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At least 721 records · Page 40Linked to original sources
PubMed1986

Principal component analysis of event-related potentials: a note on misallocation of variance.

Wood and McCarthy (1984) found a 'misallocation of variance' when applying PCA, including Varimax rotation, to simulated data. Here it is demonstrated that this effect can be produced by Varimax rotation, without PCA as an intervening step. PCA does not distort or lose information when extracting components, since it is shown that the prototypes may be perfectly reconstructed from the unrotated solution. However, it is stressed that infinitely many sets of prototypes may render the same final solution, a fact which cannot be overcome by any method. The role of the rotation step within this framework is discussed.

Electroencephalography↗
PubMed2006

Cell-cycle-dependent variations in FTIR micro-spectra of single proliferating HeLa cells: principal component and artificial neural network analysis.

We have previously reported spectral differences for cells at different stages of the eukaryotic cell division cycle. These differences are due to the drastic biochemical and morphological changes that occur as a consequence of cell proliferation. We correlate these changes in FTIR absorption and Raman spectra of individual cells with their biochemical age (or phase in the cell cycle), determined by immunohistochemical staining to detect the appearance (and subsequent disappearance) of cell-cycle-specific cyclins, and/or the occurrence of DNA synthesis. Once spectra were correlated with their cells' staining patterns, we used methods of multivariate statistics to analyze the changes in cellular spectra as a function of cell cycle phase.

Cell Cycle↗
PubMed2005

Reference-free quantification of EEG spectra: combining current source density (CSD) and frequency principal components analysis (fPCA).

OBJECTIVE: Definition of appropriate frequency bands and choice of recording reference limit the interpretability of quantitative EEG, which may be further compromised by distorted topographies or inverted hemispheric asymmetries when employing conventional (non-linear) power spectra. In contrast, fPCA factors conform to the spectral structure of empirical data, and a surface Laplacian (2-dimensional CSD) simplifies topographies by minimizing volume-conducted activity. Conciseness and interpretability of EEG and CSD fPCA solutions were compared for three common scaling methods. METHODS: Resting EEG and CSD (30 channels, nose reference, eyes open/closed) from 51 healthy and 93 clinically-depressed adults were simplified as power, log power, and amplitude spectra, and summarized using unrestricted, Varimax-rotated, covariance-based fPCA. RESULTS: Multiple alpha factors were separable from artifact and reproducible across subgroups. Power spectra produced numerous, sharply-defined factors emphasizing low frequencies. Log power spectra produced fewer, broader factors emphasizing high frequencies. Solutions for amplitude spectra showed optimal intermediate tuning, particularly when derived from CSD rather than EEG spectra. These solutions were topographically distinct, detecting multiple posterior alpha generators but excluding the dorsal surface of the frontal lobes. Instead a low alpha/theta factor showed a secondary topography along the frontal midline. CONCLUSIONS: CSD amplitude spectrum fPCA solutions provide simpler, reference-independent measures that more directly reflect neuronal activity. SIGNIFICANCE: A new quantitative EEG approach affording spectral components is developed that closely parallels the concept of an ERP component in the temporal domain.

Adolescent↗
PubMed2006

Analysis of guazatine mixture by LC and LC-MS and antimycotic activity determination of principal components.

Guazatine is a non-systemic contact fungicide, a mixture of reaction products from polyamines, comprising mainly octa-methylenediamine, iminodi(octamethylene)diamine, octamethylenebis(imino-octamethylene) diamine and carbamonitrile. In this work, the analysis of guazatine mixture by LC and LC-MS has been treated for the first time. In the guazatine mixture diamine derivatives account for 40% of the constituents of guazatine, triamines for 46%, tetramines for 11% and other amine derivatives for 3%. The most abundant individual components are the fully guanidated triamine (GGG, 30.6%) and the fully guanidated diamine (GG, 29.5%) followed by the monoguanidated diamine (GN, 9.8%) and a diguanidated triamine (GGN, 8.1%). The identification and separation of main components of commercial guazatine was performed through a new LC-MS method. The separation was performed on an Alltima C(18) column using linear gradient elution (formic acid in water and acetonitrile) with UV-detection at 200 nm and the identification was performed by ESI(+)-mass spectrometry analysis. The main components (GN, GG, GNG, GGN, GGG and GGGG) were then purified and separated from the mixture. Antimycotic activity of guazatine derivatives was determined on different species and strains belonging to genus Candida. The results obtained suggest that GNG and GGGG components can further be developed in new antifungal compounds with high potential for the treatment of Candida infections.

Antifungal Agents↗
PubMed2002

Principal components of the Beck Depression Inventory and regional cerebral metabolism in unipolar and bipolar depression.

BACKGROUND: We determined clustering of depressive symptoms in a combined group of unipolar and patients with bipolar disorder using Principle Components Analysis of the Beck Depression Inventory. Then, comparing unipolars and bipolars, these symptom clusters were examined for interrelationships, and for relationships to regional cerebral metabolism for glucose measured by positron emission tomography. METHODS: [18F]-fluoro-deoxyglucose positron emission tomography scans and Beck Depression Inventory administered to 31 unipolars and 27 bipolars, all medication-free, mildly-to-severely depressed. BDI component and total scores were correlated with global cerebral metabolism for glucose, and voxel-by-voxel with cerebral metabolism for glucose corrected for multiple comparisons. RESULTS: In both unipolars and bipolars, the psychomotor-anhedonia symptom cluster correlated with lower absolute metabolism in right insula, claustrum, anteroventral caudate/putamen, and temporal cortex, and with higher normalized metabolism in anterior cingulate. In unipolars, the negative cognitions cluster correlated with lower absolute metabolism bilaterally in frontal poles, and in right dorsolateral frontal cortex and supracallosal cingulate. CONCLUSIONS: Psychomotor-anhedonia symptoms in unipolar and bipolar depression appear to have common, largely right-sided neural substrates, and these may be fundamental to the depressive syndrome in bipolars. In unipolars, but not bipolars, negative cognitions are associated with decreased frontal metabolism. Thus, different depressive symptom clusters may have different neural substrates in unipolars, but clusters and their substrates are convergent in bipolars.

Adult↗