Transcranial alternating current stimulation in the theta band but not in the delta band modulates the comprehension of naturalistic speech in noise.

Auditory cortical activity entrains to speech rhythms and has been proposed as a mechanism for online speech processing. In particular, neural activity in the theta frequency band (4-8 â€‹Hz) tracks the onset of syllables which may aid the parsing of a speech stream. Similarly, cortical activity in the delta band (1-4 â€‹Hz) entrains to the onset of words in natural speech and has been found to encode both syntactic as well as semantic information. Such neural entrainment to speech rhythms is not merely an epiphenomenon of other neural processes, but plays a functional role in speech processing: modulating the neural entrainment through transcranial alternating current stimulation influences the speech-related neural activity and modulates the comprehension of degraded speech. However, the distinct functional contributions of the delta- and of the theta-band entrainment to the modulation of speech comprehension have not yet been investigated. Here we use transcranial alternating current stimulation with waveforms derived from the speech envelope and filtered in the delta and theta frequency bands to alter cortical entrainment in both bands separately. We find that transcranial alternating current stimulation in the theta band but not in the delta band impacts speech comprehension. Moreover, we find that transcranial alternating current stimulation with the theta-band portion of the speech envelope can improve speech-in-noise comprehension beyond sham stimulation. Our results show a distinct contribution of the theta- but not of the delta-band stimulation to the modulation of speech comprehension. In addition, our findings open up a potential avenue of enhancing the comprehension of speech in noise.

Modulation of Speech-in-Noise Comprehension Through Transcranial Current Stimulation With the Phase-Shifted Speech Envelope.

Neural activity tracks the envelope of a speech signal at latencies from 50 ms to 300 ms. Modulating this neural tracking through transcranial alternating current stimulation influences speech comprehension. Two important variables that can affect this modulation are the latency and the phase of the stimulation with respect to the sound. While previous studies have found an influence of both variables on speech comprehension, the interaction between both has not yet been measured. We presented 17 subjects with speech in noise coupled with simultaneous transcranial alternating current stimulation. The currents were based on the envelope of the target speech but shifted by different phases, as well as by two temporal delays of 100 ms and 250 ms. We also employed various control stimulations, and assessed the signal-to-noise ratio at which the subject understood half of the speech. We found that, at both latencies, speech comprehension is modulated by the phase of the current stimulation. However, the form of the modulation differed between the two latencies. Phase and latency of neurostimulation have accordingly distinct influences on speech comprehension. The different effects at the latencies of 100 ms and 250 ms hint at distinct neural processes for speech processing.

Spike sorting for large, dense electrode arrays.

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.

High-dimensional cluster analysis with the masked EM algorithm.

Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for next-generation, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.