RESUMO
A 55-year-old woman was admitted for an elevated serum carbohydrate antigen-125 (CA-125) level, and a left pleural effusion, which were detected at a routine health examination. Computed tomography of the chest was performed upon admission, revealing extensive bilateral paratracheal and mediastinal lymph node enlargement with a massive left-sided pleural effusion. Subsequent analysis of the pleural fluid demonstrated consistency with an exudate, no evidence of malignant cells, and a normal adenosine deaminase. However, the pleural fluid and serum CA-125 levels were 2,846.8 U/mL and 229.5 U/mL, respectively. A positron emission tomography did not reveal any primary focus of malignancy. Finally, a surgical mediastinoscopic biopsy of several mediastinal lymph nodes was performed, revealing non-necrotizing granulomas, consistent with sarcoidosis. After a month of treatment of prednisolone, the left pleural effusion had resolved, and after 2 months the serum CA-125 level was normalized.
RESUMO
OBJECTIVE: Recent findings have demonstrated that the EEG possesses long-range temporal (auto-) correlations (LRTC) in the dynamics of broad band oscillations. The analysis of LRTC provides a quantitative index of statistical dependencies in oscillations on different time scales. We analyzed LRTC in resting EEG signals in depressed outpatients and healthy controls. METHODS: The participants in this study were 11 non-depressed, age-matched controls, and 11 unmedicated unipolar depressed patients. EEG data were obtained from each participant during 5-min resting baseline periods with eyes closed and then analyzed with detrended fluctuation analysis (DFA), a scaling analysis method that quantifies a simple parameter to represent the correlation properties of a time series. The scaling exponent, the result of DFA, provides a quantitative measure of LRTC from the EEG. RESULTS: The present study demonstrates that all the scaling exponents in depressed patients and healthy controls were greater than 0.5 and less than 1.0, regardless of condition. Furthermore, the scaling exponents of depressed patients have relatively higher values in whole brain regions compared to healthy controls, with significant differences at F3, C3, T3, T4 and O1 channels (p<0.05). Finally, a significant linear correlation was observed between the severity of depression and the scaling exponent over most of the channels, except O2. CONCLUSIONS: These results suggest that the brain affected by a major depressive disorder shows slower decay of the LRTC, and that the persistence of the LRTC of EEG in depressed patients was associated with the severity of depression over most of the cortical areas. SIGNIFICANCE: The DFA method may broaden our understanding of the psychophysiological basis of depression.
Assuntos
Depressão/fisiopatologia , Eletroencefalografia/métodos , Pacientes Ambulatoriais , Descanso/fisiologia , Adulto , Mapeamento Encefálico , Estudos de Casos e Controles , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Dinâmica não Linear , Estudos Retrospectivos , Processamento de Sinais Assistido por ComputadorRESUMO
Fractal analysis was applied to study the trends of EEG signals in the hypnotic condition. The subjects were 19 psychiatric outpatients. Hypnotizability was measured with the Hypnotic Induction Profile (HIP). Fifty-four sets of EEG data were analyzed by detrended fluctuation analysis (DFA), a well-established fractal analysis technique. The scaling exponents, which are the results of fractal analysis, are reduced toward white noise during the hypnotic condition, which differentiates the hypnotic condition from the waking condition. Further, the decrease in the scaling exponents during hypnosis was solely associated with the eye-roll sign within specific cortical areas (F3, C4, and O1/2) closely related to eye movements and attention. In conclusion, the present study has found that the application of the fractal analysis technique can demonstrate the electrophysiological correlations with hypnotic influence on cerebral activity.