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EEG bands during wakefulness, slow-wave, and paradoxical sleep as a result of principal component analysis in the rat.

Rat EEG has been empirically divided in bands that frequently do not correspond with EEG generators nor with the functional meaning of EEG rhythms. Power spectra from wakefulness (W), slow-wave sleep (SWS), and paradoxical sleep (PS) of Wistar rats were submitted to Principal Component Analyses (PCA) to investigate which frequencies are covariant. Three independent eigenvectors were identified for SWS: a band between 1-6, an intermediate band between 7-15, and a fast band between 16-32 Hz (90.74% of the variance); two independent eigenvectors were extracted for PS: slow frequencies between 1-6 covarying together with frequencies between 11-16 Hz, and activity between 6-10 covarying together with fast frequencies between 17-32 Hz (80.38% of the variance); four eigen-vectors were obtained for W: 3-7, 8-9, 10-21 and 21-32 Hz (81.47% of the variance). Vigilance states showed significant differences in AP from 1 to 22 Hz. PCA extracted broad bands different for each vigilance state, which included the most representative EEG activities characteristic of them. These results indicate that during SWS, slow oscillations include frequencies up to 6 Hz, and spindle oscillations frequencies down to 7 Hz. No alpha frequencies were identified as an independent band. Frequencies within theta and beta were gathered in the same eigenvector during PS and in different eigenvectors during W suggesting coordinated activation of hippocampal and cortical systems during PS. These bands are consistent with the underlying neurophysiological mechanisms of sleep and wakefulness and with firing frequencies of generators of rhythmic activity obtained in cellular studies in animals.

Animals↗

Principal component analysis of the T wave in patients with chest pain and conduction disturbances.

There is a need for markers reflecting the increased risk in patients with conduction disturbances. Conduction disturbances presumably cause inhomogeneous repolarization that may create an arrhythmogenic substrate. In patients with normal conduction, parameters derived from principal components analysis (PCA) of the T wave contain prognostic information. The nondipolar PCA components are assumed to reflect repolarization inhomogeneity. This study examined the PCA parameters in relation to conduction disturbances. PCA was performed on continuously recorded 12-lead ECGs in 800 patients with chest pain and nondiagnostic ECG on admission. The patients with conduction disturbance on admission were classified into separate groups and related to comparison groups without conduction disturbance recruited from the same series. For each patient, the dipolar and nondipolar components were quantified by medians of the ratio of the two largest eigenvalues (S2/S1 Median), the residue that summarizes the eigenvalues S4-S8 (TWRabsMedian) and the ratio of this residue to the total power of the T wave (TWRrelMedian). The parameters were assessed with respect to common clinical and ECG parameters, discharge diagnosis, and total mortality during a 35-month follow-up. TWRabsMedian increased with increasing conduction disturbance. In 135 patients with conduction disturbances, ROC curves for TWRabsMedian as indicator of mortality exhibited areas under a curve of 0.66, 0.65, and 0.56 at 6-month, 24-month, and 35-month follow-up. Conduction disturbances were associated with increased nondipolar PCA component and, thus, with increased repolarization inhomogeneity. The nondipolar PCA component contained a moderate amount of prognostic information not present in a simple ECG diagnosis of a conduction disturbance.

Aged↗

Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks.

Theoretical models relating atom-based structural descriptors to 13C NMR chemical shifts were used to accurately simulate 13C NMR spectra of lignin model compounds (poly-substituted phenols). The structure-activity relationship (SAR) studies for 15 lignins using pattern recognition methods of principal component analysis (PCA) and artificial neural networks (ANNs) were performed in this work. The most important parameters affecting the 13C chemical shifts of different carbons were descriptors consisting of the charge density of the atoms at different distances from the center carbon. Among the large number of parameters, these descriptors were selected using PCA and were used as ANN input. The least square regression analyses of the results indicate correlation coefficient (R) values in excess of 0.983 for the total data set.

Carbon Isotopes↗

Principal component analysis of minimal excitatory postsynaptic potentials.

