40-Hz auditory steady-state responses and the complex information processing: An exploratory study in healthy young males

Abstract

Electroencephalographic (EEG) activity in the gamma (30–80 Hz) range is related to a variety of sensory and cognitive processes which are frequently impaired in schizophrenia. Auditory steady-state response at 40-Hz (40-Hz ASSR) is utilized as an index of gamma activity and is proposed as a biomarker of schizophrenia. Nevertheless, the link between ASSRs and cognitive functions is not clear. This study explores a possible relationship between the performance on cognitive tasks and the 40-Hz ASSRs in a controlled uniform sample of young healthy males, as age and sex may have complex influence on ASSRs. Twenty-eight young healthy male volunteers participated (mean age ± SD 25.8±3.3) in the study. The 40-Hz click trains (500 ms) were presented 150 times with an inter-stimulus interval set at 700–1000 ms. The phase-locking index (PLI) and event-related power perturbation (ERSP) of the ASSR were calculated in the 200–500 ms latency range, which corresponds to the steady part of the response. The Psychology Experiment Building Language (PEBL) task battery was used to assess five cognitive subdomains: the Choice response time task, the Stroop test, the Tower of London test, the Lexical decision task and the Semantic categorisation task. Pearson‘s correlation coefficients were calculated to access the relationships; no multiple-test correction was applied as the tests were explorative in nature. A significant positive correlation was observed for the late-latency gamma and the mean number of steps in the Tower of London task reflecting planning and problem-solving abilities. These findings support the concept that 40-Hz ASSR might highlight top-down mechanisms which are related to cognitive functioning. Therefore, 40-Hz ASSRs can be used to explore the relationship between cognitive functioning and neurophysiological indices of brain activity.

About the Authors:

Vykinta Parciauskaite

Roles Formal analysis, Investigation, Project administration, Writing – original draft, Writing – review & editing

Affiliation: Vilnius University, Institute of Biosciences, Vilnius, Lithuania

Aleksandras Voicikas

Roles Data curation, Formal analysis, Software, Writing – original draft

Affiliation: Vilnius University, Institute of Biosciences, Vilnius, Lithuania

Vytautas Jurkuvenas

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliation: Vilnius University, Institute of Psychology, Vilnius, Lithuania

Povilas Tarailis

Roles Data curation, Investigation, Writing – original draft

Affiliation: Vilnius University, Institute of Biosciences, Vilnius, Lithuania

Mindaugas Kraulaidis

Roles Data curation, Methodology, Writing – original draft

Affiliation: Vilnius University, Institute of Biosciences, Vilnius, Lithuania

Evaldas Pipinis

Roles Validation, Writing – original draft

Affiliation: Vilnius University, Institute of Biosciences, Vilnius, Lithuania

Inga Griskova-Bulanova

Roles Conceptualization, Formal analysis, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation: Vilnius University, Institute of Biosciences, Vilnius, Lithuania

ORCID logo http://orcid.org/0000-0001-5003-3300

Introduction

Accumulating evidence suggests that electroencephalographic (EEG) activity in the gamma (30–80 Hz) range is related to the information processing associated with a variety of sensory and cognitive processes such as perception [1,2], attention [3–7], memory [8–11], response speed [12], object recognition [13], and language processing [14–16]. Many of the above mentioned aspects of cognitive functioning are impaired in schizophrenia (SZ) [17], with information processing speed decline being especially important in this condition [18]. Along with poor performance on cognitive tasks, the impaired gamma oscillations were frequently reported in patients [19], suggesting that cognitive alterations, as observed in SZ, can be attributed to the deficiency within networks supporting gamma activity [20]. The presence of gamma-range activity across spatial scales and cognitive operations suggests that the study of these oscillatory responses in the scalp-recorded EEG may shed the light on the functional integrity of neural circuits [21], where synchronization seems to play a critical role in the information processing [20].

The auditory steady-state response (ASSR) is the electrical response of the brain to regularly repeating auditory stimuli [22] that is used to test the ability to generate gamma frequency range (30–80 Hz) activity in patients with neuropsychiatric disorders [23,24]. The 40-Hz ASSR is proposed to serve as a potential biomarker of schizophrenia [21,25], as the impairment of the gamma-range ASSRs is frequently reported in patients [26–28], subjects at ultra-high risk [29] and relatives [27]. However, there is no firm conclusion on the functional significance of ASSRs. Some authors suggest it is a sensory response, reflecting the integrity of auditory circuits [23,26,30,31], others see ASSRs as a reflection of rather global synchronization of neural activity over with the external environment [29,32,33].

In line with the latter view, some evidence supports the link between gamma-range ASSRs and cognitive functions. First, the wide thalamo-cortical networks are involved in the generation of ASSRs [34–36], though the main contribution is from auditory cortex [37]. Second, 40-Hz ASSRs are modulated by arousal [38–40] and attention [41–43], i.e., states that are tightly associated with cognitive functioning [44]. Third, the gamma-range ASSRs were shown to correlate with the degree of cognitive decline in Alzheimer’s disease and Mild Cognitive Impairment patients [45]. In SZ patients ASSRs correlated with working memory [27,32,46], attention [29], reasoning and problem-solving abilities [47], metacognition and insight [48]. The above-mentioned findings suggest that gamma-range ASSRs (and 40-Hz ASSR in particular) may reflect the neurobiological mechanisms underlying some cognitive processes.

