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Results for “Principal Component Analysis”

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PubMed2003

Morphological classification of the rat lateral cerebellar nuclear neurons by principal component analysis.

The deep cerebellar nuclei (DCN) constitute the major structures by which the cerebellum forwards its output to the rest of the brain. Although the connectivity of the DCN has been well studied, little is known about the interface-the neurons' soma and dendrites-between the DCN's inputs and outputs. We therefore decided to analyze the neurons' somatic and dendritic morphology by applying a multivariate approach (principal component analysis; PCA), in order to define morphological groups possibly related to distinct positions in the nuclear microcircuitry. The PCA was based on intracellularly stained neurons from the rat's lateral DCN and on 19 parameters that described the neurons' morphology. The PCA yielded two principal components that accounted for 46% of the variance. The first component, correlated with soma size, separated the majority of neurons (type I) from a population of small neurons (type II). The second component showed negative correlation with larger cells with more numerous primary dendrites and a more multipolar appearance (type Ia) and positive correlation with smaller neurons with asymmetric dendritic fields and tufted dendrites (type Ib). The preponderance of small somata in our type Ib neurons suggests that these neurons probably correspond to the inferior olive projection neurons. In summary, our results are in agreement with previous classifications, which distinguished projection neurons (type I) from local neurons (type II); furthermore, our results point to a hitherto undescribed dendritic morphological difference in the projection neurons. The latter may be important for understanding the phylogenetic changes seen in the mammalian lateral cerebellar nucleus.

Animals↗

PubMed2005

Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts.

A new method for the assessment of the atrial fibrillatory wave (AFW) from the ECG is presented. This methodology is suitable for signals registered from Holter systems, where the reduced number of leads is insufficient to exploit the spatial information of the ECG. The temporal dependence of the bio-electrical activity were exploited using principal component analysis. The main features of ventricular and atrial activity were extracted, and several basis signals for each subspace were determined. Hence, the estimated (AFW) are reconstructed exclusively from the basis signals that formed the atrial subspace. Its main advantage with respect to adaptive template subtraction techniques was its robustness to variations in the QRST morphology, which thus minimised QRST residua. The proposed approach was first validated using a database of simulated recordings with known atrial activity content. The estimated AFW was compared with the original AFW, obtaining correlation indices of 0.774 +/- 0.106. The suitability of this methodology for real recordings was also proven, though its application to a set of paroxysmal AF ECGs. In all cases, it was possible to detect the main frequency peak, which was between 4.6 Hz and 6.9 Hz for the patients under study.

Algorithms↗

PubMed2001

A principal component analysis of facial expressions.

Pictures of facial expressions from the Ekman and Friesen set (Ekman, P., Friesen, W. V., (1976). Pictures of facial affect. Palo Alto, California: Consulting Psychologists Press) were submitted to a principal component analysis (PCA) of their pixel intensities. The output of the PCA was submitted to a series of linear discriminant analyses which revealed three principal findings: (1) a PCA-based system can support facial expression recognition, (2) continuous two-dimensional models of emotion (e.g. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-1178) are reflected in the statistical structure of the Ekman and Friesen facial expressions, and (3) components for coding facial expression information are largely different to components for facial identity information. The implications for models of face processing are discussed.

Adolescent↗

PubMedDate not supplied

Beyond principal component analysis: canonical component analysis for data reduction in classification of EPs.

The authors tested a new procedure for the discrimination of EPs obtained in different stimulus situations. In contrast with principal component analysis (PCA) used so far for the purpose of data compression, the method referred to as canonical component analysis (CCA) is optimal for the purpose of discrimination. To illustrate this, the authors performed both PCA and CCA for the same material, then after carrying out discriminant analysis ( SWDA ) for the data transformed in this way, compared the performance of the two procedures in discrimination. In view of both the theoretical and practical considerations, the authors recommend that in the future researchers use CCA instead of PCA in EP studies for data reduction carried out for discrimination.

Computers↗

PubMedDate not supplied

Automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance spectra via complex principal component analysis.

