Modeling heterothermic fitness landscapes in a marsupial hibernator using changes in body composition.

Hibernation is an adaptive strategy that allows animals to enter a hypometabolic state, conserving energy and enhancing their fitness by surviving harsh environmental conditions. However, addressing the adaptive value of hibernation, at the individual level and in natural populations, has been challenging. Here, we applied a non-invasive technique, body composition analysis by quantitative magnetic resonance (qMR), to calculate energy savings by hibernation in a population of hibernating marsupials (Dromiciops gliroides). Using outdoor enclosures installed in a temperate rainforest, and measuring qMR periodically, we determined the amount of fat and lean mass consumed during a whole hibernation cycle. With this information, we estimated the daily energy expenditure of hibernation (DEEH) at the individual level and related to previous fat accumulation. Using model selection approaches and phenotypic selection analysis, we calculated linear (directional, ß), quadratic (stabilizing or disruptive, γ) and correlational (ρ) coefficients for DEEH and fat accumulation. We found significant, negative directional selection for DEEH (ßDEEH = - 0.58 ± 0.09), a positive value for fat accumulation (ßFAT = 0.34 ± 0.07), and positive correlational selection between both traits (ρDEEH × FAT = 0.24 ± 0.07). Then, individuals maximizing previous fat accumulation and minimizing DEEH were promoted by selection, which is visualized by a bi-variate selection surface estimated by generalized additive models. At the comparative level, results fall within the isometric allometry known for hibernation metabolic rate in mammals. Thus, by a combination of a non-invasive technique for body composition analysis and semi-natural enclosures, we were characterized the heterothermic fitness landscape in a semi-natural population of hibernators.

Body Composition and Energy Savings by Hibernation: Lessons from the South American Marsupial Dromiciops gliroides.

AbstractHibernation (i.e., seasonal or multiday torpor) has been described in mammals from five continents and represents an important adaptation for energy economy. However, direct quantifications of energy savings by hibernation are challenging because of the complexities of estimating energy expenditure in the field. Here, we applied quantitative magnetic resonance to determine body fat and body composition in hibernating Dromiciops gliroides (monito del monte). During an experimental period of 31 d in winter, fat was significantly reduced by 5.72±0.45 g, and lean mass was significantly reduced by 2.05±0.14 g. This fat and lean mass consumption is equivalent to a daily energy expenditure of hibernation (DEEH) of 8.89±0.6 kJ d-1, representing 13.4% of basal metabolic rate, with a proportional contribution of fat and lean mass consumption to DEEH of 81% and 18%, respectively. During the deep heterothermic bouts of monitos, body temperature remained 0.41°C ± 0.2°C above ambient temperature, typical of hibernators. Animals shut down metabolism and passively cool down to a critical defended temperature of 5.0°C ± 0.1°C, where they begin thermoregulation in torpor. Using temperature data loggers, we obtained an empirical estimation of minimum thermal conductance of 3.37±0.19 J g-1 h-1 °C-1, which is 107% of the expectation by allometric equations. With this, we parameterized body temperature/ambient temperature time series to calculate torpor parameters and metabolic rates in euthermia and torpor. Whereas the acute metabolic fall in each torpor episode is about 96%, the energy saved by hibernation is 88% (compared with the DEE of active animals), which coincides with values from the literature at similar body mass. Thus, estimating body composition provides a simple method to measure the energy saved by hibernation in mammals.

The INECO Frontal Screening for the Evaluation of Executive Dysfunction in Cerebral Small Vessel Disease: Evidence from Quantitative MRI in a CADASIL Cohort from Colombia.

OBJECTIVES: Executive dysfunction is a predominant cognitive symptom in cerebral small vessel disease (SVD). The Institute of Cognitive Neurology Frontal Screening (IFS) is a well-validated screening tool allowing the rapid assessment of multiple components of executive function in Spanish-speaking individuals. In this study, we examined performance on the IFS in subjects with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), an inherited condition leading to the early onset of SVD. We further explored associations between performance on the IFS and magnetic resonance imaging (MRI) markers of SVD. METHODS: We recruited 24 asymptomatic CADASIL subjects and 23 noncarriers from Colombia. All subjects underwent a research MRI and a neuropsychological evaluation, including the IFS. Structural MRI markers of SVD were quantified in each subject, together with an SVD Sum Score representing the overall burden of cerebrovascular alterations. General linear model, correlation, and receiver operating characteristic curve analyses were used to explore group differences on the IFS and relationships with MRI markers of SVD. RESULTS: CADASIL subjects had a significantly reduced performance on the IFS Total Score. Performance on the IFS correlated with all quantified markers of SVD, except for brain atrophy and perivascular spaces enlargement. Finally, while the IFS Total Score was not able to accurately discriminate between carriers and noncarriers, it showed adequate sensitivity and specificity in detecting the presence of multiple MRI markers of SVD. CONCLUSIONS: These results suggest that the IFS may be a useful screening tool to assess executive function and disease severity in the context of SVD.

Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study

Abstract Introduction Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques and the brain regions that allow the identification of MS patients from neuroimaging data and pattern recognition techniques. Methods The data came from the combination of computational tools of image processing and neuroimaging acquired in a 3 Tesla scanner using different techniques: Diffusion, T2 Relaxometry, Magnetization Transfer Ratio (MTR) and Structural Morphometry. Data from 126 brain regions of 203 healthy individuals and 124 MS patients were separated into two groups and processed in a data-mining program using the k-nearest-neighbor (KNN) algorithm. Results The most relevant anatomical structures in the classification procedure were: corpus callosum, precuneus, left cerebellum and fusiform. Among the quantitative techniques the most relevant was the MTR, being indicated for longitudinal studies of this disease. KNN with 5 neighbors and pre-selected attributes had a better performance with an area under the ROC curve (97.3%) and accuracy (95.7%). A restricted classification considering only brain regions previously reported in the literature as affected by MS brought slightly lower scores, area: 97.1% and accuracy: 93.2%. Conclusion The use of standard recognition techniques from quantitative neuroimaging techniques has confirmed that the white matter of the brain is the most affected tissue by MS following a global pattern with greater involvement of the left hemisphere.