However, the analysis of the relationship between parameters of 40-Hz ASSRs and the basic cognitive abilities is limited to a few studies. Of those, several studies failed to show any correlation between ASSR and cognitive parameters in both patients and controls [49,50] or the relationships obtained in patients were not observed in the healthy control sample, i.e., association between ASSRs and working memory [32,49] and attention [29]. These results would suggest that gamma-range ASSRs do not necessarily require the engagement of neural networks associated with higher cognitive functions [32,49]. However, the studied samples were heterogeneous, both in respect to participants age and gender composition. The modulating effect of these factors on ASSRs was shown previously [51,52]. Thus, the relationship between cognitive functioning and networks underlying the generation of 40-Hz ASSR needs to be investigated further in controlled uniform groups to foster its usage as an individual biological marker for disturbed functioning in neuropsychiatric disorders [49,53]. Additionally, previous ASSR studies assessed the response over the central frontal locations only [27,29,32,48–50]. However, 40Hz ASSRs were reported to be somewhat larger over the right hemisphere [54,55]—either due to a generally higher right side activation with auditory stimulation [56] or due to the anatomic features of the left temporal regions causing left signal cancellation/distortions [57]. Thus, the assessment of associations between ASSRs over the left and right frontal regions and cognitive performance might provide additional information.

The present study aims to explore the possible relationship between the complex information processing and the 40-Hz ASSRs in a uniform sample of young healthy males. The complex information processing refers to the most basic operations of the human mind that are essential both for higher cognitive abilities and in everyday living [18]. The term relates to the mechanisms within the brain systems occurring during higher cognitive processes, rather than the content of the information per se [54] and different aspects (i.e., attention, problem-solving, lexical and semantic decisions) may be assessed by standardized neurobehavioural tests [53]. We hypothesized that parameters of 40-Hz ASSR—phase-locking value and event-related spectral perturbation—would positively correlate with information processing measures—information processing speed and efficiency. This assumption is based on the fact that synchronisation is crucial when subjects engage in faster and more complex cognitive processes [58,59].

Methods

Subjects

Thirty healthy non-smoking right-handed males (females were excluded because of the potential influence of hormonal fluctuations [60]) participated in the study. Subjects were asked to abstain from alcohol for 24 h prior to the testing. Also, they were asked not to consume nicotine and caffeine-containing drinks at least one hour prior to the experiment. Exclusion criteria for the study were any reported neurological disorder or known hearing problems. The hearing thresholds of all subjects were within the normal range (<25dB HL at octave frequencies). One subject was excluded from further analysis due to the bad quality of EEG recording and one—due to a technical issue with behavioural assessment. The final sample consisted of 28 participants (mean age ± SD 25.8±3.3]. The study was a part of larger project approved by the Vilnius Regional Biomedical Research Ethics Committee and all participants gave their written informed consent.

Cognitive tasks

Each participant was given a brief description of the procedure prior to the testing. The Psychology Experiment Building Language [61] based task battery [62] consisting of the Choice response time task (CRT), the Stroop test (SOO), the Tower of London test (TOL), the Lexical decision task (LDT) and the Semantic categorisation task (SCT) was used to assess different aspects of information processing speed.

Reaction times (RTs), number of errors and response efficiency (as a mean reaction time divided by the proportion of correct responses on the test) were used as the outcome measures in the Choice response time task and Stroop test in all three conditions (congruent, incongruent and neutral). The CRT task was used to measure reaction time and psychomotor information processing speed [61,63]; SOO was used to evaluate executive function, attention, inhibition, and complex information processing speed [64]. The mean performance times, mean steps taken to perform the task and mean move time in the Tower of London test were used to measure executive function, planning speed, problem-solving and complex information processing speed [65]. The RTs, number of correct responses and efficiency scores were used to evaluate lexical memory and processing speed [62] in the Lexical decision task and semantic processing and information processing speed [66] in the Semantic categorisation task (SCT). Each task was precluded by the practice trial.

Stimulation

The 40-Hz click stimulation trials lasted 500 ms and consisted of 20 identical clicks. Each 40-Hz trial was presented 150 times with an inter-stimulus interval set at 700–1000 ms. The auditory stimuli were generated by Arduino Uno microcontroller (®Arduino,https://www.arduino.cc/) and presented binaurally through Sennheiser HD 280 PRO earphones; sound pressure level was adjusted to 60 dB with a DVM 401 dB meter (Velleman, USA). Participants were asked to focus on stimulation and try to fixate their gaze at the fixation cross on the computer screen in front of them.

EEG recording

EEG was recorded with an ANT device (ANT Neuro, The Netherlands) and a 64 channel WaveGuard EEG cap (International 10–20 System) with Ag/AgCl electrodes. Mastoids were used as a reference; the ground electrode was attached close to Fz. Impedance was kept below 20 kΩ and the sampling rate was set at 2048 Hz. Vertical and horizontal electro-occulograms (VEOG and HEOG) were recorded from above and below the left eye and from the right and left outer canthi.

EEG processing

The off-line processing of EEG data was performed in EEGLAB and ERPWAVELAB for MatLab [62,63]. The power-line noise was removed using multi-tapering and Thomas F-statistics as implemented in CleanLine plugin for EEGLAB. Data were visually inspected and channels with substantial noise throughout the recording were manually rejected. An independent component analysis (ICA) was performed on the remaining channels with the ICA-implementation of EEGLAB (‘runica’ with default settings) and independent components related to eye movements were removed.

Epochs of 1200 ms were created starting at 500 ms prior to the stimulus onset and lasting for 700 ms post-stimulus onset; the epochs were further inspected for remaining artefacts. The data were baseline-corrected to the mean of the pre-stimulus period. A wavelet transformation (complex Morlet wavelet; frequencies represented from 1 to 120 Hz, with 1 Hz intervals between each frequency) was performed.

Phase-locking index (PLI), corresponding to the phase consistency over epochs, was calculated [67]. The event-related spectral perturbation (ERSP), indicating event-related changes in power relative to a pre-stimulus baseline, was also used as this measure is commonly applied in clinical ASSR-related studies [67]. The calculations were made according to the formulaswhere for every channel c, frequency f and time point t a measure is calculated by taking time-frequency decomposition X of each trial n.

