ABSTRACT
While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition. We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.
ABSTRACT
The representation of the human electroencephalogram (EEG) records by neurophysiologists demands standardized time-amplitude scales for their correct conventional interpretation. In a suite of graphical experiments involving scaling affine transformations we have been able to convert electroencephalogram samples corresponding to any particular sleep phase and relaxed wakefulness into each other. We propound a statistical explanation for that finding in terms of data collapse. As a sequel, we determine characteristic time and amplitude scales and outline a possible physical interpretation. An analysis for characteristic times based on lacunarity is also carried out as well as a study of the synchrony between left and right EEG channels.
Subject(s)
Brain/physiology , Electroencephalography/methods , Evoked Potentials/physiology , Models, Neurological , Sleep Stages/physiology , Wakefulness/physiology , Computer Simulation , Data Interpretation, Statistical , Diagnosis, Computer-Assisted/methods , Humans , Models, StatisticalABSTRACT
INTRODUCTION AND AIMS: EEG signals emerge from the collective behaviour of large neuronal aggregates and betrays the information processed by neocortex. This electrophysiological collective activity varies with the brain function. Thus, one can ask whether there exists any indication of that neuronal activity in the EEG record. In this work this question is considered in the particular case of sleep/awake EEG s. To this aim, the concept of lacunarity is proposed as a tool to analyse the texture of the EEG samples. From the resulting lacunarity profiles an index is defined which represents a characteristic time of each phase. PATIENTS AND METHODS: The samples analysed corresponds to 30 seconds epochs from polysomnographic night records. Essential details for the computation of lacunarity patterns and the propounded index are given. The mean values of characteristic times (in seconds) are: 0.43 (phase I); 0.73 (phase II); 1.12 (phase III/IV); 0.65 (REM); 0.12 (relaxed wakefulness). RESULTS AND CONCLUSIONS: With this criterium, the awake state is clearly distinguished from phase I and REM sleep, whereas REM sleep comes out to be similar to phase II. Finally, statistical analysis of results suggests the possibility to interpret this index as a complementary tool for reading polysomnographies as well as its application to different physiological or pathological situations of EEG records.
Subject(s)
Electroencephalography , Sleep/physiology , Wakefulness/physiology , Humans , Polysomnography , Time FactorsABSTRACT
Introduction y objetivos. La señal EEG es el resultado del comportamiento medio de grandes poblaciones de neuronas agregadas y, por tanto, refleja en gran medida la información procesada en el neocórtex. Esta actividad electrofisiológica colectiva será diferente para las distintas funciones cerebrales. Cabe, pues, plantear la siguiente pregunta: ¿Existe en los trazados EEG alguna indicación que pueda cuantificarse de aquella actividad neuronal colectiva? Esta es la cuestión que abordamos en el presente trabajo, y nos limitamos a registros EEG de sueño y vigilia en estado de relajación mental. Proponemos aplicar a estos EEG el concepto de lagunaridad como un método para analizar la textura de las muestras. A partir de los patrones obtenidos, definimos un índice que representa un tiempo característico de cada fase. Pacientes y métodos. Las muestras analizadas corresponden a fragmentos de 30 segundos obtenidos durante registros polisomnográficos nocturnos. Los detalles esenciales para el cálculo de los patrones de lagunaridad y del índice propuesto se describen en el texto. Estos tiempos característicos toman los siguientes valores (en segundos): 0,43 (fase-I); 0,73 (fase-II); 1,12 (fase-III/IV); 0,65 (REM); 0,12 (vigilia en relajación mental). Resultados y conclusiones. Con este criterio, la vigilia se diferencia claramente de la fase-I (somnolencia) y del sueño paradójico (REM). A su vez, el sueño REM resulta ser semejante a la fase-II y claramente distinto de la fase-I. Finalmente, sobre la base del análisis estadístico de los resultados, se apunta la posibilidad de incorporar este índice como un elemento de ayuda adicional en la lectura de polisomnografías, o su aplicación a diferentes situaciones fisiológicas o incluso patológicas del registro EEG (AU)
