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At least 145 records · Page 8Linked to original sources
PubMed1986

Geometrical principal component analysis of planar-segments of the three-channel Lissajous' trajectory of human auditory brain stem evoked potentials.

Three-Channel Lissajous' Trajectories (3CLTs) of Auditory Brain Stem Evoked Potentials (ABEP) were obtained from 15 normal humans. Planar-segments of 3CLT were identified and the orientations of the first two geometrical principal components, which interact to produce the planar-segments, were calculated. Each principal component's orientation in voltage space was quantified by its coefficients (A, B and C). Intersubject variability of these orientations was comparable to the variability of plane orientations. The principal components of planar-segments can indicate the type of generator activity that is involved in the formation of planar-segments. The results of this analysis indicate that planarity of each 3CLT component is produced by the interaction of simultaneous multiple generators, or by a single synchronous generator which changes its orientation. The coefficients of these principal components may complement plane coefficients as quantitative indices of 3CLT of ABEP.

Adult↗
PubMed1998

The Influence Function of Principal Component Analysis by Self-Organizing Rule.

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

Journal Article↗
PubMedDate not supplied

Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia.

Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm.

Cluster Analysis↗
PubMed2003

Shrinkage estimation for functional principal component scores with application to the population kinetics of plasma folate.

We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved estimation in the neighborhood of and at the diagonal of the covariance surface, where the measurement errors are reflected. The presence of additive measurement errors motivates shrinkage estimates for the functional principal component scores. Shrinkage estimates are developed through best linear prediction and in a generalized version, aiming at minimizing one-curve-leave-out prediction error. The estimation of individual trajectories combines data obtained from that individual as well as all other individuals. We apply our methods to new data regarding the analysis of the level of 14C-folate in plasma as a function of time since dosing of healthy adults with a small tracer dose of 14C-folic acid. A time transformation was incorporated to handle design irregularity concerning the time points on which the measurements were taken. The proposed methodology, incorporating shrinkage and data-adaptive features, is seen to be well suited for describing population kinetics of 14C-folate-specific activity and random effects, and can also be applied to other functional data analysis problems.

Adult↗
PubMed2005

Principal component analysis of complex multijoint coordinative movements.

Principal components analysis (PCA) has not been very much in vogue within the field of movement coordination even though it is useful to reduce data dimensionality and to reveal underlying data structures. Traditionally, studies of coordination between two joints have predominantly made use of relative phase analyses. This has resulted in the identification of principal constraints that govern the Central Nervous System's organization and the control of coordination patterns. However, relative phase analyses on pairwise joints have some drawbacks because they are not optimal for revealing convergent patterns among multijoint coordination modes and for unraveling generic control strategies. In this paper, we present a method to analyze multijoint coordination based on the properties of PC, more specifically the eigenvalues and eigenvectors of the covariance matrix. The comparison between relative phase analysis and PCA shows that both provide similar and consistent results, underscoring the latter technique's sensitivity to the study of coordination performance. In addition, it provides a method for automatic pattern detection as well as an index of performance for each joint within the context of the global coordination pattern. Finally, the merit of the PCA technique within the context of central pattern generators (CPG) will be discussed.

Adult↗
PubMedDate not supplied

The detection of abnormal neonatal cerebral haemodynamics using principal component analysis of the Doppler ultrasound waveform.

Doppler ultrasound recordings were made from the anterior cerebral arteries of 40 normal full-term babies and 14 mature babies with intracranial pathology. The maximum velocity waveforms were extracted using spectral analysis and further analyzed using the Pourcelot resistance index method and the principal component method. Principal component analysis was found to be superior to the currently widely used resistance index method and may significantly enhance the value of Doppler ultrasound for detecting abnormal cerebral haemodynamics in neonates.

Cerebral Arteries↗
PubMed1985

Principal components as indicators of body fatness and subcutaneous fat patterning.

Principal component analysis was performed on the skinfold data of contrasting groups of 50 New Guinean men, 47 New Guinean women and 138 Italian men. The common components of fatness and limb-trunk fat patterning were identified and the relationship of component scores to fatness and fat content, determined densitometrically, and to fat distribution as evinced from simple skinfold ratios, were ascertained. The interpretation of the components appeared correct: correlation coefficient between fat mass and first component scores was 0.89, P less than 0.001, and between abdomen/thigh skinfolds and second component scores was -0.78, P less than 0.001. However, log sigma skinfolds was as good an indicator of fatness and not all limb/trunk skinfold ratios were highly correlated with second component scores. The identification of the simplest and most rigorous method of describing fat patterning and distribution and the factors affecting them require further investigation but principal component analysis is proving useful.

