Endocranial Morphology in Metopism

TL;DR

This study explores how metopism, a condition where a skull suture remains open, affects the shape of the inner skull, finding distinct differences in specific areas.

Contribution

The study is the first to investigate endocranial morphology in metopism using morphometric analysis and machine learning.

Findings

Metopic crania show longer segments in the anterior and middle cranial fossae.

The landmark endobregma is closer to the anterior and middle cranial base in metopic cases.

A machine learning model achieved 85% accuracy in classifying metopic vs. control crania.

Abstract

Metopism is a condition in which the metopic suture, which usually closes within the first postnatal year, persists in adult individuals, and the frontal bone is bipartite. Comparative investigations have demonstrated that metopism is associated with a specific configuration of the cranial vault, a delayed closure of the major sutures, a presence of supernumerary bones, and an underdevelopment of the frontal sinus. However, there are no comparative data concerning the endocranial morphology in this condition. In this study, we aimed to test the hypothesis that in metopism the differences in the vault are also related to differences in the endocranial morphology. For this purpose, we compared the metopic and control series using morphometric analysis and machine learning approaches. The obtained results showed that in metopism the anterior sections of the cranial base as well as the vault were specific, and this finding confirmed our hypothesis. However, the peculiarities of the base and vault were expressed in different directions. This discrepancy showed that besides the closely interrelated development of the cranial base and vault, in metopism the morphology of the vault did not entirely reflect the morphology of the base and vice versa.

Comparative investigations on homogenous cranial series have demonstrated that metopism is linked to a specific configuration of the cranial vault; however, there are no comparative data concerning the endocranial morphology in this condition. This study aimed to compare the endocranial space in metopic and control crania using morphometric analysis and machine learning algorithms. For this purpose, a series of 230 (184 control and 46 metopic) dry crania of contemporary adult Bulgarian males were scanned using an industrial µCT system. The 3D coordinates of 47 landmarks were collected on the endocranial surface. All possible measurements between the landmarks were calculated as Euclidean distances. The resultant 1081 measurements represented the initial dataset, which was reduced to smaller datasets applying different criteria. The derived datasets were used for learning a set of classification models by machine learning algorithms. The morphometric analysis showed that in the metopic crania some segments of the anterior and middle cranial fossae were significantly longer, and the landmark endobregma was significantly closer to the anterior and middle sections of the cranial base. The most accurate model, with a classification accuracy of 85%, was the Naive Bayes one learned on a dataset of 69 attributes assembled after an attribute selection procedure.

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