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PubMed1993

Functional connectivity: the principal-component analysis of large (PET) data sets.

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.

Algorithms↗
PubMed2006

Application of principal component analysis and Raman spectroscopy in the analysis of polycrystalline BaTiO3 at high pressure.

The principal component analysis (PCA) was applied to Raman spectra of polycrystalline BaTiO(3) under pressure from atmospheric pressure to approximately 6.72 GPa. For the system utilized, PCA was able to distinguish spectral features and to determine the phase transition pressure: tetragonal to cubic at approximately 2.0 GPa. The present study demonstrates the potentialities of the application of PCA to the investigation on phase transitions at high pressure by Raman spectroscopy.

Barium Compounds↗
PubMed2006

Megavariate analysis of environmental QSAR data. Part I--a basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD).

This paper introduces principal component analysis (PCA), partial least squares projections to latent structures (PLS), and statistical molecular design (SMD) as useful tools in deriving multi- and megavariate quantitative structure-activity relationship (QSAR) models. Two QSAR data sets from the fields of environmental toxicology and environmental chemistry are worked out in detail, showing the benefits of PCA, PLS and SMD. PCA is useful when overviewing a data set and exploring relationships among compounds and relationships among variables. PLS is the regression extension of PCA and is used for establishing QSARs. SMD is essential for selecting informative training and test sets of compounds for QSAR calibration and validation.

Data Interpretation, Statistical↗
PubMed2004

Principal component analysis is a powerful instrument in occupational hygiene inquiries.

Several investigators have successfully used principal component analysis (PCA) in interpreting occupational hygiene data. However, traditional textbooks in occupational hygiene provide no guidance for the application and interpretation of PCA. In this article I briefly review the basics of PCA (for those not statistically inclined), provide some guidelines for performing PCA (and designing studies that use the power of PCA), illustrate its application in understanding exposure to mixtures and the characterization of 'peak exposure', and highlight other benefits that occupational hygienists stand to gain by including PCA in their 'statistical toolkit'. I hope that this article will promote greater use and understanding of a data analysis approach that has long been helping investigators outside the field of occupational hygiene to unravel the structure behind the complex relationships among multiple correlated variables.

Humans↗
PubMed1986

The principal component structure of the General Health Questionnaire among Greek and Turkish adolescents.

The 28-item version of the General Health Questionnaire was administered to Greek and Turkish school pupils in their mother country as well as to Greeks in Munich. Principal component analysis with varimax rotation was carried out separately for both the individual populations and sexes separately. Visual inspection of the matrices suggested overall agreement with the sub-scales obtained by Goldberg and Hillier (1979). The coefficient of factor similarity, calculated between matrices, suggested a highly similar principal component structure between the population samples as a whole and separately for the sexes living in their own country, but not between male and female Greek pupils in Germany. Highly similar component matrices were obtained for Greek males living in Greece with those in Germany, but not for females.

Adolescent↗
PubMed2000

Application of principal component regression to luminescence data for the screening of ciprofloxacin and enrofloxacin in animal tissues.

A new screening method for the analysis of enrofloxacin and ciprofloxacin in edible animal tissues is described. The method is based on the application of principal component regression to luminescence measurements after reaction of quinolones with terbium(III) in a micellar medium. The method was used, first, to discriminate between quinolone-containing or quinolone-not-containing samples (concentration below the detection limit) and, then, to quantify the sum of both analytes. Standards in a pure-water matrix, using the first three principal components, were used for the determination. RRMSE range from 4 to 10% depending on the analyte. Calibration was successfully applied to the analysis of spiked chicken and trout muscle at concentrations between 10 and 50 micrograms kg-1.

Animals↗
PubMed2005

Principal component analysis of trace elements in Serbian wheat.

Trace elements (Cu, Fe, Pb, Hg, Cd, As, Mn, Zn) were analyzed quantitatively in 14 wheat samples collected from fields in all Serbian growing regions, harvested in 2002. Microelements were determined according to an atomic absorption spectrophotometric method. Principal component analyses (PCA) were performed on data matrices consisting of contents of trace elements in wheats (columns) and all Serbian wheat-growing regions (rows). It was found that four principal components account for 87.2% of the total variance in the data. The plot of component loadings showed significant groupings for concentration of some microelements. The component scores indicated the similarities among the Serbian wheat-growing regions. The loading plot reveals that there is no need to measure all of the variables to achieve the same classification. It is enough to measure one variable per group. Naturally, this conclusion is valid only within the limits of the present study of wheat grain samples from different parts of Serbia.

Seeds↗
PubMed1983

Quantitative gait evaluation of hip diseases using principal component analysis.

