Movement-related phase locking in the delta-theta frequency band.

Movements result from a complex interplay of multiple brain regions. These regions are assembled into distinct functional networks depending on the specific properties of the action. However, the nature and details of the dynamics of this complex assembly process are unknown. In this study, we sought to identify key markers of the neural processes underlying the preparation and execution of motor actions that always occur irrespective of differences in movement initiation, hence the specific neural processes and functional networks involved. To this end, EEG activity was continuously recorded from 18 right-handed healthy participants while they performed a simple motor task consisting of button presses with the left or right index finger. The movement was performed either in response to a visual cue or at a self-chosen, i.e., non-cued point in time. Despite these substantial differences in movement initiation, dynamic properties of the EEG signals common to both conditions could be identified using time-frequency and phase locking analysis of the EEG data. In both conditions, a significant phase locking effect was observed that started prior to the movement onset in the δ-θ frequency band (2-7Hz), and that was strongest at the electrodes nearest to the contralateral motor region (M1). This phase locking effect did not have a counterpart in the corresponding power spectra (i.e., amplitudes), or in the event-related potentials. Our finding suggests that phase locking in the δ-θ frequency band is a ubiquitous movement-related signal independent of how the actual movement has been initiated. We therefore suggest that phase-locked neural oscillations in the motor cortex are a prerequisite for the preparation and execution of motor actions.

Different functions of QTL for estrogen-dependent tumor growth of the rat pituitary.

Estrogen treatment to rats of the Fischer 344 (F344) strain induces growth of pituitary tumors that exhibit accelerated cell proliferation, breakdown of basement membrane, and formation of hemorrhagic lakes. Estrogen-dependent pituitary growth is due to variation in a group of quantitative trait loci (QTL), called Edpm for estrogen-dependent pituitary mass, that we previously identified in an F(2) intercross of F344 and the tumor-resistant Brown Norway strain. We previously identified 5 QTL, and microsatellite markers developed since our earlier work have allowed us to scan new chromosomal regions, resulting in two new QTL for estrogen-dependent pituitary mass: Edpm9-2 and a possible QTL on the X Chromosome (Chr). Here we report evidence that these QTL differ from each other in how they affect growth. To examine the effect of the Edpm QTL on biochemical components of tumor growth, we tested their effects in 138 progeny of a backcross to the F344 strain which were given a 10-week chronic estrogen treatment. Hemoglobin/DNA ratio (a measure of blood volume relative to cell number) and total pituitary DNA (a measure of cell number) correlated only weakly, and very large pituitaries were observed which had a low hemoglobin/DNA ratio resembling a normal gland. Through QTL mapping, we found that Edpm2-1, Edpm3, Edpm5, and Edpm9-2 all had significant effects on pituitary mass, but Edpm2-1 and Edpm9-2 primarily affected DNA content, Edpm5 primarily affected hemoglobin/DNA ratio, and Edpm3 affected all traits equally.

[Definition of a leak model and derivation of formulas for quantifying the accompanying leak surfaces in the anesthetic system]. / Definition eines Leckagemodells und Herleitung von Formeln zur Quantifizierung der zugehörigen Leckflächen im Narkosesystem.

Our intention was to conceive a simple model for the evaluation of well-defined leakages in the anesthetic breathing system. Utilization of any model requires a formula to calculate the corresponding leakage surfaces. METHOD. The leak is defined as the projection of a circle (radius r) onto the surface of a cylinder (radius R) in which anesthetic gas is flowing. If we remove the leakage surface from the cylinder and flatten it out this produces neither a circular nor an elliptic shape. We will develop an expression for the leakage surface depending on the two radii, r and R, in a two-dimensional coordinate system. RESULTS. Formulas (3), (4) and (5) can be used to compute leakage areas for our model. An analytic solution of the equations is impossible by the application of calculus, but a PC program for numeric integration can yield values with a sufficient degree of accuracy. Some results for well-defined leakages in breathing tubes (R = 11 mm) are shown. These show that the difference between the leakage area and the projected circle (radius r) can practically be neglected for some values of r. CONCLUSION. Leakages in most anesthetic breathing systems cause some gas loss. The main causes are leaking plug connections and screw joints. Damage to the breathing tubes or bellows is less significant. Part of the tidal volume will disperse into the environment via the leak. This might be hazardous for the patient because the breathing volume and inspiratory oxygen concentration are reduced and for the operating team because of air pollution. To examine how such parameters as flow, compliance and resistance cause loss of pressure and volume and variations in gas concentration in the case of leakages an appropriate model of practical use is needed. Application of the formulas derived from our model makes it possible to compare measurements gained from studying the impacts of leakages in cylindrical tubes of different diameters. By experimental research of pressure and volume loss we intend to gather reliable information that will allow us to make proper recommendations for efficient setting of the pressure disconnection alarm.