Adipose Tissue↗
PubMed2005

Identification of distinct characteristics of postural sway in Parkinson's disease: a feature selection procedure based on principal component analysis.

We selected descriptive measures of the centre of pressure (CoP) displacement in quiet standing, by means of a procedure based on principal component analysis, in two groups particularly different in terms of postural behaviours, such as subjects with Parkinson's disease (PD) in the levodopa off and on states. We computed 14 measures of the CoP: 5 measures of CoP trajectory over the support surface, 3 measures that estimated the area covered by the CoP, 1 measure that estimated the principal CoP sway direction, 1 measure that quantified the CoP total power, 1 measure that estimated the variability of CoP frequency content and 3 measures of characteristic CoP frequencies [L. Rocchi, L. Chiari, A. Cappello, Feature selection of stabilometric parameters based on principal component analysis, Med. Biol. Eng. Comput. 42 (2004) 71-79; L. Rocchi, L. Chiari, F.B. Horak, Effects of deep brain stimulation and levodopa on postural sway in Parkinson's disease, J. Neurol. Neurosurg. Psychiatry, 73 (2002) 267-274]. The feature selection, independently applied to the measures obtained in the two groups, resulted in different principal component (PC) subspaces of the 14-dimension original data set (4 PCs in the off and 3 PCs in the on state to account for over 90% of the original variance), but in the same 5 CoP measures (selected features) needed to describe the different postural behaviours: root mean square distance; mean velocity; principal sway direction; centroidal frequency of the power spectrum; frequency dispersion. The five selected features were found to provide insight into the postural control mechanisms and to describe changes in postural strategies in the two groups of PD subjects, off and on levodopa. Thus, the five selected features may be recommended for use in clinical practice and in research, in the direction toward the definition of a standard protocol in quantitative posturography.

Aged↗
PubMed1984

Principal component analysis as a tool for studying interdependences among trace metals in edible mussels from the Gulf of Trieste.

Principal component analysis has been applied to analyze the correlation matrix obtained from a (6 X 44) data matrix. The 6 trace metals are Co, Ni, Cu, Cd, Hg, and Pb, which are contained in the soft part of edible mussels (Mytilus galloprovincialis Lamarck) from a hatchery on the gulf of Trieste. In the space of "eigenvectors", the dimensions are reduced to 3 principal axes, which account for about 77% of the total variance. The unrotated factor matrix obtained by the principal components shows that Co, Ni and Cu are bonded to the first principal component, Cd and Pb to the second, Hg to the third. This picture is also assessed by the Varimax-rotated matrix and it is tentatively explained, at least in part, by considering the associations of the trace metals from various geographical sources.

Animals↗
PubMed2001

The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data.

By using principal components analysis (PCA) we demonstrate here that the information relevant to tumor line classification linked to the activity of 1375 genes expressed in 60 tumor cell lines can be reproduced by only five independent components. These components can be interpreted as cell motility and migration, cellular trafficking and endo/exocytosis, and epithelial character. PCA, at odds with cluster analysis methods routinely used in microarray analysis, allows for the participation of individual genes to multiple biochemical pathways, while assigning to each cell line a quantitative score reflecting fundamental biological functions.

Cell Movement↗
PubMed2000

Principal component analysis of dynamical features in the peroxidase-oxidase reaction

Inherent variance due to oscillations in the peroxidase-oxidase (PO) reaction was studied using principal component analysis (PCA). The substrates were oxygen and reduced nicotinamide adenine dinucleotide (NADH). Horseradish peroxidase (HRP) catalyzed the reaction. The concentration of a cofactor, methylene blue (MB), was varied, and 2,4-dichlorophenol was kept constant. Increase in the NADH influx was used to change the reaction dynamics from periodic to chaotic. The reaction space was abstracted to the most significant, mutually independent, pairs of absorption and kinetic basis vectors (principal components). Typically, two significant principal components were extracted from the periodic time series and three from the chaotic data. The PCA models accounted for 70-97% of experimental variance. The greatest fraction of the total variance was accounted for in experiments exhibiting periodic dynamics and less than 25 nM MB. More MB induced an increased contribution of NADH to the PO oscillator variance, as did increased NADH influx. A simulated absorption time series, computed from a mass-action model of the chemistry, was analyzed by PCA as well. The comparison of simulation with experiment indicates that the chemical model renders the time series for HRP oxidation forms with fidelity, but incompletely represents NADH chemistry and other salient processes underlying the observed dynamics.

