Skip to main content
NCBI home page
Cognitive Neurodynamics logoLink to Cognitive Neurodynamics
. 2022 Jul 7;17(2):399–410. doi: 10.1007/s11571-022-09834-x

Enhancement of the neural response during 40 Hz auditory entrainment in closed-eye state in human prefrontal region

Chuanliang Han 1,✉,#, Xixi Zhao 2,3,#, Meijia Li 4, Naem Haihambo 4, Jiayi Teng 5,6, Sixiao Li 5,7, Jinyi Qiu 8, Xiaoyang Feng 9, Michel Gao 5,✉
PMCID: PMC10050539  PMID: 37007205

Abstract

Gamma-band activity was thought to be related to several high-level cognitive functions, and Gamma ENtrainment Using Sensory stimulation (GENUS, 40 Hz sensory combined visual and auditory stimulation) was found to have positive effects on patients with Alzheimer’s dementia. Other studies found, however, that neural responses induced by single 40 Hz auditory stimulation were relatively weak. To address this, we included several new experimental conditions (sounds with sinusoidal or square wave; open-eye and closed-eye state) combined with auditory stimulation with the aim of investigating which of these induces a stronger 40 Hz neural response. We found that when participant´s eyes were closed, sounds with 40 Hz sinusoidal wave induced the strongest 40 Hz neural response in the prefrontal region compared to responses in other conditions. More interestingly, we also found there is a suppression of alpha rhythms with 40 Hz square wave sounds. Our results provide potential new methods when using auditory entrainment, which may result in a better effect in preventing cerebral atrophy and improving cognitive performance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09834-x.

Keywords: Entrainment, Prefrontal cortex, 40 Hz auditory sound, Gamma-band activity, Alpha-band activity

Introduction

Previous studies have commonly found gamma-band activity (peaking around 40 Hz) in the brain (Draguhn and Buzsáki 2004; Fries 2005, 2009; Cardin et al. 2009; Wang 2010; Buzśaki and Wang 2012; Xing et al. 2012b, a, 2014; Jia et al. 2013a, b; Han et al. 2020, 2021a, b, c), which have been thought to contribute to high-level cognitive functions such as attention(Fries et al. 2001; Jensen et al. 2007; Gregoriou et al. 2009; Vinck et al. 2013), memory (Pesaran et al. 2002; Carr et al. 2012; Kucewicz et al. 2017), learning(Bauer et al. 2007) and visual functions (Singer and Gray 1995; Singer 1999). Abnormal gamma-band activity was, on the other hand, found to be related to neuropsychiatric disorders (Uhlhaas and Singer 2006, 2012) such as Alzheimer’s disease (AD; Palop and Mucke 2016; Mably and Colgin 2018), autism (Uhlhaas and Singer 2007; Wilson et al. 2007; An et al. 2018), and schizophrenia (Lewis et al. 2005; Uhlhaas and Singer 2010; Hirano et al. 2015; Tada et al. 2016).

More recently, the evoked steady-state response in gamma-band activity in the brain has received increased attention (Galambos et al. 1981; Ross et al. 2005; Roth et al. 2013; McFadden et al. 2014; Korostenskaja et al. 2016; Ho et al. 2019). Especially in the recent five years, non-invasive gamma entrainment using sensory stimulation (GENUS) at 40 Hz entrainment was found to be a potentially useful therapeutic tool for AD (Adaikkan and Tsai 2020) in both animal models (Iaccarino et al. 2016; Adaikkan et al. 2019; Martorell et al. 2019) and in human clinical treatment (Chan et al. 2021). To clarify, neural entrainment is the process where neural activity is synchronized to the frequency of repetitive sensory rhythms, which can be observed as an increase in the electroencephalogram (EEG) power spectrum at driving frequency, -also known as the steady-state response(Obleser and Kayser 2019).

Previous studies have found that 40 Hz entrainment induced by 40 Hz sensory stimulation (single visual, or synchronized visual and auditory stimulation) shows promising results in the treatment of the AD symptoms by improving cognitive functions, and improving AD-related degeneration of the brain structure and functional MRI biomarkers(Iaccarino et al. 2016; Adaikkan et al. 2019; Martorell et al. 2019; Adaikkan and Tsai 2020). The procedures used in these studies required participants to be subjected to a one-hour long stimulation, along with additional visual and auditory stimulation daily at home, before finding significant results after the procedure was followed for a total of 3 months (Chan et al. 2021). An important concern is that long-term frequent visual flickering poses health-related risks, such as risks to the eyes and other risks (Hermes et al. 2017).

