Analysis of brain complexity and mental disorders

Abstract

Knowledge on the brain processes underlying mental disorders has significantly increased in recent decades, but in spite of the very important research efforts being made, there is no biological marker available for such disorders. For example, neurophysiological techniques (EEG or MEG), have been widely utilized in the investigation of the most important psychiatric syndromes such as schizophrenia, major depression, bipolar disorder or obsessive/compulsive disorder. The outcomes of some of those neurophysiological studies have made it possible to develop statistical models having very high sensitivity and specificity, although those models have not been incorporated into the day to day clinical practice. A possible explanation for this situation is that an inadequate analysis procedure which might be missing some important quantums of information contained in brain signals is being used. In this sense, new methods of non-linear analysis have been proposed for the investigation of neurophysiological data. Particularly, the analysis of brain signal complexity has been widely utilized in the investigation of psychiatric disorders. Parameters of EEG or MEG complexity usually estimate the predictability of brain oscillations and/or the number of independent oscillators underlying the observed signals. More importantly, complexity parameters seem to be sensitive to the temporal components of brain activity, and therefore might reflect the dynamical nature of psychiatric disorders. This paper reviews some of the most relevant studies within this field, especially those focusing on the diagnosis, follow-up and prediction of response to treatment.