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40 Hz-Transcranial alternating current stimulation (tACS) selectively modulates speech perception
40 Hz-Transcranial alternating current stimulation (tACS) selectively modulates speech perception Katharina S. Rufener, Tino Zaehle, Mathias S. Oechslin, Martin Meyer PII: DOI: Reference:
S0167-8760(16)30002-2 doi: 10.1016/j.ijpsycho.2016.01.002 INTPSY 11073
To appear in:
International Journal of Psychophysiology
Received date: Revised date: Accepted date:
21 May 2015 7 January 2016 8 January 2016
Please cite this article as: Rufener, Katharina S., Zaehle, Tino, Oechslin, Mathias S., Meyer, Martin, 40 Hz-Transcranial alternating current stimulation (tACS) selectively modulates speech perception, International Journal of Psychophysiology (2016), doi: 10.1016/j.ijpsycho.2016.01.002
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40 Hz - transcranial Alternating Current Stimulation (tACS)
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selectively modulates speech perception
1 University
Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland
Research Unit for Neuroplasticity and Learning of the Healthy Aging Brain, University of Zurich, Switzerland
3International
Normal Aging and Plasticity Imaging Center, Zurich, Switzerland
Department of Neurology, Otto-von-Guericke University Magdeburg, Germany Psychology Unit (CPU), University of Klagenfurt, Klagenfurt am Wörthersee, Austria
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5 Cognitive
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2
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Katharina S. Rufener1,2§ , Tino Zaehle4, Mathias S.Oechslin3, Martin Meyer1,2,3,5
§ Corresponding author
Correspondence to: Katharina Rufener Department of Neurology Leipzigerstrasse 44 D-39120 Magdeburg E-mail: [email protected] Phone: +49 391 67 21678
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40Hz tACS selectively modulates speech processing
Abstract
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The present study investigated the functional relevance of gamma oscillations for the processing of rapidly changing acoustic features in speech signals. For this purpose
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we analyzed repetition-induced perceptual learning effects in 18 healthy adult participants. The participants received either 6 Hz of 40 Hz tACS over the bilateral
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auditory cortex, while repeatedly performing a phoneme categorization task.
In result, we found that 40 Hz tACS led to a specific alteration in repetition-induced perceptual learning. While participants in the non-stimulated control group as well as
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those in the experimental group receiving 6Hz-tACS considerably improved their perceptual performance, the application of 40 Hz tACS selectively attenuated the
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repetition-induced improvement in phoneme categorization abilities.
Our data provide causal evidence for a functional relevance of gamma oscillations
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during the perceptual learning of acoustic speech features. Moreover, we demonstrate that even less than twenty minutes of alternating current stimulation
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below the individual perceptual threshold is sufficient to affect speech perception. This finding is relevant in that this novel approach might have implications with
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respect to impaired speech processing in dyslexics and older adults.
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1. Introduction
Speech perception is based on both spectral (i.e. F0 and harmonics that characterize
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the pitch and timbre of one’s speech) and temporal information (i.e. phoneme features and syllables) that are available in the acoustic signal. However, previous
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studies consistently show that successful speech processing relies mainly on temporal patterns (Shannon et al., 1995) such as the voice onset time (VOT). The VOT describes a short delay between the release of closure and the start of voicing,
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which enables the distinction of voiced (i.e. /da/) from voiceless stop-consonants (i.e. /ta/). VOT can thus be considered an important acoustic cue for the encoding of linguistically relevant information (Lisker et al., 1964). The ability to distinguish two
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acoustic signals as discrete, as is the case with the VOT, requires a sufficient resolution of the incoming acoustic features as well as the formation of a temporally precise representation of those events. There is evidence regarding the deficient
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processing of rapid auditory information in children with developmental dyslexia, and also in adults with reduced reading and writing skills (Chobert et al., 2012; Gaab et
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al, 2007; Raschle et al., 2013).
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Neurophysiologically, current studies suggest a functional relationship between endogenous neuronal oscillations in the gamma range (30 - 60 Hz) and phoneme
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processing (Luo et al., 2012; Giraud et al., 2012; Peña et al., 2012; Gross et al. 2013). By tracking the acoustic signal, neural oscillations in the gamma range are assumed to parse the incoming auditory information into units on the phonematic scale. Furthermore, intra-individually, the resonance frequency of the auditory system in the gamma-range (Zaehle et al., 2010a) seems to determine the individual auditory temporal resolution abilities (Baltus et al., 2015). In contrast, oscillations in the theta range (approximately 3 – 8 Hz) have been consistently associated with the processing of linguistic features evolving over enhanced time ranges such as intonation contour (Ghitza, 2012, 2013; Peelle et al., 2012). This functional dissociation between gamma and theta oscillations for different aspects of speech processing is in line with the “Asymmetric Sampling in Time” hypothesis (Giraud et al., 2012; Giraud et al., 2007; Poeppel, 2003), which is a theoretical framework delimiting the initial steps of auditory language integration. The AST-model is able to
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bridge the gap between linguistics and neuroscience by proposing an isomorphism in fundamental oscillation patterns, both in the speech and in the brain signal. Further evidence for the AST hypothesis is also provided by data from fMRI resting state
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activity in the auditory cortex regions with lateralized endogenous theta and gamma oscillations in Heschl’s gyrus, emphasizing the functional relevance of these
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frequencies in speech processing (Giraud et al., 2007). Importantly, although a funtional dominance of gamma oscillations in the processing of phonemes, and theta oscillations in the processing of the intonation contour exists, there is no clear
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dichotomy between the two neural oscillatory frequencies in the processing of these linguistic features; rather, it has been described as a preference (Poeppel, 2003).
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Usually, when a given task is performed repeatedly, improvements in the perceptual performance can be observed. Perception thus changes with experience (Goldstone, 1998; Seitz et al. 2007). Regarding the auditory modality, such repetition-induced
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behavioral improvements have been shown for gap detection thresholds (Smith et al., 2008; Mishra et al., 2015) and the perception of time-compressed speech (Peelle
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et al., 2005), which indicates that the processing of rapidly changing acoustic cues benefits from repeated stimulus exposure, i.e. it underlies perceptual learning.
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Perceptual learning already takes place after some tens of repetitions but it also improves over days (Smith et al., 2008) and months (Schwab et al., 1985).
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Perceptual learning is gated by alterations in underlying neuronal mechanisms of sensory processing that allow the system to process the incoming signal more precisely (Gilbert et al., 2001; Fahle, 2005). This process, in turn, requires the adequate encoding of the incoming acoustic features.
A limitation of most of the aforementioned studies is their correlative methodological approach. Although brain imaging techniques (e.g. Electroencephalography or structural and functional Magnetic Resonance Imaging) are state of the art approaches in cognitive neuroscience, these methods strictly speaking do not allow for the establishment of a causal relationship between the dependent variable and a set of independent variables. In contrast, non-invasive electrical brain stimulation techniques are able to directly influence cortical brain areas and, therefore, to demonstrate a causal brain – behavior relationship. Transcranial alternating current
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stimulation (tACS) has recently drawn the attention of neuroscientists and clinicians. Through the application of a sinusoidal current over two electrodes on the scalp, this technique is able to interact with rhythmic neural activity (Fröhlich, 2015; Herrmann et
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al., 2013; Zaehle et al., 2010).
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The electrical stimulation is delivered with a battery-driven stimulator by means of (usually) two electrodes: a negative cathode and a positive anode. In contrast to tDCS, in which the current continuously flows from the anode to the cathode
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(meaning from the negative to the positive electrode), the electrode polarity changes in a determined frequency, resulting in a sinusoidal alternating current (Zaghi et al., 2010; Herrmann et al., 2013; Herrmann et al., 2015). Current research indicates that
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tACS entrains cortical oscillations by inducing specific frequency patterns (Fröhlich et al., 2010; Zaehle et al., 2010b; Antal et al., 2013; Herrmann et al., 2013; Helfrich et al. 2014). The stimulation at physiologically meaningful frequencies seems to have
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domain-specific effects on cognitive activities, which supports the causal implication of cortical rhythms in cognitive function (Thut et al., 2009). Thus, tACS allows us to
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study the influence of such oscillation patterns on the corresponding perceptual and cognitive functions. It is noteworthy, that the majority of previous studies on neural
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oscillation patterns have investigated clinical patients using repeated trains of rhythmic TMS. However, to date, only few studies have explored the effects of
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potential frequency ‘entrainment’ after the application of rhythmic stimulation. A number of studies focusing on the functional relevance of alpha oscillations (8 – 12 Hz) showed a relationship between parieto-occipital tACS and visual perception (Kanai et al., 2008; Brignani et al., 2013; Helfrich et al., 2014). Neuling et al. (2012) found alterations in the auditory detection threshold when applying alpha tACS over the bilateral auditory cortex regions. Considering cognitive processes, studies applying theta oscillations have provided evidence for a functional role of slower oscillation patterns during working memory (Polania et al., 2012; Jausovec el al., 2014a; Jausovec el al., 2014b).
Based on these findings, the goal of the present study was to provide causal evidence for a functional relationship between neuronal gamma oscillations and phoneme feature processing. In line with the AST-framework (Giraud et al., 2007;
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Giraud et al., 2012; Poeppel, 2003) and with recent studies using tACS on perception (Neuling et al., 2012; Polania et al., 2012; Herrmann et al., 2013; Riecke et al., 2015) we hypothesized that applying a functionally relevant oscillation (i.e. lower gamma in
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VOT perception) would interfere with ability to categorize phonemes. In contrast, applying functionally irrelevant oscillations (such as in the slower theta range) should
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not have affected phoneme processing.
2. Methods and Materials
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2.1 Participants
Twenty-one healthy young adults (11 females, 20 – 28 years, M = 24.3, SD = 2.0) participated in this study. Participants were recruited via advertisement at the
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University of Zurich. They were native speakers of Swiss German or Standard German and right-handed according to the Edinburgh Handedness Inventory
between
500
Hz
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(Oldfield, 1971). All participants were screened for auditory acuity for frequencies and
6000
Hz
using
MAICO
ST20
(http://www.maico-
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diagnostic.com/). Hearing performance of all participants was < 25 dB SPL for the frequencies tested. None of them reported a history of psychiatric or neurological
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disease and were not currently taking medication that would affect the central nervous system. Participants were informed about all aspects of the study excluding the main hypothesis and the specific order of the two applied stimulation parameters. Before the experiment all participants gave their written informed consent. The procedure was approved by the local ethics committee and is in accordance with the declaration of Helsinki.
In an additional control experiment, a separate sample of 17 participants (11 females, 22 – 32 years, M = 27.5; SD = 3.3 years) was assessed to measure the mere learning effect over the five consecutive runs on VOT categorization. This control group took part in one experimental session and performed the same behavioral task but did not receive any electrical stimulation. The instructions and the task procedure
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to the participants of the control group were comparable to the stimulation group,
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- Insert Figure 1 about here -
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except that no tACS was applied.
2.2 Procedure
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TACS was applied by means of a battery-driven stimulator (NeuroConn, Ilmenau, Germany) using two rubber electrodes placed in 0.9% saline-soaked synthetic sponges. Two 5 x 7 cm stimulation electrodes were placed over T7 and T8 according
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to the 10 –20 system for EEG electrode placement (Fig. 1A). Impedance was kept below 10 kOhm. We applied oscillating sinusoidal currents at 40 Hz (gamma band) and 6 Hz (theta band). Prior to the experiment, all participants underwent a tACS-
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threshold-measurement procedure to determine their thresholds for skin sensation and phosphenes as induced by tACS. We applied tACS stimulation at 40 Hz and 6
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Hz with 1.0 mA and either increased or decreased the amplitude stepwise in increments of 0.1 mA. Participants were asked to keep their eyes open and to
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indicate the presence of any sensation. Stimulation intensity was kept 0.1 mA below the lower threshold for either phosphenes or skin sensations with a 10s fade in/out
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sequence. This procedure allows for adequate stimulation at threshold intensities, which are determined by individual physiological and metabolic parameters (stimulation mean intensity 6 Hz: 1.0 mA; SD = 0.401 and 40 Hz: 1.1 mA; SD = 0.395). The sequence of stimulation frequencies was randomized between subjects. To avoid possible carry-over effects, the two tACS frequencies were applied on two different days with an interval of at least seven days in between them. All participants performed both stimulations settings. The experiment started with a pre-tACS run of the VOT categorization task (see Fig. 1 B). At the end of this first block, the stimulation was turned on and was applied over three additional consecutive runs of VOT categorization. A final run of VOT categorization was then completed without the application of any stimulation (posttACS).
