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PubMed1996

Statistical approaches to human brain mapping by functional magnetic resonance imaging.

Proper use of functional neuro-imaging through effective experimental design and modern statistical analysis provides new insights in current brain research. This tutorial has two aims: to describe aspects of this technology to applied statisticians and to provide some statistical ideas to neuroscientists unfamiliar with quantitative analytic methods that accommodate randomness. Introductory background material and ample references to current literature on the physics of magnetic resonance imaging, Fourier methods for image reconstruction and measures of image quality are included. Two of the statistical approaches mentioned here are extensions of established methods for longitudinal data analysis to the frequency domain. A recent case study provides real-world instances of approaches, problems and open questions encountered in current functional neuro-imaging research and an introduction to the analysis of spatial time series in this context.

Algorithms↗
PubMed2002

A signal process method for DNA segments separation in micro-channel electrophoresis.

This paper presents a signal process method for DNA segments separation in micro-channel electrophoresis. It is developed and optimized by using a laser induced fluorescence (LIF) based detection system. In this detection system, signal is sampled and processed through a novel signal process module. The results show that this signal process method provides good signal-to-noise ratios and lower limit of detection (LOD).

DNA↗
PubMed2003

Extraction of short-latency evoked potentials using a combination of wavelets and evolutionary algorithms.

Somatosensory evoked potentials, recorded at the spine or scalp of a patient, are contaminated by noise. It is common practice to use ensemble averaging to remove the noise, which usually requires a large number of responses to produce one averaged signal. In this paper a post-processing technique is shown which uses a combination of wavelets and evolutionary algorithms to produce a representative waveform with fewer responses. The most suitable wavelets and a set of weights are selected by an evolutionary algorithm to form a filter bank, which enhances the extraction of evoked potentials from noisy recordings.

Algorithms↗
PubMed2005

Risk of severe asthma episodes predicted from fluctuation analysis of airway function.

Asthma is an increasing health problem worldwide, but the long-term temporal pattern of clinical symptoms is not understood and predicting asthma episodes is not generally possible. We analyse the time series of peak expiratory flows, a standard measurement of airway function that has been assessed twice daily in a large asthmatic population during a long-term crossover clinical trial. Here we introduce an approach to predict the risk of worsening airflow obstruction by calculating the conditional probability that, given the current airway condition, a severe obstruction will occur within 30 days. We find that, compared with a placebo, a regular long-acting bronchodilator (salmeterol) that is widely used to improve asthma control decreases the risk of airway obstruction. Unexpectedly, however, a regular short-acting beta2-agonist bronchodilator (albuterol) increases this risk. Furthermore, we find that the time series of peak expiratory flows show long-range correlations that change significantly with disease severity, approaching a random process with increased variability in the most severe cases. Using a nonlinear stochastic model, we show that both the increased variability and the loss of correlations augment the risk of unstable airway function. The characterization of fluctuations in airway function provides a quantitative basis for objective risk prediction of asthma episodes and for evaluating the effectiveness of therapy.

Adult↗
PubMed1994

A method of using noise as a test pattern for determining image enhancement filters.

Many digitally based medical imaging systems include both reconstruction algorithms and additional image filters designed to enhance certain image features. However, the manufacturers usually consider these algorithms and filters to be proprietory information. The purpose of this note is to describe a simple procedure for determining the spatial frequency response of these proprietary enhancement filters. The technique uses image noise as a test pattern. The procedure consists of acquiring a small number of noise-only data sets (say 10) of a uniform phantom and reconstructing the images using the different filters with repeated use of the noise data sets. A straightforward analysis then yields the enhancement filter frequency responses.

Algorithms↗
PubMed2004

Correcting log ratios for signal saturation in cDNA microarrays.