'Minimal' excitatory postsynaptic potentials (EPSPs) are often recorded from central neurones, specifically for quantal analysis. However the EPSPs may emerge from activation of several fibres or transmission sites so that formal quantal analysis may give false results. Here we extended application of the principal component analysis (PCA) to minimal EPSPs. We tested a PCA algorithm and a new graphical 'alignment' procedure against both simulated data and hippocampal EPSPs. Minimal EPSPs were recorded before and up to 3.5 h following induction of long-term potentiation (LTP) in CA1 neurones. In 29 out of 45 EPSPs, two (N=22) or three (N=7) components were detected which differed in latencies, rise time (Trise) or both. The detected differences ranged from 0.6 to 7.8 ms for the latency and from 1.6-9 ms for Trise. Different components behaved differently following LTP induction. Cases were found when one component was potentiated immediately after tetanus whereas the other with a delay of 15-60 min. The immediately potentiated component could decline in 1-2 h so that the two components contributed differently into early (< 1 h) LTP1 and later (1-4 h) LTP2 phases. The noise deconvolution techniques was applied to both conventional EPSP amplitudes and scores of separate components. Cases are illustrated when quantal size (upsilon) estimated from the EPSP amplitudes increased whereas upsilon estimated from the component scores was stable during LTP1. Analysis of component scores could show apparent double-fold increases in upsilon which are interpreted as reflections of synchronized quantal releases. In general, the results demonstrate PCA applicability to separate EPSPs into different components and its usefulness for precise analysis of synaptic transmission.

Algorithms↗

Correlative motions and memory effects in molecular dynamics simulations of molecules: principal components and rescaled range analysis suggest that the motions of native BPTI are more correlated than those of its mutants.

In this work MD simulations of the native bovine pancreatic trypsin inhibitor (BPTI) and 16 mutants were done in vacuum in order to study memory effects in the mutants using principal component analysis (PCA) and the rescaled range analysis (Hurst exponents). Both PCA and the rescaled range analysis support our previous proposition, based on PCA of lysozyme, that the motions of a native protein are more correlated than those of mutants. The methods are compared, the nature and applications of the rule and the role of the long-range correlations in MD time series (i.e. memory) are discussed in the context of collective motions.

Algorithms↗

Forecasting peak daily ozone levels--I. A regression with time series errors model having a principal component trigger to fit 1991 ozone levels.

This research was motivated by the need to warn the population of Milwaukee, WI, on high-ozone days. A statistical model for the peak daily 1-hr ozone level is proposed. A Regression with Time Series Errors (RTSE) model, which includes a principal component (PC) trigger, is the basis for forecasting the peak daily 1-hr ozone level. The RTSE model, with a PC trigger, is first employed to estimate daily peak ozone measured at the University of Wisconsin, Milwaukee-North (UWM-N), during the 1991 ozone season. The RTSE model uses peak daily temperature, morning vector average wind direction, and the PC trigger as predictor variables. The PC trigger was designed to summarize atmospheric circumstances when peak ozone was greater than 100 parts per billion (ppb). It is verified that the RTSE model, with a PC trigger, significantly improves the prediction of peak daily ozone, particularly peak ozone greater than 100 ppb. In comparison with the RTSE model without the PC trigger, the RTSE model with a PC trigger raised the R2 from 0.680 to 0.809. It is suggested that the RTSE model, with the PC trigger, is an adequate statistical model that has the potential for real-time ozone forecasting.

Air Pollutants↗

NMR of biofluids and pattern recognition: assessing the impact of NMR parameters on the principal component analysis of urine from rat and mouse.

The ability to interpret metabolic responses to toxic insult as expressed in altered urine composition and measured by NMR spectroscopy is dependent upon a database of proton NMR spectra of urine collected from both control and treated animals. Pattern recognition techniques, such as principal component analysis (PCA), can be used to establish whether the spectral data cluster according to a dose response. However, PCA will be sensitive to other variables that might exist in the data, such as those arising from the NMR instrument itself. Thus, studies were conducted to determine the impact that NMR-related variables might impart on the data, with a view towards understanding and minimizing variables that could interfere with the interpretation of a biological effect. This study has focused on solvent suppression methods, as well as instrument-to-instrument variability, including field strength. The magnitude of the NMR-induced variability was assessed in the presence of an established response to the nephrotoxin bromoethanamine. Changes caused by the model toxin were larger and easily distinguished from those caused by using different solvent suppression methods and field strengths.

Animals↗

A comparative investigation of the principal component structure of the 28 item version of the General Health Questionnaire (GHQ). 15-year-old schoolgirls in England, Greece, Turkey and West Germany.

The 28-item version of the General Health Questionnaire of 15-year-old schoolgirls obtained under identical conditions in two separate studies was subjected to principal component analysis (PCA). Varimax rotation produced different numbers of components for the different groups, but restricting the number of components to be rotated to four produced similar component structures, as supported by the coefficient of factor similarity, for both Turkish and Greek groups in their home countries and a heterogeneous non-British group in London in comparison to British girls. Different structures were obtained in schoolgirls from Greece, in Munich, and from the Indian subcontinent in London. Analysis of variance of the factor scores of a combined PCA produced significant overall group differences for all components and specific group differences for anxiety and insomnia, social dysfunction, and severe depression. Somatic symptoms and anxiety and insomnia subscales, either alone or in combination with other subscales, contributed most frequently to morbidity.

