence and weak stability of estimated values as well as a low degree of statistical confidence. Factors that hamper the estimation of strange attractor dimensions with reasonable accuracy are discussed by the example of analysis of human electrocardiograms. A method for the statistcal estimation of dimensions of attractors is proposed based on multidimensional imitational modeling.