MOTIVATION: Pixel saturation occurs when the pixel intensity exceeds a threshold and the recorded pixel intensity is truncated. Microarray experiments are commonly afflicted with saturated pixels. As a result, estimators of gene expression are biased, with the amount of bias increasing as a function of the proportion of pixels saturated. Saturation is directly related to the photomultiplier tube (PMT) voltage settings and RNA abundance and is not necessarily associated with poor array or poor spot quality. When choosing PMT settings, higher PMT settings are desired because of improved signal-to-noise ratios of low-intensity spots. This improved signal is somewhat offset by saturation of high-intensity spots. In practice, spots with saturated pixels are discarded or the biased value is used. Neither of these approaches is appealing, particularly the former approach when a highly expressed gene is discarded because of saturation. RESULTS: We present a method to correct for saturation using pixel-level data. The method is based on a censored regression model. Evaluations on several arrays indicate that the method performs well. Simulation studies suggest that the method is robust under certain model violations.

Algorithms↗
PubMed2002

A computer-aided detection of EEG seizures in infants: a singular-spectrum approach and performance comparison.

This paper presents a scalp electroencephalogram (EEG) seizure detection scheme based on singular spectrum analysis (SSA) and Rissanen minimum description length (MDL) model-order selection (SSA-MDL). Preprocessing of the signals allows for the drastic reduction of the number of false alarms. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed on both synthetic data and real EEG seizures. Monte Carlo simulations based on synthetic infant EEG seizure data reveals some detection drawbacks on a large variety of seizure waveforms. Detection using both Monte Carlo and four real infant scalp EEG signals shows the superiority of the SSA-MDL method with an average good detection rate of >93% and false detection rate <4%.

Computer Simulation↗
PubMed2002

Laser-induced fluorescence detection on multichannel electrophoretic microchips using microprocessor-embedded acousto-optic laser beam scanning.

An improved method for fast scanning and fluorescence detection on multimicrochannel microchips is presented using acousto-optic-deflection-driven laser-beam scanning. A microprocessor embedded subsystem used in conjunction with LabView program as the human-machine interface for control of laser-beam scanning and data preprocessing allowed faster scanning and addressing speeds to be attained and improved attenuation calibration and the data sampling speed. This system allows for flexible, high-resolution fluorescence detection for multimicrochannel electrophoresis in a manner that can be applied to a number of high-throughput analysis applications. Incorporating an F-theta focusing lens into the optical set-up allowed for a laser spot as small as 10 microm to accurately be addressed to the center of microchannels. With this spot size, it will be possible to further increase the channel density in the scanning range without encountering crosstalk. Using a six-channel microchip (four separation channels, two alignment channels), the simultaneous separation and fluorescence detection of amino acids and DNA digest samples in four channels is illustrated. User-friendly interpretation of the separation data is facilitated not only by a peak alignment/normalization routine developed within the software, but also through improved signal-to-noise ratios obtained through exploitation of signal processing.

Acoustics↗
PubMed2003

A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio.

We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signal-to-noise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this problem and, thus, in previous studies on experimental neurophysiology, only action potentials with sufficiently large amplitude have been detected and analyzed. In order to lower the level of SNR required for successful detection, we propose an action potential detector based on a prudent combination of wavelet coefficients of multiple scales and demonstrate its performance for neural signal recording with varying degrees of similarity between signal and noise. The experimental data include recordings from the rat somatosensory cortex, the giant medial nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed method was tested for various SNR values and degrees of spectral similarity. The method was superior to the Teager energy operator and even comparable to or better than the optimal linear detector. A detection ratio higher than 80% at a false alarm ratio lower than 10% was achieved, under an SNR of 2.35 for the rat cortex data where the spectral similarity was very high.

Action Potentials↗
PubMed2003

Using the fast orthogonal search with first term reselection to find subharmonic terms in spectral analysis.