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During the experiment, participants were seated in a comfortable chair at a distance of approximately 0.80 m from a monitor in an electromagnetic and sound shielded booth. Stimulus material was presented via in-ear headphones (Sennheiser CX271)
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at 65dB SPL. Participants were instructed to keep their eyes open during the experiment and to fixate on a white cross, which was presented on a black screen.
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Finally, after completing the experiment, participants were asked to fill out a short questionnaire about their physical state both during and after the stimulation. Analysis of this questionnaire did not reveal a difference between the two stimulation
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protocols. Thus, the placebo effect, as well as tACS-frequency related differences in alertness, fatigue and/or physical sensations (e.g. itching, heat sensation), could be
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excluded with a sufficiently high degree of probability.
2.3 Stimulus material
Stimuli consisted of a VOT continuum ranging from the syllable /da/ to the syllable
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/ta/. The continuum comprised of 21 stimuli from VOT 20 ms to VOT 40 ms in 1 ms steps (for a more detailed explanation see Zaehle et al., 2007). Using a phoneme
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category-labeling task participants were instructed to decide whether the stimulus represented /da/ or /ta/ and to give their response via a button press (left or right
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mouse button with the right hand). Notably, category labeling as a task places relatively low demands on short-term memory (Manis et al., 1997; McBride-Chang,
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1996). In order to prevent fatigue and loss of concentration, the stimuli were presented in five consecutive runs, each separated by a short break. In each run, all 21 VOT stimuli were presented six times in randomized order. This procedure took six minutes duration per run. Thus, participants received 18 minutes of tACS. To familiarize participants with the relevant stimulus dimension they were presented with two examples of VOT stimuli (VOT 20 and VOT 40) prior to the experiment. We used the Presentation Software, Version 16.0 (http://www.neurobs.com/) to run the experiment and to record the participants’ category labeling and response times.
2.4 Statistical Analysis Analysis of the VOT discrimination task was statistically evaluated by fitting each participant’s data using a logistic regression analysis and then extracting the slope
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parameter (beta value of the binary logistic regression), the intercept parameter (representing the number of /da/ at 20 ms-VOT, or the consistency of detection of the voiced feature), and the category boundary (VOT at 50% /da/-responses) of the
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well as for pre-tACS and post-tACS run separately.
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individual curves (Breier et al., 2001). This procedure was run for each participant as
Hosmer Lemeshow tests were carried out to control for the goodness of fit (GOF) of the logistic regression for each participant, and also separately for the pre- and the
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post-assessment. Due to the fact that this analysis revealed an insufficient model fit (p < .05) for three subjects (2 females) of the experimental group in at least one condition (6 Hz tACS, 40 Hz tACS) or time point of measurement (pre-tACS, post-
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tACS), the data from these participants were excluded from any further statistical analysis.
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To analyze repetition-induced perceptual learning we compared the slope parameter between pre-tACS and post-tACS using separate dependent samples t-tests for both
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tACS-conditions and for the control group. Following on that, the pre-to-post percentage change in the slope parameter was compared between the two tACS
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frequencies using a dependent samples t-test. Additionally, we compared the pre-topost percentage change in the slope parameter between the tACS frequencies and
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the control group using two separate independent samples t-tests. Finally and in order to control for differences in the baseline performance the slope parameter in the pre-tACS run was compared between the tACS frequencies and the control group using two, separate, independent-samples t-tests. In addition, we run dependent-samples t-tests to compare the baseline performance between the two tACS-frequencies. The same statistical approach was also used in order to analyze the influence of tACS on the intercept and the category boundary.
3. Results
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Hosmer Lemeshow tests confirmed the goodness of fit (GOF) of the logistic regression for each participant and pre-and post-assessment respectively (p > .05). Analysis of the GOF showed no statistically significant differences in the model fit of
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the logistic regression between the tACS conditions (i.e. 6 Hz, 40 Hz); neither in the pre-tACS (T(17) = .016; p = .998) nor in the post-tACS run (T(17) = .264; p = .795).
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Furthermore, no statistically significant differences in the GOF were found between the separately measured control group and the 40 Hz tACS condition in the pre-tACS run (T(33) = .393; p = .697). This finding also holds true for the comparison between
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the control group and the 6 Hz tACS condition (T(33) = .414; p = .682). In the posttACS run, the control group revealed enhanced GOF measures when compared to the 6 Hz tACS condition (T(33) = 2.152; p= .039), as well as to the 40 Hz tACS
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condition (T(33) = 2.324; p = .026). This effect is mainly driven by a significant pre-topost increment in GOF in the separately measured control group (T(16) = -2.605; p = .019). However, all stimulation condition revealed a sufficient model fit, both in the
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pre-tACS run (M 6 Hz: 0.457; M 40 Hz: 0.456; M control: 0.501), and in the post-
Analysis
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tACS run (M 6 Hz: 0.506; M 40 Hz: 0.475; M control: 0.734),
of the beta coefficient revealed that the participants of the control group (M
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pre = 0.367, SD = 0.368; M post = 0.587, SD = 0.471) improved their performance from pre-assessment to post-assessment (T(16) = - 3.479; p = .003). The same
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effect was found after 6 Hz stimulation (M pre = 0.453, SD = 0.142; M post = 0.784, SD = 0.485) in the tACS-group (T(17) = 2.911; p = .010), whereas no significant difference between pre-to-post measures (M pre = 0.527, SD = 0.241; M post = 0.645, SD = 0.348) was found after 40 Hz tACS (T(17) = -1.480; p = .157). No statistically significant difference was found between 6 Hz tACS (M = 0.453, SD = 0.142) and 40 Hz tACS (M = 0.527, SD = 0.241) in the pre-tACS run (T(17) = -1.148; p = .267). Furthermore, no significant differences were found in the pre-tACS run between the control group and the experimental group, neither for the 6 Hz tACS condition (T(33) = 0.915; p = .367), nor for the 40 Hz tACS condition (T(33) = 0.136; p = .682).
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Figure 2 illustrates the repetition-induced improvement in the beta coefficient (i.e. the
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percentage change from pre- to post-assessment) for the two stimulation conditions and the control group separately. Whereas both the control group and the
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participants in the 6 Hz tACS stimulation condition improved their accuracy in the VOT categorization task, for the 40 Hz tACS stimulation this effect was considerably reduced. This finding was confirmed by statistical analysis of the pre-to-post
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percentage changes of the beta coefficient revealing a significantly enhanced improvement for the control group compared to the 40 Hz tACS group (T(33) = 2.341; p = .029). Similarly, the percentage improvement was enhanced after 6 Hz
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compared to 40 Hz tACS (T(17) = 2.963; p = .009). No such difference was found in
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the comparison between the control group and 6 Hz tACS (T(33) = .035; p = .972).
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The averaged VOT categorizations for both stimulation conditions and for the separately measured control group are presented in Figure 3. As depicted, in the 40
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Hz condition the participants tend to categorize the ambiguous stimuli less consistently compared to the 6 Hz condition and compared to the control group. This, in turn, led to a shallower slope after 40 Hz tACS as confirmed by the abovementioned statistical analysis of the beta coefficient. No such effect seems present for stimuli at the end of the VOT continuum. This finding was confirmed by statistical analyses of the intercept parameter, revealing no significant difference between the stimulation conditions. Finally, no statistically significant differences between the stimulation conditions were found for the category boundary, or for the RT. In sum, the data of the present study indicate that 40 Hz tACS, but not 6 Hz tACS, applied over the bilateral auditory cortex regions interacts with the process of repetition-induced improvement in a VOT categorization task.
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4. Discussion
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The aim of this study was to investigate the specific influence of gamma tACS on the processing of rapidly changing acoustic features in the speech signal. Based on
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recent studies that had emphasized the functional relevance of gamma oscillations in phoneme processing we hypothesized that the application of 40 Hz tACS over the auditory cortex regions would affect repetition-induced improvement in a VOT categorization task. The results of the present study show that 40 Hz tACS applied
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over the bilateral auditory cortex interferes with the increment in participants’ perceptual learning of VOT stimuli. By comparing pre-to-post changes when
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stimulating with 40 Hz tACS and 6 Hz tACS as well as with a separately measured non-stimulated control group, our data indicate that 40 Hz tACS selectively attenuates the perceptual learning to categorize VOT stimuli. These findings provide
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causal evidence for the functional relevance of auditory gamma oscillations in the
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encoding of phoneme features.
The observation that tACS directly affects perception and behavior is in good
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agreement with previous studies which have demonstrated the influence of tACS on visual (Strüber et al., 2013) and auditory perception (Riecke et al., 2015; Neuling et
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al., 2012) as well as on cognitive processes (Vosskuhl et al., 2015; Jaušovec et al., 2014; Jaušovec et. al., 2014a; Jaušovec et. al., 2014b; Polania et al., 2012). There is a broad consensus that tACS synchronizes and entrains neural oscillations. This, in turn, allows for the assumption of a causal link between rhythmic activation patterns and behavior. In the same vein, Strüber and colleagues (2013) demonstrated that 40 Hz tACS, but not 6 Hz tACS, applied over the parietal cortex regions leads to changes in visual motion perception. These findings indicated that it was not tACS per se but rather the application of task- or feature-relevant neural oscillations that modulated perception. Accordingly, stimulating the (auditory) cortex with alternating currents allows for the assessment of feature-relevant oscillatory rhythms. In the present study, we found evidence for a frequency specific effect of tACS on the participants’ increments in VOT categorization. Whereas 6 Hz tACS led to a similar improvement in VOT categorization as that shown by the control group, application of
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40 Hz tACS considerably hampered repetition-induced learning of the presented stimuli. These findings indicate that 40 Hz tACS modulates relevant processes in the context of repetition-induced learning to categorize VOT features. However, since the
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aim of this study was to investigate the influence of gamma and theta tACS on preto-post measures of VOT categorization we did not assess the participants’ ability to
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categorize the VOT stimuli during the application of tACS.
With regards to the underlying neurophysiological mechanism of auditory perceptual learning, a recent study on animal models suggested the interplay of separate
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inhibiting neuromodulatory systems as being the basis of neural plasticity (Martins et al., 2015). The potential capability of transcranial electrical stimulation (tES) to gate or to inhibit neurotransmitter systems has already been demonstrated (Chaieb et al.,
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2015; Krause et al., 2013; Krause et al., 2014; Monte-Silva et al., 2009). One can thus speculate that 40 Hz tACS impairs perceptual learning by interfering with the relevant neurotransmitters in the auditory system. Nevertheless, we cannot fully
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exclude the possibility of learning mechanisms other than those involved in phoneme
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processing having influenced our findings.
