Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

Principal-Agent Theory and the World Trade Organization: Complex Agency and ‘Missing Delegation’

This paper analyses the World Trade Organization within a principal-agent framework. The concept of complex agency is introduced to focus on the variety of actors that comprise an international organization. Special attention is paid to the relationship between contracting parties’ representatives and the Secretariat. In the empirical part, the paper analyses the role of the Secretariat in assisting negotiations and presents evidence of declining influence. It is shown how principal-agent theory can contribute to addressing this ‘puzzle of missing delegation’. The paper concludes with a cautionary note as to the ‘location’ of international organizations’ emerging pathologies and calls for additional research to address the relationship between material and social sources to explain behaviour of the key actors within the complex agency.

Digital Personal Assistant for Cognitive Cities: A Paper Prototype

This chapter presents an evaluation and initial testing of a meta-application (meta-app) for enhanced communication and improved interaction (e.g., appointment scheduling) between stakeholders (e.g., citizens) in cognitive cities. The underlying theoretical models as well as the paper prototype are presented to ensure the comprehensibility of the user interface. This paper prototype of the meta-app was evaluated through interviews with various experts in different fields (e.g., a strategic consultant, a small and medium-sized enterprises cofounder in the field of online marketing, an IT project leader, and an innovation manager). The results and implications of the evaluation show that the idea behind this meta-app has the potential to improve the living standards of citizens and to lead to a next step in the realization and maturity of the meta-app. The meta-app helps citizens more effectively manage their time and organize their personal schedules and thus allows them to have more leisure time and take full advantage of it to ensure a good work-life balance to enable them to be the most efficient and productive during their working time.

Personal Digital Assistant 2.0 - A Software Prototype for Cognitive Cities

This paper presents a software prototype of a personal digital assistant 2.0. Based on soft computing methods and cognitive computing this mobile application prototype improves calendar and mobility management in cognitive cities. Applying fuzzy cognitive maps and evolutionary algorithms, the prototype represents a next step towards the realization of cognitive cities (i.e., smart cities enhanced with cognition). A user scenario and a test version of the prototype are included for didactical reasons.