Halasz, V. | bioRxiv Science

bioRxiv2024

Connecting crowns: Analyzing morphological covariation in the modern human postcanine dentition

Morphological covariation within the modern human postcanine dentition remains an open field of study. Analysis of covariation patterns of the three-dimensional (3D) shape between different tooth types has been seldom conducted, but it is relevant for the advancement of human biology and evolution, as well as dental anthropology, phylogeny, and medicine. Here, we analysed 3D shape covariation of the postcanine dentition (excluding third molars), both within and between dental arches using geometric morphometrics (GM). Based on high-resolution ({micro}CT) scans of 526 teeth from 136 individuals we found high pairwise correlation in tooth pairs within the dental arches (lower P3 and P4, r1 = 0.89; upper P3 and P4, r1 = 0.81; upper M1 and M2, r1 = 0.86). The correlation values between antagonists varied notably from the highest value detected between upper and lower M1s (r = 0.9), to the lowest between upper P4s and lower M1s (r = 0.58). Of all analysed tooth types, only the upper M1s showed moderate to high correlation in every pair analysis. Noticeably, unusually high covariation was detected between some of the tooth type pairs that do not articulate in a normal dentition (e.g., lower P3 and upper M2, r1 = 0.88). Furthermore, a relatively high covariation was found in the pairs of lower P4s and M1s (r1 = 0.79), and upper P4s and M1s (r1 = 0.77), which are the only tooth type pairs of the postcanine dentition belonging to different tooth classes (premolars and molars, respectively) and still serving similar masticatory functions. This study points to the fact that higher morphological integration seems to characterize teeth within the same dental arch rather than between antagonistic teeth. With this study, we provided an overview of pairwise correlations and strength of covariation between different tooth types. This information might inform future studies aimed at understanding developmental, phylogenetic, and functional aspects of the human postcanine dentition, including possible phenotype-genotype associations. However, with this study being the first one performed on a 3D sample of this size, we also report on obstacles and peculiarities that have been determined.

evolutionary biology↗

bioRxiv2016

Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli

Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between two alternative computational models. The Opposition model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the Interaction model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modelling (DCM) showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain.

neuroscience↗