Although our study design does not allow to distinguishing completely between
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perceptual learning to categorize VOT stimuli and the processing of rapidly changing acoustic features we argue that perceptual learning (i.e. the increased ability to
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detect and to extract relevant stimulus information as the result of experience) requires an adequate sensory processing of the relevant stimulus features (Gibson, 1969; Adolph et al., 2015). In the (audio-)visual domain there is a broad consensus that perceptual learning is based on changes in the visual cortex associated with the perception of stimuli (Watanabe et al., 2015, Adini et al., 2002, Bejjanki et al., 2011, Choi et al., 2012, Karni et al., 1991). Furthermore, the effects of a learning process are highly specific to sensory features such as location, orientation, and motion (Irvine et al., 2000; Ahissar et al., 1997, Crist et al 1997, Karni et al., 1991). Regarding the auditory domain, there is evidence of learning-induced changes in the structure of the auditory cortex as a result of both phonetic (Engineer et al., 2015; Vandermosten et al., 2015) and musical experience (Tierney et al., 2015; Seppänen et al. 2014; Tervaniemi et al., 2012; Trainor et al., 2003). These findings suggest that perceptual learning in the auditory domain emerges out of the processes of
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differentiation, selection and the extraction of relevant information as the consequence of an adaptation in the primary cortical regions. Furthermore, the specificity of the learning process would argue against the assumption that there is a
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general, feature-independent mechanism underlying it. Finally, the effect of tACS on auditory and visual perception has already been demonstrated in numerous previous
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studies (Strüber et al., 2013; Riecke et al., 2015; Helfrich et al., 2014). Thus, it seems plausible that the reported effects of our study (i.e. the frequency specific pre-to-post changes in VOT categorization) are the result of specifically modulated perceptual
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mechanisms in the auditory cortex regions (that is sensory cortex regions associated with bottom-up processes).
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However, and in contrast to the aforementioned studies, we found a diminished performance when applying feature-relevant oscillations, and then comparing these results with those of a non-stimulated control group compared to a non-stimulated
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control group. TACS is assumed to synchronize the firing rate of neurons via entrainment and this, in turn, enhances the power (i.e. amplitude) of oscillatory in
the
stimulated
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rhythms
regions
as can
be
measured
by means of
electroencephalography (Helfrich et al. 2014; Reato et al. 2013; Fröhlich et al., 2010).
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In our sample of healthy young adults, this neuromodulatory effect (i.e. the increased gamma amplitudes) was paralleled by decreased perceptual learning in a phoneme
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categorization task. This mitigating effect may be related to a non-linear relationship between gamma oscillations in the vicinity of the auditory cortex and the auditory system’s ability for repetition-induced perceptual learning of phoneme features. Accordingly, in an optimal and unaffected level of neuronal activity, an increase of the specific neural oscillation will deteriorate the associated perceptual process. Such an inverted U-shape relation has been demonstrated for the influences of auditory tDCS on acoustic processing (Heimrath et al., 2014), the influence of psychotropic drugs on tDCS effects (Monte-Silva et al., 2009), and for the association between memory functions and gamma oscillations (Baldi et al., 2005; Callicott et al., 1999). Given a relatively optimal level of gamma oscillations in the auditory cortices of our participants, 40 Hz tACS-related entrainment of endogenous gamma band activity may have led to a deteriorated performance.
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By using a bilateral electrode placement over the auditory cortex regions (i.e. over T7 and T8) the alternating current introduces a phase shift of 180 degrees at the contralateral electrode (Riecke et al., 2015). Due to the fact that auditory processing is
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normally associated with a synchronous neural phase angle in bilateral auditory cortex regions this phase shift may result in deviating perception (Neuling et al.,
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2012). Accordingly, we cannot fully exclude the possibility, that this phase shift may have influenced the present findings.
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However, the data of the present study indicated that 40 Hz tACS specifically, and not 6 Hz tACS, led to altered repetition-induced perceptual learning of VOT categorization when compared to a non-stimulated control group. This finding
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emphasizes the relevance of 40 Hz oscillations in the context of VOT processing.
5. Conclusion
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Our study is the first to provide evidence for a causal relationship between 40 Hz oscillations and repetition-induced improvement in phoneme categorization. This
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finding encourages the notion of transcranial alternating current stimulation as a powerful device to systematically investigate the relationship between neural
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oscillations and the corresponding behavior. Our findings indicate the importance of further investigations on the efficacy of tACS to modulate the processing of other crucial linguistic features in order to develop successful protocols for therapeutic application.
Acknowledgment This research was supported by the Jacobs Foundation Research Grant to Katharina Rufener. The authors would also like to express their appreciation to the “Fonds zur Förderung
des
Akademischen
Nachwuchses”
(FAN)
des
“Zürcher
Universitätsvereins” (ZUNIV) and the University Research Priority Program “Dynamics of Healthy Aging” of the University of Zurich.
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References
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Adini, Y., Sagi, D., & Tsodyks, M. (2002). Context-enabled learning in the human visual system. Nature, 415(6873), 790–793. http://doi.org/10.1038/415790a
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Adolph, K. E., & Kretch, K. S. (2015). Gibson's Theory of Perceptual Learning. In: Wrigh, JD. (Ed.), International Encyclopedia of the Social & Behavioral Sciences (pp. 127–134). Elsevier. http://doi.org/10.1016/B978-0-08-097086-8.99002-0
Ahissar, M., & Hochstein, S. (1997). Task difficulty and the specificity of perceptual learning.
NU
Nature, 387(6631), 401–406. http://doi.org/10.1038/387401a0
Antal, A., & Paulus, W. (2013). Transcranial alternating current stimulation (tACS). Frontiers in Human Neuroscience, 7, 1–4. http://doi.org/10.3389/fnhum.2013.00317
MA
Baldi, E., & Bucherelli, C. (2005). The Inverted “U-Shaped” Dose-Effect Relationships in Learning and Memory: Modulation of Arousal and Consolidation. Nonlinearity in Biology, Toxicology, and Medicine, 3(1), 9–21. http://doi.org/10.2201/nonlin.003.01.002
ED
Baltus, A., & Herrmann, C. S. (2015). Auditory temporal resolution is linked toresonance frequency of the auditory cortex. International Journal of Psychophysiology, 1–31.
PT
http://doi.org/10.1016/j.ijpsycho.2015.08.003 Bejjanki, V. R., Beck, J. M., Lu, Z.-L., & Pouget, A. (2011). Perceptual learning as improved
CE
probabilistic inference in early sensory areas. Nature Neuroscience, 14(5), 642–648. http://doi.org/10.1038/nn.2796 Breier, J. I., Gray, L., Fletcher, J. M., Diehl, R. L., Klaas, P., Foorman, B. R., & Molis, M. R.
AC
(2001). Perception of voice and tone onset time continua in children with dyslexia with and without attention deficit/hyperactivity disorder. Journal of Experimental Child Psychology, 80(3), 245–270. http://doi.org/10.1006/jecp.2001.2630 Brignani, D., Ruzzoli, M., Mauri, P., & Miniussi, C. (2013). Is Transcranial Alternating Current Stimulation Effective in Modulating Brain Oscillations? PLoS ONE, 8(2), e56589. http://doi.org/10.1371/journal.pone.0056589.g003 Callicott, J. H., Mattay, V. S., Bertolino, A., Finn, K., Coppola, R., Frank, J. A., et al. (1999). Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. Cerebral Cortex, 9(1), 20–26. http://doi.org/ 10.1093/cercor/9.1.20 Chaieb, L., Antal, A., & Paulus, W. (2015). Transcranial random noise stimulation-induced plasticity
is
NMDA-receptor
benzodiazepines
sensitive.
independent
Frontiers
in
but
sodium-channel
Neuroscience,
9,
125.
blocker
and
http://doi.org/
10.3389/fnins.2015.00125 Chobert, J., François, C., Habib, M., & Besson, M. (2012). Deficit in the preattentive
16 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
processing of syllabic duration and VOT in children with dyslexia. Neuropsychologia, 50(8), 2044–2055. http://doi.org/10.1016/j.neuropsychologia.2012.05.004 Choi, H., & Watanabe, T. (2012). Is perceptual learning associated with changes in a
PT
sensory region? F1000 Reports Biology, 4(24). http://doi.org/ 10.3410/B4-24 Crist, R. E., Kapadia, M. K., & Westheimer, G. (1997). Perceptual learning of spatial
SC RI
localization: specificity for orientation, position, and context. J Neurophysiolgy. 78(6):2889-94.
Engineer, C. T., Rahebi, K. C., Buell, E. P., Fink, M. K., & Kilgard, M. P. (2015). Behavioural Brain Research, 287, 256–264. http://doi.org/10.1016/j.bbr.2015.03.044
NU
Fahle, M. (2005). Perceptual learning: specificity versus generalization. Current Opinion in Neurobiology, 15(2), 154–160. http://doi.org/ 10.1016/j.conb.2005.03.010 Fröhlich, F. (2015). Experiments and models of cortical oscillations as a target for brain
stimulation.
doi:10.1016/bs.pbr.2015.07.025
Progress
MA
noninvasive
in
Brain
Research;
222,
41
-
73.
Fröhlich, F., & McCormick, D. A. (2010). Endogenous Electric Fields May Guide Neocortical
ED
Network Activity. Neuron, 67(1), 129–143. http://doi.org/10.1016/j.neuron.2010.06.005 Gaab, N., Gabrieli, J. D. E., & Tallal, P. (2007). Neural correlates of rapid auditory processing
PT
are disrupted in children with developmental dyslexia and ameliorated with training: an fMRI study. Restorative Neurology and Neuroscience, 25(3-4), 295-310.
CE
Ghitza, O. (2012). On the role of theta-driven syllabic parsing in decoding speech: intelligibility of speech with a manipulated modulation spectrum. Frontiers in Psychology. http://doi.org/10.3389/fpsyg.2012.00238
AC
Ghitza, O. (2013). The theta-syllable: a unit of speech information defined by cortical function. Frontiers in Psychology, 4, 138. http://doi.org/10.3389/fpsyg.2013.00138 Gilbert, C. D., Sigman, M., & Crist, R. E. (2001). The neural basis of perceptual learning. Neuron, 31(5), 681–697. Giraud, A.-L., Kleinschmidt, A., Poeppel, D., Lund, T. E., Frackowiak, R. S. J., & Laufs, H. (2007). Endogenous Cortical Rhythms Determine Cerebral Specialization for Speech Perception
and
Production.
Neuron,
56(6),
1127–1134.
http://doi.org/10.1016/j.neuron.2007.09.038 Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational
principles
and
operations.
Nature,
15(4),
511–517.
doi.org/10.1038/nn.3063 Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49(1), 585–612. Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., & Garrod, S. (2013). Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain. PLoS
17 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
Biology, 11(12). http://doi.org/10.1371/journal.pbio.1001752.s005 Heimrath, K., Kuehne, M., Heinze, H. J., & Zaehle, T. (2014). Transcranial direct current stimulation (tDCS) traces the predominance of the left auditory cortex for processing of changing
acoustic
information.
Neuroscience,
PT
rapidly
http://doi.org/10.1016/j.neuroscience.2013.12.031
261,
68–73.
SC RI
Helfrich, R. F., Schneider, T. R., Rach, S., Trautmann-Lengsfeld, S. A., Engel, A. K., & Herrmann, C. S. (2014). Entrainment of Brain Oscillations by Transcranial Alternating Current
Stimulation.
Current
Biology,
http://doi.org/10.1016/j.cub.2013.12.041
24(3),
333–339.
NU
Herrmann, C. S., Rach, S., Neuling, T., & Strüber, D. (2013). Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive Frontiers
in
10.3389/fnhum.2013.00279
Human
Neuroscience,
7,
1–13.
http://doi.org/
MA
processes.
