Biomedical subjects

Matthew C Wiener

Publications and source records attributed to Matthew C Wiener.

7 recordsLinked to original sources

PubMed2006

Quantitative analysis of complex peptide mixtures using FTMS and differential mass spectrometry.

Label-free LC-MS profiling is a powerful quantitative proteomic method to study relative peptide abundances between two or more biological samples. Here we demonstrate the use of a previously described comparative LC-MS method, differential mass spectrometry (dMS), to analyze high-resolution Fourier transform mass spectrometry (FTMS) data for detection and quantification of known peptide differences between two sets of complex mixtures. Six standard peptides were spiked into a processed plasma background at fixed ratios from 1.25:1 to 4:1 to make two sets of samples. The resulting mixtures were analyzed by microcapillary LC-FTMS and dMS. dMS successfully identified five out of the six peptides as statistically significant differences (p <or= 0.005). In this experiment, the smallest fold change reliably detected by our method was 1.5:1, and the errors of estimated ratios of concentrations were less than 20% for peptides spiked at 1.5:1 to 4:1. We conclude that LC-FTMS coupled with dMS is a useful label-free quantitative MS method that can be used to detect subtle yet statistically significant peptide differences in complex protein mixtures, including plasma samples.

Amino Acid Sequence↗

PubMed2006

Surface enhanced laser desorption ionization spectrometry reveals biomarkers for drug treatment but not dose.

Here we describe the use of SELDI-MS to detect dose-dependent peptide changes in plasma from mice treated with vehicle or rosiglitazone at one of two doses (10 and 30 mg/kg). SELDI features differentiating spectra from the three conditions were found and used to train classifiers. Samples treated with vehicle could be reliably distinguished from samples treated with either dose, but samples treated with the different doses could not be reliably distinguished from one another. We conclude that while SELDI-TOF mass spectra can be used to distinguish treated from untreated samples, the reproducibility and information content of SELDI-TOF are currently not sufficient as a pharmacodynamic readout to distinguish between mice treated with 10 or 30 mg/kg of rosiglitazone. This raises more general questions about whether SELDI's sensitivity is sufficient for detecting dose-dependent changes in plasma.

Animals↗

PubMed2004

Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures.

Efficiently identifying and quantifying disease- or treatment-related changes in the abundance of proteins is an important area of research for the pharmaceutical industry. Here we describe an automated, label-free method for finding differences in complex mixtures using complete LC-MS data sets, rather than subsets of extracted peaks or features. The method selectively finds statistically significant differences in the intensity of both high-abundance and low-abundance ions, accounting for the variability of measured intensities and the fact that true differences will persist in time. The method was used to compare two complex peptide mixtures with known peptide differences. This controlled experiment allowed us to assess the validity of each difference found and so to analyze the method's sensitivity and specificity. The method detects both presence versus absence and a 2-fold change in peptide concentration near the limit of detection of the instrument used, where chromatographic peaks may not be sufficiently well defined to be detected in individual samples. The method is more sensitive and gives fewer false positives than subtractive methods that ignore signal variability. Differential mass spectrometry combined with targeted MS/MS analysis of only identified differences may save both computation time and human effort compared to shotgun proteomics approaches.

Chromatography, Liquid↗

PubMed2003

Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model.

In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do not. Order statistics are time-consuming to compute in the general case. We demonstrate that data from neurons in primary visual cortex are well fit by a mixture of Poisson processes; in this special case, our computations are substantially faster. In these data, spike timing contributed information beyond that available from the spike count throughout the trial. At the end of the trial, a decoder based on the mixture-of-Poissons model correctly decoded about three times as many trials as expected by chance, compared with approximately twice as many as expected by chance using the spike count only. If our model perfectly described the spike trains, and enough data were available to estimate model parameters, then our Bayesian decoder would be optimal. For four-fifths of the sets of stimulus-elicited responses, the observed spike trains were consistent with the mixture-of-Poissons model. Most of the error in estimating stimulus probabilities is attributable to not having enough data to specify the parameters of the model rather than to misspecification of the model itself.

Action Potentials↗

PubMed2003

An adjustment to the time-rescaling method for application to short-trial spike train data.

It is important to validate models of neural data using appropriate goodness-of-fit measures. Models summarizing some response features--for example, spike count distributions or peristimulus time histograms--can be assessed using standard statistical tools. Measuring the fit of a full point-process model of spike trains is more difficult. Recently, Barbieri, Quirk, Frank, Wilson, and Brown (2001) and Brown, Barbieri, Ventura, Kass, and Frank (2002) presented a method for rescaling time so that if an underlying description correctly describes the conditional intensity function of a point process, the rescaling will convert the process into a homogeneous Poisson process. The corresponding interevent intervals are exponential with mean 1 and can be transformed to be uniform; tests of the uniformity of the transformed intervals are thus tests of how well the model fits the data. When the lengths of interevent intervals are comparable to the length of the observation window, as can happen in common neurophysiology paradigms using short trials, the fact that long intervals cannot be observed (are censored) can cause the tests based on time rescaling to reject a correct model inappropriately. This article presents a simple adjustment to the time-rescaling method to account for interval censoring, avoiding inappropriate rejection of acceptable models for short-trial data. We illustrate the adjustment's effect using both simulated data and short-trial data from monkey primary visual cortex.

Action Potentials↗

PubMed2002

What geometric visual hallucinations tell us about the visual cortex.

Many observers see geometric visual hallucinations after taking hallucinogens such as LSD, cannabis, mescaline or psilocybin; on viewing bright flickering lights; on waking up or falling asleep; in "near-death" experiences; and in many other syndromes. Klüver organized the images into four groups called form constants: (I) tunnels and funnels, (II) spirals, (III) lattices, including honeycombs and triangles, and (IV) cobwebs. In most cases, the images are seen in both eyes and move with them. We interpret this to mean that they are generated in the brain. Here, we summarize a theory of their origin in visual cortex (area V1), based on the assumption that the form of the retino-cortical map and the architecture of V1 determine their geometry. (A much longer and more detailed mathematical version has been published in Philosophical Transactions of the Royal Society B, 356 [2001].) We model V1 as the continuum limit of a lattice of interconnected hypercolumns, each comprising a number of interconnected iso-orientation columns. Based on anatomical evidence, we assume that the lateral connectivity between hypercolumns exhibits symmetries, rendering it invariant under the action of the Euclidean group E(2), composed of reflections and translations in the plane, and a (novel) shift-twist action. Using this symmetry, we show that the various patterns of activity that spontaneously emerge when V1's spatially uniform resting state becomes unstable correspond to the form constants when transformed to the visual field using the retino-cortical map. The results are sensitive to the detailed specification of the lateral connectivity and suggest that the cortical mechanisms that generate geometric visual hallucinations are closely related to those used to process edges, contours, surfaces, and textures.

Hallucinations↗

PubMedDate not supplied

Model based decoding of spike trains.

Reliably decoding neuronal responses requires knowing what aspects of neuronal responses are stimulus related, and which aspects act as noise. Recent work shows that spike trains can be viewed as stochastic samples from the rate variation function, as estimated by the time dependent spike density function (or normalized peristimulus time histogram). Such spike trains are exactly described by order statistics, and can be decoded millisecond-by-millisecond by iterative application of order statistics.

Action Potentials↗