Several algorithms for automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance (MR) spectra are investigated. First, a careful comparison between the complex principal component analysis (PCA) and the Hankel total least squares-based methods for quantifying the resonances in the spectral sets of magnetic resonance spectroscopy imaging (MRSI) spectra is presented. Afterward, we discuss a method based on complex PCA plus linear regression and a method based on cross correlation of the magnitude spectra for correcting frequency shifts of resonances in sets of MR spectra. Their advantages and limitations are demonstrated on simulated MR data sets as well as on an in vivo MRSI data set of the human brain.

Brain↗

PubMed2004

Flexibility of beta-sheets: principal component analysis of database protein structures.

Protein folds are built primarily from the packing together of two types of structures: alpha-helices and beta-sheets. Neither structure is rigid, and the flexibility of helices and sheets is often important in determining the final fold (e.g., coiled coils and beta-barrels). Recent work has quantified the flexibility of alpha-helices using a principal component analysis (PCA) of database helical structures (J. Mol. Bio. 2003, 327, pp. 229-237). Here, we extend the analysis to beta-sheet flexibility using PCA on a database of beta-sheet structures. For sheets of varying dimension and geometry, we find two dominant modes of flexibility: twist and bend. The distributions of amplitudes for these modes are found to be Gaussian and independent, suggesting that the PCA twist and bend modes can be identified as the soft elastic normal modes of sheets. We consider the scaling of mode eigenvalues with sheet size and find that parallel beta-sheets are more rigid than antiparallel sheets over the entire range studied. Finally, we discuss the application of our PCA results to modeling and design of beta-sheet proteins.

Databases, Protein↗

PubMed2004

Evaluation of the salt accumulation process during inundation in water resource of Contas river basin (Bahia-Brazil) applying principal component analysis.

The high salinization in some reservoirs of the Contas river basin (Bahia-Brazil) has been erroneously attributed only to concentration by evaporation. However, recent studies of this basin have shown that in period of intense rainfalls, occur an increase of the saline concentration in the flowing rivers of the reservoirs. The application of statistical methods (cluster and principal components analysis) have shown that this fact can be attributed to the discharge of saline waters from the small reservoirs of every drained area, provoked by inundation, is also an important factor in the salinization process. Thus the study of the geochemical variables: Na(+), K(+), Ca(2+), Mg(2+), Cl(-), SO(4)(2-) and CO(3)(2-), showed one group formed by Na(+) and Cl(-), attributed to the discharge of saline water provoked by inundation from a small reservoir, and a second group constituted by Ca(2+), Mg(2+), K(+) and SO(4)(2-), due to an increase provoked by the evaporation in the salinization process.

Disasters↗

PubMedDate not supplied

A general treatment of solubility. 3. Principal component analysis (PCA) of the solubilities of diverse solutes in diverse solvents.

A phenomenological study of solubility has been conducted using a combination of quantitative structure-property relationship (QSPR) and principal component analysis (PCA). A solubility database of 4540 experimental data points was used that utilized available experimental data into a matrix of 154 solvents times 397 solutes. Methodology in which QSPR and PCA are combined was developed to predict the missing values and to fill the data matrix. PCA on the resulting filled matrix, where solutes are observations and solvents are variables, shows 92.55% of coverage with three principal components. The corresponding transposed matrix, in which solvents are observations and solutes are variables, showed 62.96% of coverage with four principal components.

Journal Article↗

PubMed2004

A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line.

The monocyte-like human histiocytic lymphoma cell line U937 can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. We have used two-dimensional gel electrophoresis (2-DE), oligonucleotide microarrays and principal component analysis (PCA) to characterize the U937 cell line as a model system for the differentiation of monocytes into macrophages. A total of 226 differentially expressed proteins were found, of which 41 were selected by PCA for identification using matrix-assisted laser desorption/ionization tandem mass spectrometry. Based on the PCA results, three marker proteins were selected for confirmation of differential expression using Western blot and quantitative real time-PCR. The selected marker proteins were: gamma interferon inducible lysosomal thiol reductase, cathepsin D and adipocyte-fatty acid binding protein. All three proved to be good differentiation markers for macrophage maturation of U937 cells as well as peripheral blood-derived macrophages. The transcriptomics data revealed a large number of additional putative differentiation markers in U937 macrophages, many of which are known to be expressed in peripheral blood-derived macrophages. These include osteospontin, matrix metalloproteinase 9, and HC-gp39. Our results show that the characteristics of U937 macrophages resemble those of inflammatory (exudate) macrophages, exemplified by the down-regulation of 5' nucleotidase and the up-regulation of leucine aminopeptidase mRNAs. In conclusion, using the powerful combination of transcriptomics, 2-DE and PCA, our results show that U937 cells differentiated by PMA treatment are an excellent model system for monocyte derived macrophage generation from blood.