Herrmann,, C. S., Strüber, D., Helfrich, R. F., & Engel, A. K. (2015). EEG oscillations: From correlation to causality. International Journal of Psychophysiology. 1–10. http://doi.org/
ED
10.1016/j.ijpsycho.2015.02.003
Irvine, D. R. F., Martin, R. L., Klimkeit, E., & Smith, R. (2000). Specificity of perceptual
PT
learning in a frequency discrimination task. The Journal of the Acoustical Society of America, 108(6), 2964–2968. http://doi.org/10.1121/1.1323465
CE
Jaušovec, N., & Jaušovec, K. (2014a). Increasing working memory capacity with theta transcranial alternating current stimulation (tACS). Biological Psychology, 96, 42–47. http://doi.org/10.1016/j.biopsycho.2013.11.006
AC
Jaušovec, N., Jaušovec, K., & Pahor, A. (2014b). The influence of theta transcranial alternating current stimulation (tACS) on working memory storage and processing functions. Acta Psychologica, 146, 1–6. http://doi.org/ 10.1016/j.actpsy.2013.11.011 Kanai, R., Chaieb, L., Antal, A., Walsh, V., & Paulus, W. (2008). Frequency-Dependent Electrical Stimulation of the Visual Cortex. Current Biology, 18(23), 1839–1843. http://doi.org/10.1016/j.cub.2008.10.027 Karni, A., & Sagi, D. (1991). Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proceedings of the National Academy of Sciences of the United States of America, 88(11), 4966-4970. Krause, B., Márquez-Ruiz, J, & Cohen Kadosh, R. (2013). The effect of transcranial direct current stimulation: a role for cortical excitation/inhibition balance? Frontiers in Human Neuroscience, 7, 602. http://doi.org/10.3389/fnhum.2013.00602 Krause, B., & Kadosh, R. C. (2014). Not all brains are created equal: the relevance of individual differences in responsiveness to transcranial electrical stimulation. Frontiers in
18 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
Systems Neuroscience, 8. http://doi.org/10.3389/fnsys.2014.00025. Lisker, L., & Abramson, A. S. (1964). A cross-language study of voicing in initial stops: Acoustical measurements. Word, 20(3), 384–422.
PT
Luo, H., & Poeppel, D. (2012). Cortical oscillations in auditory perception and speech: evidence for two temporal windows in human auditory cortex. Frontiers in Psychology
SC RI
3,170. 1–10. http://doi.org/10.3389/fpsyg.2012.00170
Martins, A. R. O., & Froemke, R. C. (2015). Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex. Nature Neuroscience, 1–12. http://doi.org/ 10.1038/nn.4090
NU
Manis, F. R., McBride-Chang, C., Seidenberg, M. S., Keating, P., Doi, L. M., Munson, B., & Petersen, A. (1997). Are speech perception deficits associated with developmental dyslexia?
Journal
of
Experimental
Psychology,
66(2),
211–235.
MA
http://doi.org/10.1006/jecp.1997.2383
Child
McBride-Chang, C. (1996). Models of Speech Perception and Phonological Processing in Reading. Child Development, 67(4), 1836–1856. http://doi.org/10.2307/1131735?
ED
Mishra, S. K., Dey, R., & Davessar, J. L. (2015). Temporal Resolution of the Normal Ear in Listeners with Unilateral Hearing Impairment. Journal of the Association for Research in
PT
Otolaryngology. http://doi.org/ 10.1007/s10162-015-0536-6 Monte-Silva, K., Kuo, M. F., Thirugnanasambandam, N., Liebetanz, D., Paulus, W., &
CE
Nitsche, M. A. (2009). Dose-Dependent Inverted U-Shaped Effect of Dopamine (D2-Like) Receptor Activation on Focal and Nonfocal Plasticity in Humans. Journal of Neuroscience, 29(19), 6124–6131. http://doi.org/ 10.1523/JNEUROSCI.0728-09.2009
AC
Neuling, T., Rach, S., Wagner, S., Wolters, C. H., & Herrmann, C. S. (2012). Good vibrations: Oscillatory phase shapes perception. NeuroImage, 63(2), 771–778. http://doi.org/ 10.1016/j.neuroimage.2012.07.024 Oldfield, R. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 9(1), 97–113. Peelle, J. E., & Wingfield, A. (2005). Dissociations in Perceptual Learning Revealed by Adult Age Differences in Adaptation to Time-Compressed Speech. Journal of Experimental Psychology. Human Perception and Performance, 31(6), 1315–1330. Peelle, J. E., & Davis, M. H. (2012). Neural Oscillations Carry Speech Rhythm through to Comprehension.
Frontiers
in
Psychology,
3,
320.
http://doi.org/10.3389/fpsyg.2012.00320 Peña, M., & Melloni, L. (2012). Brain oscillations during spoken sentence processing. Journal of Cognitive Neuroscience, 24(5), 1149–1164. http://doi.org/10.1162/jocn_a_00144 Poeppel, D. (2003). The analysis of speech in different temporal integration windows:
19 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
cerebral lateralization as “asymmetric sampling in time.” Speech Communication, 41(1), 245–255. http://doi.org/10.1016/S0167-6393(02)00107-3 PolanIa, R., Nitsche, M. A., Korman, C., Batsikadze, G., & Paulus, W. (2012). The
PT
Importance of Timing in Segregated Theta Phase-Coupling for Cognitive Performance. Current Biology, 22(14), 1314–1318. http://doi.org/10.1016/j.cub.2012.05.021
SC RI
Raschle, N. M., Stering, P. L., Meissner, S. N., & Gaab, N. (2013). Altered Neuronal Response During Rapid Auditory Processing and Its Relation to Phonological Processing in
Prereading
Children
at
Familial
Risk
http://doi.org/10.1093/cercor/bht104
for
Dyslexia.
Cerebral
Cortex.
NU
Reato, D., Rahman, A., Bikson, M., & Parra, L. C. (2013). Effects of weak transcranial alternating current stimulation on brain activity-a review of known mechanisms from animal
studies.
Frontiers
in
Neuroscience,
7,
687.
MA
http://doi.org/10.3389/fnhum.2013.00687
Human
Riecke, L., Formisano, E., Herrmann, C. S., & Sack, A. T. (2015). 4-Hz Transcranial Alternating Current Stimulation Phase Modulates Hearing. Brain Stimulation, 1–7.
ED
doi.org/10.1016/j.brs.2015.04.004
Schwab, E. C., & Nusbaum, H. C. (1985). Some effects of training on the perception of
PT
synthetic speech. Human Factors. 27(4): 395–408. Seitz, A. R., & Dinse, H. R. (2007). A common framework for perceptual learning. Current
CE
Opinion in Neurobiology, 17(2), 148–153. http://doi.org/10.1016/j.conb.2007.02.004 Seppänen, M., Hämäläinen, J., Pesonen, A.-K., & Tervaniemi, M. (2013). Biological Psychology, 94(2), 341–353. http://doi.org/10.1016/j.biopsycho.2013.07.004
AC
Shannon, R. V., Zeng, F. G., Kamath, V., Wygonski, J., & Ekelid, M. (1995). Speech recognition with primarily temporal cues. Science, 270(5234), 303–304. Smith, N. A., Trainor, L. J., Gray, K., Plantinga, J. A., & Shore, D. I. (2008). Stimulus, task, and learning effects on measures of temporal resolution: implications for predictors of language outcome. Journal of Speech, Language, and Hearing Research, 51(6), 1630– 1642. http://doi.org/10.1044/1092-4388(2008/07-0058) Strüber, D., Rach, S., Trautmann-Lengsfeld, S. A., Engel, A. K., & Herrmann, C. S. (2013). Antiphasic 40 Hz Oscillatory Current Stimulation Affects Bistable Motion Perception. Brain Topography, 1–14. http://doi.org/10.1007/s10548-013-0294-x Tervaniemi, M. S. A. M. (2012). Music training enhances rapid neural plasticity of N1 and P2 source activation for unattended sounds, Frontiers in Human Neuroscience. 1–13. http://doi.org/10.3389/fnhum.2012.00043/abstract Thut, G., & Miniussi, C. (2009). New insights into rhythmic brain activity from TMS-EEG studies.
Trends
in
Cognitive
Sciences,
13(4),
182–189.
20 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
http://doi.org/10.1016/j.tics.2009.01.004 Tierney, A. T., Krizman, J., & Kraus, N. (2015). Music training alters the course of adolescent auditory development. Proceedings of the National Academy of Sciences of the United
PT
States of America, 112(32), 10062–10067. http://doi.org/10.1073/pnas.1505114112 Trainor, L. J., Shahin, A., & Roberts, L. E. (2003). Effects of musical training on the auditory
SC RI
cortex in children. Annals of the New York Academy of Sciences, 999, 506–513. Vandermosten, M., Price, C. J., & Golestani, N. (2015). Plasticity of white matter connectivity in phonetics experts. Brain Structure and Function, 1–9. http://doi.org/10.1007/s00429015-1114-8
NU
Watanabe, T., & Sasaki, Y. (2015). Perceptual Learning: Toward a Comprehensive Theory. Annual Review of Psychology, 66(1), 197–221. doi.org/10.1146/annurev-psych-010814015214
MA
Vosskuhl, J., Huster, R. J., & Herrmann, C. S. (2015). Increase in short-term memory capacity induced by down-regulating individual theta frequency via transcranial alternating
current
stimulation.
Frontiers
in
Human
Neuroscience,
9.
ED
http://doi.org/10.3389/fnhum.2015.00257 Zaehle, T., Jäncke, L., & Meyer, M. (2007). Electrical brain imaging evidences left auditory
PT
cortex involvement in speech and non-speech discrimination based on temporal features. Behavioral and Brain Functions, 3(1), 63. http://doi.org/10.1186/1744-9081-3-
CE
63
Zaehle, T., Lenz, D., Ohl, F. W., & Herrmann, C. S. (2010a). Resonance phenomena in the human auditory cortex: individual resonance frequencies of the cerebral cortex
AC
determine electrophysiological responses. Experimental Brain Research, 203(3), 629– 635. http://doi.org/10.1007/s00221-010-2265-8 Zaehle, T., Rach, S., & Herrmann, C. S. (2010b). Transcranial Alternating Current Stimulation Enhances Individual Alpha Activity in Human EEG. PLoS ONE, 5(11), e13766. http://doi.org/10.1371/journal.pone.0013766.g002 Zaghi, S., Acar, M., Hultgren, B., Boggio, P. S., & Fregni, F. (2010). Noninvasive Brain Stimulation with Low-Intensity Electrical Currents: Putative Mechanisms of Action for Direct and Alternating Current Stimulation. The Neuroscientist, 16(3), 285–307. http://doi.org/10.1177/1073858409336227
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Figure 1: Experimental Setup and Procedure. A: Electrode positioning. Grey rectangles represent the 5 x 7 cm tACS-electrodes placed horizontally over T7 and T8; B: Experimental procedure: pre-tACS assessment of VOT categorization was followed by three blocks where tACS was applied. Short breaks of approximately 2 minutes were inserted between consecutive blocks, in order to avoid the subjects becoming fatigued and/or losing concentration. Finally, post-tACS categorization was measured.
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Figure 2: Pre-to-post percentage change in VOT categorization (slope parameter). The figure demonstrates the percentage increase in the slope parameter over the
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course of the experiment for the two applied tACS frequencies (6 Hz-tACS: white bar, 40 Hz-tACS: black bar). The data of the separately measured control group shows the percentage increase without any tACS-stimulation (grey bar). Asterisks indicate
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statistically significant differences between the conditions (p < .05). Error bars represent the standard errors (SE).
