Tag Archives: Craniosynostosis

Chiari malformation and the posterior fossa

SyringomyeliaChiari Malformation type 1 (CM-I) is an anatomical hindbrain abnormality having various symptoms (headache, pain in the neck and shoulders) because of obstruction of cerebrospinal fluid circulation and compression of hindbrain tissues such as the cerebellum, brain stem and spinal nerve. Most CM-I have syringomyelia. There is no direct test for CM-I and often symptoms are misinterpreted. Indicating tests are MRI, CT, neurological tests and CINE PC MRI. Treatment is a surgical operation called “posterior fossa compression”. Recently, researchers from the Netherlands and Turkey conducted different studies to examine this disorder. Akar et al. tested the usefulness of fractal analysis to examine the morphological complexity features of CM-I. Fractal Dimension (FD) analysis conducts the structural differences between patients with MCI (n=17) and healthy control subjects (n=16). Results showed that patients with CMI have larger cerebellar gray matter (GM) areas compared to controls, in contrast to other studies. FD could be a significant indicator for brain abnormalities in the cerebellum of CMI. It seems to be the case that the higher the FD value of cerebellar , the more complex object structure was. Rijken et al. found by examining 28 not operated, 85 operated craniosynostosis patients and 34 control that development of CMI is more likely to be supra tentorial. Craniosynostosis patients with CMI have similar cerebellar volume (CV) and posterior fossa volume (PFV) to control subjects, but they do have a significantly higher CV/PFV ratio. A higher CV/PFV ratio can be regarded as a predisposing factor for the development of CMI. In the end Rijken et al. advise to focus more on the skull vault itself.

Johannes Freiherr von Boeselager

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Diagnosing cranial synostosis

Craniosynostosis (J.M. de la Cuetara)During normal cranial morphogenesis, most neurocranial sutures remain open till advanced adult stages, closing long after the brain is fully grown and developed. In some cases, however, sutures may close prematurely causing a pathological condition known as craniosynostosis which is associated with different neurocranial malformations depending on the suture involved. Apart from its aesthetic consequences, craniosynostosis is relevant from a biomedical perspective as malformations due to premature fusion of sutures may result in limited or abnormal brain growth and development, higher intra-cranial pressure, as well as respiratory and visual impairments. Moreover, a complete understanding on craniosynostotic processes is also relevant for evolutionary biologists, as species-specific cranial and cerebral morphologies may be somewhat influenced by changes on the particular tempo and sequence of suture closure (see for example the case of the australopithecine metopic suture).

From the above lines, it seems clear that an early and accurate diagnosis of the disease is essential for the correct management and prevention of possible clinical complications, as well as proper planning of surgical interventions. In this context, Carlos S. Mendoza and colleagues have recently published an automated pipeline that allows untrained observers to diagnose early craniosynostotic individuals with a 96% accuracy. In brief, their novel methodology is based on the automated computation of suture fusion indices and on the quantitative characterization of local deformation and curvature changes of bones and sutural areas. For the former task, the proposed pipeline incorporates a dedicated algorithm based on graph-cut techniques which is capable for automatically detecting and labeling both bones and sutures. After the identification of skeletal and sutural components, open and fused sutures are detected following a ‘contact degree’ criteria and fusion indices are computed as the number of voxels belonging to different bones that are in contact. On the other hand, normal neurocranial shape variation is modeled using a novel landmark-free methodology and principal component analysis (PCA). The resultant shape space is then used as a multi-atlas reference to compare and evaluate any potential patient that is to be diagnosed. Although this methodology may show some limitations, validation analyses presented by Mendoza and colleagues have demonstrated that by considering together fusion indices and shape descriptors it is possible to automatically discriminate between normal synostotic patients, as well as between different phenotypes of craniosynostosis. Moreover, this methodology not only enables the fast and accurate diagnosis of particular clinical cases (with all its associated medical advantages), but it also provides a methodological framework for the statistical analysis of large datasets, which in turn could provide important information regarding the structural and functional role of sutures during ontogeny and phylogeny.

José Manuel de la Cuétara