The mean PLIs and ERSPs were extracted focusing on the frequency window of 35–45 Hz by averaging the data over 200–500 ms. Data were baseline-corrected by subtracting the mean of the pre-stimulus period of 200 ms prior to the stimulus onset. In the next step PLI and ERSP values were grouped for the left (F3, F1, FC1, C1, FC3, C3), central (Fz, FCz, Cz), and right (F4, F2, FC2, C2, FC4, C4) regions.

Statistics

Pearson‘s correlation coefficients were calculated to assess the relationship between mean PLI and ERSP values of the 40-Hz ASSR and measures of cognitive tasks. P values less than 0.05 were regarded as significant and no multiple-test correction was applied as the tests were exploratory in nature. The PLI and ERSP values between the regions (left vs. center vs. right) were compared by means of one-way ANOVA with subsequent post hoc tests. Statistical evaluation was performed using SPSSv20 (SPSS Inc., Chicago, Illinois, USA).

Results

Auditory steady-state responses

The topographies of PLIs and ERSPs in response to 40-Hz stimulation within 0–500 ms range in 100 ms windows are presented in Fig 1. The steady-state part of the response was established at around 200 ms and lasted till the end of stimulation. Analyses were performed on this fronto-central activation in 200–500 ms window corresponding to previous reports [32,68,69]. The time courses of PLI and ERSP values separately for left, central and right regions are plotted in Fig 2. The means and standard deviations of PLIs and ERSPs for the left, right and central regions are presented in Table 1. Somewhat larger PLI and ERSP values were observed over the central region, however differences were insignificant.

[Figure omitted. See PDF.]

Fig 1. The topographies of PLI (upper panel) and ERSP (lower panel) within 0–500 ms time window in 100 ms bins in response to 40-Hz stimulation.

https://doi.org/10.1371/journal.pone.0223127.g001

[Figure omitted. See PDF.]

Fig 2. Time course of PLIs (upper panel) and ERSPs (lower panel) for the left, central and right regions in response to 40-Hz stimulation.

https://doi.org/10.1371/journal.pone.0223127.g002

[Figure omitted. See PDF.]

Table 1. Means and standard deviations of PLIs and ERSPs values during the 40-Hz stimulation for the left, central and right regions.

https://doi.org/10.1371/journal.pone.0223127.t001

Cognitive tasks

Performance on cognitive tasks was similar to the results of previous research [66]. Means and SDs of quantified indices are presented in Table 2.

[Figure omitted. See PDF.]

Table 2. Means and standard deviations of cognitive indices.

https://doi.org/10.1371/journal.pone.0223127.t002

Correlations

Pearson’s correlation coefficients and corresponding p values for correlation between PLIs/ERSPs in response to the 40-Hz stimulation and cognitive indices are presented in Table 3. The significant correlations between the mean number of steps on the Tower of London task and PLI/ERSP values were observed for the left (PLI: r = 0.55, p < 0.01; ERSP: r = 0.57, p < 0.01), center (PLI: r = 0.37, p = 0.05; ERSP: r = 0.42, p = 0.03) and right regions (PLI: r = 0.43, p = 0.02; ERSP: r = 0.46, p = 0.01). Scatterplots of ERSPs against mean moves in the Tower of London Task are presented in Fig 3.

[Figure omitted. See PDF.]

Table 3. Correlation coefficients and corresponding p values between 40-Hz ASSR parameters and cognitive task scores.

https://doi.org/10.1371/journal.pone.0223127.t003

[Figure omitted. See PDF.]

Fig 3. Scatterplots of PLIs and ERSPs against mean moves in the Tower of London Task.

Plots are presented separately for the left, central and right regions.

https://doi.org/10.1371/journal.pone.0223127.g003

Discussion

The involvement of the gamma activity in various cognitive functions is a well-documented finding [9,70] as are the alterations of gamma-band in schizophrenia (SZ) [20,21,23]. However, the extent of the relationship between various cognitive domains and gamma responses elicited with 40-Hz stimulation remains unclear. We explored the possible relationship between cognitive functioning indices in the dimensions that are more frequently reported to be altered in SZ and the measures of 40-Hz ASSR in a healthy uniform group of male subjects. The phase-locking properties and event-related changes in power relative to a pre-stimulus baseline were evaluated. The only significant correlation was observed for the steady part of 40-Hz ASSR and the mean number of moves on the Tower of London task (Fig 3). The association was evident both for PLI and for ERSP over the left, center and right regions, and somewhat stronger correlation was observed for the left side response.

The Tower of London (ToL) task reflects executive function, planning and problem-solving [65,71]. Unexpectedly, the stronger and more synchronized responses were observed in subjects who performed more steps in ToL, i.e., were less efficient on the task. This contradicts recent observation by Sun et al. [47], who showed a positive correlation between another test for reasoning and problem solving—Mazes test from the Matrics Consensus cognitive battery (MCCB) [72]—and the phase-locking properties of 40-Hz ASSR in both SZ patients and controls, indicating better performance in subjects with more synchronized ASSRs. However, the discrepancy between the tests and in the scoring approaches in used ToL variations makes comparison to previous results difficult. The ToL task, as implemented in Brief Assessment of Cognition in Schizophrenia (BACS) battery [73], was previously used by Tada et al. [29]. Although they did not test healthy controls, the relationship between measures of 40-Hz ASSR and a number of correct responses as an outcome score in their ultra-high risk and first episode SZ subjects was not observed. Earlier, Diez et al. [74] reported a negative correlation between the total gamma power (35–45 Hz) while performing P300 oddball task and the Tower of London task scores on BACS battery in both SZ patients and their relatives. However, no association in healthy controls was detected. The ToL outcome, as implemented in BACS, is based on the number of correct responses (ranging 0–22), where higher scores indicate better performance. Thus, a negative correlation between gamma power and ToL scores in a study by Díez et al. [75] would also indicate poorer performance in subjects with higher gamma, similar to our observation. Díez et al. [74] obtained comparable results when evaluating gamma noise power—the loading of frontal lateralized component was significantly and inversely related to problem-solving in patients. Diez et al. [74] have speculated that higher gamma in patients and their relatives is a sign of overactivation that prevents good performance on ToL. This might be the case in our subjects, who were able to generate strong ASSRs, i.e., were more reactive to gamma stimulation, but were less efficient in ToL.