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Figure 3: Percentage of the presented stimuli identified as /da/ plotted as a function of VOT for the post-tACS run. Grey line: separately measured control group, dashed
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The study investigates the functional relevance of gamma oscillations in phoneme processing Perceptual learning by means of a repeatedly performed phoneme categorization task was assessed Transcranial alternating current stimulation allows to draw causal conclusions between entrained cortical oscillations and the related behavior 40 Hz – tACS but not 6 Hz-tACS interferes with perceptual learning in the phoneme categorization task Results indicate that 40 Hz oscillations represent a functionally relevant neural oscillation pattern in the context of phoneme processing
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S0167-8760(16)30002-2 doi: 10.1016/j.ijpsycho.2016.01.002 INTPSY 11073
To appear in:
International Journal of Psychophysiology
Received date: Revised date: Accepted date:
21 May 2015 7 January 2016 8 January 2016
Please cite this article as: Rufener, Katharina S., Zaehle, Tino, Oechslin, Mathias S., Meyer, Martin, 40 Hz-Transcranial alternating current stimulation (tACS) selectively modulates speech perception, International Journal of Psychophysiology (2016), doi: 10.1016/j.ijpsycho.2016.01.002
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ACCEPTED MANUSCRIPT Word count Abstract: 166 Word count Manuscript: max. 3902
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40 Hz - transcranial Alternating Current Stimulation (tACS)
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selectively modulates speech perception
1 University
Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland
Research Unit for Neuroplasticity and Learning of the Healthy Aging Brain, University of Zurich, Switzerland
3International
Normal Aging and Plasticity Imaging Center, Zurich, Switzerland
Department of Neurology, Otto-von-Guericke University Magdeburg, Germany Psychology Unit (CPU), University of Klagenfurt, Klagenfurt am Wörthersee, Austria
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5 Cognitive
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Katharina S. Rufener1,2§ , Tino Zaehle4, Mathias S.Oechslin3, Martin Meyer1,2,3,5
§ Corresponding author
Correspondence to: Katharina Rufener Department of Neurology Leipzigerstrasse 44 D-39120 Magdeburg E-mail: [email protected] Phone: +49 391 67 21678
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Abstract
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The present study investigated the functional relevance of gamma oscillations for the processing of rapidly changing acoustic features in speech signals. For this purpose
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we analyzed repetition-induced perceptual learning effects in 18 healthy adult participants. The participants received either 6 Hz of 40 Hz tACS over the bilateral
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auditory cortex, while repeatedly performing a phoneme categorization task.
In result, we found that 40 Hz tACS led to a specific alteration in repetition-induced perceptual learning. While participants in the non-stimulated control group as well as
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those in the experimental group receiving 6Hz-tACS considerably improved their perceptual performance, the application of 40 Hz tACS selectively attenuated the
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repetition-induced improvement in phoneme categorization abilities.
Our data provide causal evidence for a functional relevance of gamma oscillations
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during the perceptual learning of acoustic speech features. Moreover, we demonstrate that even less than twenty minutes of alternating current stimulation
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below the individual perceptual threshold is sufficient to affect speech perception. This finding is relevant in that this novel approach might have implications with
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respect to impaired speech processing in dyslexics and older adults.
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1. Introduction
Speech perception is based on both spectral (i.e. F0 and harmonics that characterize
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the pitch and timbre of one’s speech) and temporal information (i.e. phoneme features and syllables) that are available in the acoustic signal. However, previous
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studies consistently show that successful speech processing relies mainly on temporal patterns (Shannon et al., 1995) such as the voice onset time (VOT). The VOT describes a short delay between the release of closure and the start of voicing,
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which enables the distinction of voiced (i.e. /da/) from voiceless stop-consonants (i.e. /ta/). VOT can thus be considered an important acoustic cue for the encoding of linguistically relevant information (Lisker et al., 1964). The ability to distinguish two
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acoustic signals as discrete, as is the case with the VOT, requires a sufficient resolution of the incoming acoustic features as well as the formation of a temporally precise representation of those events. There is evidence regarding the deficient
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processing of rapid auditory information in children with developmental dyslexia, and also in adults with reduced reading and writing skills (Chobert et al., 2012; Gaab et
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al, 2007; Raschle et al., 2013).
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Neurophysiologically, current studies suggest a functional relationship between endogenous neuronal oscillations in the gamma range (30 - 60 Hz) and phoneme
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processing (Luo et al., 2012; Giraud et al., 2012; Peña et al., 2012; Gross et al. 2013). By tracking the acoustic signal, neural oscillations in the gamma range are assumed to parse the incoming auditory information into units on the phonematic scale. Furthermore, intra-individually, the resonance frequency of the auditory system in the gamma-range (Zaehle et al., 2010a) seems to determine the individual auditory temporal resolution abilities (Baltus et al., 2015). In contrast, oscillations in the theta range (approximately 3 – 8 Hz) have been consistently associated with the processing of linguistic features evolving over enhanced time ranges such as intonation contour (Ghitza, 2012, 2013; Peelle et al., 2012). This functional dissociation between gamma and theta oscillations for different aspects of speech processing is in line with the “Asymmetric Sampling in Time” hypothesis (Giraud et al., 2012; Giraud et al., 2007; Poeppel, 2003), which is a theoretical framework delimiting the initial steps of auditory language integration. The AST-model is able to
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bridge the gap between linguistics and neuroscience by proposing an isomorphism in fundamental oscillation patterns, both in the speech and in the brain signal. Further evidence for the AST hypothesis is also provided by data from fMRI resting state
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activity in the auditory cortex regions with lateralized endogenous theta and gamma oscillations in Heschl’s gyrus, emphasizing the functional relevance of these
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frequencies in speech processing (Giraud et al., 2007). Importantly, although a funtional dominance of gamma oscillations in the processing of phonemes, and theta oscillations in the processing of the intonation contour exists, there is no clear
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dichotomy between the two neural oscillatory frequencies in the processing of these linguistic features; rather, it has been described as a preference (Poeppel, 2003).
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Usually, when a given task is performed repeatedly, improvements in the perceptual performance can be observed. Perception thus changes with experience (Goldstone, 1998; Seitz et al. 2007). Regarding the auditory modality, such repetition-induced
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behavioral improvements have been shown for gap detection thresholds (Smith et al., 2008; Mishra et al., 2015) and the perception of time-compressed speech (Peelle
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et al., 2005), which indicates that the processing of rapidly changing acoustic cues benefits from repeated stimulus exposure, i.e. it underlies perceptual learning.
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Perceptual learning already takes place after some tens of repetitions but it also improves over days (Smith et al., 2008) and months (Schwab et al., 1985).
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Perceptual learning is gated by alterations in underlying neuronal mechanisms of sensory processing that allow the system to process the incoming signal more precisely (Gilbert et al., 2001; Fahle, 2005). This process, in turn, requires the adequate encoding of the incoming acoustic features.
A limitation of most of the aforementioned studies is their correlative methodological approach. Although brain imaging techniques (e.g. Electroencephalography or structural and functional Magnetic Resonance Imaging) are state of the art approaches in cognitive neuroscience, these methods strictly speaking do not allow for the establishment of a causal relationship between the dependent variable and a set of independent variables. In contrast, non-invasive electrical brain stimulation techniques are able to directly influence cortical brain areas and, therefore, to demonstrate a causal brain – behavior relationship. Transcranial alternating current
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stimulation (tACS) has recently drawn the attention of neuroscientists and clinicians. Through the application of a sinusoidal current over two electrodes on the scalp, this technique is able to interact with rhythmic neural activity (Fröhlich, 2015; Herrmann et
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al., 2013; Zaehle et al., 2010).
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The electrical stimulation is delivered with a battery-driven stimulator by means of (usually) two electrodes: a negative cathode and a positive anode. In contrast to tDCS, in which the current continuously flows from the anode to the cathode
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(meaning from the negative to the positive electrode), the electrode polarity changes in a determined frequency, resulting in a sinusoidal alternating current (Zaghi et al., 2010; Herrmann et al., 2013; Herrmann et al., 2015). Current research indicates that
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tACS entrains cortical oscillations by inducing specific frequency patterns (Fröhlich et al., 2010; Zaehle et al., 2010b; Antal et al., 2013; Herrmann et al., 2013; Helfrich et al. 2014). The stimulation at physiologically meaningful frequencies seems to have
ED
domain-specific effects on cognitive activities, which supports the causal implication of cortical rhythms in cognitive function (Thut et al., 2009). Thus, tACS allows us to
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study the influence of such oscillation patterns on the corresponding perceptual and cognitive functions. It is noteworthy, that the majority of previous studies on neural
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oscillation patterns have investigated clinical patients using repeated trains of rhythmic TMS. However, to date, only few studies have explored the effects of
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potential frequency ‘entrainment’ after the application of rhythmic stimulation. A number of studies focusing on the functional relevance of alpha oscillations (8 – 12 Hz) showed a relationship between parieto-occipital tACS and visual perception (Kanai et al., 2008; Brignani et al., 2013; Helfrich et al., 2014). Neuling et al. (2012) found alterations in the auditory detection threshold when applying alpha tACS over the bilateral auditory cortex regions. Considering cognitive processes, studies applying theta oscillations have provided evidence for a functional role of slower oscillation patterns during working memory (Polania et al., 2012; Jausovec el al., 2014a; Jausovec el al., 2014b).
Based on these findings, the goal of the present study was to provide causal evidence for a functional relationship between neuronal gamma oscillations and phoneme feature processing. In line with the AST-framework (Giraud et al., 2007;
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Giraud et al., 2012; Poeppel, 2003) and with recent studies using tACS on perception (Neuling et al., 2012; Polania et al., 2012; Herrmann et al., 2013; Riecke et al., 2015) we hypothesized that applying a functionally relevant oscillation (i.e. lower gamma in
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VOT perception) would interfere with ability to categorize phonemes. In contrast, applying functionally irrelevant oscillations (such as in the slower theta range) should
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not have affected phoneme processing.
2. Methods and Materials
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2.1 Participants
Twenty-one healthy young adults (11 females, 20 – 28 years, M = 24.3, SD = 2.0) participated in this study. Participants were recruited via advertisement at the
ED
University of Zurich. They were native speakers of Swiss German or Standard German and right-handed according to the Edinburgh Handedness Inventory
between
500
Hz
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(Oldfield, 1971). All participants were screened for auditory acuity for frequencies and
6000
Hz
using
MAICO
ST20
(http://www.maico-
CE
diagnostic.com/). Hearing performance of all participants was < 25 dB SPL for the frequencies tested. None of them reported a history of psychiatric or neurological
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disease and were not currently taking medication that would affect the central nervous system. Participants were informed about all aspects of the study excluding the main hypothesis and the specific order of the two applied stimulation parameters. Before the experiment all participants gave their written informed consent. The procedure was approved by the local ethics committee and is in accordance with the declaration of Helsinki.
In an additional control experiment, a separate sample of 17 participants (11 females, 22 – 32 years, M = 27.5; SD = 3.3 years) was assessed to measure the mere learning effect over the five consecutive runs on VOT categorization. This control group took part in one experimental session and performed the same behavioral task but did not receive any electrical stimulation. The instructions and the task procedure
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to the participants of the control group were comparable to the stimulation group,
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- Insert Figure 1 about here -
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except that no tACS was applied.
2.2 Procedure
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TACS was applied by means of a battery-driven stimulator (NeuroConn, Ilmenau, Germany) using two rubber electrodes placed in 0.9% saline-soaked synthetic sponges. Two 5 x 7 cm stimulation electrodes were placed over T7 and T8 according
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to the 10 –20 system for EEG electrode placement (Fig. 1A). Impedance was kept below 10 kOhm. We applied oscillating sinusoidal currents at 40 Hz (gamma band) and 6 Hz (theta band). Prior to the experiment, all participants underwent a tACS-
ED
threshold-measurement procedure to determine their thresholds for skin sensation and phosphenes as induced by tACS. We applied tACS stimulation at 40 Hz and 6
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Hz with 1.0 mA and either increased or decreased the amplitude stepwise in increments of 0.1 mA. Participants were asked to keep their eyes open and to
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indicate the presence of any sensation. Stimulation intensity was kept 0.1 mA below the lower threshold for either phosphenes or skin sensations with a 10s fade in/out
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sequence. This procedure allows for adequate stimulation at threshold intensities, which are determined by individual physiological and metabolic parameters (stimulation mean intensity 6 Hz: 1.0 mA; SD = 0.401 and 40 Hz: 1.1 mA; SD = 0.395). The sequence of stimulation frequencies was randomized between subjects. To avoid possible carry-over effects, the two tACS frequencies were applied on two different days with an interval of at least seven days in between them. All participants performed both stimulations settings. The experiment started with a pre-tACS run of the VOT categorization task (see Fig. 1 B). At the end of this first block, the stimulation was turned on and was applied over three additional consecutive runs of VOT categorization. A final run of VOT categorization was then completed without the application of any stimulation (posttACS).