Cell Differentiation↗

PubMed2003

Two-dimensional infrared spectroscopy and principal component analysis studies on a new azobenzene derivative supramolecular system based on hydrogen bonds.

Infrared (IR) spectra of a supramolecular assembly with an azobenzene derivative and intermolecular hydrogen bonds have been measured in the temperature range from 30 to 200 degrees C to investigate heat-induced structural changes and thermal stability. Principal component analysis (PCA) and two kinds of two-dimensional (2D) correlation spectroscopy, variable-variable (VV) 2D and sample-sample (SS) 2D spectroscopy, have been employed to analyze the observed temperature-dependent spectral variations. The PCA and SS 2D correlation analyses have demonstrated that the complete decoupling of hydrogen bonds in the supramolecular assembly occurs between 110 and 115 degrees C, which is in good agreement with the results of a differential scanning calorimetry (DSC) study for the heating process. The PCA of the IR spectra in the region of 3600-3100 cm(-1) has illustrated that there are at least four principal components for the different NH2 and CONH species in the present supramolecular system. The VV 2D correlation spectroscopy study has provided information about the structure and strength of hydrogen bonds of NH2 and CONH groups and their temperature-dependent variations. The different species of hydrogen-bonded NH2 and CONH groups in the supramolecular system can be clarified by the VV 2D correlation analysis. The VV 2D correlation analysis has also revealed the specific order of the temperature-induced changes in the hydrogen bonds of NH2 and CONH groups.

Azo Compounds↗

PubMed2003

Fatigue among caregivers of chronic renal failure patients: a principal components analysis.

Quality of life for caregivers of ESRD patients has not been well addressed. The physical and psychological status of this overlooked group can be important in the recovery or adaptation of patients with chronic renal failure. One particular symptom of a reduced quality of life of such caregivers is that of fatigue. The study tested the reliability of both existing and newer fatigue measures. Measures with high reliability yielded a single construct of fatigue in a principal components analysis in this study of 99 caregivers. Implications for practice are addressed. Potential for further study is recommended.

Caregivers↗

PubMed2004

Feature selection of stabilometric parameters based on principal component analysis.

This study addresses the challenge of identifying the features of the Centre of pressure (COP) trajectory that are most sensitive to postural performance, with the aim of avoiding redundancy and allowing a straightforward interpretation of the results. Postural sway in 50 young, healthy subjects was measured by a force platform. Thirty-seven stabilometric parameters were computed from the one-dimensional and two-dimensional COP time series. After normalisation to the relevant biomechanical factors, by means of multiple regression models, a feature selection process was performed based on principal component analysis. Results suggest that COP two-dimensional time series can be primarily characterised by four parameters, describing the size of the COP path over the support surface; the principal sway direction; and the shape and bandwidth of the power spectral density plot. COP one-dimensional time series (antero-posterior (AP) and medio-lateral (ML)) can be characterised by six parameters describing COP dispersion along the AP direction; mean velocity along the ML and AP directions; the contrast between ML and AP regulatory activity; and two parameters describing the spectral characteristics of the COP along the AP direction. On the basis of the results obtained, some guidelines are suggested for the choice of stabilometric parameters to use, with the aim of promoting standardisation in quantitative posturography.

Adult↗

PubMed2006

Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis.