Adolescent↗

Tests of human olfactory function: principal components analysis suggests that most measure a common source of variance.

It is not known whether nominally different olfactory tests actually measure dissimilar perceptual attributes. In this study, we administered nine olfactory tests, including tests of odor identification, discrimination, detection, memory, and suprathreshold intensity and pleasantness perception, to 97 healthy subjects. A principal components analysis performed on the intercorrelation matrix revealed four meaningful components. The first was comprised of strong primary loadings from most of the olfactory test measures, whereas the second was comprised of primary loadings from intensity ratings given to a set of suprathreshold odorant concentrations. The third and fourth components had primary loadings that reflected, respectively, mean suprathreshold pleasantness ratings and a response bias measure derived from a yes/no odor identification signal detection task. In an effort to adjust for potential confounding influences of age, gender, smoking, and years of schooling on the component structure, a matrix of residuals from a multiple regression analysis, which included these variables, was also analyzed. A similar component pattern emerged. Overall, these findings suggest, in healthy subjects spanning a wide range, that (1) a number of nominally distinct tests of olfactory function are measuring a common source of variance, and (2) some suprathreshold odor intensity and pleasantness rating tests may be measuring sources of variance different from this common source.

Adult↗

Analysis of MTR histograms in multiple sclerosis using principal components and multiple discriminant analysis.

Magnetization transfer ratio (MTR) histograms have the potential to characterize subtle diffuse changes in multiple sclerosis (MS) and other white matter disease. A new method is described which gives improved correlation with the Expanded Disability Status Scale (EDSS). Classification of individual subjects into normal and MS subgroups is shown. Principal component analysis (PCA) and multiple discriminant analysis (MDA) are shown to give results superior to methods of MTR histogram analysis using traditional features such as peak height and peak location. Scatterplots confirm the improved separation between groups achieved using the MDA score. The histogram analysis provides a comparison of two classification approaches, based on PCA and MDA, to recognize differences between normal controls and the four different subgroups of MS disease (and all MS patients). Multiple linear regression of these PCs vs. EDSS established an MR-based measure of disease. Using a central 60-mm slab of brain tissue, the success rate of binary classification between control and MS subgroups using MDA was 75-95%, depending on which two groups were being compared. Multiple regression analysis of EDSS with the first three PCs as independent variables was significant (r = 0.83 for secondary progressive MS, and r = 0.80 for all MS patients).

Brain↗

Positive and negative symptoms in the psychoses: principal components analysis of items from the Scale for the Assessment of Positive Symptoms and the Scale for the Assessment of Negative Symptoms.

The present study investigated the factor structure of the items contained in Andreasen's scales for the assessment of positive and negative symptoms (SAPS and SANS) by use of a series of principal components analyses (PCAs) with oblique rotations of the axes. It was found that the structure could be summarized by three major components labeled negative symptoms, thought disorder, and delusions/hallucinations. Dimensionality could meaningfully be increased to five components. Negative symptoms was found to separate into two components that we labeled negative signs and social dysfunctions. The delusions/hallucinations factor could be separated into two components, delusions and hallucinations, with "loss of boundary" delusions being related to both factors. Delusions of persecution were independent of other symptoms. The thought disorder factor did not decompose meaningfully within the investigated dimensionality. A two-factor solution did not explain the correlation between symptoms adequately. The results do not support the simple dichotomy between positive and negative symptoms in psychosis, but suggest that a wider dimensional concept may be more useful in future studies.

Delusions↗

Inclusion of the standard deviation of data in principal component analysis. A graphical approximation.

The adsorption capacity and specific adsorption surface area of 13 anti-hypoxia drugs were determined in three chromatographic systems using methanol-carbon tetrachloride, chloroform-carbon tetrachloride and acetonitrile-carbon tetrachloride mixtures as eluents. The retention behaviours of the anti-hypoxia drugs were compared using principal component analysis (PCA). A graphical approximation was used for the inclusion of the standard deviations of both the variables and observations in PCA and the results were visualized by two-dimensional nonlinear mapping and cluster analysis. The results indicated that the graphical approximation can be successfully used for the inclusion of the standard deviation of data in PCA calculations. Nonlinear mapping and cluster analysis resulted in similar, but not identical, classification of drugs and chromatographic systems, indicating that each multivariate method can be successfully used for the comparison of solutes and chromatographic systems.