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During the experiment, participants were seated in a comfortable chair at a distance of approximately 0.80 m from a monitor in an electromagnetic and sound shielded booth. Stimulus material was presented via in-ear headphones (Sennheiser CX271)
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at 65dB SPL. Participants were instructed to keep their eyes open during the experiment and to fixate on a white cross, which was presented on a black screen.
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Finally, after completing the experiment, participants were asked to fill out a short questionnaire about their physical state both during and after the stimulation. Analysis of this questionnaire did not reveal a difference between the two stimulation
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protocols. Thus, the placebo effect, as well as tACS-frequency related differences in alertness, fatigue and/or physical sensations (e.g. itching, heat sensation), could be
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excluded with a sufficiently high degree of probability.
2.3 Stimulus material
Stimuli consisted of a VOT continuum ranging from the syllable /da/ to the syllable
ED
/ta/. The continuum comprised of 21 stimuli from VOT 20 ms to VOT 40 ms in 1 ms steps (for a more detailed explanation see Zaehle et al., 2007). Using a phoneme
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category-labeling task participants were instructed to decide whether the stimulus represented /da/ or /ta/ and to give their response via a button press (left or right
CE
mouse button with the right hand). Notably, category labeling as a task places relatively low demands on short-term memory (Manis et al., 1997; McBride-Chang,
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1996). In order to prevent fatigue and loss of concentration, the stimuli were presented in five consecutive runs, each separated by a short break. In each run, all 21 VOT stimuli were presented six times in randomized order. This procedure took six minutes duration per run. Thus, participants received 18 minutes of tACS. To familiarize participants with the relevant stimulus dimension they were presented with two examples of VOT stimuli (VOT 20 and VOT 40) prior to the experiment. We used the Presentation Software, Version 16.0 (http://www.neurobs.com/) to run the experiment and to record the participants’ category labeling and response times.
2.4 Statistical Analysis Analysis of the VOT discrimination task was statistically evaluated by fitting each participant’s data using a logistic regression analysis and then extracting the slope
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40Hz tACS selectively modulates speech processing
parameter (beta value of the binary logistic regression), the intercept parameter (representing the number of /da/ at 20 ms-VOT, or the consistency of detection of the voiced feature), and the category boundary (VOT at 50% /da/-responses) of the
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well as for pre-tACS and post-tACS run separately.
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individual curves (Breier et al., 2001). This procedure was run for each participant as
Hosmer Lemeshow tests were carried out to control for the goodness of fit (GOF) of the logistic regression for each participant, and also separately for the pre- and the
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post-assessment. Due to the fact that this analysis revealed an insufficient model fit (p < .05) for three subjects (2 females) of the experimental group in at least one condition (6 Hz tACS, 40 Hz tACS) or time point of measurement (pre-tACS, post-
MA
tACS), the data from these participants were excluded from any further statistical analysis.
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To analyze repetition-induced perceptual learning we compared the slope parameter between pre-tACS and post-tACS using separate dependent samples t-tests for both
PT
tACS-conditions and for the control group. Following on that, the pre-to-post percentage change in the slope parameter was compared between the two tACS
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frequencies using a dependent samples t-test. Additionally, we compared the pre-topost percentage change in the slope parameter between the tACS frequencies and
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the control group using two separate independent samples t-tests. Finally and in order to control for differences in the baseline performance the slope parameter in the pre-tACS run was compared between the tACS frequencies and the control group using two, separate, independent-samples t-tests. In addition, we run dependent-samples t-tests to compare the baseline performance between the two tACS-frequencies. The same statistical approach was also used in order to analyze the influence of tACS on the intercept and the category boundary.
3. Results
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Hosmer Lemeshow tests confirmed the goodness of fit (GOF) of the logistic regression for each participant and pre-and post-assessment respectively (p > .05). Analysis of the GOF showed no statistically significant differences in the model fit of
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the logistic regression between the tACS conditions (i.e. 6 Hz, 40 Hz); neither in the pre-tACS (T(17) = .016; p = .998) nor in the post-tACS run (T(17) = .264; p = .795).
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Furthermore, no statistically significant differences in the GOF were found between the separately measured control group and the 40 Hz tACS condition in the pre-tACS run (T(33) = .393; p = .697). This finding also holds true for the comparison between
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the control group and the 6 Hz tACS condition (T(33) = .414; p = .682). In the posttACS run, the control group revealed enhanced GOF measures when compared to the 6 Hz tACS condition (T(33) = 2.152; p= .039), as well as to the 40 Hz tACS
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condition (T(33) = 2.324; p = .026). This effect is mainly driven by a significant pre-topost increment in GOF in the separately measured control group (T(16) = -2.605; p = .019). However, all stimulation condition revealed a sufficient model fit, both in the
ED
pre-tACS run (M 6 Hz: 0.457; M 40 Hz: 0.456; M control: 0.501), and in the post-
Analysis
PT
tACS run (M 6 Hz: 0.506; M 40 Hz: 0.475; M control: 0.734),
of the beta coefficient revealed that the participants of the control group (M
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pre = 0.367, SD = 0.368; M post = 0.587, SD = 0.471) improved their performance from pre-assessment to post-assessment (T(16) = - 3.479; p = .003). The same
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effect was found after 6 Hz stimulation (M pre = 0.453, SD = 0.142; M post = 0.784, SD = 0.485) in the tACS-group (T(17) = 2.911; p = .010), whereas no significant difference between pre-to-post measures (M pre = 0.527, SD = 0.241; M post = 0.645, SD = 0.348) was found after 40 Hz tACS (T(17) = -1.480; p = .157). No statistically significant difference was found between 6 Hz tACS (M = 0.453, SD = 0.142) and 40 Hz tACS (M = 0.527, SD = 0.241) in the pre-tACS run (T(17) = -1.148; p = .267). Furthermore, no significant differences were found in the pre-tACS run between the control group and the experimental group, neither for the 6 Hz tACS condition (T(33) = 0.915; p = .367), nor for the 40 Hz tACS condition (T(33) = 0.136; p = .682).
- Insert Figure 2 about here -
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Figure 2 illustrates the repetition-induced improvement in the beta coefficient (i.e. the
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percentage change from pre- to post-assessment) for the two stimulation conditions and the control group separately. Whereas both the control group and the
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participants in the 6 Hz tACS stimulation condition improved their accuracy in the VOT categorization task, for the 40 Hz tACS stimulation this effect was considerably reduced. This finding was confirmed by statistical analysis of the pre-to-post
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percentage changes of the beta coefficient revealing a significantly enhanced improvement for the control group compared to the 40 Hz tACS group (T(33) = 2.341; p = .029). Similarly, the percentage improvement was enhanced after 6 Hz
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compared to 40 Hz tACS (T(17) = 2.963; p = .009). No such difference was found in
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the comparison between the control group and 6 Hz tACS (T(33) = .035; p = .972).
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- Insert Figure 3 about here -
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The averaged VOT categorizations for both stimulation conditions and for the separately measured control group are presented in Figure 3. As depicted, in the 40
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Hz condition the participants tend to categorize the ambiguous stimuli less consistently compared to the 6 Hz condition and compared to the control group. This, in turn, led to a shallower slope after 40 Hz tACS as confirmed by the abovementioned statistical analysis of the beta coefficient. No such effect seems present for stimuli at the end of the VOT continuum. This finding was confirmed by statistical analyses of the intercept parameter, revealing no significant difference between the stimulation conditions. Finally, no statistically significant differences between the stimulation conditions were found for the category boundary, or for the RT. In sum, the data of the present study indicate that 40 Hz tACS, but not 6 Hz tACS, applied over the bilateral auditory cortex regions interacts with the process of repetition-induced improvement in a VOT categorization task.
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4. Discussion
PT
The aim of this study was to investigate the specific influence of gamma tACS on the processing of rapidly changing acoustic features in the speech signal. Based on
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recent studies that had emphasized the functional relevance of gamma oscillations in phoneme processing we hypothesized that the application of 40 Hz tACS over the auditory cortex regions would affect repetition-induced improvement in a VOT categorization task. The results of the present study show that 40 Hz tACS applied
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over the bilateral auditory cortex interferes with the increment in participants’ perceptual learning of VOT stimuli. By comparing pre-to-post changes when
MA
stimulating with 40 Hz tACS and 6 Hz tACS as well as with a separately measured non-stimulated control group, our data indicate that 40 Hz tACS selectively attenuates the perceptual learning to categorize VOT stimuli. These findings provide
ED
causal evidence for the functional relevance of auditory gamma oscillations in the
PT
encoding of phoneme features.
The observation that tACS directly affects perception and behavior is in good
CE
agreement with previous studies which have demonstrated the influence of tACS on visual (Strüber et al., 2013) and auditory perception (Riecke et al., 2015; Neuling et
AC
al., 2012) as well as on cognitive processes (Vosskuhl et al., 2015; Jaušovec et al., 2014; Jaušovec et. al., 2014a; Jaušovec et. al., 2014b; Polania et al., 2012). There is a broad consensus that tACS synchronizes and entrains neural oscillations. This, in turn, allows for the assumption of a causal link between rhythmic activation patterns and behavior. In the same vein, Strüber and colleagues (2013) demonstrated that 40 Hz tACS, but not 6 Hz tACS, applied over the parietal cortex regions leads to changes in visual motion perception. These findings indicated that it was not tACS per se but rather the application of task- or feature-relevant neural oscillations that modulated perception. Accordingly, stimulating the (auditory) cortex with alternating currents allows for the assessment of feature-relevant oscillatory rhythms. In the present study, we found evidence for a frequency specific effect of tACS on the participants’ increments in VOT categorization. Whereas 6 Hz tACS led to a similar improvement in VOT categorization as that shown by the control group, application of
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40 Hz tACS considerably hampered repetition-induced learning of the presented stimuli. These findings indicate that 40 Hz tACS modulates relevant processes in the context of repetition-induced learning to categorize VOT features. However, since the
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aim of this study was to investigate the influence of gamma and theta tACS on preto-post measures of VOT categorization we did not assess the participants’ ability to
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categorize the VOT stimuli during the application of tACS.
With regards to the underlying neurophysiological mechanism of auditory perceptual learning, a recent study on animal models suggested the interplay of separate
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inhibiting neuromodulatory systems as being the basis of neural plasticity (Martins et al., 2015). The potential capability of transcranial electrical stimulation (tES) to gate or to inhibit neurotransmitter systems has already been demonstrated (Chaieb et al.,
MA
2015; Krause et al., 2013; Krause et al., 2014; Monte-Silva et al., 2009). One can thus speculate that 40 Hz tACS impairs perceptual learning by interfering with the relevant neurotransmitters in the auditory system. Nevertheless, we cannot fully
ED
exclude the possibility of learning mechanisms other than those involved in phoneme
PT
processing having influenced our findings.