Journal Article↗
PubMed1997

Using principal-components regression to stabilize EMG-muscle force parameter estimates of torso muscles.

Models for estimating muscle force from surface electromyographic (EMG) recordings require parameter estimates with low intertrial variability. The inclusion of multiple muscles in multivariate statistical models can lead to multicollinearity, especially when there are significant correlations between synergist muscles. One result of multicollinearity is that parameter estimates are very sensitive to changes in the independent variables. This study compared the parameter variability of multiple regression and principal-components regression techniques when applied to a six muscle EMG analysis of the lumbar region of the torso. Nine subjects participated. Twenty-three percent of the traditional multiple-regression parameters had incorrect signs, but none of the principal-components regression parameters did. The principal-components regression technique also produced parameter estimates having an order of magnitude smaller parameter variability. It was concluded that principal-components regression is an effective method of mitigating the effect of multicollinearity in torso EMG models.

Adult↗
PubMed1992

Observations on a principal components analysis of head-related transfer functions.

A recent principal components analysis (Kistler and Wightman, 1992) has shown that the transfer functions of the human external ear, for a wide range of source locations, can be expressed as weighted sums of a small number of basis vectors. Directional transfer functions obtained in this laboratory, using substantially different measurement techniques, yielded principal component basis vectors that are remarkably similar to those reported by Kistler and Wightman. When this subject population was divided in half according to the overall physical sizes of subjects, basis vectors computed for the subpopulation of smaller subjects were shifted systematically to higher frequencies relative to those computed for the subpopulation of larger subjects.

Ear, External↗
PubMed1984

Principal component analysis of event-related potentials: simulation studies demonstrate misallocation of variance across components.

Simulated event-related potential (ERP) components were used to investigate the ability of principal component analysis (PCA), Varimax rotation and univariate analysis of variance (ANOVA) to reconstruct component wave shapes, to allocate variance correctly across components, and to identify the correct locus of simulated experimental treatments. The simulated ERPs consisted of 800 randomly weighted combinations of three 64-point components, corresponding to a 2 X 2 X 10 repeated-measures design with 20 subjects. Covariance PCAs, Varimax rotations and univariate ANOVAs were performed on each of 400 such simulations, 100 with no effect of any experimental treatment and 100 each with main effects on each of the 3 components. Eight hundred additional simulations were performed to investigate the effects of systematic variations in the size of the experimental treatments and the number of subjects per experiment. The wave shapes of the simulated components were reconstructed reasonably well, although not completely, by the rotated principal component (PC) loadings. However, comparison of rotated PC scores with the random weights used to generate the simulated ERPs indicated that PCA incorrectly allocated variance across overlapping components, producing dramatic increases in type I error (the largest in excess of 80%) for ANOVAs on one component when the true treatment effect was on another. Although these results should not be overgeneralized, they clearly demonstrate that the PCA-Varimax-ANOVA strategy can incorrectly distribute variance across components, resulting in serious misinterpretation of treatment effects. Additional simulation studies are needed to determine the generality of the variance misallocation problem; pending the outcome of such studies, results obtained with the PCA-Varimax-ANOVA strategy should be interpreted cautiously.

Analysis of Variance↗
PubMed2002

Block principal component analysis with application to gene microarray data classification.

We propose a block principal component analysis method for extracting information from a database with a large number of variables and a relatively small number of subjects, such as a microarray gene expression database. This new procedure has the advantage of computational simplicity, and theory and numerical results demonstrate it to be as efficient as the ordinary principal component analysis when used for dimension reduction, variable selection and data visualization and classification. The method is illustrated with the well-known National Cancer Institute database of 60 human cancer cell lines data (NCI60) of gene microarray expressions, in the context of classification of cancer cell lines.

DNA, Neoplasm↗
PubMed1986

Principal-component analysis of macaque lateral geniculate nucleus chromatic data.

