Fibromyalgia is a chronic syndrome of widespread pain and fatigue; its aetiology is still a mater of debate. It has been suggested that it is a psychosomatic response to psychological distress, but several underlying organic factors exist and symptoms have intriguing and ambiguous characteristics.
To our knowledge, this disorder is explained by the dissociated state concept. A dissociated state (DS) is a state that gathers characteristics from two functional states that shouldnt coexist. A clear example of that is alpha-delta sleep pattern. The presence of alpha rhythm pattern is typical from awake disappearing at sleep onset, while delta EEG patterns are characteristic of sleep. In FMS these two features coexist reflecting a sleeping awake state.
In this project will test the hypothesis that FMS is due to several brain-body DS, in which there are specific structures (thalamus, thalamo-cortical networks, hypothalamus and limbic system) and systems involved (thermoregulation and autonomic nervous system).
A brain microstate (MS) is a functional brain state (of neuronal populations) with finite duration, characterized by fixed spatial distribution and time-varying intensity. In this concern, we aim to describe the various functional MSs involved in different specific behavioral situations that incorporate FMS, namely, in sleep and awaked states, in physical exercise, in pain and pleasure feelings and in cognitive tests.
Furthermore, we will generalize brain MS concept (which only integrates brain measures, e.g., EEG and Magnetic resonance imaging MRI) including on the analysis other functional states. These MSs, that we will name as disseminated microstates (DMS), include not only awake and sleep EEG data, but also other neurophysiologic data, such as event related potentials (ERP), electromyograms (EMG), electrocardiograms (ECG), heart rate variability (HRV), temperature and neuroendocrine patterns.
The correlation between DMS and pain, affective and cognitive behaviors, thermoregulation and circadian neuroendocrine levels (hypothalamus-pituitary-adrenals, HPA and hypothalamus-pituitary-gonads, HPG axis) will allow us to detect the DS that we believe to be FMS substrate. These correlations are complex and uneasy, due to the huge amount of data involved. These data are multimodal and are contaminated by other neurophysiologic mechanisms that occur simultaneously. For this reason, statistical techniques (Hidden Markov Models) must be employed, as well as data mining, in order to identify hidden relationships between DMS and the symptoms and behavioral patterns of subjects with and without FMS. The goal is to identify clusters, DMS and to design a classification method that translates a strategy to the dysfunction diagnosis. In addition, the cluster analysis of DMS, in which MS (of the brain, of the autonomic system and of the neuroendocrine sleep/awake/specific behavioral situations) are involved, have a central role in the characterization and explanation of the disorder, specifically, through DS detection, which are essential, from our point of view. Considering this level of analysis, we believe that this study has a very important role in understanding FMS as well as strong impact in future therapeutical methods.
Linear processing techniques are limited when studying biological signals. Regarding this, we will use to EEG and ECG, non linear techniques with entropy measurements, multiscale entropy, Lempel Ziv complexity, symbolic dynamic, detrended fluctuation and 1/f slope.
Finally, the previous described MS will be integrated using the brain mapping technique, in order to determine if the brain regions involved are in accordance with our hypothesis. In data acquisition we will sample 2 groups: 20 healthy subjects (paired to sex and age) and 20 subjects with FMS typical symptoms.
Therefore we will use several complementary approaches, that we split in successive phases: i) to quantify circulating hormone levels, both across time and before and after the behavioral situations defined subsequently; ii) to analyze patients reactivity to multimodal stimulation, simulating the behavioral changes typical of FMS; iii) to apply Hidden Markov processes and cluster analysis to several arrays of variables (EEG, ECG and EMG activity) during REM and NREM sleep and during awake in specific manipulated states (event related potentials) in order to define MS; iv) to brain map MS; v) to do a non linear analysis of several of MSD; vi) to build up a dysfunctional model in FMS.
The research team is composed of a group of researchers used to team work, which has been collaborating in the last years in pre and post graduate Biomedical Engineering courses, supervising several degree and master degree students, but it also has some registered patents. The researchers knowledge is diversified and multidisciplinary (Electrotechnic Engineering, Physics; Neurology, Neurophysiology, Sleep Medicine and Psychobiology) which, together with the involved man-power, are useful and needed in this kind of project.