In addition to potential health impacts, light flickering at 40 Hz may cause problems with artifacts. Of interest is that results of human EEG (Chan et al. 2021), demonstrated that neural response induced by entrainment of single 40 Hz auditory stimuli is weaker than that of visual stimuli. Suggesting that the occurrence of eye artifacts, such as regular blinks or eye movements, are hard to eliminate during the intervention, and so, associated EEG signals may be stronger than the brain signals of interest. Additionally, keeping eyes open may increase occipital alpha and a changed topology and activity in different frequency bands compared to eyes closed (Barry et al. 2007). A possible solution to make the participant more comfortable and reduce artifacts could be to let the participant close their eyes. This idea is feasible when visual input is not needed. To our knowledge, existing literature do not include closed-eye condition using 40 Hz GENUS. Addressing both health and artifact limitations could increase the popularity of auditory stimulation as a preventative measure and intervention for pathological changes in the brain, especially considering evidence of successfully entrained gamma frequency oscillations and its non-invasive nature.

In sum, how the auditory entrainment effects are modulated by the attention states (open-eye or closed-eye) and the type of the 40 Hz sound (sinusoidal or square wave form) remains unclear. For this reason, in the current study, we investigated the effect of several new experimental conditions (sounds with sinusoidal or square wave; open-eye and closed-eye state) using the auditory stimulation to figure out which condition can induce a stronger 40 Hz neural response.

Materials and methods

Subjects

In this study, ten healthy participants (seven males and three females, mean age = 35.1 (Std = 5.7)) were recruited from local universities or communities. This study was approved by the Ethics Committee of Beijing Anding Hospital (ID: 201723SF-2), Capital Medical University, China and all methods were performed in accordance with the relevant guidelines and regulations.

Auditory stimuli

A 40 Hz auditory stimulus was presented to the subject during each condition, generating a 40 Hz SSAEP response. Two types of 40 Hz auditory waveform with a 400 Hz barrier were used in this study. The first type of stimulation consisted of a series of 12.5 ms square waves with a 12.5 ms interval between the onset of each pulse. The second type of stimulation is the sinusoidal function with a period of 25 ms. These conditions were counterbalanced across participants.

Electrophysiological recording and processing

We recorded EEG signals using an 18-channel dry electrode cap using the DSI-24 device (Wearable sensing, USA) at a sampling rate of 300 Hz. Electrodes on the cap are placed according to the international 10–20 system. The impedance values were less than 50 kW, and the Pz electrode was used as the reference electrode. The data were analyzed offline with EEGlab. The raw EEG recordings were re-referenced to the average amplitude of the signals from the left and right earlobe. A bandpass filter was applied from 0.5 to 50 Hz. All trials were visually inspected for electrical or obvious movement artifacts, and sections of data containing artifacts were removed manually. Then, we used independent component analysis (ICA) to decompose the measured EEG signals into independent components, and the components sensitive to eye blinks, eye movements, head movements, heartbeats, and other visually identified artifacts were removed.

Procedure

The participants were seated comfortably in a dimly lit room with a low level of environmental noise. They were instructed to remain relaxed in open-eye or closed-eye state and listened to the auditory stimulation (sinusoidal or square wave, 2 min each). Participants experienced one experimental condition during each experimental session. Each experimental session was broken up into three segments: (1) resting state (30 s), (2) auditory stimulation (i.e., 120 s sinusoidal or square wave sound), and (3) post-exposure resting (30 s). The data collection procedure lasted for 12 min for each subject.

Power spectrum and time–frequency analysis

Power spectral densities (PSDs) were computed using the multi-taper method using the Chronux toolbox (Bell et al. 1993; Percival and Walden 1993), which was implemented using custom software written in MATLAB. Time–frequency power spectra were also calculated using the Chronux toolbox using a moving window of size 1000 ms and step size of 1000 ms, giving a frequency resolution of 1 Hz. Essentially, the multi-taper method attempts to reduce the variance of spectral estimates by pre-multiplying the data with several orthogonal tapers known as Slepian functions. The frequency decomposition of multi-tapered data segments, therefore, provides a set of independent spectral estimates that, once averaged, provides an ensemble estimate that is more reliable for noisy data.