Chromatography↗

The use of principal components in the quantitative analysis of gamma camera dynamic studies.

The reduction of the enormous quantity of data in a radionuclide dynamic study to a few diagnostic parameters presents a problem. Conventional methods of data reduction using regions-of-interest or functional images have several defects which potentially limit their usefulness. Using a principal components analysis of the elemental curves representing the change of activity with time in each pixel, followed by a further factor analysis, it is possible to extract the fundamental functional changes of activity which underly the observed variation of activity. An example of this analysis on a dynamic brain scan suggests that the three fundamental phases of activity represent activity in the arterial system, the venous system and diffusion of tracer into the tissues.

Brain↗

Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

Cerebrovascular Disorders↗

A model of motion adaptation and motion after-effects based upon principal component regression.

A computational model to help explain effects of adaptation to moving signals is compared with established energy (linear regression) models of motion detection. The proposed model assumes that processed image signals are subject to error in both dimensions of space and time. This assumption constrains models of motion perception to be based upon principal component regression rather than linear regression. It is shown that response suppression of model complex cell neurons that input into the model may account for (1) increases in perceived speed after adaptation to static patterns and testing with slowly moving patterns, (2) significant increases in perceived speed after adaptation to patterns moving at a medium speed and testing at high speed, and (3) decreases in perceived speed in the opponent direction to a quickly moving adapting signal. Neither of predictions (2) or (3) are general features of established accounts of motion detection by visual processes based upon linear regression. Comparisons of the proposed model's speed transfer function with existing psychophysical data suggests that the visual system processes motion signals with the tacit assumption that image measurements are subject to error in both space and time.

Animals↗

A multisensor array for visualizing continuous state transitions in biopharmaceutical processes using principal component analysis.

An array of sensors with varying sensitivities, a so-called multisensor array, has been used for monitoring the growth and production states of biopharmaceutical processes. The sensor array produced continuous and characteristic response patterns from the processes due to the differences of the sensors. By analysing these patterns with the multivariate method principal component analysis, the state as well as the change of state of the bioprocesses could be visualized. The sensors used in the array were well-known semiconductor and optical gas sensors and the array was connected in an on-line set-up to the bioreactor's headspace effluent. The sensor array was applied to the monitoring of two recombinant bioprocesses, the production of human growth hormone in Escherichia coli and human factor VIII in Chinese ovary hamster cells. The sensor array could clearly visualize the characteristic transitions during the main growth or production phases of these two bioprocesses.

Animals↗

Molecular dynamics of apo-adenylate kinase: a principal component analysis.

Adenylate kinase from E. coli (AKE) is studied with molecular dynamics. AKE undergoes large-scale motions of its Lid and AMP-binding domains when its open form closes over its substrates, AMP and Mg2+-ATP. The third domain, the Core, is relatively stable during closing. The resulting trajectory is analyzed with a principal component analysis method that decomposes the atom motions into modes ordered by their decreasing contributions to the total protein fluctuation. Simulations at 303 K (normal T) and 500 K (high T) reveal that at both temperatures the first three modes account for 70% of the total fluctuation. The residues that contribute the most to these three modes are concentrated in the Lid and AMP-binding domains. Analysis of the normal T modes indicates that the Lid and AMP-binding domains sample a broad distribution of conformations indicating that AKE is designed to provide its substrates with a large set of conformations. The high T results show that the Lid initially closes toward the Core. Subsequently, the Lid rotates to a new stable conformation that is different from what is observed in the substrate-bound AKE. These results are discussed in the context of experimental data that indicate that adenylate kinases do sample more than one conformational state in solution and that each of these conformational states undergoes substantial fluctuations. A pair of residues is suggested for labeling that would be useful for monitoring distance fluctuations by energy transfer experiments.

Adenylate Kinase↗

A principal components analysis of the Comprehensive Test of Adaptive Behavior.

The Comprehensive Test of Adaptive Behavior (CTAB) yields indices of functioning in six categories (Self-Help Skills, Home Living Skills, Independent Living Skills, Social Skills, Sensory and Motor Skills, and Language Concepts and Academic-Skills) and a composite Total Score. A single factor, extracted through principal components analysis of standardization sample data, accounted for approximately 86% of the variance in the six category scores, portraying the CTAB as unidimensional rather than multidimensional. Therefore, rather than use of category scores, cautious interpretation of idiographic item clusters and the Total Score may be the most psychometrically sound procedure for evaluating CTAB results.

Activities of Daily Living↗