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At least 127 records · Page 7Linked to original sources
PubMed1999

Generalizable patterns in neuroimaging: how many principal components?

Generalization can be defined quantitatively and can be used to assess the performance of principal component analysis (PCA). The generalizability of PCA depends on the number of principal components retained in the analysis. We provide analytic and test set estimates of generalization. We show how the generalization error can be used to select the number of principal components in two analyses of functional magnetic resonance imaging activation sets.

Algorithms↗
PubMed2003

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images.

Adult↗
PubMed1984

Principal-components analysis of prehistoric South Asian crania.

Principal-components analysis is used as an investigative procedure for establishing temporal, spatial and evolutionary-developmental changes in Homo sapiens skeletal specimens from prehistoric sites in South Asia. It is concluded that cranial variables which cluster hunter-gatherers within the sample are related to facial architecture with respect to robusticity and size. Older models presumably reflecting genetic affinities and racial classifications of prehistoric South Asians which were based upon univariate-bivariate statistical analyses are not supported by the results of this principal-components analysis.

Asia, Western↗
PubMed2006

Can principal components yield a dimension reduced description of protein dynamics on long time scales?

The suitability of principal component analysis (PCA) to yield slow collective coordinates for use within a dimension reduced description of conformational motions in proteins is evaluated. Two proteins are considered, T4 lysozyme and crambin. We present a quantitative evaluation of the convergence of conformational coordinates obtained with principal component analysis. Detailed analyses of (>200 ns) molecular dynamics trajectories and crystallographic data suggests that simulations of a few nanoseconds should generally provide a stable and statistically reliable definition of the essential and near constraints subspaces. Moreover, a systematic assessment of the density of states of the dynamics of all principal components showed that for an optimal separation of time scales it is crucial to include also side chain atoms in the PCA.

Algorithms↗
PubMed2005

Fungus covered insulator materials studied with laser-induced fluorescence and principal component analysis.

A method combining laser-induced fluorescence and principal component analysis to detect and discriminate between algal and fungal growth on insulator materials has been studied. Eight fungal cultures and four insulator materials have been analyzed. Multivariate classifications were utilized to characterize the insulator material, and fungal growth could readily be distinguished from a clean surface. The results of the principal component analyses make it possible to distinguish between algae infected, fungi infected, and clean silicone rubber materials. The experiments were performed in the laboratory using a fiber-optic fluorosensor that consisted of a nitrogen laser and an optical multi-channel analyzer system.

Equipment Contamination↗
PubMed2002

A near infrared spectroscopic discrimination of noodle flours using a principal-component analysis coupled with chemical information.

Using a portable near infrared (NIR) spectrometer, we discriminated flours for making Japanese noodles (Soba), not only relying on a statistical and mathematical approach, but also on a chemical interpretation of the NIR spectra. In original NIR spectra, the particle-size difference, which results in an undesired systematic variation, was extracted and interpreted as the first-principal component factor by a principal-component analysis. The discrimination of flour materials cannot be satisfied by this factor. However, after a standardized treatment for the original spectra, the particle-size effects were eliminated; alternatively, differences in the chemical contents were extracted as principal-component factors. Using these factors, flour material discrimination was achieved much better. This study suggests a novel idea of utilizing the wavelength contribution ratio spectra for interpreting the factors extracted from the principal-component analysis for the NIR spectra. This report also describes the relationship between the NIR spectra and the chemical-analysis data.

Fagopyrum↗
PubMed1992

Principal components and multidimensional scaling of auditory and visual event-related potential topography.

Principal components analysis and metric multidimensional scaling were used to assess auditory and visual event-related potential topography in healthy late-middle-aged and elderly adults (n = 20). Binaurally elicited auditory evoked potentials and full-field checkerboard pattern reversal visual event-related potentials were recorded from 28 scalp sites. The zero-lag, cross-correlations of all waveforms for 300 msec post-stimulus epochs were obtained and separate analyses of the auditory and visual data were performed. Ultimately, three of the four dimensions identified in the principal components and multidimensional scaling solutions were similar and represented electrode site differences in (1) the anterior-posterior plane; (2) laterality; and, (3) the proximal-distal relation to midline. The remaining multidimensional scaling axis appeared to reflect effects specific to the modality of stimulation. Under auditory stimulation, the temporal and central-parietal sites were distinct from other scalp regions, whereas under visual stimulation, the occipital and frontal sites were distinctive. Although the results of the principal component analyses were conceptually similar to the multidimensional scaling outcomes, there were consistent differences between them. The findings provide empirical support for the validity of these multivariate methods in topographic analysis.

Acoustic Stimulation↗
PubMed2001

NMR spectral quantitation by principal component analysis.

The use of principal component analysis (PCA) for simultaneous spectral quantitation of a single resonant peak across a series of spectra has gained popularity among the NMR community. The approach is fast, requires no assumptions regarding the peak lineshape and provides quantitation even for peaks with very low signal-to-noise ratio. PCA produces estimates of all peak parameters: area, frequency, phase and linewidth. If desired, these estimates can be used to correct the original data so that the peak in all spectra has the same lineshape. This ability makes PCA useful not only for direct peak quantitation, but also for processing spectral data prior to application of pattern recognition/classification techniques. This article briefly reviews the theoretical basis of PCA for spectral quantitation, addresses issues of data processing prior to PCA, describes suitable and unsuitable datasets for PCA applications and summarizes the developments and the limitations of the method.

Magnetic Resonance Spectroscopy↗
PubMed2005

Use of principal component analysis to evaluate the partial organ tolerance of normal tissues to radiation.

PURPOSE: To describe a novel method of analyzing partial volume effects of normal tissues to radiation. With this approach, principal component analysis (PCA) is used to efficiently describe the variance in cumulative dose-volume histogram (cDVH) morphology. The independent features of cDVHs that describe the largest variance are then investigated regarding complication risk. METHODS AND MATERIALS: Principal component analysis was used to describe the variance in the morphology of normal tissue cDVHs, irrespective of complication, by summarizing the largest source of variation within the first principal component (PC), the next largest in the second PC, and so on. Plots relating the most meaningful PCs were constructed. Ideally, cDVHs associated with a complication would yield PC values that could be easily segregated from cDVHs without a complication. Two data sets were evaluated with this approach: 90 parotid gland cDVHs (36 with complications) and 203 liver cDVHs (19 with complications). RESULTS: Ninety-four percent and 80% of the variation in cDVH morphology was described with two PCs for the parotid gland and the liver data sets, respectively. Plots of the first and second PC values on a Cartesian plane for both data sets revealed "clusters." For the parotid gland, one cluster contained PCs from parotid gland cDVHs with complications, and the other primarily contained PCs from cDVHs without complications. The first PC value, corresponding to a larger volume treated with 10-60 Gy (2 Gy per fraction), was more likely to be larger in parotid gland cDVHs associated with complications than those without complications. In the plots of PC values of liver cDVHs, whole liver radiation cDVHs were segregated from the other cDVHs. There was a trend for cDVHs with a higher first PC, corresponding to increased volume treated with approximately 10-40 Gy (1.5 Gy b.i.d.), to be associated with increased risk of complication. For partial liver radiation cDVHs there was a trend for cDVHs with a higher first PC, corresponding to an increased volume treated with 5-50 Gy, to be associated with a complication. For each data set, logistic regression modeling revealed that the first PC was significantly associated with a complication developing (p < 0.02). CONCLUSIONS: Principal component analysis can be used to summarize the variance in parallel normal tissue cDVHs, and it can help segregate cDVHs at high or low risk for complications.

Adult↗
PubMed2003

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

Biomechanical Phenomena↗
PubMed1989

Principal-Component Analysis of the Characteristics Desirable in Baker's Yeasts.

Twenty-seven properties considered to be required for good bakery products were examined in 56 industrial and 2 laboratory yeast strains. The data obtained were applied to principal-component analysis, one of the multivariate statistical analyses. The first and second principal components together were extracted, and these accounted for 77.7% of the variance. The first principal component was interpreted as the glycolytic activity of yeast in dough, and the second one was interpreted as the balance of leavening abilities in sweet and flour doughs from the factor loadings. The scattergram on the two principal components was effective in grouping the 58 yeast strains used.

Journal Article↗
PubMed1989

Assessment of air pollution sources in an industrial atmosphere using principal component and multilinear regression analysis.

Aerosol samples collected in the industrial area of Estarreja, Portugal, were used to assess the source classes responsible for the particulate levels observed in the local atmosphere. Principal Component Analysis was applied separately to the concentrations of aerosol constituents and meteorological variables to obtain the number of Principal Components and to verify the influence of weather conditions on ambient air quality. The technique led to the conclusion that soil and transport emissions represent important aerosol sources even in this industrial environment. The quantitative contribution of each source class was calculated using Multilinear Regression Analysis; 37% of the aerosol mass had a soil origin, 8% was from sea spray, 18% resulted from transport emissions and 24% contained ammonium salts. Twelve percent of total suspended particle (TSP) mass could not be explained by any of the six Principal Components retained. Ammonium salts and two other minor Principal Components seem to result mainly from industrial emissions. More specific information about the contribution of each particular source was not possible with this technique.