Statistical analysis

The bootstrapping method was mainly adopted in this study, which is one of the non-parametric tests. Advantages of this method include its robustness with small sample size and not relying on a specific theoretical distribution. We used the bootstrapping procedure to test the difference of the 40 Hz normalized power in multiple experimental conditions (auditory sound in 40 Hz square waves in open-eye or closed eye state, auditory sound in 40 Hz sinusoidal waves in open-eye or closed-eye state). We also used the bootstrapping procedure to test the difference of the normalized power in the alpha band in multiple experimental conditions (auditory sound in 40 Hz square waves in open-eye or closed-eye state, auditory sound in 40 Hz sinusoidal waves in open-eye or closed-eye state).

Results

We recorded the scalp EEG (18 channels) from normal adults. Participants were told to open or close their eyes and passively listen to the 40 Hz sound (entrainment) in square or sinusoidal type (Fig. 1A). For each electrode, the power spectrum in different time bins was estimated by a multi-taper method (Fig. 1B), and normalized by power in the resting state without any entrainment (Fig. 1C).

Fig. 1.

Fig. 1

Open in a new tab

Effect of 40 Hz Entrainment in gamma frequency band. A Time course of the task, the subject was at resting state (open-eye or closed-eye state) for 30 s and then there would be a 40 Hz (sinusoidal or square wave) sound entrainment for 120 s, after which the subject was at resting state again (open-eye or closed-eye state) for 30 s. B Absolute spectrogram of the EEG to the 40 Hz entrainment (sinusoidal type in closed-eye state) is coded by color. Around 40 Hz (marked by two horizontal dashed lines), there is sustained power after stimulus onset (marked by the first vertical dashed line). C The raw power spectrum was converted into the relative power spectrum by normalizing the power during the blank period at each frequency

Clear 40 Hz oscillation could be induced in both open-eye and closed-eye state

A typical example has shown that a clear elevation of power in 40 Hz was found during the 40 Hz entrainment (Fig. 1, sinusoidal type in closed-eye state). We next asked whether this effect of the entrainment would also appear in different experimental conditions (Fig. 2). From the results of population average, we found that no matter the open-eye or closed-eye state, the 40 Hz entrainment (sinusoid or square type) would induce clear 40 Hz peak power (Fig. 2A–D). Based on the observation in Fig. 2, we roughly found that the 40 Hz power was strongest in frontal regions of the brain and it is also stronger in closed-eye state during the 40 Hz sinusoidal type sound than that of other experimental conditions. Hence, we next quantified the power to find the region that could induce the strongest 40 Hz response and explore whether the simulation type and open-eye or closed-eye state have different effects on neural entrainment.

Fig. 2.

Fig. 2

Open in a new tab

Effect of 40 Hz Entrainment on gamma-band activity in multiple experimental conditions. A Population-averaged (n = 10) relative power spectrum in gamma band (25-50 Hz) for all 18 EEG electrodes in open-eye state during the sinusoidal auditory 40 Hz entrainment. The onset and offset of the auditory stimuli were marked by two horizontal dashed lines. B is the same as A, except for that in closed-eye state during the sinusoidal auditory 40 Hz entrainment. C is the same as A, except for that in open-eye state during the square auditory 40 Hz entrainment. D is the same as A, except for that in closed-eye state during the square auditory 40 Hz entrainment

In the closed-eye state, sounds with 40 Hz sinusoidal wave could induce the strongest 40 Hz neural response in the prefrontal region

In all four experimental conditions, we found that the 40 Hz neural response entrained by 40 Hz sound was strongest in the frontal region (Fig. 3A). Especially for the 40 Hz power induced by the sinusoidal wave in the closed-eye state, the 40 Hz response in the frontal region is the strongest compared with the other three conditions (Fig. 3B). To make this comparison more intuitive, we then averaged the power in the frontal-central region (Fig. 3B red square), and parietal-occipital region (Fig. 3B blue square) respectively and compared the statistical significance among these four conditions using a bootstrap test. We found that the 40 Hz response in the central-frontal region is significantly stronger (bootstrap test, p < 0.05) than that of the other three conditions, while in the posterior region, we did not find this difference.

Fig. 3.