The fast orthogonal search (FOS) algorithm has been shown to accurately model various types of time series by implicitly creating a specialized orthogonal basis set to fit the desired time series. When the data contain periodic components, FOS can find frequencies with a resolution greater than the discrete Fourier transform (DFT) algorithm. Frequencies with less than one period in the record length, called subharmonic frequencies, and frequencies between the bins of a DFT, can be resolved. This paper considers the resolution of subharmonic frequencies using the FOS algorithm. A new criterion for determining the number of non-noise terms in the model is introduced. This new criterion does not assume the first model term fitted is a dc component as did the previous stopping criterion. An iterative FOS algorithm called FOS first-term reselection (FOS-FTR), is introduced. FOS-FTR reduces the mean-square error of the sinusoidal model and selects the subharmonic frequencies more accurately than does the unmodified FOS algorithm.

Algorithms↗
PubMed2003

Improved temporal coding of sinusoids in electric stimulation of the auditory nerve using desynchronizing pulse trains.

Rubinstein et al. [Hearing Res. 127, 108-118 (1999)] suggested that the representation of electric stimulus waveforms in the temporal discharge patterns of auditory-nerve fiber (ANF) might be improved by introducing an ongoing, high-rate, desynchronizing pulse train (DPT). To test this hypothesis, activity of ANFs was studied in acutely deafened, anesthetized cats in response to 10-min-long, 5-kpps electric pulse trains that were sinusoidally modulated for 400 ms every second. Two classes of responses to sinusoidal modulations of the DPT were observed. Fibers that only responded transiently to the unmodulated DPT showed hyper synchronization and narrow dynamic ranges to sinusoidal modulators, much as responses to electric sinusoids presented without a DPT. In contrast, fibers that exhibited sustained responses to the DPT were sensitive to modulation depths as low as 0.25% for a modulation frequency of 417 Hz. Over a 20-dB range of modulation depths, responses of these fibers resembled responses to tones in a healthy ear in both discharge rate and synchronization index. This range is much wider than the dynamic range typically found with electrical stimulation without a DPT, and comparable to the dynamic range for acoustic stimulation. These results suggest that a stimulation strategy that uses small signals superimposed upon a large DPT to encode sounds may evoke temporal discharge patterns in some ANFs that resemble responses to sound in a healthy ear.

Animals↗
PubMed1997

Fractal character of the neural spike train in the visual system of the cat.

We used a variety of statistical measures to identify the point process that describes the maintained discharge of retinal ganglion cells (RGC's) and neurons in the lateral geniculate nucleus (LGN) of the cat. These measures are based on both interevent intervals and event counts and include the interevent-interval histogram, rescaled range analysis, the event-number histogram, the Fano factor, Allan factor, and the periodogram. In addition, we applied these measures to surrogate versions of the data, generated by random shuffling of the order of interevent intervals. The continuing statistics reveal 1/f-type fluctuations in the data (long-duration power-law correlation), which are not present in the shuffled data. Estimates of the fractal exponents measured for RGC- and their target LGN-spike trains are similar in value, indicating that the fractal behavior either is transmitted form one cell to the other or has a common origin. The gamma-r renewal process model, often used in the analysis of visual-neuron interevent intervals, describes certain short-term features of the RGC and LGN data reasonably well but fails to account for the long-duration correlation. We present a new model for visual-system nerve-spike firings: a gamma-r renewal process whose mean is modulated by fractal binomial noise. This fractal, doubly stochastic point process characterizes the statistical behavior of both RGC and LGN data sets remarkably well.

Animals↗
PubMed2001

Neural coding and decoding: communication channels and quantization.

We present a novel analytical approach for studying neural encoding. As a first step we model a neural sensory system as a communication channel. Using the method of typical sequence in this context, we show that a coding scheme is an almost bijective relation between equivalence classes of stimulus/response pairs. The analysis allows a quantitative determination of the type of information encoded in neural activity patterns and, at the same time, identification of the code with which that information is represented. Due to the high dimensionality of the sets involved, such a relation is extremely difficult to quantify. To circumvent this problem, and to use whatever limited data set is available most efficiently, we use another technique from information theory--quantization. We quantize the neural responses to a reproduction set of small finite size. Among many possible quantizations, we choose one which preserves as much of the informativeness of the original stimulus/response relation as possible, through the use of an information-based distortion function. This method allows us to study coarse but highly informative approximations of a coding scheme model, and then to refine them automatically when more data become available.

