scholarly journals Greater variability in chimpanzee ( Pan troglodytes ) brain structure among males

2020 ◽  
Vol 287 (1925) ◽  
pp. 20192858 ◽  
Author(s):  
Alex R. DeCasien ◽  
Chet C. Sherwood ◽  
Steven J. Schapiro ◽  
James P. Higham

Across the animal kingdom, males tend to exhibit more behavioural and morphological variability than females, consistent with the ‘greater male variability hypothesis'. This may reflect multiple mechanisms operating at different levels, including selective mechanisms that produce and maintain variation, extended male development, and X chromosome effects. Interestingly, human neuroanatomy shows greater male variability, but this pattern has not been demonstrated in any other species. To address this issue, we investigated sex-specific neuroanatomical variability in chimpanzees by examining relative and absolute surface areas of 23 cortical sulci across 226 individuals (135F/91M), using permutation tests of the male-to-female variance ratio of residuals from MCMC generalized linear mixed models controlling for relatedness. We used these models to estimate sulcal size heritability, simulations to assess the significance of heritability, and Pearson correlations to examine inter-sulcal correlations. Our results show that: (i) male brain structure is relatively more variable; (ii) sulcal surface areas are heritable and therefore potentially subject to selection; (iii) males exhibit lower heritability values, possibly reflecting longer development; and (iv) males exhibit stronger inter-sulcal correlations, providing indirect support for sex chromosome effects. These results provide evidence that greater male neuroanatomical variability extends beyond humans, and suggest both evolutionary and developmental explanations for this phenomenon.

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.


Biometrics ◽  
2004 ◽  
Vol 60 (4) ◽  
pp. 1043-1052 ◽  
Author(s):  
Yutaka Yasui ◽  
Ziding Feng ◽  
Paula Diehr ◽  
Dale McLerran ◽  
Shirley A. A. Beresford ◽  
...  

2011 ◽  
Vol 2 (4) ◽  
pp. 428-435 ◽  
Author(s):  
Ya–Hsiu Chuang ◽  
Sati Mazumdar ◽  
Taeyoung Park ◽  
Gong Tang ◽  
Vincent. C. Arena ◽  
...  

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