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Investments into other costly aspects of an organism's biology may therefore place important constraints on brain size AJP D620S.
Sexual traits are often costly and could therefore be traded off against neural investment. However, brain size may itself be under sexual selection through mate choice on cognitive ability. Here, we use guppy Poecilia AJP D620S lines selected for large and AJP D620S brain size relative to body size to investigate the relationship between brain size, a large suite of male primary and secondary sexual traits, and body condition index. Our data do not support the hypothesis that investment into sexual traits is an important limiting factor to brain size evolution, but instead suggest that brain size and several sexual traits are positively genetically correlated. Introduction Brain size is highly variable among animals, but despite over a century of research in this area, our understanding of the evolutionary processes and mechanisms that have generated this variation remains inconclusive.
The theoretical framework in this field is based on the general idea that relative brain size evolves through a balance between the positive fitness effects of AJP D620S cognitive ability and the prohibiting effects of the energetic costs of developing and maintaining a larger brain e. Recently, experimental evidence for a causal link between brain size and cognitive ability was provided based on artificial selection on brain size in the guppy Poecilia reticulata. Interestingly, very few studies have tested for negative associations between brain size and sexual traits and the results remain inconclusive. One rationale behind this idea is that individuals with larger brains are better at foraging and exploiting food resources, obtain overall better condition and therefore develop more elaborate sexual traits.
Condition can be broadly defined as the total pool of resources available for allocation to different traits Rowe and Houle Functional resting-state data were acquired using an echo-planar imaging EPI sequence 36 axial slices of 3. These skull-stripped images served as input for processing within the FreeSurfer 5. Briefly, pre-processing of the T1-weighted anatomical images included motion correction, registration to a neuroanatomical Talairach atlas and intensity correction. This was followed by a segmentation of the brain into gray matter, white matter and cerebrospinal fluid.
Using a tessellation procedure, a white matter surface mesh model was created boundary between white and gray matter.
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To receive the pial surface, the mesh of vertices is inflated until the white matter surface AJP D620S the boundary between the pial surface and the cerebrospinal fluid Dale et al. After surface reconstruction, the local gyrification index LGI was calculated as implemented in FreeSurfer Schaer et al.
The gyrification index was initially defined by Zilles et AJP D620S. Schaer et al. For each vertex on the pial surface, the LGI is calculated as the ratio between a region of interest ROI on the pial surface and a corresponding region of the outer hull surface with a defined radius of 25 mm around the center vertex; Schaer et al. Thus, higher LGI is associated with higher local gyrification, and vice versa, lower LGI with reduced local gyrification. While gyrification overall is early determined during development, changes in the measure of the LGI was repeatedly interpreted as signs of local brain atrophy Magnotta et al.
The preprocessing consisted of: Afterwards, variance caused by nuisance factors that could lead to spurious correlations in the following RSFC analysis was removed from each voxel's time series Weissenbacher et al. This included the six motion parameters derived from the image realignment head motion correction as well as their first derivatives. These parameters were included as first and second order terms, resulting in 24 motion regressors Satterthwaite et al. Furthermore, linear tissue regression of the mean gray, and white matter signal as well as cerebrospinal fluid was performed. In addition to that, artificial and AJP D620S signals were further removed by a PCA denoising Behzadi et al.
Data were band-pass filtered to retain only those frequencies which AJP D620S relevant neuronal signal, i. For the purpose of the current study, we examined the influence of B1 and B6 on resting state functional connectivity of the seed regions with all other gray matter voxels, while correcting for age, AJP D620S, head motion using DVARS and gray matter volume. The statistical analysis was carried out using SPM8. Each VOI was defined as the peak vertex of each significant cluster, with a 5-mm-sphere around it. Characteristic time-series of each seed region see above of definition of seed regions based on the significant clusters from the structural analysis was calculated for each subject by computing the first eigenvariate of the preprocessed time-series.
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Afterwards, linear Pearson correlation coefficients between the VOI's time series and time series of all other gray matter voxels were calculated and transformed into Fisher's Z-scores. Statistical Analysis B Vitamin Level Impact on Cognition First, blood levels of vitamin B6 and vitamin B1 were related to 14 different cognitive functions in the domains of attention, executive functions, language and episodic and working memory using partial correlation analyses, with age and gender added as covariates of no interest implemented in SPSS B Vitamin Level Impact on Cortical Structure The correlation between blood levels of vitamin B6 and vitamin B1 and vertex-wise local AJP D620S was examined using general linear models GLM and Monte Carlo simulations for generation of the null-hypothesis using Qdec http: For both analyses, age and gender were included of covariates of no interest.
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Results were reported at two different significance levels, i. B Vitamin Level Impact on AJP D620S Connectivity Linear Pearson correlation coefficients were calculated between time series extracted from each seed region see above and each gray matter voxel in the brain.
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The resulting correlation coefficients were then transformed into Fisher's Z-scores and a second-level analysis of variance ANOVA was used to test for consistency across subjects. Results Blood levels of vitamin B6 0— Cognitive AJP D620S in Relation to Vitamin Blood levels No significant effects were found when relating vitamin B6 blood levels to cognitive performance.
Thus, the higher vitamin B1 blood AJP D620S, the worse the performance in visual-spatial working memory. AJP Sound Card Drivers.
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