Is gender crucial for cardiovascular adjustments induced by exercise training in female spontaneously hypertensive rats?

Evidence of mild hypertension in women and female rats and our preliminary observation showing that training is not effective to reduce pressure in female as it does in male spontaneously hypertensive rats (SHR) prompt us to investigate the effects of gender on hemodynamic pattern and microcirculatory changes induced by exercise training. Female SHR and normotensive controls (Wistar- Kyoto rats) were submitted to training (55% VO2 peak; 3 months) or kept sedentary and instrumented for pressure and hindlimb flow measurements at rest and during exercise. Heart, kidney, and skeletal muscles (locomotor/ nonlocomotor) were processed for morphometric analysis of arterioles, capillaries, and venules. High pressure in female SHR was accompanied by an increased arteriolar wall: lumen ratio in the kidney (+30%; P < 0.01) but an unchanged ratio in the skeletal muscles and myocardium. Female SHR submitted to training did not exhibit further changes on the arteriolar wall: lumen ratio and pressure, showing additionally increased hindlimb resistance at rest (+29%; P < 0.05). On the other hand, female SHR submitted to training exhibited increased capillary and venular densities in locomotor muscles (+50% and 2.3- fold versus sedentary SHR, respectively) and normalized hindlimb flow during exercise hyperemia. Left ventricle pressure and weight were higher in SHR versus WKY rats, but heart performance (positive dP/dt(max) and negative dP/dt(max)) was not changed by hypertension or training, suggesting a compensated heart function in female SHR. In conclusion, the absence of training- induced structural changes on skeletal muscle and myocardium arterioles differed from changes observed previously in male SHR, suggesting a gender effect. This effect might contribute to the lack of pressure fall in trained female SHRs.

Multilevel Training of Binary Morphological Operators

The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.