Although our study design does not allow to distinguishing completely between
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perceptual learning to categorize VOT stimuli and the processing of rapidly changing acoustic features we argue that perceptual learning (i.e. the increased ability to
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detect and to extract relevant stimulus information as the result of experience) requires an adequate sensory processing of the relevant stimulus features (Gibson, 1969; Adolph et al., 2015). In the (audio-)visual domain there is a broad consensus that perceptual learning is based on changes in the visual cortex associated with the perception of stimuli (Watanabe et al., 2015, Adini et al., 2002, Bejjanki et al., 2011, Choi et al., 2012, Karni et al., 1991). Furthermore, the effects of a learning process are highly specific to sensory features such as location, orientation, and motion (Irvine et al., 2000; Ahissar et al., 1997, Crist et al 1997, Karni et al., 1991). Regarding the auditory domain, there is evidence of learning-induced changes in the structure of the auditory cortex as a result of both phonetic (Engineer et al., 2015; Vandermosten et al., 2015) and musical experience (Tierney et al., 2015; Seppänen et al. 2014; Tervaniemi et al., 2012; Trainor et al., 2003). These findings suggest that perceptual learning in the auditory domain emerges out of the processes of
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differentiation, selection and the extraction of relevant information as the consequence of an adaptation in the primary cortical regions. Furthermore, the specificity of the learning process would argue against the assumption that there is a
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general, feature-independent mechanism underlying it. Finally, the effect of tACS on auditory and visual perception has already been demonstrated in numerous previous
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studies (Strüber et al., 2013; Riecke et al., 2015; Helfrich et al., 2014). Thus, it seems plausible that the reported effects of our study (i.e. the frequency specific pre-to-post changes in VOT categorization) are the result of specifically modulated perceptual
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mechanisms in the auditory cortex regions (that is sensory cortex regions associated with bottom-up processes).
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However, and in contrast to the aforementioned studies, we found a diminished performance when applying feature-relevant oscillations, and then comparing these results with those of a non-stimulated control group compared to a non-stimulated
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control group. TACS is assumed to synchronize the firing rate of neurons via entrainment and this, in turn, enhances the power (i.e. amplitude) of oscillatory in
the
stimulated
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rhythms
regions
as can
be
measured
by means of
electroencephalography (Helfrich et al. 2014; Reato et al. 2013; Fröhlich et al., 2010).
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In our sample of healthy young adults, this neuromodulatory effect (i.e. the increased gamma amplitudes) was paralleled by decreased perceptual learning in a phoneme
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categorization task. This mitigating effect may be related to a non-linear relationship between gamma oscillations in the vicinity of the auditory cortex and the auditory system’s ability for repetition-induced perceptual learning of phoneme features. Accordingly, in an optimal and unaffected level of neuronal activity, an increase of the specific neural oscillation will deteriorate the associated perceptual process. Such an inverted U-shape relation has been demonstrated for the influences of auditory tDCS on acoustic processing (Heimrath et al., 2014), the influence of psychotropic drugs on tDCS effects (Monte-Silva et al., 2009), and for the association between memory functions and gamma oscillations (Baldi et al., 2005; Callicott et al., 1999). Given a relatively optimal level of gamma oscillations in the auditory cortices of our participants, 40 Hz tACS-related entrainment of endogenous gamma band activity may have led to a deteriorated performance.
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By using a bilateral electrode placement over the auditory cortex regions (i.e. over T7 and T8) the alternating current introduces a phase shift of 180 degrees at the contralateral electrode (Riecke et al., 2015). Due to the fact that auditory processing is
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normally associated with a synchronous neural phase angle in bilateral auditory cortex regions this phase shift may result in deviating perception (Neuling et al.,
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2012). Accordingly, we cannot fully exclude the possibility, that this phase shift may have influenced the present findings.
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However, the data of the present study indicated that 40 Hz tACS specifically, and not 6 Hz tACS, led to altered repetition-induced perceptual learning of VOT categorization when compared to a non-stimulated control group. This finding
ED
MA
emphasizes the relevance of 40 Hz oscillations in the context of VOT processing.
5. Conclusion
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Our study is the first to provide evidence for a causal relationship between 40 Hz oscillations and repetition-induced improvement in phoneme categorization. This
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finding encourages the notion of transcranial alternating current stimulation as a powerful device to systematically investigate the relationship between neural
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oscillations and the corresponding behavior. Our findings indicate the importance of further investigations on the efficacy of tACS to modulate the processing of other crucial linguistic features in order to develop successful protocols for therapeutic application.
Acknowledgment This research was supported by the Jacobs Foundation Research Grant to Katharina Rufener. The authors would also like to express their appreciation to the “Fonds zur Förderung
des
Akademischen
Nachwuchses”
(FAN)
des
“Zürcher
Universitätsvereins” (ZUNIV) and the University Research Priority Program “Dynamics of Healthy Aging” of the University of Zurich.
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References
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Adini, Y., Sagi, D., & Tsodyks, M. (2002). Context-enabled learning in the human visual system. Nature, 415(6873), 790–793. http://doi.org/10.1038/415790a
SC RI
Adolph, K. E., & Kretch, K. S. (2015). Gibson's Theory of Perceptual Learning. In: Wrigh, JD. (Ed.), International Encyclopedia of the Social & Behavioral Sciences (pp. 127–134). Elsevier. http://doi.org/10.1016/B978-0-08-097086-8.99002-0
Ahissar, M., & Hochstein, S. (1997). Task difficulty and the specificity of perceptual learning.
NU
Nature, 387(6631), 401–406. http://doi.org/10.1038/387401a0
Antal, A., & Paulus, W. (2013). Transcranial alternating current stimulation (tACS). Frontiers in Human Neuroscience, 7, 1–4. http://doi.org/10.3389/fnhum.2013.00317
MA
Baldi, E., & Bucherelli, C. (2005). The Inverted “U-Shaped” Dose-Effect Relationships in Learning and Memory: Modulation of Arousal and Consolidation. Nonlinearity in Biology, Toxicology, and Medicine, 3(1), 9–21. http://doi.org/10.2201/nonlin.003.01.002
ED
Baltus, A., & Herrmann, C. S. (2015). Auditory temporal resolution is linked toresonance frequency of the auditory cortex. International Journal of Psychophysiology, 1–31.
PT
http://doi.org/10.1016/j.ijpsycho.2015.08.003 Bejjanki, V. R., Beck, J. M., Lu, Z.-L., & Pouget, A. (2011). Perceptual learning as improved
CE
probabilistic inference in early sensory areas. Nature Neuroscience, 14(5), 642–648. http://doi.org/10.1038/nn.2796 Breier, J. I., Gray, L., Fletcher, J. M., Diehl, R. L., Klaas, P., Foorman, B. R., & Molis, M. R.
AC
(2001). Perception of voice and tone onset time continua in children with dyslexia with and without attention deficit/hyperactivity disorder. Journal of Experimental Child Psychology, 80(3), 245–270. http://doi.org/10.1006/jecp.2001.2630 Brignani, D., Ruzzoli, M., Mauri, P., & Miniussi, C. (2013). Is Transcranial Alternating Current Stimulation Effective in Modulating Brain Oscillations? PLoS ONE, 8(2), e56589. http://doi.org/10.1371/journal.pone.0056589.g003 Callicott, J. H., Mattay, V. S., Bertolino, A., Finn, K., Coppola, R., Frank, J. A., et al. (1999). Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. Cerebral Cortex, 9(1), 20–26. http://doi.org/ 10.1093/cercor/9.1.20 Chaieb, L., Antal, A., & Paulus, W. (2015). Transcranial random noise stimulation-induced plasticity
is
NMDA-receptor
benzodiazepines
sensitive.
independent
Frontiers
in
but
sodium-channel
Neuroscience,
9,
125.
blocker
and
http://doi.org/
10.3389/fnins.2015.00125 Chobert, J., François, C., Habib, M., & Besson, M. (2012). Deficit in the preattentive
16 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
processing of syllabic duration and VOT in children with dyslexia. Neuropsychologia, 50(8), 2044–2055. http://doi.org/10.1016/j.neuropsychologia.2012.05.004 Choi, H., & Watanabe, T. (2012). Is perceptual learning associated with changes in a
PT
sensory region? F1000 Reports Biology, 4(24). http://doi.org/ 10.3410/B4-24 Crist, R. E., Kapadia, M. K., & Westheimer, G. (1997). Perceptual learning of spatial
SC RI
localization: specificity for orientation, position, and context. J Neurophysiolgy. 78(6):2889-94.
Engineer, C. T., Rahebi, K. C., Buell, E. P., Fink, M. K., & Kilgard, M. P. (2015). Behavioural Brain Research, 287, 256–264. http://doi.org/10.1016/j.bbr.2015.03.044
NU
Fahle, M. (2005). Perceptual learning: specificity versus generalization. Current Opinion in Neurobiology, 15(2), 154–160. http://doi.org/ 10.1016/j.conb.2005.03.010 Fröhlich, F. (2015). Experiments and models of cortical oscillations as a target for brain
stimulation.
doi:10.1016/bs.pbr.2015.07.025
Progress
MA
noninvasive
in
Brain
Research;
222,
41
-
73.
Fröhlich, F., & McCormick, D. A. (2010). Endogenous Electric Fields May Guide Neocortical
ED
Network Activity. Neuron, 67(1), 129–143. http://doi.org/10.1016/j.neuron.2010.06.005 Gaab, N., Gabrieli, J. D. E., & Tallal, P. (2007). Neural correlates of rapid auditory processing
PT
are disrupted in children with developmental dyslexia and ameliorated with training: an fMRI study. Restorative Neurology and Neuroscience, 25(3-4), 295-310.
CE
Ghitza, O. (2012). On the role of theta-driven syllabic parsing in decoding speech: intelligibility of speech with a manipulated modulation spectrum. Frontiers in Psychology. http://doi.org/10.3389/fpsyg.2012.00238
AC
Ghitza, O. (2013). The theta-syllable: a unit of speech information defined by cortical function. Frontiers in Psychology, 4, 138. http://doi.org/10.3389/fpsyg.2013.00138 Gilbert, C. D., Sigman, M., & Crist, R. E. (2001). The neural basis of perceptual learning. Neuron, 31(5), 681–697. Giraud, A.-L., Kleinschmidt, A., Poeppel, D., Lund, T. E., Frackowiak, R. S. J., & Laufs, H. (2007). Endogenous Cortical Rhythms Determine Cerebral Specialization for Speech Perception
and
Production.
Neuron,
56(6),
1127–1134.
http://doi.org/10.1016/j.neuron.2007.09.038 Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational
principles
and
operations.
Nature,
15(4),
511–517.
doi.org/10.1038/nn.3063 Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49(1), 585–612. Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., & Garrod, S. (2013). Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain. PLoS
17 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
Biology, 11(12). http://doi.org/10.1371/journal.pbio.1001752.s005 Heimrath, K., Kuehne, M., Heinze, H. J., & Zaehle, T. (2014). Transcranial direct current stimulation (tDCS) traces the predominance of the left auditory cortex for processing of changing
acoustic
information.
Neuroscience,
PT
rapidly
http://doi.org/10.1016/j.neuroscience.2013.12.031
261,
68–73.
SC RI
Helfrich, R. F., Schneider, T. R., Rach, S., Trautmann-Lengsfeld, S. A., Engel, A. K., & Herrmann, C. S. (2014). Entrainment of Brain Oscillations by Transcranial Alternating Current
Stimulation.
Current
Biology,
http://doi.org/10.1016/j.cub.2013.12.041
24(3),
333–339.
NU
Herrmann, C. S., Rach, S., Neuling, T., & Strüber, D. (2013). Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive Frontiers
in
10.3389/fnhum.2013.00279
Human
Neuroscience,
7,
1–13.
http://doi.org/
MA
processes.
