Topic: alpha

The brain exhibits rich rhythmic activity generated by synchronous neural firing. These neural rhythms, which possess a variety of spatiotemporal patterns, are linked to cognition and vigilance. Neural rhythms are a defining feature of arousal states: in humans, alpha (8-12Hz) is a key marker of quiet wakefulness, and is associated with attentional processes, while delta (1-4Hz) is the most prominent rhythm during non-REM sleep, and is tied to memory consolidation and brain waste clearance. Prior work sought to localize the oscillatory generators of specific rhythms, but it is not clear what b

Network hyperexcitability in Alzheimer’s Disease (AD) is claimed to be an important and potentially treatable pathophysiological mechanism. Empirical and modeling studies suggest that functional connectivity (FC) are promising quantitative neurophysiological markers for network hyperexcitability. This study examines how robust FC measures differ between AD patients with and without interictal epileptiform discharges (IEDs). We obtained eyes closed source-level magnetoencephalography recordings from demographically matched biomarker confirmed AD patients in the mild cognitive impairment and dem

The aim of this study is to compare and explore the frequency characteristics of non-ordinary states of consciousness (NSCs) induced by hypnosis and bilateral alternating stimulations (BAS) as used in EMDR and the depth of these states. The neurophysiological processes underlying these states are investigated by measuring power spectral density (PSD) in frequency bands of the EEG signal and the deepness of NSC is assessed through visual analog scales on intensity, absorption, dissociation and immersion during memory recall. 25 healthy participants were included in the study. Each participant w

Neural oscillations have been associated with different functions and disorders of the brain. Recently, the non-sinusoidal waveform of oscillations has gained interest as a biomarker for neurological and psychiatric diseases, and as an indicator of underlying network physiology. However, waveform analysis in the time domain has been limited to dominant neuronal oscillations and by pre-selection of waveform features. Here, we contribute a novel Fourier-series waveform analysis that is noise resistant and provides a complete description of waveforms. We applied Fourier-based waveform analysis to