Air Pollutants, Occupational↗
PubMed1990

[Multivariate analysis for the study of craniofacial structure. Selection of parameters on using the principal component analysis].

The present study was undertaken to discuss the effects of parameters on using the principal component analysis for the evaluation of the craniofacial structures. Materials consisted of lateral roentgenocephalograms of 100 adult Japanese females. They were divided into three groups by ANB angle. Linear and angular measurements were selected as parameters to apply the principal component analysis, and scattergrams made by the first and second principal component were compared. The results of the analysis were varied by the parameters. Especially there was a great difference between the real size and the value corrected by isometric method on scattergrams made by the first and second components. Results indicated the importance of using appropriate parameters for the research purpose.

Adult↗
PubMed1999

Principal component analysis of measured quantities during degradation of hydroperoxides in oxidized vegetable oils.

Decomposition of hydroperoxides in sunflower oil under strictly oxygen-free conditions was followed by measuring peroxide values against time, absorbance values at 232 and 268 nm, para-anisidine values, and by quantitative analyses of volatile products using various additives. The results were arranged in a matrix form and subjected to principal component analysis. Three principal components explained 89-97% of the total variance in the data. The measured quantities and the effect of additives were closely related. Characteristic plots showed similarities among the measured quantities (loading plots) and among the additives (score plots). Initial decomposition rate of hydroperoxides and the amount of volatile products formed were similar to each other. The outliers, the absorbance values, were similar to each other but carried independent information from the other quantities. Para-anisidine value (PAV) was a unique parameter. Since PAV behaved differently during the course of hydroperoxide degradation, it served as a kinetic indicator. Most additives were similar in their effects on the mentioned quantities, but two outliers were also observed. Rotation of the principal component axes did not change the dominant patterns observed. The investigations clearly showed which variables were worth measuring to evaluate different additives.

Analysis of Variance↗
PubMed2005

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis.

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.

Aminopyrine↗
PubMed1996

Principal components describing biological activities and molecular diversity of heterocyclic aromatic ring fragments.

Ten physicochemical variables have been calculated for each of 100 different aromatic rings. These variables were selected because of their potential involvement in the molecular recognition of drug-receptor binding interactions, and they include size, lipophilicity, dipole magnitude and orientation, HOMO and LUMO energies, and electronic point charges. A total of 59 different aromatic ring systems were studied including monocyclics and [5.5]-, [6.5]- and [6.6]-fused bicyclics. A principal components analysis of b1ese results generated four principal components which account for 84% of the total variance in the data. These principal components provide a quantitative measure of molecular diversity, and their relevance for structure-activity relationships is discussed. The principal components correlate with the in vitro biological activity of heterocyclic aromatic fragments within a series of previously reported HIV-1 reverse transcriptase inhibitors.

Chemical Phenomena↗
PubMed1996

[Fast and accurate numerical method for principal components analysis dealing with large image data sets].

The principal components analysis has been applied to various imaging studies in nuclear medicine. This technical report describes a fast and accurate numerical method of calculating eigenvalues and eigenvectors in the principal components analysis dealing with larger image data sets. The method employs both data transformation and matrix transpose of original data sets to calculate a variance-covariance or correlation matrix. The method was tested on actual image data sets using a common workstation, confirming faster execution time and efficient accuracy in comparison to a standard method.

Image Processing, Computer-Assisted↗
PubMed1984

PCAVR: a portable laboratory program for performing varimax-rotated principal components analysis of event-related potentials.

A portable laboratory computer program for performing varimax-rotated principal components analysis (PCA) of event-related potentials (ERPs) is described. The program is written in FORTRAN 77; its compiled version requires 429, 140 bytes of memory. The program reads a matrix of numbers from an input file. The PCA can be performed on either the variance/covariance or the correlation matrix. The program computes the first six principal components. The user is given the option of rotating as many of the principal components as desired based upon the percentages of variance that they account for. Eigenvalues, percentages of variance, cumulative percentages of variance, factor loadings, and factor scores are written to an output file. Description of the program is preceded by a conceptual overview both of PCA as a factor analytic technique and of the application of PCA to ERP data analysis.

Brain↗