How to evaluate Quality of Life.

In medical terminology, it has become more and more common the use of the expression "Quality of Life" (QoL) to define a series of aspects that go beyond the traditional, clinical and "objective" evaluation of the medical intervention. The attention to QoL comes from the need to find tools that are able to reveal important aspects of the life of the patient that cannot be measured by a laboratory exam and/or a radiological procedure. The QoL is measured through multidimensional questionnaires on, at the very least, the domains of physical, psychological and social health. The improvement of the health care standards and the technological progress in medical matters have brought about an increase in the average age of the population, and as a consequence, an increase of the chronic and degenerative disease, which can negatively influence the patient's quality of life. Amongst these pathologies, heart failure (HF) has a high prevalence in patients who are at least 70 years old, and it's the cause of frequent and repeated hospitalizations. The estimate of the QoL becomes then a very important piece of the puzzle to figure out, as important as the clinical parameters, to allow the patient to become an integral part of the physician's decisions and to reach more quickly and with better results the therapeutic objectives.

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.