Principal-component analysis was used to find a set of orthonormal basis vectors that could span the chromatic space of neurons in the parvocellular layers of the macaque monkey lateral geniculate nucleus (LGN). A total of 441 spectral curves measured on 147 cells from a well-known study [R. L. De Valois, I. Abramov, and G. H. Jacobs, J. Opt. Soc. Am. 56, 966 (1966)] were used as the input data. The principal-component technique, in a completely objective manner, showed that 93% of the variance in the data could be accounted for by only three eigenvectors. An orthogonal Varimax rotation of the first two eigenvectors matched the mean spectra of the previously identified GR and YB opponent cell groups. A different rotation gave a photopic luminosity curve.

Animals↗
PubMed1999

Principal component analysis of neuronal ensemble activity reveals multidimensional somatosensory representations.

Principal components analysis (PCA) was used to define the linearly dependent factors underlying sensory information processing in the vibrissal sensory area of the ventral posterior medial (VPM) thalamus in eight awake rats. Ensembles of up to 23 single neurons were simultaneously recorded in this area, either during long periods of spontaneous behavior (including exploratory whisking) or controlled deflection of single whiskers. PCA rotated the matrices of correlation between these n neurons into a series of n uncorrelated principal components (PCs), each successive PC oriented to explain a maximum of the remaining variance. The fact that this transformation is mathematically equivalent to the general Hebb algorithm in linear neural networks provided a major rationale for performing it here on data from real neuronal ensembles. Typically, most information correlated across neurons in the ensemble was concentrated within the first 3-8 PCs. Each of these was found to encode distinct, and highly significant informational factors. These factor encodings were assessed in two ways, each making use of fact that each PC consisted of a matrix of weightings, one for each neuron. First, the neurons were rank ordered according to the locations of the central whiskers in their receptive fields, allowing their weightings within different PCs to be viewed as a function of their position within the whisker representation in the VPM. Each PC was found to define a distinctly different topographic mapping of the cutaneous surface. Next, the PCs were used to weight-sum the neurons' simultaneous activities to create population vectors (PVs). Each PV consisted of a single continuous time series which represented the expression of each PC's 'magnitude' in response to stimulation of different whiskers, or during behavioral events such as active tactile whisking. These showed that each PC functioned as a feature detector capable of selectively predicting significant sensory or behavioral events with far greater statistical reliability than could any single neuron. The encoding characteristics of the first few PCs were remarkably consistent across all animals and experimental conditions, including both spontaneous exploration and direct sensory stimulation: PC1 positively weighted all neurons, mainly according to their covariance. Thus it encoded global magnitude of ensemble activity, caused either by combined sensory inputs or intrinsic network activity, such as spontaneous oscillations. PC2 encoded spatial position contrast, generally in the rostrocaudal dimension, across the whole cutaneous surface represented by the ensemble. PC3 more selectively encoded contrast in an orthogonal (usually dorsoventral) dimension. A variable number of higher numbered PCs encoded local position contrast within one or more smaller regions of the cutaneous surface. The remaining PCs typically explained residual 'noise', i.e. the uncorrelated variance that constituted a major part of each neuron's activity. Differences in behavioral or sensory experience produced relatively little in the PC weighting patterns but often changed the variance they explained (eigenvalues) enough to alter their ordering. These results argue that PCA provides a powerful set of tools for selectively measuring neural ensemble activity within multiple functionally significant 'dimensions' of information processing. As such, it redefines the 'neuron' as an entity which contributes portions of its variance to processing not one, but several tasks.

Animals↗
PubMed2002

Principal component analysis of TOF-SIMS images of organic monolayers.

Principal component analysis (PCA) is a statistical method used to find combinations of variables or factors that describe the most important trends in the data. PCA has been combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) data to extract new information and find relations between species contained in complex systems. Monolayers of dipalmitoylphosphatidylcholine alone and mixed with palmitoyloleoylphosphatidylglycerol prepared using the Langmuir-Blodgett technique are discussed. PCA software provides image scores and corresponding loadings for each significant principal component. Image plots of the scores show the spatial distribution and intensity of the species defined by the loading plots (mass spectral features). The intensity and resolution of the image scores can result in substantial improvement over that of the regular TOF-SIMS images especially when static conditions are used for small analysis areas. Also, some of the effects of topography and matrix in the images can be removed, allowing for a better presentation of chemical variations.

Journal Article↗