Cazalis et al. [76] have suggested that superior performers and standard performers in ToL could potentially elaborate different strategies, as indicated by different patterns of activation. Authors proposed that standard performers might use a larger working memory span compared to superior performers [76]. We did not evaluate working memory in our group, however, prior research has indicated a positive relationship between working memory and measures of ASSRs [29,32,46]. It is possible that a positive relationship between 40-Hz ASSRs and mean move times in ToL is indicating the working-memory related aspects of the task. This assumption should be tested in further studies.

We did not evaluate some domains that were shown to be correlated to 40-Hz ASSR measures before, like abstract verbal reasoning [27] or working memory [46]. These associations should be tested in future research. However, the discrepancies in the observed relationships between performance on cognitive tests and 40-Hz ASSRs are inconsistent even when the same stimulation, analysis and scoring settings are elaborated. For example, Rass et al. [27] observed a positive relationship between gamma range ASSRs and verbal reasoning in SZ patients, their relatives and control subjects; however, in a sample of bipolar patients and matched controls no such association was observed in a study with similar experimental settings [50]. Light et al. [32] observed a positive correlation with working memory in SZ patients; however, Kirihara et al. [49], using the same stimulation settings and assessment tools, failed to show any relationship in their group. These results suggest that relationships might depend on the individual characteristics of the groups tested.

Only young male subjects were enrolled in the current study, thus any potential gender-related aspects were avoided. Some of the employed cognitive tasks are known to be differently performed by males and females [77] and 40-Hz ASSRs were previously shown to differ between sexes [49,51]. This could have affected the observed relationships in prior reports. Another factor potentially modulating the relationship between cognitive domain and electrophysiological indices is subjects’ age. The decline in performance on cognitive tasks is a well-established observation [78] and changes of ASSRs with age were also showed [52,79]. The age representation of our sample was narrow, thus observed relationships were not affected by potential aging effects; this could also cause less variable data and thus hamper some associations potentially explaining why we did not observe any other correlations. Nevertheless, based on the prior reports and our current observation, the 40-Hz ASSRs were related to more complex tasks having executive aspect, and supporting the notion that synchronisation is crucial when subjects engage in faster and more complex cognitive processes [58,59]. It is possible that other tasks employed in this study were not complex enough. This feature should be addressed further.

Auditory stimulation is frequently used to test gamma activity as it produces the strongest EEG responses in the gamma range [79–81]. However, it is unclear whether auditory sensory modality is the most optimal for detecting gamma-band abnormalities [82]. Thus, further studies should include stimuli of other modalities to test associations of periodic responses to performance in cognitive domain.

Finally, ASSR assessment approach could influence the observed relationships. As detected in the current study, weak correlations between measures over the central locations (that are commonly used for the assessment of associations to cognitive symptoms (for example 27,29,49] and ToL were observed, opposite to the lateralized left and right sides. This is in line with Díez et al. [72,73], who reported relationship to responses from lateralized locations in their groups. Thus, lateral locations should also be included in the evaluation of ASSRs in the future studies.

Conclusions

In this study, aiming to assess the relationship between performance on cognitive tasks and measures of 40-Hz ASSR, the positive correlation between the strength and synchronicity during the steady part (200–500 ms) of the response and the mean number of steps on the Tower of London task was observed. This association potentially indicates that the late-latency gamma in response to auditory 40-Hz stimulation might index abilities for planning and problem-solving. The finding supports the concept that 40-Hz ASSR might highlight the top-down mechanisms which are related to cognitive functioning and can be a useful tool to explore the relationship between cognitive functioning and neurophysiological indices of brain activity.

Acknowledgments

The authors would like to thank all the volunteers who participated in the experiment.

Citation: Parciauskaite V, Voicikas A, Jurkuvenas V, Tarailis P, Kraulaidis M, Pipinis E, et al. (2019) 40-Hz auditory steady-state responses and the complex information processing: An exploratory study in healthy young males. PLoS ONE 14(10): e0223127. https://doi.org/10.1371/journal.pone.0223127

References

1. Rodriguez E, George N, Lachaux JP, Martinerie J, Renault B, Varela FJ. Perception’s shadow: long-distance synchronization of human brain activity. Nature. 1999; 397: 430–433. https://doi.org/10.1038/17120 pmid:9989408

2. Tallon-Baudry C, Bertrand O, Delpuech C, Pernier J. Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J Neurosci Off J Soc Neurosci. 1996; 16: 4240–4249. https://doi.org/10.1523/JNEUROSCI.16-13-04240.1996 pmid:8753885

3. Villena-González M, Palacios-García I, Rodríguez E, López V. Beta Oscillations Distinguish Between Two Forms of Mental Imagery While Gamma and Theta Activity Reflects Auditory Attention. Front Hum Neurosci. 2018; 12: 389. https://doi.org/10.3389/fnhum.2018.00389 pmid:30337865

4. Debener S, Herrmann CS, Kranczioch C, Gembris D, Engel AK. Top-down attentional processing enhances auditory evoked gamma band activity. Neuroreport. 2003; 14: 683–686. https://doi.org/10.1097/00001756-200304150-00005 pmid:12692463

5. Tallon-Baudry C, Bertrand O, Hénaff M-A, Isnard J, Fischer C. Attention Modulates Gamma-band Oscillations Differently in the Human Lateral Occipital Cortex and Fusiform Gyrus. Cereb Cortex. 2005; 15: 654–662. https://doi.org/10.1093/cercor/bhh167 pmid:15371290

6. Tiitinen H, Sinkkonen J, Reinikainen K, Alho K, Lavikainen J, Näätänen R. Selective attention enhances the auditory 40-Hz transient response in humans. Nature. 1993; 364: 59–60. https://doi.org/10.1038/364059a0 pmid:8316297

7. Yordanova J, Kolev V, Demiralp T. The phase-locking of auditory gamma band responses in humans is sensitive to task processing. Neuroreport. 1997; 8: 3999–4004. https://doi.org/10.1097/00001756-199712220-00029 pmid:9462481

8. Gruber T, Tsivilis D, Montaldi D, Müller MM. Induced gamma band responses: an early marker of memory encoding and retrieval. Neuroreport. 2004; 15: 1837–1841. https://doi.org/10.1097/01.wnr.0000137077.26010.12 pmid:15257158

9. Herrmann CS, Munk MHJ, Engel AK. Cognitive functions of gamma-band activity: memory match and utilization. Trends Cogn Sci. 2004; 8: 347–355. https://doi.org/10.1016/j.tics.2004.06.006 pmid:15335461

10. Kucewicz MT, Berry BM, Kremen V, Brinkmann BH, Sperling MR, Jobst BC, et al. Dissecting gamma frequency activity during human memory processing. Brain J Neurol. 2017; 140: 1337–1350. https://doi.org/10.1093/brain/awx043 pmid:28335018

11. Tallon-Baudry C, Bertrand O, Peronnet F, Pernier J. Induced gamma-band activity during the delay of a visual short-term memory task in humans. J Neurosci Off J Soc Neurosci. 1998; 18: 4244–4254. https://doi.org/10.1523/JNEUROSCI.18-11-04244.1998 pmid:9592102

12. Andino SLG, Michel CM, Thut G, Landis T, Peralta RG. Prediction of response speed by anticipatory high-frequency (gamma band) oscillations in the human brain. Hum Brain Mapp. 2005; 24: 50–58. https://doi.org/10.1002/hbm.20056 pmid:15593272

13. Csibra G, Davis G, Spratling MW, Johnson MH. Gamma oscillations and object processing in the infant brain. Science. 2000; 290: 1582–1585. https://doi.org/10.1126/science.290.5496.1582 pmid:11090357

14. Benasich AA, Gou Z, Choudhury N, Harris KD. Early cognitive and language skills are linked to resting frontal gamma power across the first 3 years. Behav Brain Rese. 2008; 195: 215–222. https://doi.org/10.1016/j.bbr.2008.08.049 pmid:18831992

15. Ford JM, Gray M, Faustman WO, Heinks TH, Mathalon DH. Reduced gamma-band coherence to distorted feedback during speech when what you say is not what you hear. Int J Psychophysiol. 2005; 57: 143–150. https://doi.org/10.1016/j.ijpsycho.2005.03.002 pmid:15967529

16. Palva S, Palva JM, Shtyrov Y, Kujala T, Ilmoniemi RJ, Kaila K, et al. Distinct gamma-band evoked responses to speech and non-speech sounds in humans. J Neurosci Off J Soc Neurosci. 2002; 22: RC211. https://doi.org/10.1523/JNEUROSCI.22-04-j0003.2002

17. Bora E, Yücel M, Pantelis C. Cognitive Impairment in Schizophrenia and Affective Psychoses: Implications for DSM-V Criteria and Beyond. Schizophr Bull. 2010; 36: 36–42. https://doi.org/10.1093/schbul/sbp094 pmid:19776206

18. Deluca J. Information processing speed: How fast, how slow, and how come? Information processing speed in clinical populations. Philadelphia, PA, US: Taylor & Francis; 2008. pp. 265–273.

19. Senkowski D, Gallinat J. Dysfunctional prefrontal gamma-band oscillations reflect working memory and other cognitive deficits in schizophrenia. Biol Psychiatry. 2015; 77: 1010–1019. https://doi.org/10.1016/j.biopsych.2015.02.034 pmid:25847179

20. Shin Y-W, O’Donnell BF, Youn S, Kwon JS. Gamma oscillation in schizophrenia. Psychiatry Investig. 2011; 8: 288–296. https://doi.org/10.4306/pi.2011.8.4.288 pmid:22216037

21. Thuné H, Recasens M, Uhlhaas PJ. The 40-Hz Auditory Steady-State Response in Patients With Schizophrenia: A Meta-analysis. JAMA Psychiatry. 2016; 73: 1145–1153. https://doi.org/10.1001/jamapsychiatry.2016.2619 pmid:27732692

22. Picton TW, John MS, Purcell DW, Plourde G. Human auditory steady-state responses: the effects of recording technique and state of arousal. Anesth Analg. 2003; 97: 1396–1402. https://doi.org/10.1213/01.ANE.0000082994.22466.DD pmid:14570657

23. Brenner CA, Krishnan GP, Vohs JL, Ahn W-Y, Hetrick WP, Morzorati SL, et al. Steady state responses: electrophysiological assessment of sensory function in schizophrenia. Schizophr Bull. 2009; 35: 1065–1077. https://doi.org/10.1093/schbul/sbp091 pmid:19726534

24. Isomura S, Onitsuka T, Tsuchimoto R, Nakamura I, Hirano S, Oda Y, et al. Differentiation between major depressive disorder and bipolar disorder by auditory steady-state responses. J Affect Disord. 2016; 190: 800–806. https://doi.org/10.1016/j.jad.2015.11.034 pmid:26625092

25. O’Donnell BF, Vohs JL, Krishnan GP, Rass O, Hetrick WP, Morzorati SL. The auditory steady-state response (ASSR): a translational biomarker for schizophrenia. Suppl Clin Neurophysiol. 2013; 62: 101–112. https://doi.org/10.1016/B978-0-7020-5307-8.00006-5 pmid:24053034

26. Hamm JP, Gilmore CS, Picchetti NAM, Sponheim SR, Clementz BA. Abnormalities of Neuronal Oscillations and Temporal Integration to Low- and High-Frequency Auditory Stimulation in Schizophrenia. Biol Psychiatry. 2011; 69: 989–996. https://doi.org/10.1016/j.biopsych.2010.11.021 pmid:21216392

27. Rass O, Forsyth JK, Krishnan GP, Hetrick WP, Klaunig MJ, Breier A, et al. Auditory steady state response in the schizophrenia, first-degree relatives, and schizotypal personality disorder. Schizophr Res. 2012; 136: 143–149. https://doi.org/10.1016/j.schres.2012.01.003 pmid:22285558

28. Spencer KM, Salisbury DF, Shenton ME, McCarley RW. γ-Band Auditory Steady-State Responses Are Impaired in First Episode Psychosis. Biol Psychiatry. 2008; 64: 369–375. https://doi.org/10.1016/j.biopsych.2008.02.021 pmid:18400208

29. Tada M, Nagai T, Kirihara K, Koike S, Suga M, Araki T, et al. Differential Alterations of Auditory Gamma Oscillatory Responses Between Pre-Onset High-Risk Individuals and First-Episode Schizophrenia. Cereb Cortex. 2016; 26: 1027–1035. https://doi.org/10.1093/cercor/bhu278 pmid:25452567

30. Teale P, Carlson J, Rojas D, Reite M. Reduced laterality of the source locations for generators of the auditory steady-state field in schizophrenia. Biol Psychiatry. 2003; 54: 1149–1153. https://doi.org/10.1016/S0006-3223(03)00411-6 pmid:14643081

31. Spencer KM, Niznikiewicz MA, Nestor PG, Shenton ME, McCarley RW. Left auditory cortex gamma synchronization and auditory hallucination symptoms in schizophrenia. BMC Neurosci. 2009; 10: 85. https://doi.org/10.1186/1471-2202-10-85 pmid:19619324

32. Light GA, Hsu JL, Hsieh MH, Meyer-Gomes K, Sprock J, Swerdlow NR, et al. Gamma band oscillations reveal neural network cortical coherence dysfunction in schizophrenia patients. Biol Psychiatry. 2006; 60: 1231–1240. https://doi.org/10.1016/j.biopsych.2006.03.055 pmid:16893524

33. Koenig T, van Swam C, Dierks T, Hubl D. Is gamma band EEG synchronization reduced during auditory driving in schizophrenia patients with auditory verbal hallucinations? Schizophr Res. 2012; 141: 266–270. https://doi.org/10.1016/j.schres.2012.07.016 pmid:22892287

34. Reyes SA, Salvi RJ, Burkard RF, Coad ML, Wack DS, Galantowicz PJ, et al. PET imaging of the 40 Hz auditory steady state response. Hear Res. 2004; 194: 73–80. https://doi.org/10.1016/j.heares.2004.04.001 pmid:15276678

35. Pastor M, Artieda J, Arbizu J, Martí-Climent J, Penuelas I, Masdeu JC. Activation of Human Cerebral and Cerebellar Cortex by Auditory Stimulation at 40 Hz. J Neurosci Off J Soc Neurosci. 2003; 22: 10501–6. https://doi.org/10.1523/JNEUROSCI.22-23-10501.2002 pmid:12451150

36. Bish JP, Martin T, Houck J, Ilmoniemi RJ, Tesche C. Phase shift detection in thalamocortical oscillations using magnetoencephalography in humans. Neurosci Lett. 2004; 362: 48–52. https://doi.org/10.1016/j.neulet.2004.02.032 pmid:15147778

37. Pantev C, Elbert T, Makeig S, Hampson S, Eulitz C, Hoke M. Relationship of transient and steady-state auditory evoked fields. Electroencephalogr Clin Neurophysiol Potentials Sect. 1993; 88: 389–396. https://doi.org/10.1016/0168-5597(93)90015-H

38. Górska U, Binder M. Low and medium frequency auditory steady-state responses decrease during NREM sleep. Int J Psychophysiol Off J Int Organ Psychophysiol. 2018; https://doi.org/10.1016/j.ijpsycho.2018.11.003 pmid:30452935

39. Griskova I, Morup M, Parnas J, Ruksenas O, Arnfred SM. The amplitude and phase precision of 40 Hz auditory steady-state response depend on the level of arousal. Exp Brain Res. 2007; 183: 133–138. https://doi.org/10.1007/s00221-007-1111-0 pmid:17828530

40. Griskova-Bulanova I, Ruksenas O, Dapsys K, Maciulis V, Arnfred SMH. Distraction task rather than focal attention modulates gamma activity associated with auditory steady-state responses (ASSRs). Clin Neurophysiol. 2011; 122: 1541–1548. https://doi.org/10.1016/j.clinph.2011.02.005 pmid:21377412

41. Gander PE, Bosnyak DJ, Roberts LE. Evidence for modality-specific but not frequency-specific modulation of human primary auditory cortex by attention. Hear Res. 2010; 268: 213–226. https://doi.org/10.1016/j.heares.2010.06.003 pmid:20547217

42. Skosnik PD, Krishnan GP, O’Donnell BF. The effect of selective attention on the gamma-band auditory steady-state response. Neurosci Lett. 2007; 420: 223–228. https://doi.org/10.1016/j.neulet.2007.04.072 pmid:17556098

43. Voicikas A, Niciute I, Ruksenas O, Griskova-Bulanova I. Effect of attention on 40Hz auditory steady-state response depends on the stimulation type: Flutter amplitude modulated tones versus clicks. Neurosci Lett. 2016; 629: 215–220. https://doi.org/10.1016/j.neulet.2016.07.019 pmid:27424792

44. Logue SF, Gould TJ. The neural and genetic basis of executive function: Attention, cognitive flexibility, and response inhibition. Pharmacol Biochem Behav. 2014; 123: 45–54. https://doi.org/10.1016/j.pbb.2013.08.007 pmid:23978501

45. van Deursen JA, Vuurman EFPM, van Kranen-Mastenbroek VHJM, Verhey FRJ, Riedel WJ. 40-Hz steady state response in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging. 2011; 32: 24–30. https://doi.org/10.1016/j.neurobiolaging.2009.01.002 pmid:19237225

46. Puvvada KC, Summerfelt A, Du X, Krishna N, Kochunov P, Rowland LM, et al. Delta Vs Gamma Auditory Steady State Synchrony in Schizophrenia. Schizophr Bull. 2018; 44: 378–387. https://doi.org/10.1093/schbul/sbx078 pmid:29036430

47. Sun C, Zhou P, Wang C, Fan Y, Tian Q, Dong F, et al. Defects of Gamma Oscillations in Auditory Steady-State Evoked Potential of Schizophrenia. Shanghai Arch Psychiatry. 2018; 30: 27–38. pmid:29719356

48. Leonhardt BL, Vohs JL, Bartolomeo LA, Visco A, Hetrick WP, Bolbecker AR, et al. Relationship of Metacognition and Insight to Neural Synchronization and Cognitive Function in Early Phase Psychosis. Clin EEG Neurosci. 2019; 1550059419857971. https://doi.org/10.1177/1550059419857971 pmid:31241355

49. Kirihara K, Rissling AJ, Swerdlow NR, Braff DL, Light GA. Hierarchical organization of gamma and theta oscillatory dynamics in schizophrenia. Biol Psychiatry. 2012; 71: 873–880. https://doi.org/10.1016/j.biopsych.2012.01.016 pmid:22361076

50. Rass O, Krishnan G, Brenner CA, Hetrick WP, Merrill CC, Shekhar A, et al. Auditory steady state response in bipolar disorder: relation to clinical state, cognitive performance, medication status, and substance disorders. Bipolar Disord. 2010; 12: 793–803. https://doi.org/10.1111/j.1399-5618.2010.00871.x pmid:21176026

51. Melynyte S, Pipinis E, Genyte V, Voicikas A, Rihs T, Griskova-Bulanova I. 40 Hz Auditory Steady-State Response: The Impact of Handedness and Gender. Brain Topogr. 2018; 31: 419–429. https://doi.org/10.1007/s10548-017-0611-x pmid:29218677

52. Griskova-Bulanova I, Dapsys K, Maciulis V. Does brain ability to synchronize with 40 Hz auditory stimulation change with age? Acta Neurobiol Exp (Warsz). 2013; 73: 564–570.

53. Oda Y, Onitsuka T, Tsuchimoto R, Hirano S, Oribe N, Ueno T, et al. Gamma band neural synchronization deficits for auditory steady state responses in bipolar disorder patients. PloS One. 2012; 7: e39955. https://doi.org/10.1371/journal.pone.0039955 pmid:22792199

54. Ross B, Herdman AT, Pantev C. Right hemispheric laterality of human 40 Hz auditory steady-state responses. Cereb Cortex. 2005; 15: 2029–2039. https://doi.org/10.1093/cercor/bhi078 pmid:15772375

55. Draganova R, Ross B, Wollbrink A, Pantev C. Cortical steady-state responses to central and peripheral auditory beats. Cereb Cortex. 2008; 18: 1193–1200. https://doi.org/10.1093/cercor/bhm153 pmid:17827173

56. Gilmore CS, Clementz BA, Berg P. Hemispheric differences in auditory oddball responses during monaural versus binaural stimulation. Int J Psychophysiol. 2009; 73: 326–333. https://doi.org/10.1016/j.ijpsycho.2009.05.005 pmid:19463866

57. Shaw ME, Hämäläinen MS, Gutschalk A. How anatomical asymmetry of human auditory cortex can lead to a rightward bias in auditory evoked fields. Neuroimage. 2013; 74: 22–29. https://doi.org/10.1016/j.neuroimage.2013.02.002 pmid:23415949

58. Violante IR, Li LM, Carmichael DW, Lorenz R, Leech R, Hampshire A, et al. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance. Hamilton R, editor. eLife. 2017; 6: e22001. https://doi.org/10.7554/eLife.22001 pmid:28288700

59. Takeuchi H, Sugiura M, Sassa Y, Sekiguchi A, Yomogida Y, Taki Y, et al. Neural Correlates of the Difference between Working Memory Speed and Simple Sensorimotor Speed: An fMRI Study. PLoS One. 2012; 7. https://doi.org/10.1371/journal.pone.0030579 pmid:22291992

60. Griskova-Bulanova I, Grikšienė R, Korostenskaja M, Ruksenas O. 40 Hz auditory steady-state response in females: When is it better to entrain? Acta Neurobiol Exp (Warsz). 2014; 74: 91–7.

61. Mueller ST, Piper BJ. The Psychology Experiment Building Language (PEBL) and PEBL Test Battery. J Neurosci Methods. 2014; 222: 250–259. https://doi.org/10.1016/j.jneumeth.2013.10.024 pmid:24269254

62. Jurkuvėnas V. Relations among age, simple information processing speed, complex information processing speed, memory, and set-shifting. Psychology. 2015; 51: 81–98. https://doi.org/10.15388/Psichol.2015.51.8258

63. Heitz RP. The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Front Neurosci. 2014; 8. https://doi.org/10.3389/fnins.2014.00150 pmid:24966810

64. Phillips LH, Bull R, Adams E, Fraser L. Positive mood and executive function: evidence from stroop and fluency tasks. Emot Wash DC. 2002; 2: 12–22. https://doi.org/10.1037/1528-3542.2.1.12

65. Kremen WS, Jacobson KC, Panizzon MS, Xian H, Eaves LJ, Eisen SA, et al. Factor structure of planning and problem-solving: a behavioral genetic analysis of the Tower of London task in middle-aged twins. Behav Genet. 2009; 39: 133–144. https://doi.org/10.1007/s10519-008-9242-z pmid:19085096

66. Hargreaves IS, White M, Pexman PM, Pittman D, Goodyear BG. The question shapes the answer: the neural correlates of task differences reveal dynamic semantic processing. Brain Lang. 2012; 120: 73–78. https://doi.org/10.1016/j.bandl.2011.10.004 pmid:22078639

67. Mørup M, Hansen LK, Arnfred SM. ERPWAVELAB: A toolbox for multi-channel analysis of time-frequency transformed event related potentials. J Neurosci Methods. 2007; 161: 361–368. https://doi.org/10.1016/j.jneumeth.2006.11.008 pmid:17204335

68. Ross B, Picton TW, Pantev C. Temporal integration in the human auditory cortex as represented by the development of the steady-state magnetic field. Hear Res. 2002; 165: 68–84. https://doi.org/10.1016/S0378-5955(02)00285-X pmid:12031517

69. Korostenskaja M, Ruksenas O, Pipinis E, Griskova-Bulanova I. Phase-locking index and power of 40-Hz auditory steady-state response are not related to major personality trait dimensions. Exp Brain Res. 2016; 234: 711–719. https://doi.org/10.1007/s00221-015-4494-3 pmid:26586270

70. Kaiser J, Lutzenberger W. Human gamma-band activity: a window to cognitive processing. Neuroreport. 2005; 16: 207–211. https://doi.org/10.1097/00001756-200502280-00001 pmid:15706221

71. Unterrainer JM, Rahm B, Kaller CP, Leonhart R, Quiske K, Hoppe-Seyler K, et al. Planning abilities and the Tower of London: is this task measuring a discrete cognitive function? J Clin Exp Neuropsychol. 2004; 26: 846–856. https://doi.org/10.1080/13803390490509574 pmid:15370380

72. Nuechterlein KH, Green MF, Kern RS, Baade LE, Barch DM, Cohen JD, et al. The MATRICS Consensus Cognitive Battery, Part 1: Test Selection, Reliability, and Validity. Am J Psychiatry. 2008; 165: 203–213. https://doi.org/10.1176/appi.ajp.2007.07010042 pmid:18172019

73. Keefe RSE, Goldberg TE, Harvey PD, Gold JM, Poe MP, Coughenour L. The Brief Assessment of Cognition in Schizophrenia: reliability, sensitivity, and comparison with a standard neurocognitive battery. Schizophr Res. 2004; 68: 283–297. https://doi.org/10.1016/j.schres.2003.09.011 pmid:15099610

74. Díez Á, Suazo V, Casado P, Martín-Loeches M, Perea MV, Molina V. Frontal gamma noise power and cognitive domains in schizophrenia. Psychiatry Res Neuroimaging. 2014; 221: 104–113. https://doi.org/10.1016/j.pscychresns.2013.11.001 pmid:24300084

75. Díez Á, Suazo V, Casado P, Martín-Loeches M, Molina V. Gamma Power and Cognition in Patients with Schizophrenia and Their First-Degree Relatives. Neuropsychobiology. 2014; 69: 120–128. https://doi.org/10.1159/000356970 pmid:24732388

76. Cazalis F, Valabrègue R, Pélégrini‐Issac M, Asloun S, Robbins TW, Granon S. Individual differences in prefrontal cortical activation on the Tower of London planning task: implication for effortful processing. Eur J Neurosci. 2003; 17: 2219–2225. https://doi.org/10.1046/j.1460-9568.2003.02633.x pmid:12786989

77. Halpern DF, LaMay ML. The Smarter Sex: A Critical Review of Sex Differences in Intelligence. Educ Psychol Rev. 2000; 12: 229–246. https://doi.org/10.1023/A:1009027516424

78. Verhaeghen P. The Elements of Cognitive Aging: Meta-Analyses of Age-Related Differences in Processing Speed and Their Consequences. Oxford University Press; 2013. https://doi.org/10.1093/acprof:oso/9780195368697.001.0001

79. Edgar JC, Fisk CL, Chen Y-H, Stone-Howell B, Hunter MA, Huang M, et al. By our bootstraps: Comparing methods for measuring auditory 40 Hz steady-state neural activity. Psychophysiology. 2017; 54: 1110–1127. https://doi.org/10.1111/psyp.12876 pmid:28421620

80. Giani AS, Ortiz E, Belardinelli P, Kleiner M, Preissl H, Noppeney U. Steady-state responses in MEG demonstrate information integration within but not across the auditory and visual senses. NeuroImage. 2012; 60: 1478–1489. https://doi.org/10.1016/j.neuroimage.2012.01.114 pmid:22305992

81. Galambos R, Makeig S, Talmachoff PJ. A 40-Hz auditory potential recorded from the human scalp. Proc Natl Acad Sci USA. 1981; 78: 2643–2647. https://doi.org/10.1073/pnas.78.4.2643 pmid:6941317

82. Picton TW, John MS, Dimitrijevic A, Purcell D. Human auditory steady-state responses. Int J Audiol. 2003; 42: 177–219. https://doi.org/10.3109/14992020309101316 pmid:12790346