The accuracy of predictions of muscle force based on electromyography (EMG) is an important issue in biomechanics and kinesiology. Since human skeletal muscles show a high diversity and heterogeneity in their fiber architecture, it is difficult to properly align electrodes to the muscle fiber direction. Against this background, we analyzed the effect of different bipolar configuration directions on EMG-based force estimation. In addition, we investigated whether principal component analysis (PCA) can improve this estimation. High-density surface-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) between predicted and measured force was determined. We found the best bipolar configuration direction to cause a 13% lower RMSD relative to the worst direction. Optimal results were obtained with electrodes aligned with the expected main muscle fiber direction. We found that PCA reduced RMSD by about 40% compared to conventional bipolar electrodes and by about 12% compared to optimally aligned multiple bipolar electrodes. Thus, PCA contributes to the accuracy of EMG-based estimation of muscle force when using a high-density EMG grid.

Algorithms↗

PubMed2005

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

Antiviral Agents↗

PubMed2005

A construction of an averaged representation of human cortical gyri using non-linear principal component analysis.

Because of the complex shape of human cortical gyri and great variation between individuals, development of effective representation schemes which allow establishment of correspondence between individuals, extraction of average structure of a population, and co-registration has proved very difficult. We introduce an approach which extracts line representations of gyri at different depths from high resolution MRI, labels main gyri semi-automatically, and extracts a template from a population using non-linear principal component analysis. The method has been tested on data from 96 healthy human volunteers. The model captures the most salient shape features of all major cortical gyri, and can be used for inter-subject registration, for investigating regionalized inter-subject variability, and for inter-hemispheric comparisons.

Algorithms↗

PubMed1998

Addressing misallocation of variance in principal components analysis of event-related potentials.

Interpretation of evoked response potentials is complicated by the extensive superposition of multiple electrical events. The most common approach to disentangling these features is principal components analysis (PCA). Critics have demonstrated a number of caveats that complicate interpretation, notably misallocation of variance and latency jitter. This paper describes some further caveats to PCA as well as using simulations to evaluate three potential methods for addressing them: parallel analysis, oblique rotations, and spatial PCA. An improved simulation model is introduced for examining these issues. It is concluded that PCA is an essential statistical tool for event-related potential analysis, but only if applied appropriately.

Analysis of Variance↗

PubMed1992

A model of head-related transfer functions based on principal components analysis and minimum-phase reconstruction.

Free-field to eardrum transfer functions (HRTFs) were measured from both ears of 10 subjects with sound sources at 265 different positions. A principal components analysis of the resulting 5300 HRTF magnitude functions revealed that the HRTFs can be modeled as a linear combination of five basic spectral shapes (basis functions), and that this representation accounts for approximately 90% of the variance in the original HRTF magnitude functions. HRTF phase was modeled by assuming that HRTFs are minimum-phase functions and that interaural phase differences can be approximated by a simple time delay. Subjects' judgments of the apparent directions of headphone-presented sounds that had been synthesized from the modeled HRTFs were nearly identical to their judgments of sounds synthesized from measured HRTFs. With fewer than five basis functions used in the model, a less faithful reconstruction of the HRTF was produced, and the frequency of large localization errors increased dramatically.

Attention↗

PubMed2005

Neuropathological changes in ten cases of neuronal intermediate filament inclusion disease (NIFID): a study using alpha-internexin immunohistochemistry and principal components analysis (PCA).

Ten cases of neuronal intermediate filament inclusion disease (NIFID) were studied quantitatively. The alpha-internexin positive neurofilament inclusions (NI) were most abundant in the motor cortex and CA sectors of the hippocampus. The densities of the NI and the swollen achromatic neurons (SN) were similar in laminae II/III and V/VI but glial cell density was greater in V/VI. The density of the NI was positively correlated with the SN and the glial cells. Principal components analysis (PCA) suggested that PC1 was associated with variation in neuronal loss in the frontal/temporal lobes and PC2 with neuronal loss in the frontal lobe and NI density in the parahippocampal gyrus. The data suggest: 1) frontal and temporal lobe degeneration in NIFID is associated with the widespread formation of NI and SN, 2) NI and SN affect cortical laminae II/III and V/VI, 3) the NI and SN affect closely related neuronal populations, and 4) variations in neuronal loss and in the density of NI were the most important sources of pathological heterogeneity.

Adult↗