Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).

Coping with breast cancer and its consequences has now become a major socioeconomic challenge. The BOUNCE EU H2020 project aims at building a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. This paper gives an overview of the project targets and on the algorithmic methods focusing on modeling the psychological resilience trajectories during breast cancer treatment.

Breast cancer is the most common cancer disease in women and is rapidly becoming a chronic illness due recent advances in treatment methods. As such, coping with cancer has become a major socio-economic challenge leading to an increasing need for predicting resilience of women to the variety of stressful experiences and practical challenges they face. In this paper, we present the data infrastructure developed for this purpose, demonstrating the various components that will contribute to the developing the resilience trajectory predictor. Special emphasis is given to the semantic tier, presenting the project solution already implemented for effectively collecting, ingesting, cleaning, modelling and processing data that will be used throughout the lifetime of the project.

Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.

This paper focuses on the analysis of head movements in the context of speech during stress and neutral conditions. An experimental protocol was followed including tasks of neutral and stressful emotional states induced by different types of stressors. Translational and rotational head movements and velocities were computationally estimated from 2-dimensional facial landmarks in order to assess stress states. In parallel, the effect of speech production on head motility was investigated. The results indicate that stress conditions increase head motility, in both translational or rotational movement features. Besides, there is a clear involvement of speech in the increase of head motility. However, the intensity of head motility can be attributed to the combined effect of stress or arousal and speech and not just the effect of speech production.