Effect of exposure to 900 MHz radiofrequency on the MEG alpha band activity at rest

With the extensive use of mobile phones several studies have been realized to understand the effects of radiofrequency exposure on brain activity. Results show changes in the alpha band spectral power. To better understand the cortical structures involved in these changes after exposure at 900 MHz, we carried out electroencephalography and magnetoencephalography recordings followed by the anatomical magnetic resonance imaging. Healthy volunteers were selected according to strict inclusion criteria. Preliminary results on MEG sensor space showed a modification of the alpha band right after exposure. Data analyses of MEG recordings on sensor space and source space are still under process and ready results will be presented at the conference

Human resting-state EEG and radiofrequency GSM mobile phone exposure: Exploring the individual sensitivity hypothesis

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Radiofrequency signal affects alpha band in resting electroencephalogram

International audienceObjective: The aim of the present work was to investigate the effects of the radiofrequency (RF) electromagnetic fields (EMFs) on human resting EEG with a control of some parameters that are known to affect alpha band such as electrode impedance, salivary cortisol and caffeine.Methods: Eyes open and eyes-closed resting EEG data were recorded in 26 healthy young subjects under two conditions: sham exposure and real exposure in double-blind, counterbalanced, crossover design. Spectral power of EEG rhythms was calculated for the alpha band (8-12Hz). Saliva samples were collected before and after the study. Salivary cortisol and caffeine were assessed respectively by Enzyme linked immunosorbent assay (ELISA) and high performance liquid chromatography (HPLC). The electrode impedance was recorded at the beginning of each run.Results: Compared with sham session, the exposure session showed a statistically significant (p < 0.0001) decrease of the alpha band spectral power during closed eyes condition. This effect persisted in the post-exposure session (p < 0.0001). No significant changes were detected in electrode impedance, salivary cortisol and caffeine in the sham session when compared to the exposure one.Conclusions: These results suggest that GSM-EMFs of a mobile phone affect alpha band within spectral power of resting human EEG

GSM 900MHz signal affects a spectral power of alpha band in the resting human electroencephalogram

The aim of this study was to examine the possible effect of exposure to radiofrequency electromagnetic field (RF EMF) GSM 900 MHz, on brain electrical activity. 26 healthy volunteers aged 18-35 years underwent two experimental sessions in which one session served as control (sham) and second one as exposure session. Subjects were exposed to mobile phone signals at intensity of 0.49 W/kg. In each session, waking EEG was recorded before, during and after an exposure period. These results suggest that GSM-EMFs of a mobile phone affect alpha band within spectral power of resting human EEG

Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy

International audiencePrecise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients’ seizures. These key parameters together with their personalized model determine a given patient’s EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non–seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy