Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT05962658 |
Other study ID # |
Fibro |
Secondary ID |
|
Status |
Completed |
Phase |
|
First received |
|
Last updated |
|
Start date |
April 1, 2021 |
Est. completion date |
December 31, 2022 |
Study information
Verified date |
July 2023 |
Source |
Universidade Federal de Pernambuco |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Studies with quantitative electroencephalogram (qEEG) in people with fibromyalgia showed the
existence of distinct patterns of brain electrical activity when compared to healthy
individuals. Such dysfunctional patterns may be correlated to clinical symptoms of the
syndrome as chronic pain and emotional disorders (depression and anxiety). As chronic pain
can be considered a multidimensional symptom, its evaluation should consider beyond others,
two main dimensions: the sensitive-discriminative dimension and the affective-motivational
dimension. Previous studies have been describing distinct brain areas as neural substrates
for processing such dimensions of pain. Thus, the identification of electrophysiological
biomarkers (i.e., as qEEG measures) allowing to perform an evaluation between dysfunctional
patterns of brain electrical activity and different dimensions of pain seems to be a
promising path in the search for a better understanding of the syndrome as well as for more
individualized and effective therapeutic approaches. Our objective was to investigate whether
dysfunctional patterns of brain electrical activity in frontal and central areas of people
with fibromyalgia are differently related to dimensions of pain (sensory-discriminative and
affective-motivational) and to emotional disorders (depression and anxiety).
Description:
The study was a pilot study with a cross-sectional and exploratory design carried out from
2020 to 2022. A quantitative electroencephalographic analysis was performed with women with
Fibromyalgia and pain-free individuals as a control group, matched for gender and age. The
sample was not probabilistic and recruitment occurred in a random manner. All volunteers were
recruited from reference services in the state of Pernambuco, referred from other centers as
well as through announcements in digital media.
Procedures All procedures were conducted respecting the Declaration of Helsinki (1964) and
approved by the local ethical committee. Before clinical and sociodemographic evaluation, all
volunteers signed a written informed consent, including all information regarding the risks
and benefits of their participation in the study. During the study, all individuals with
fibromyalgia were instructed not to change their medication use as well as eating habits.
Clinical assessments and qEEG data acquisition took place in one single visit to the
laboratory lasting around two hours. After signing the written informed consent, all
volunteers were taken to an isolated room to perform an EEG evaluation. Then, they underwent
sociodemographic and clinical assessments (only individuals with fibromyalgia).
EEG data acquisition and processing For each volunteer, signals were recorded using digital
EEG equipment for 120 seconds in an isolated room - without any communication with the
external environment - with volunteers rested, seated in a comfortable chair, and with closed
eyes. Signal recording was performed through 19 Ag/AgCl electrodes positioned on the scalp
following the predetermined points of the international 10-20 system of
electroencephalography and, always maintaining a maximum impedance of 10 kΩ. Additionally, a
ground electrode was positioned on the lateral third of the right clavicle, while two
reference electrodes were positioned on the region of the right and left mastoid processes. A
sampling rate for recording the 500 Hz signal was captured by the NeuronSpectrum signal
amplifier and recorded by the Neuron-Spectrum/NET omega software. Additionally, the high-pass
(0.5 Hz), low-pass (35 Hz), and notch (60Hz; suitable for 220V mains) filters were applied
during data acquisition and processing.
Then, the collected data were pre-processed using the EEGLab toolbox in MATLAB® version
R2014a software for Windows. In addition, an Independent Component analysis was performed
using the Independent Components Analysis (ICA) algorithm to separate the components related
to biological artifacts. The rejection of these components was done through the Multiple
Artefact Rejection Algorithm (MARA) considering a 50% cutoff point. For time-frequency
analysis of the relative spectral power for each epoch, the fast Fourrier transform method
was used. The dominant frequency in each patient was identified in the following points of
the international 10-20 EEG system: F3, F4, Fz, F7, F8 (frontal area), and C3, C4, Cz
(central area) during rest. Spectral power density assessment was also performed, for each
frequency band, considering the following bands: delta (0,5 a ≤ 4 Hz); theta ( > 4 a ≤ 8 Hz);
alpha (> 8 a ≤13 Hz) e beta (> 13 a ≤ 30 Hz). For relative spectral power distribution
calculations, the absolute spectral power of each frequency band was divided by the total
power of all bands present in the 0.5-35Hz.