The measurement of five gait parameters, namely, joint angular displacement of lower extremities, floor reaction forces, trajectory for a point of force application, temporal factor and distance factor has been performed with ease and high speed using mini-computer on-line real-time processing. Gait data of 211 patients with hip diseases was normalized, quantified and summarized by the principal component analysis. A 'gait evaluation plane' was formed according to the results obtained by the principal component analysis. The gait evaluation using the plane was compared with clinical conditions of patients, and it was evident that this system can evaluate the recovery of the gait by treatment.

Biomechanical Phenomena↗
PubMed2000

Human arm movements described by a low-dimensional superposition of principal components.

A new method for analyzing kinematic patterns during smooth movements is proposed. Subjects are asked to move the end of a two-joint manipulandum to copy a smooth initial target path. On subsequent trials the target path is the subject's actual movement from the preceding trial. Using Principal Components Analysis, it is shown that the trajectories have very low dimension and that they converge toward a linear superposition of the first few principal components. We show similar results for handwriting on an electronic pen tablet. We hypothesize that the low dimensionality and convergence are attributable to combined properties of the internal controller and the musculoskeletal system. The low dimensionality may allow for efficient descriptions of a large class of arm movements.

Arm↗
PubMed2003

Conditional pairwise estimation in the Rasch model for ordered response categories using principal components.

In the Rasch model for items with more than two ordered response categories, the thresholds that define the successive categories are an integral part of the structure of each item in that the probability of the response in any category is a function of all thresholds, not just the thresholds between any two categories. This paper describes a method of estimation for the Rasch model that takes advantage of this structure. In particular, instead of estimating the thresholds directly, it estimates the principal components of the thresholds, from which threshold estimates are then recovered. The principal components are estimated using a pairwise maximum likelihood algorithm which specialises to the well known algorithm for dichotomous items. The method of estimation has three advantageous properties. First, by considering items in all possible pairs, sufficiency in the Rasch model is exploited with the person parameter conditioned out in estimating the item parameters, and by analogy to the pairwise algorithm for dichotomous items, the estimates appear to be consistent, though unlike for the dichotomous case, no formal proof has yet been provided. Second, the estimates of each item parameter is a function of frequencies in all categories of the item rather than just a function of frequencies of two adjacent categories. This stabilizes estimates in the presence of low frequency data. Third, the procedure accounts readily for missing data. All of these properties are important when the model is used for constructing variables from large scale data sets which must account for structurally missing data. A simulation study shows that the quality of the estimates is excellent.

Algorithms↗
PubMed1994

Independence of soleus H-reflex tests in control and spastic subjects shown by principal components analysis.

Different soleus H-reflex tests are used in the study of neurophysiological mechanisms of motor control. We studied the interdependence pattern of a number of soleus H-reflex tests, i.e., vibratory inhibition, the ratio of the reflex response to direct muscle potential (H/M ratio) and the homonymous recovery curve with a principal components analysis in 48 healthy controls and 38 patients with signs of the upper motoneuron syndrome. In controls, the analysis showed 3 independent principal components (PCs). Vibratory inhibition and H/M ratio loaded on separate components. Late facilitation and late inhibition variables of the recovery curve loaded on the third component due to the positive correlation (P < 0.001) between these variables. In spastic patients the analysis identified 4 independent PCs corresponding with vibratory inhibition, H/M ratio, late facilitation and late inhibition variables, respectively. The findings suggest that the mutual independence of the different soleus H-reflex tests in patients with the upper motoneuron syndrome has retained the control situation to a large extent.

Adolescent↗
PubMed1995

Harmonicity and anharmonicity in protein dynamics: a normal mode analysis and principal component analysis.

A comparison of a normal mode analysis and principal component analysis of a 200-ps molecular dynamics trajectory of bovine pancreatic trypsin inhibitor in vacuum has been made in order to further elucidate the harmonic and anharmonic aspects in the dynamics of proteins. An anharmonicity factor is defined which measures the degree of anharmonicity in the modes, be they principal modes or normal modes, and it is shown that the principal mode system naturally divides into anharmonic modes with peak frequencies below 80 cm-1, and harmonic modes with frequencies above this value. In general the larger the mean-square fluctuation of a principal mode, the greater the degree of anharmonicity in its motion. The anharmonic modes represent only 12% of the total number of variables, but account for 98% of the total mean-square fluctuation. The transitional nature of the anharmonic motion is demonstrated. The results strongly suggest that in a large subspace, the free energy surface, as probed by the simulation, is approximated by a multi-dimensional parabola which is just a rescaled version of the parabola corresponding to the harmonic approximation to the conformational energy surface at a single minimum. After 200 ps, the rescaling factor, termed the "normal mode rescaling factor," has apparently converged to a value whereby the mean-square fluctuation within the subspace is about twice that predicted by the normal mode analysis.

Animals↗
PubMed1999

Principal component analysis of proton nuclear magnetic resonance spectra of lipoprotein fractions from patients with coronary heart disease and healthy subjects.

