Pontomedullary and hypothalamic distribution of Fos-like immunoreactive neurons after acute exercise in rats

During exercise, intense brain activity orchestrates an increase in muscle tension. Additionally, there is an increase in cardiac output and ventilation to compensate the increased metabolic demand of muscle activity and to facilitate the removal of CO2 from and the delivery of O-2 to tissues. Here we tested the hypothesis that a subset of pontomedullary and hypothalamic neurons could be activated during dynamic acute exercise. Male Wistar rats (250-350 g) were divided into an exercise group (n = 12) that ran on a treadmill and a no-exercise group (n = 7). Immunohistochemistry of pontomedullary and hypothalamic sections to identify activation (c-Fos expression) of cardiorespiratory areas showed that the no-exercise rats exhibited minimal Fos expression. In contrast, there was intense activation of the nucleus of the solitary tract, the ventrolateral medulla (including the presumed central chemoreceptor neurons in the retrotrapezoid/parafacial region), the lateral parabrachial nucleus, the Kolliker-Fuse region, the perifornical region, which includes the perifornical area and the lateral hypothalamus, the dorsal medial hypothalamus, and the paraventricular nucleus of the hypothalamus after running exercise. Additionally, we observed Fos immunoreactivity in catecholaminergic neurons within the ventrolateral medulla (C1 region) without Fos expression in the A2, A5 and A7 neurons. In summary, we show for the first time that after acute exercise there is an intense activation of brain areas crucial for cardiorespiratory control. Possible involvement of the central command mechanism should be considered. Our results suggest whole brain-specific mobilization to correct and compensate the homeostatic changes produced by acute exercise. (c) 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells

We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the NeuroMorpho.org database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.