PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 6674824

The COMSTAT algorithm for multimodal factor analysis: an improvement of Tucker's three-mode factor analysis method.

J Röhmel·B Streitberg·W M Herrmann
View original source ↗Export metadata ↓

Abstract

Three-mode factor analysis, as developed by Tucker in 1966, is a method for nonredundant representation of data arrays with three subscripts (e.g. observations classified according to persons by variables by conditions). Tucker , however, did not succeed in obtaining a least-squares solution for his model. We derive in this paper a necessary condition for a least-squares solution and construct an algorithm which improves any given initial solution in the least-squares sense. We show that this algorithm converges to a representation satisfying the necessary conditions for a least-squares solution. The method is in no way restricted to three dimensions, but can be applied to any multimodal data array.

Explore related subjects

Alpha RhythmDelta RhythmElectroencephalographyFactor Analysis, StatisticalHumansTheta RhythmJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J Röhmel, B Streitberg, W M Herrmann. 1983. The COMSTAT algorithm for multimodal factor analysis: an improvement of Tucker's three-mode factor analysis method.. https://doi.org/10.1159/000118004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2006

Heritability of bipolar EEG spectra in a large sib-pair population.

The additive genetic heritability of both monopolar and bipolar EEG spectral power in a sample of 305 non-twin sibships comprising 690 individuals (age range 7-65) was estimated in order to investigate their regional variation. The heritabilities of the bipolar EEG spectral power ranged from 0.10 to 0.63 in 38 electrode-pairs, and those of monopolar power ranged from 0.23 to 0.68 in 19 electrodes in six frequency bands from theta to high beta. The bipolar data shows significantly greater topographic variation compared to that of the monopolar data. The mean of bivariate genetic correlations were consistently lower for the bipolar data and the coefficients of variation consistently higher when compared to those of the monopolar data for each of the frequency bands. The results from the bipolar derivations are in greater accord with genetic findings in brain anatomy and show the possibility of multiple genetic sources for the phenotypic variability of EEG activity.

Alpha Rhythm↗
PubMed2006

Theory of the normal waking EEG: from single neurones to waveforms in the alpha, beta and gamma frequency ranges.

The classic alpha rhythm, recorded intracortically, consists of alternating surface-negative troughs and briefer surface-positive peaks. The troughs are associated with neuronal hyperpolarization, the peaks with brief depolarization and burst firing. Each hyperpolarization is mainly a potassium potential, lasting approximately 100 ms. Depolarization and burst firing arise when this inactivates. In the desynchronized state, membrane potential is poised just below threshold. Firing in vivo is somewhat irregular and non-bursting. It is suggested that EEG bistability (classic alpha vs desynchronization) corresponds to bistability of single pyramidal cells. In vitro, paired pulses lead to depression of synaptic transmission in synapses linking two pyramidal cells, but to facilitation in synapses linking pyramidal cells to inhibitory neurones. These effects should be recruited by burst firing in vivo. Thus, enhancement of inhibitory and excitatory transmission occur respectively during the classic alpha rhythm, and the desynchronized state. As a result both states tend to be self-sustaining. In the desynchronized state high frequency (gamma or beta) activity predominates. In simulations, gamma activity has been modeled as the behaviour of cortical networks where populations of excitatory and inhibitory neurones interact. These simulations assume conduction times between neurones to be negligible. However, this is not true for long-distance interactions. Introduction into the models of plausible conduction delays should slow the oscillation frequency. The activated cortex can then produce not only gamma activity but also beta, and sometimes alpha activity. Thus, alpha frequencies can arise both in the "idling" cortex (classic alpha), and in the activated cortex, although the respective mechanisms are quite different.

Alpha Rhythm↗
PubMed2006

Just a phase they're going through: the complex interaction of intrinsic high-threshold bursting and gap junctions in the generation of thalamic alpha and theta rhythms.

Rhythms in the alpha frequency band (8-13 Hz) are a defining feature of the human EEG during relaxed wakefulness and are known to be influenced by the thalamus. In the early stages of sleep and in several neurological and psychiatric conditions alpha rhythms are replaced by slower activity in the theta (3-7 Hz) band. Of particular interest is how these alpha and theta rhythms are generated at the cellular level. Recently we identified a subset of thalamocortical (TC) neurons in the lateral geniculate nucleus (LGN) which exhibit rhythmic high-threshold (>-55 mV) bursting at approximately 2-13 Hz and which are interconnected by gap junctions (GJs). These cells combine to generate a locally synchronized continuum of alpha and theta oscillations, thus providing direct evidence that the thalamus can act as an independent pacemaker of alpha and theta rhythms. Interestingly, GJ coupled pairs of TC neurons can exhibit both in-phase and anti-phase synchrony and will often spontaneously alternate between these two states. This dictates that the local field oscillation amplitude is not simply linked to the extent of cell recruitment into a single synchronized neuronal assembly but also to the degree of destructive interference between dynamic, spatially overlapping, competing anti-phase groups of continuously bursting neurons. Thus, the waxing and waning of thalamic alpha/theta rhythms should not be assumed to reflect a wholesale increase and reduction, respectively, in underlying neuronal synchrony. We argue that these network dynamics might have important consequences for relating changes in the amplitude of EEG alpha and theta rhythms to the activity of thalamic networks.

Alpha Rhythm↗