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This paper discusses the development of a statistical shape model (SSM) of the skull from a South African population. A total of 16 skulls is used together with a reference from the Basel Face Model. A free-form deformation model is built using the reference model and the squared exponential kernel. The freeform model is used to establish dense correspondence across the skull sample and the in-correspondence skulls are then used to build an SSM. The validity of the SSM is assessed using leave-one-out cross-validation with its generality ranging between 1.47 mm and 1.84 mm and, the specificity ranging between 1.74 mm and 2.11 mm.

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Over the last few decades, electroencephalography (EEG) has evolved from being a method that purely relies on visual inspection into a quantitative method. Quantitative EEG, or QEEG, enables the assessment of neurological disorders based on spectral features, dynamic characterizations of EEG resting-state activity, brain connectivity analyzes or quantification of EEG signal complexity. The information contained in EEG is multidimensional: Electrodes, positioned at different scalp locations, provide a spatial dimension to the analysis of EEG while time provides a dynamic dimension: This multidimensional property of EEG makes its quantification a challenging task. In this narrative review we present quantitative models focused on different aspects of EEG: While microstate models focus more on the quantification of the dynamic aspects of EEG, spectral methods, connectivity analysis and entropy based models are more concerned with its spatial aspects. Nevertheless, these diverse approaches have provided neurophysiology based biomarkers, especially for monitoring and predicting the course of various neurodegenerative disorders. However, their translation into clinical practice crucially depends on the ability to automate the analysis of EEG in a user-friendly manner, without compromising on the validity of the provided results. Once this has been accomplished, EEG would provide an inexpensive and widely available method for monitoring disease progression, identifying patients at risk of neurodegeneration—especially before the onset of clinical symptoms, and predicting future cognition. For stratification of patients to clinical trials, EEG would allow shortening the trial duration and lowering the number of necessary participants by identifying patients at risk of fast cognitive decline.