Theta

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

…at precise phases of hippocampal theta during high-density electrophysiological recordings in head-fixed mice while they navigate a virtual environment. The precise timing of single-unit spiking relative to network-wide oscillations (i.e., phase locking) has long been thought to maintain excitatory-inhibitory homeostasis and coordinate cognitive processes, but due to intense experimental demands, the causal influence of this phenomenon has never b

Experimental evidence suggests that the brain is able to encode memories in the phase of ongoing low-frequency oscillations. Recently, trainable recurrent neural networks (RNNs) have emerged as promising models of how neural circuits process information. However, short-term memory maintenance in these networks has mostly been implemented through static attractors. Thus, it remains unclear if and how RNNs can encode memories in the relative phase of oscillations. Here we show that such phase-dependent coding can arise through the training of RNNs on memory tasks. We characterize how the emerg