Abstract
This work presents a screening and evaluation platform for depression and suicidality that has been tested in the scope of primary healthcare. The main objective is to improve the efficiency and effectiveness of screening processes. A web-based, decision support platform was provided for qualified healthcare professionals. The platform provides several assessment tools for patient evaluation and monitoring of their treatment, along with follow up appointment management. A preliminary evaluation process was carried out to understand the health professional's satisfaction. This revealed there was general satisfaction with its integrated functions and all the provided methods of assessment. In conclusion, the project sustains the goal of improving the treatment outcomes for clinical depression by refining the screening methods and consequently increase the screening effectiveness and efficiency.

Abstract
Background: Hemiballism may arise as a rare consequence of focal basal ganglia lesions. Pathophysiologically, there is a controversy between the role of the STN as the exclusive lesion localization as opposed to several brain regions in which lesions may induce hemiballism. This is most likely due to a motor circuit affection. Objectives: To study the affection of neural networks in the pathogenesis of hemiballism. Methods: We analysed focal vascular lesions inducing hemiballism (n = 8), their localizations and connectivity profiles. Probabilistic tractography (FSL: http://fsl.fmrib.ox.ac.uk/fsl/) was used to study connectivity. Results: Lesions inducing hemiballism were distributed across several anatomic regions (basal ganglia, thalamus, caudate, internal capsule) without a clear predilection. However, we detected increased connectivity for these lesions toward the STN and mesial cortical motor regions (pre-SMA/SMA). These regions are interconnected via subthalamo-pallido-thalamo-cortical networks. Conclusions: We provide evidence for the involvement of the subthalamo-pallido-thalamic pathways in the pathogenesis of hemiballism, which is consistent with data on experimental hemiballism in animals. Electrophysiological basal ganglia recordings and functional MRI would complement our findings to assess the activation patters within these circuits.

Abstract
The recent advent of three-dimensional echocardiography has led to an increased interest from the scientific community in left ventricle segmentation frameworks for cardiac volume and function assessment. An automatic orientation of the segmented left ventricular mesh is an important step to obtain a point-To-point correspondence between the mesh and the cardiac anatomy. Furthermore, this would allow for an automatic division of the left ventricle into the standard 17 segments and, thus, fully automatic per-segment analysis, e.g. regional strain assessment. In this work, a method for fully automatic short axis orientation of the segmented left ventricle is presented. The proposed framework aims at detecting the inferior right ventricular insertion point. 211 three-dimensional echocardiographic images were used to validate this framework by comparison to manual annotation of the inferior right ventricular insertion point. A mean unsigned error of 8, 05° ± 18, 50° was found, whereas the mean signed error was 1, 09°. Large deviations between the manual and automatic annotations (> 30°) only occurred in 3, 79% of cases. The average computation time was 666ms in a non-optimized MATLAB environment, which potentiates real-Time application. In conclusion, a successful automatic real-Time method for orientation of the segmented left ventricle is proposed. © 2016 SPIE.

Abstract
In a dynamically changing social environment, humans have to face the challenge of prioritizing stimuli that compete for attention. In the context of social communication, the voice is the most important sound category. However, the existing studies do not directly address whether and how the salience of an unexpected vocal change in an auditory sequence influences the orientation of attention. In this study, frequent tones were interspersed with task-relevant infrequent tones and task-irrelevant infrequent vocal sounds (neutral, happy and angry vocalizations). Eighteen healthy college students were asked to count infrequent tones. A combined event-related potential (ERP) and EEG time-frequency approach was used, with the focus on the P3 component and on the early auditory evoked gamma band response, respectively. A spatial-temporal principal component analysis was used to disentangle potentially overlapping ERP components. Although no condition differences were observed in the 210-310 ms window, larger positive responses were observed for emotional than neutral vocalizations in the 310-410 ms window. Furthermore, the phase synchronization of the early auditory evoked gamma oscillation was enhanced for happy vocalizations. These findings support the idea that the brain prioritizes the processing of emotional stimuli, by devoting more attentional resources to salient social signals even when they are not task-relevant.

Abstract
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm for its identification. The advantage of the proposed approach is that it overcomes the "curse of dimensionality", a hurdle commonly encountered with classical bilinear subspace identification algorithms. Simulation results show that the algorithm converges quickly and provides new alternatives for modeling/identifying nonlinear repetitive processes.

Abstract
Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure -induced body movements - called seizure semiology. Thus, synchronous EEG and (2D) video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p< 0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p< 0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p< 0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.