Daily resting-state intracranial EEG connectivity for seizure risk forecasts.

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.

No Evidence of Attentional Modulation of the Neural Response to the Temporal Fine Structure of Continuous Musical Pieces.

Speech and music are spectrotemporally complex acoustic signals that are highly relevant for humans. Both contain a temporal fine structure that is encoded in the neural responses of subcortical and cortical processing centers. The subcortical response to the temporal fine structure of speech has recently been shown to be modulated by selective attention to one of two competing voices. Music similarly often consists of several simultaneous melodic lines, and a listener can selectively attend to a particular one at a time. However, the neural mechanisms that enable such selective attention remain largely enigmatic, not least since most investigations to date have focused on short and simplified musical stimuli. Here, we studied the neural encoding of classical musical pieces in human volunteers, using scalp EEG recordings. We presented volunteers with continuous musical pieces composed of one or two instruments. In the latter case, the participants were asked to selectively attend to one of the two competing instruments and to perform a vibrato identification task. We used linear encoding and decoding models to relate the recorded EEG activity to the stimulus waveform. We show that we can measure neural responses to the temporal fine structure of melodic lines played by one single instrument, at the population level as well as for most individual participants. The neural response peaks at a latency of 7.6 msec and is not measurable past 15 msec. When analyzing the neural responses to the temporal fine structure elicited by competing instruments, we found no evidence of attentional modulation. We observed, however, that low-frequency neural activity exhibited a modulation consistent with the behavioral task at latencies from 100 to 160 msec, in a similar manner to the attentional modulation observed in continuous speech (N100). Our results show that, much like speech, the temporal fine structure of music is tracked by neural activity. In contrast to speech, however, this response appears unaffected by selective attention in the context of our experiment.