Sexual selection predicts brain structure in dragon lizards.

Phenotypic traits such as ornaments and armaments are generally shaped by sexual selection, which often favours larger and more elaborate males compared to females. But can sexual selection also influence the brain? Previous studies in vertebrates report contradictory results with no consistent pattern between variation in brain structure and the strength of sexual selection. We hypothesize that sexual selection will act in a consistent way on two vertebrate brain regions that directly regulate sexual behaviour: the medial preoptic nucleus (MPON) and the ventromedial hypothalamic nucleus (VMN). The MPON regulates male reproductive behaviour whereas the VMN regulates female reproductive behaviour and is also involved in male aggression. To test our hypothesis, we used high-resolution magnetic resonance imaging combined with traditional histology of brains in 14 dragon lizard species of the genus Ctenophorus that vary in the strength of precopulatory sexual selection. Males belonging to species that experience greater sexual selection had a larger MPON and a smaller VMN. Conversely, females did not show any patterns of variation in these brain regions. As the volumes of both these regions also correlated with brain volume (BV) in our models, we tested whether they show the same pattern of evolution in response to changes in BV and found that the do. Therefore, we show that the primary brain nuclei underlying reproductive behaviour in vertebrates can evolve in a mosaic fashion, differently between males and females, likely in response to sexual selection, and that these same regions are simultaneously evolving in concert in relation to overall brain size.

Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment.

BACKGROUND: Magnetic resonance diffusion tensor imaging (DTI) shows promise in the early detection of microstructural pathophysiological changes in the brain. OBJECTIVES: To measure microstructural differences in the brains of participants with amnestic mild cognitive impairment (MCI) compared with an age-matched control group using an optimised DTI technique with fully automated image analysis tools and to investigate the correlation between diffusivity measurements and neuropsychological performance scores across groups. METHODS: 34 participants (17 participants with MCI, 17 healthy elderly adults) underwent magnetic resonance imaging (MRI)-based DTI. To control for the effects of anatomical variation, diffusion images of all participants were registered to standard anatomical space. Significant statistical differences in diffusivity measurements between the two groups were determined on a pixel-by-pixel basis using gaussian random field theory. RESULTS: Significantly raised mean diffusivity measurements (p<0.001) were observed in the left and right entorhinal cortices (BA28), posterior occipital-parietal cortex (BA18 and BA19), right parietal supramarginal gyrus (BA40) and right frontal precentral gyri (BA4 and BA6) in participants with MCI. With respect to fractional anisotropy, participants with MCI had significantly reduced measurements (p<0.001) in the limbic parahippocampal subgyral white matter, right thalamus and left posterior cingulate. Pearson's correlation coefficients calculated across all participants showed significant correlations between neuropsychological assessment scores and regional measurements of mean diffusivity and fractional anisotropy. CONCLUSIONS: DTI-based diffusivity measures may offer a sensitive method of detecting subtle microstructural brain changes associated with preclinical Alzheimer's disease.

MRI based diffusion and perfusion predictive model to estimate stroke evolution.

In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/- 0.19 and 1.20 +/- 0.36. For recovered tissue the mean values were 0.99 +/- 0.25 and 1.87 +/- 0.71, respectively. There was a significant difference between these two regions for both measures (p < 0.003 and p < 0.001, respectively). Mean absolute measures of CBF (ml/100g/min) and CBV (ml/100g) for the total infarcted territory were 33.9 +/- 9.7 and 4.2 +/- 1.9. For recovered MTT tissue, the mean values were 41.5 +/- 7.2 and 5.3 +/- 1.2, respectively. A significant difference was also found for these regions (p < 0.009 and p < 0.036, respectively). The mean measures of sensitivity, specificity, positive and negative predictive values for modeling infarct evolution for the validation patient data were 0.72 +/- 0.05, 0.97 +/- 0.02, 0.68 +/- 0.07 and 0.97 +/- 0.02. We propose that this automated strategy may allow possible guided therapeutic intervention to stroke patients and evaluation of efficacy of novel stroke compounds in clinical drug trials.

4D deformation modeling of cortical disease progression in Alzheimer's dementia.

This work describes the development of a model of cerebral atrophic changes associated with the progression of Alzheimer's disease (AD). Linear registration, region-of-interest analysis, and voxel-based morphometry methods have all been employed to elucidate the changes observed at discrete intervals during a disease process. In addition to describing the nature of the changes, modeling disease-related changes via deformations can also provide information on temporal characteristics. In order to continuously model changes associated with AD, deformation maps from 21 patients were averaged across a novel z-score disease progression dimension based on Mini Mental State Examination (MMSE) scores. The resulting deformation maps are presented via three metrics: local volume loss (atrophy), volume (CSF) increase, and translation (interpreted as representing collapse of cortical structures). Inspection of the maps revealed significant perturbations in the deformation fields corresponding to the entorhinal cortex (EC) and hippocampus, orbitofrontal and parietal cortex, and regions surrounding the sulci and ventricular spaces, with earlier changes predominantly lateralized to the left hemisphere. These changes are consistent with results from post-mortem studies of AD.