Tractography of the Spider Monkey (\u3cem\u3eAteles geoffroyi\u3c/em\u3e) Corpus Callosum Using Diffusion Tensor Magnetic Resonance Imaging

The objective of this research was to describe the organization, connectivity and microstructure of the corpus callosum of the spider monkey (Ateles geoffroyi). Non-invasive magnetic resonance imaging and diffusion-tensor imaging were obtained from three subjects using a 3T Philips scanner. We hypothesized that the arrangement of fibers in spider monkeys would be similar to that observed in other non-human primates. A repeated measure (n = 3) of fractional anisotropy values was obtained of each subject and for each callosal subdivision. Measurements of the diffusion properties of corpus callosum fibers exhibited a similar pattern to those reported in the literature for humans and chimpanzees. No statistical difference was reached when comparing this parameter between the different CC regions (p = 0.066). The highest fractional anisotropy values corresponded to regions projecting from the corpus callosum to the posterior cortical association areas, premotor and supplementary motor cortices. The lowest fractional anisotropy corresponded to projections to motor and sensory cortical areas. Analyses indicated that approximately 57% of the fibers projects to the frontal cortex and 43% to the post-central cortex. While this study had a small sample size, the results provided important information concerning the organization of the corpus callosum in spider monkeys

Automatic quantification of abdominal subcutaneous and visceral adipose tissue in children, through MRI study, using total intensity maps and Convolutional Neural Networks

Childhood overweight and obesity is one of the main health problems in the world since it is related to the early appearance of different diseases, in addition to being a risk factor for later developing obesity in adulthood with its health and economic consequences. Visceral abdominal tissue (VAT) is strongly related to the development of metabolic and cardiovascular diseases compared to abdominal subcutaneous adipose tissue (ASAT). Therefore, precise and automatic VAT and ASAT quantification methods would allow better diagnosis, monitoring and prevention of diseases caused by obesity at any stage of life. Currently, magnetic resonance imaging is the standard for fat quantification, with Dixon sequences being the most useful. Different semiautomatic and automatic ASAT and VAT quantification methodologies have been proposed. In particular, the semi-automated quantification methodology used commercially through the cloud-based service AMRA R Researcher stands out due to its extensive validation in different studies. In the present work, a database made up of Dixon MRI sequences, obtained from children between 7 and 9 years of age, was studied. Applying a preprocessing to obtain what we call total intensity maps, a convolutional neural network (CNN) was proposed for the automatic quantification of ASAT and VAT. The quantifications obtained from the proposed methodology were compared with quantifications previously made through AMRA R Researcher. For the comparison, correlation analysis, Bland-Altman graphs and non-parametric statistical tests were used. The results indicated a high correlation and similar precisions between the quantifications of this work and those of AMRA R Researcher. The final objective is that the proposed methodology can serve as an accessible and free tool for the diagnosis, monitoring and prevention of diseases related to childhood obesity.Comment: 14 pages, 9 figures, 3 table

Brain Connectivity in \u3cem\u3eAteles geoffroyi\u3c/em\u3e: Resting-State Functional Magnetic Resonance Imaging of Working Memory and Executive Control

The objective of this research was to describe the organization and connectivity of the working memory (WM) and executive control (EC) networks in Ateles geoffroyi in resting-state conditions. Recent studies have shown that resting-state activity may underlie rudimentary brain functioning, showing that several brain regions can be tonically active at rest, maximizing the efficiency of information transfer while preserving a low physical connection cost. Whole-brain resting-state images were acquired from three healthy adult Ateles monkeys (2 females, 1 male; mean age 10.5 ± SD 2.5 years). Data were analyzed with independent component analysis, and results were grouped together using the GIFT software. The present study compared the EC and WM networks obtained with human data and with results found in the literature in other primate species. Nine resting-state networks were found, which were similar to resting networks found in healthy human adults in the prefrontal basal portion and frontopolar area. Additionally, components of the WM network were found to be extending into the hypothalamus and the olfactory areas. A key finding was the discovery of connections in the WM and EC networks to the hypothalamus, the motor cortex, and the entorhinal cortex, suggesting that information is integrated from larger brain areas. The correlated areas suggest that many elements of WM and EC may be conserved across primate species. Characterization of these networks in resting-state conditions in nonhuman primate brains is a fundamental prerequisite for understanding of the neural bases underlying the evolution and function of this cognitive system