Electrophysiological and behavioral correlates of stable automatic semantic retrieval in aging

Previous studies have shown both declining and stable semantic-memory abilities during healthy aging. There is consistent evidence that semantic processes involving controlled mechanisms weaken with age. In contrast, results of aging studies on automatic semantic retrieval are often inconsistent, probably due to methodological limitations and differences. The present study therefore examines age-related alterations in automatic semantic retrieval and memory structure with a novel combination of critical methodological factors, i.e., the selection of subjects, a well-designed paradigm, and electrophysiological methods that result in unambiguous signal markers. Healthy young and elderly participants performed lexical decisions on visually presented word/non-word pairs with a stimulus onset asynchrony (SOA) of 150 ms. Behavioral and electrophysiological data were measured, and the N400-LPC complex, an event-related potential component sensitive to lexical-semantic retrieval, was analyzed by power and topographic distribution of electrical brain activity. Both age groups exhibited semantic priming (SP) and concreteness effects in behavioral reaction time and the electrophysiological N400-LPC complex. Importantly, elderly subjects did not differ significantly from the young in their lexical decision and SP performances as well as in the N400-LPC SP effect. The only difference was an age-related delay measured in the N400-LPC microstate. This could be attributed to existing age effects in controlled functions, as further supported by the replicated age difference in word fluency. The present results add new behavioral and neurophysiological evidence to earlier findings, by showing that automatic semantic retrieval remains stable in global signal strength and topographic distribution during healthy aging.

Topographic and age-dependent distribution of subchondral bone density in the elbow joints of clinically normal dogs

OBJECTIVE: To investigate topographic and age-dependent adaptation of subchondral bone density in the elbow joints of healthy dogs by means of computed tomographic osteoabsorptiometry (CTOAM). Animals-42 elbow joints of 29 clinically normal dogs of various breeds and ages. PROCEDURES: Subchondral bone densities of the humeral, radial, and ulnar joint surfaces of the elbow relative to a water-hydroxyapatite phantom were assessed by means of CTOAM. Distribution patterns in juvenile, adult, and geriatric dogs (age, < 1 year, 1 to 8 years, and > 8 years, respectively) were determined and compared within and among groups. RESULTS: An area of increased subchondral bone density was detected in the humerus distomedially and cranially on the trochlea and in the olecranon fossa. The ulna had maximum bone densities on the anconeal and medial coronoid processes. Increased bone density was detected in the craniomedial region of the joint surface of the radius. A significant age-dependent increase in subchondral bone density was revealed in elbow joint surfaces of the radius, ulna, and humerus. Mean subchondral bone density of the radius was significantly less than that of the ulna in paired comparisons for all dogs combined and in adult and geriatric, but not juvenile, dog groups. CONCLUSIONS AND CLINICAL RELEVANCE: An age-dependent increase in subchondral bone density at the elbow joint was revealed. Maximal relative subchondral bone densities were detected consistently at the medial coronoid process and central aspect of the humeral trochlea, regions that are commonly affected in dogs with elbow dysplasia.

Topographic electrophysiological signatures of FMRI Resting State Networks

Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.

From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

In this work, we present a multichannel EEG decomposition model based on an adaptive topographic time-frequency approximation technique. It is an extension of the Matching Pursuit algorithm and called dependency multichannel matching pursuit (DMMP). It takes the physiologically explainable and statistically observable topographic dependencies between the channels into account, namely the spatial smoothness of neighboring electrodes that is implied by the electric leadfield. DMMP decomposes a multichannel signal as a weighted sum of atoms from a given dictionary where the single channels are represented from exactly the same subset of a complete dictionary. The decomposition is illustrated on topographical EEG data during different physiological conditions using a complete Gabor dictionary. Further the extension of the single-channel time-frequency distribution to a multichannel time-frequency distribution is given. This can be used for the visualization of the decomposition structure of multichannel EEG. A clustering procedure applied to the topographies, the vectors of the corresponding contribution of an atom to the signal in each channel produced by DMMP, leads to an extremely sparse topographic decomposition of the EEG.