Computer Simulation↗
PubMed2000

Role of noise in image processing by the human perceptive system.

Two psychophysics experiments are described, pointing out the significant role played by stochastic resonance in recognition of capital stylized noisy letters by the human perceptive apparatus. The first experiment shows that an optimal noise level exists at which the letter is recognized for a minimum threshold contrast. A simple two-parameter model that best fits the experimental data is also discussed. In the second experiment we show that a dramatically increased ability of the visual system in letter recognition occurs in an extremely narrow range of increasing noise. Possible interesting future investigations suggested by these experimental results and based on functional imaging techniques are discussed.

Brain↗
PubMed2000

Spatial structure and fluctuations in the contact process and related models.

The contact process is used as a simple spatial model in many disciplines, yet because of the buildup of spatial correlations, its dynamics remain difficult to capture analytically. We introduce an empirically based, approximate method of characterizing the spatial correlations with only a single adjustable parameter. This approximation allows us to recast the contact process in terms of a stochastic birth-death process, converting a spatiotemporal problem into a simpler temporal one. We obtain considerably more accurate predictions of equilibrium population than those given by pair approximations, as well as good predictions of population variance and first passage time distributions to a given (low) threshold. A similar approach is applicable to any model with a combination of global and nearest-neighbor interactions.

Animals↗
PubMed1986

An electroencephalographic processing algorithm specifically intended for analysis of cerebral electrical activity.

This article describes a computer procedure for the examination and analysis of cerebral electrical activity (CEA). Changes in CEA generate random electrical activity and may include transitory events, such as burst episodes. As yet, there are no standard techniques for evaluating the statistical process of the CEA. This article proposes a computerized method of analyzing the stochastic character of CEA using a computer algorithm. Using a real-time wave-by-wave technique, the algorithm characterizes CEA by the frequency and amplitude of each CEA waveform. This algorithm produces digital packets of information that describe individual CEA waveforms.

Analog-Digital Conversion↗
PubMed2006

A pharmacokinetic formalism explicitly integrating the patient drug compliance.

The adherence phenomenon is now well recognized to seriously compromise drug efficacy. In this paper, we analyze the role of compliance through drug intake history as an integral part of the pharmacokinetic process. Being concerned with what is accessible in medical practice, we develop a stochastic approach to model the drug intake behavior that we combine with a conventional pharmacokinetic model in order to investigate the effect of drug intake history on the pharmacokinetic time-course. For this purpose, we explicitly formalize the plasma concentration variations for the most common administration routes. This analytical approach allows to characterize drug concentration variations directly inherited from patient compliance.

Animals↗
PubMed2004

Prestimulus oscillations enhance psychophysical performance in humans.

The presence of various ongoing oscillations in the brain is correlated with behavioral states such as restful wakefulness or drowsiness. However, even when subjects aim to maintain a high level of vigilance, ongoing oscillations exhibit large amplitude variability on time scales of hundreds of milliseconds to seconds, suggesting that the functional state of local cortical networks is continuously changing. How this volatility of ongoing oscillations influences the perception of sensory stimuli has remained essentially unknown. We investigated the relationship between prestimulus neuronal oscillations and the subjects' ability to consciously perceive and react to somatosensory stimuli near the threshold of detection. We show that, for prestimulus oscillations at approximately 10, 20, and 40 Hz detected over the sensorimotor cortex, intermediate amplitudes were associated with the highest probability of conscious detection and the shortest reaction times. In contrast, for 10 and 20 Hz prestimulus oscillations detected over the parietal region, the largest amplitudes were associated with the best performance. Our data indicate that the prestimulus oscillatory activity detected over sensorimotor and parietal cortices has a profound effect on the processing of weak stimuli. Furthermore, the results suggest that ongoing oscillations in sensory cortices may optimize the processing of sensory stimuli with the same mechanism as noise sources in intrinsic stochastic resonance.

Adult↗