Blood plasma was drawn from 12 healthy subjects and 12 patients with coronary heart disease (CHD). The lipoproteins were fractionated by serial ultracentrifugation. The methyl and methylene regions of proton nuclear magnetic resonance (1 H NMR) spectra of the lipoproteins very low density lipoprotein (VLDL), low density lipoprotein (LDL) and high density lipoprotein (HDL) were analysed by principal component analysis (PCA). Grouping patterns in the score plots and the profiles of the principal components revealed several characteristics of the spectra. LDL subparticle size among the CHD group was consistently skewed against smaller, denser subparticles. This feature was independent of the concentration of LDL cholesterol. Analysis of the LDL spectra by soft independent modelling of class analogy (SIMCA) showed that none of the samples from the CHD group could be assigned as healthy subjects (p < 0.05). We also found that the samples from the healthy subjects were associated with a higher concentration of HDL cholesterol and larger VLDL subparticles. The approach presented, in which PCA is used in combination with NMR spectroscopy, might be implemented in clinical studies to give information about lipoprotein subparticle distribution and lipid content.

Adult↗
PubMed2000

Assignment of enzyme substrate specificity by principal component analysis of aligned protein sequences: an experimental test using DNA glycosylase homologs.

We have studied the relationship between amino acid sequence and substrate specificity in a DNA glycosylase family by characterizing experimentally the specificity of four new members of the family. We show that principal component analysis (PCA) of the sequence family correctly predicts the substrate specificity of one of the novel homologs even though conventional sequence analysis methods fail to group this homolog with other sequences of the same specificity. PCA also suggested, correctly, that another homolog characterized previously differs in its specificity from those sequences with which it clusters by conventional criteria. These results suggest that principal component analysis of sequence families can be a useful tool in annotating genome sequences when there is ambiguity concerning which subfamily a new homolog belongs to. Published 2000 Wiley-Liss, Inc.

Amino Acid Sequence↗
PubMed1999

Inter- and intra-individual variability of ground reaction forces during sit-to-stand with principal component analysis.

Variable reduction is an important issue in biomechanics, because the definition of a non-redundant set of variables necessary for a complete description of a given motor act provides information about the motor strategy. A systematic tool for dealing with variable reduction problems is Principal Component Analysis. In this paper, as an example of an application of this technique, the set of Ground Reaction Forces (GRFs) provided by a six-component force plate, gained during standing up in a heterogeneous population of 82 normal individuals, was reduced to a set of fewer variables. Each subject was required to stand up from a chair five times at different, randomly self selected, speeds, obtaining a data set of 410 trials. Principal Components (PCs) of GRFs were computed for each trial. On average, over the ensemble of trials, first and second PCs (PC1 and PC2) explained together 90% of PCs. Inter- and intra-individual repeatability of the first two PCs was investigated by examining the correlation coefficient between PC waveforms obtained from the whole set of trials and within the set of trials performed by the same subject, respectively. While the PC1 exhibited repeatable patterns, the second one, although repeatable within the group of trials performed by the same subject, displayed marked inter-individual variability. Therefore, PC1 was related to intrinsic aspects of the motor task and PC2 to inter-subject features.

Adult↗
PubMed2002

Principal component analysis of biogenic amines and polyphenols in Hungarian wines.

Biogenic amines, polyphenols, and resveratrol were analyzed quantitatively in 25 different Hungarian wines from the same wine-making region, harvest of 1998. Polyphenols were determined according to a spectrophotometric method, whereas other substrates were analyzed using overpressured-layer chromatography (OPLC). Principal component analyses (PCA) were performed on data matrices consisting of substrates (columns) and different sorts of wines (rows) from the region of Pécs (southern Hungary). It was found that four (unrotated) principal components account for >80% of the total variance in the data. The plots of component loadings showed significant groupings for concentrations of biogenic amines (and polyphenols). Similarly, the component scores grouped according to the different sorts of wines. The loading plots reveal that there is no need to measure all of the variables to achieve the same characterization. It is enough to measure one variable per group. Naturally, this conclusion is valid only within the limits of the present study; wines from other regions may behave differently.

Biogenic Amines↗
PubMed2006

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data.

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Adolescent↗
PubMed1980

[Evaluation of functional scintigraphy of gastric emptying by the principal component method (author's transl)].

Gastric emptying of a standard semifluid test-meal, labeled with 99mTc-DTPA, was studied by functional scintigraphy in 88 subjects (normals, patients with duodenal and gastric ulcer before and after selective proximal vagotomy with and without pyloroplasty). Gastric emptying curves were analysed by the method of principal components. Patients after selective proximal vagotomy with pyloroplasty showed an rapid initial emptying, whereas this was a rare finding in patients after selective proximal vagotomy without pyloroplasty. The method of principal components is well suited for mathematical analysis of gastric emptying; nevertheless the results are difficult to interpret. The method has advantages when looking at larger collectives and allows a separation into groups with different gastric emptying.

Evaluation Studies as Topic↗