Herrmann,, C. S., Strüber, D., Helfrich, R. F., & Engel, A. K. (2015). EEG oscillations: From correlation to causality. International Journal of Psychophysiology. 1–10. http://doi.org/
ED
10.1016/j.ijpsycho.2015.02.003
Irvine, D. R. F., Martin, R. L., Klimkeit, E., & Smith, R. (2000). Specificity of perceptual
PT
learning in a frequency discrimination task. The Journal of the Acoustical Society of America, 108(6), 2964–2968. http://doi.org/10.1121/1.1323465
CE
Jaušovec, N., & Jaušovec, K. (2014a). Increasing working memory capacity with theta transcranial alternating current stimulation (tACS). Biological Psychology, 96, 42–47. http://doi.org/10.1016/j.biopsycho.2013.11.006
AC
Jaušovec, N., Jaušovec, K., & Pahor, A. (2014b). The influence of theta transcranial alternating current stimulation (tACS) on working memory storage and processing functions. Acta Psychologica, 146, 1–6. http://doi.org/ 10.1016/j.actpsy.2013.11.011 Kanai, R., Chaieb, L., Antal, A., Walsh, V., & Paulus, W. (2008). Frequency-Dependent Electrical Stimulation of the Visual Cortex. Current Biology, 18(23), 1839–1843. http://doi.org/10.1016/j.cub.2008.10.027 Karni, A., & Sagi, D. (1991). Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proceedings of the National Academy of Sciences of the United States of America, 88(11), 4966-4970. Krause, B., Márquez-Ruiz, J, & Cohen Kadosh, R. (2013). The effect of transcranial direct current stimulation: a role for cortical excitation/inhibition balance? Frontiers in Human Neuroscience, 7, 602. http://doi.org/10.3389/fnhum.2013.00602 Krause, B., & Kadosh, R. C. (2014). Not all brains are created equal: the relevance of individual differences in responsiveness to transcranial electrical stimulation. Frontiers in
18 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
Systems Neuroscience, 8. http://doi.org/10.3389/fnsys.2014.00025. Lisker, L., & Abramson, A. S. (1964). A cross-language study of voicing in initial stops: Acoustical measurements. Word, 20(3), 384–422.
PT
Luo, H., & Poeppel, D. (2012). Cortical oscillations in auditory perception and speech: evidence for two temporal windows in human auditory cortex. Frontiers in Psychology
SC RI
3,170. 1–10. http://doi.org/10.3389/fpsyg.2012.00170
Martins, A. R. O., & Froemke, R. C. (2015). Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex. Nature Neuroscience, 1–12. http://doi.org/ 10.1038/nn.4090
NU
Manis, F. R., McBride-Chang, C., Seidenberg, M. S., Keating, P., Doi, L. M., Munson, B., & Petersen, A. (1997). Are speech perception deficits associated with developmental dyslexia?
Journal
of
Experimental
Psychology,
66(2),
211–235.
MA
http://doi.org/10.1006/jecp.1997.2383
Child
McBride-Chang, C. (1996). Models of Speech Perception and Phonological Processing in Reading. Child Development, 67(4), 1836–1856. http://doi.org/10.2307/1131735?
ED
Mishra, S. K., Dey, R., & Davessar, J. L. (2015). Temporal Resolution of the Normal Ear in Listeners with Unilateral Hearing Impairment. Journal of the Association for Research in
PT
Otolaryngology. http://doi.org/ 10.1007/s10162-015-0536-6 Monte-Silva, K., Kuo, M. F., Thirugnanasambandam, N., Liebetanz, D., Paulus, W., &
CE
Nitsche, M. A. (2009). Dose-Dependent Inverted U-Shaped Effect of Dopamine (D2-Like) Receptor Activation on Focal and Nonfocal Plasticity in Humans. Journal of Neuroscience, 29(19), 6124–6131. http://doi.org/ 10.1523/JNEUROSCI.0728-09.2009
AC
Neuling, T., Rach, S., Wagner, S., Wolters, C. H., & Herrmann, C. S. (2012). Good vibrations: Oscillatory phase shapes perception. NeuroImage, 63(2), 771–778. http://doi.org/ 10.1016/j.neuroimage.2012.07.024 Oldfield, R. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 9(1), 97–113. Peelle, J. E., & Wingfield, A. (2005). Dissociations in Perceptual Learning Revealed by Adult Age Differences in Adaptation to Time-Compressed Speech. Journal of Experimental Psychology. Human Perception and Performance, 31(6), 1315–1330. Peelle, J. E., & Davis, M. H. (2012). Neural Oscillations Carry Speech Rhythm through to Comprehension.
Frontiers
in
Psychology,
3,
320.
http://doi.org/10.3389/fpsyg.2012.00320 Peña, M., & Melloni, L. (2012). Brain oscillations during spoken sentence processing. Journal of Cognitive Neuroscience, 24(5), 1149–1164. http://doi.org/10.1162/jocn_a_00144 Poeppel, D. (2003). The analysis of speech in different temporal integration windows:
19 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
cerebral lateralization as “asymmetric sampling in time.” Speech Communication, 41(1), 245–255. http://doi.org/10.1016/S0167-6393(02)00107-3 PolanIa, R., Nitsche, M. A., Korman, C., Batsikadze, G., & Paulus, W. (2012). The
PT
Importance of Timing in Segregated Theta Phase-Coupling for Cognitive Performance. Current Biology, 22(14), 1314–1318. http://doi.org/10.1016/j.cub.2012.05.021
SC RI
Raschle, N. M., Stering, P. L., Meissner, S. N., & Gaab, N. (2013). Altered Neuronal Response During Rapid Auditory Processing and Its Relation to Phonological Processing in
Prereading
Children
at
Familial
Risk
http://doi.org/10.1093/cercor/bht104
for
Dyslexia.
Cerebral
Cortex.
NU
Reato, D., Rahman, A., Bikson, M., & Parra, L. C. (2013). Effects of weak transcranial alternating current stimulation on brain activity-a review of known mechanisms from animal
studies.
Frontiers
in
Neuroscience,
7,
687.
MA
http://doi.org/10.3389/fnhum.2013.00687
Human
Riecke, L., Formisano, E., Herrmann, C. S., & Sack, A. T. (2015). 4-Hz Transcranial Alternating Current Stimulation Phase Modulates Hearing. Brain Stimulation, 1–7.
ED
doi.org/10.1016/j.brs.2015.04.004
Schwab, E. C., & Nusbaum, H. C. (1985). Some effects of training on the perception of
PT
synthetic speech. Human Factors. 27(4): 395–408. Seitz, A. R., & Dinse, H. R. (2007). A common framework for perceptual learning. Current
CE
Opinion in Neurobiology, 17(2), 148–153. http://doi.org/10.1016/j.conb.2007.02.004 Seppänen, M., Hämäläinen, J., Pesonen, A.-K., & Tervaniemi, M. (2013). Biological Psychology, 94(2), 341–353. http://doi.org/10.1016/j.biopsycho.2013.07.004
AC
Shannon, R. V., Zeng, F. G., Kamath, V., Wygonski, J., & Ekelid, M. (1995). Speech recognition with primarily temporal cues. Science, 270(5234), 303–304. Smith, N. A., Trainor, L. J., Gray, K., Plantinga, J. A., & Shore, D. I. (2008). Stimulus, task, and learning effects on measures of temporal resolution: implications for predictors of language outcome. Journal of Speech, Language, and Hearing Research, 51(6), 1630– 1642. http://doi.org/10.1044/1092-4388(2008/07-0058) Strüber, D., Rach, S., Trautmann-Lengsfeld, S. A., Engel, A. K., & Herrmann, C. S. (2013). Antiphasic 40 Hz Oscillatory Current Stimulation Affects Bistable Motion Perception. Brain Topography, 1–14. http://doi.org/10.1007/s10548-013-0294-x Tervaniemi, M. S. A. M. (2012). Music training enhances rapid neural plasticity of N1 and P2 source activation for unattended sounds, Frontiers in Human Neuroscience. 1–13. http://doi.org/10.3389/fnhum.2012.00043/abstract Thut, G., & Miniussi, C. (2009). New insights into rhythmic brain activity from TMS-EEG studies.
Trends
in
Cognitive
Sciences,
13(4),
182–189.
20 / 25
ACCEPTED MANUSCRIPT
40Hz tACS selectively modulates speech processing
http://doi.org/10.1016/j.tics.2009.01.004 Tierney, A. T., Krizman, J., & Kraus, N. (2015). Music training alters the course of adolescent auditory development. Proceedings of the National Academy of Sciences of the United
PT
States of America, 112(32), 10062–10067. http://doi.org/10.1073/pnas.1505114112 Trainor, L. J., Shahin, A., & Roberts, L. E. (2003). Effects of musical training on the auditory
SC RI
cortex in children. Annals of the New York Academy of Sciences, 999, 506–513. Vandermosten, M., Price, C. J., & Golestani, N. (2015). Plasticity of white matter connectivity in phonetics experts. Brain Structure and Function, 1–9. http://doi.org/10.1007/s00429015-1114-8
NU
Watanabe, T., & Sasaki, Y. (2015). Perceptual Learning: Toward a Comprehensive Theory. Annual Review of Psychology, 66(1), 197–221. doi.org/10.1146/annurev-psych-010814015214
MA
Vosskuhl, J., Huster, R. J., & Herrmann, C. S. (2015). Increase in short-term memory capacity induced by down-regulating individual theta frequency via transcranial alternating
current
stimulation.
Frontiers
in
Human
Neuroscience,
9.
ED
http://doi.org/10.3389/fnhum.2015.00257 Zaehle, T., Jäncke, L., & Meyer, M. (2007). Electrical brain imaging evidences left auditory
PT
cortex involvement in speech and non-speech discrimination based on temporal features. Behavioral and Brain Functions, 3(1), 63. http://doi.org/10.1186/1744-9081-3-
CE
63
Zaehle, T., Lenz, D., Ohl, F. W., & Herrmann, C. S. (2010a). Resonance phenomena in the human auditory cortex: individual resonance frequencies of the cerebral cortex
AC
determine electrophysiological responses. Experimental Brain Research, 203(3), 629– 635. http://doi.org/10.1007/s00221-010-2265-8 Zaehle, T., Rach, S., & Herrmann, C. S. (2010b). Transcranial Alternating Current Stimulation Enhances Individual Alpha Activity in Human EEG. PLoS ONE, 5(11), e13766. http://doi.org/10.1371/journal.pone.0013766.g002 Zaghi, S., Acar, M., Hultgren, B., Boggio, P. S., & Fregni, F. (2010). Noninvasive Brain Stimulation with Low-Intensity Electrical Currents: Putative Mechanisms of Action for Direct and Alternating Current Stimulation. The Neuroscientist, 16(3), 285–307. http://doi.org/10.1177/1073858409336227
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Figure 1: Experimental Setup and Procedure. A: Electrode positioning. Grey rectangles represent the 5 x 7 cm tACS-electrodes placed horizontally over T7 and T8; B: Experimental procedure: pre-tACS assessment of VOT categorization was followed by three blocks where tACS was applied. Short breaks of approximately 2 minutes were inserted between consecutive blocks, in order to avoid the subjects becoming fatigued and/or losing concentration. Finally, post-tACS categorization was measured.
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Figure 2: Pre-to-post percentage change in VOT categorization (slope parameter). The figure demonstrates the percentage increase in the slope parameter over the
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course of the experiment for the two applied tACS frequencies (6 Hz-tACS: white bar, 40 Hz-tACS: black bar). The data of the separately measured control group shows the percentage increase without any tACS-stimulation (grey bar). Asterisks indicate
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statistically significant differences between the conditions (p < .05). Error bars represent the standard errors (SE).
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Figure 3: Percentage of the presented stimuli identified as /da/ plotted as a function of VOT for the post-tACS run. Grey line: separately measured control group, dashed
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The study investigates the functional relevance of gamma oscillations in phoneme processing Perceptual learning by means of a repeatedly performed phoneme categorization task was assessed Transcranial alternating current stimulation allows to draw causal conclusions between entrained cortical oscillations and the related behavior 40 Hz – tACS but not 6 Hz-tACS interferes with perceptual learning in the phoneme categorization task Results indicate that 40 Hz oscillations represent a functionally relevant neural oscillation pattern in the context of phoneme processing
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