Fig. 3

Open in a new tab

Comparison of the entrainment effect in 40 Hz neural response among the multiple experimental conditions. A Topographic maps of the 40 Hz relative power in four experimental conditions (n = 10). B Left: the comparison of 40 Hz neural response in all 18 electrodes among four conditions; Right: the comparison in central-frontal and posterior regions, respectively

Suppression of the alpha rhythms under the sounds with 40 Hz square wave in the prefrontal region

The alpha rhythm (around 8-13 Hz) was assumed to reflect the inattentive state of the brain. Results unveiled that the 40 Hz entrainment affected the alpha oscillations in these different experimental conditions (Fig. 4A). A demo of the F4 electrode was shown in Fig. 4, illustrating the decreased alpha power during the 40 Hz entrainment in sound with square wave in the closed-eye state. The strength of the alpha rhythm decreased after the onset of the 40 Hz entrainment (Fig. 4B; Fig S1). We then normalized the temporal power spectrum by the power before the onset of the 40 Hz entrainment, and results revealed a clear blue trace that is lower than zero in the alpha band (Fig. 4C). When plotting all the electrodes in all four experimental conditions (Fig. 5A–D), we found that most conditions did not have the suppression phenomenon in alpha band, but in square type 40 Hz sound in the closed-eye state (Fig. 5D). From the topographic map, we found this suppression happened most significantly in the central-frontal region (Fig. 6AB). The alpha suppression during square type 40 Hz entrainment in the closed-eye state is significantly stronger than that of the other three conditions (bootstrap test, p < 0.01) (Fig. 6B).

Fig. 4.

Fig. 4

Open in a new tab

Effect of 40 Hz Entrainment in alpha frequency band. A Time course of the task, the subject was at resting state (open-eye or closed-eye state) for 30 s and then there would be a 40 Hz (sinusoidal or square wave) sound entrainment for 120 s, after which the subject was at resting state again (open-eye or closed-eye state) for 30 s. B Absolute spectrogram of the EEG to the 40 Hz entrainment (square type in closed-eye state) is coded by color. Around 40 Hz (marked by two horizontal dashed lines), there is sustained power after stimulus onset (marked by the first vertical dashed line). C The raw power spectrum was converted into the relative power spectrum by normalizing by the power during the blank period at each frequency

Fig. 5.

Fig. 5

Open in a new tab

Effect of 40 Hz Entrainment in multiple experimental conditions. A Population-averaged (n = 10) relative power spectrum in low-frequency band (3–25 Hz) for all 18 EEG electrodes in open-eye state during the sinusoidal auditory 40 Hz entrainment. The onset and offset of the auditory stimuli were marked by two horizontal dashed lines. B is the same as A, except for that in closed-eye state during the sinusoidal auditory 40 Hz entrainment. C is the same as A, except for that in open-eye state during the square auditory 40 Hz entrainment. D is the same as A, except for that in closed-eye state during the square auditory 40 Hz entrainment

Fig. 6.

Fig. 6

Open in a new tab

Comparison of the entrainment effect in alpha-band activity among the multiple experimental conditions. A Topographic maps of the relative power in alpha band in four experimental conditions (n = 10). B Left: the comparison of alpha neural response in all 18 electrodes among four conditions; Right: the comparison in central-frontal and posterior region respectively

Discussion

In this study, we explored neural responses in multiple experimental conditions using the 40 Hz entrainment for the first time. We found that during the closed-eye state, entrainment with 40 Hz sinusoidal wave induced the strongest 40 Hz neural response in the prefrontal brain region compared with other conditions, and we also found that there is a stronger suppression of alpha rhythms with 40 Hz square wave sounds in the prefrontal region. These new findings might give new insight into the early prevention or treatment of Alzheimer’s disease.

Neural mechanism of the 40 Hz neural response during different types of the entrainment

40 Hz neural response entrained by auditory stimulation is found conditionally. Results of the 40 Hz neural response during the 40 Hz entrainment showed a preference for the closed-eye state and the sinusoidal sound wave. In other conditions, however, we could also intrigue the 40 Hz peak, however, this was weaker in strength. It should be noted that the combination of sinusoidal sound wave simulation while eyes are closed achieved the strongest neural response to the entrainment. A possible reason might be that if participants open their eyes, the visual input from the outside may not contribute to the integration of auditory entrained information. As for the wave type, the 40 Hz neural response induced by the sine wave is stronger than that induced by the square wave. We speculate that the sine wave is simpler in oscillation while the square wave contains more oscillatory components. The property of the sine wave might be more natural to the brain and therefore make it easier to synchronize. These two factors together make significant entrainment in neuronal response, but the intrinsic neural mechanisms merit additional consideration and investigation.

As for the brain regions where the neural responses were found, we found that the entrainment mostly happened in the central-frontal regions. Previous studies have reported gamma-band activity in many brain regions (Buzsáki 2009; Wang 2010), including the entorhinal cortex (Chrobak and Buzsáki 1998; Quilichini et al. 2010), hippocampus (Bragin et al. 1995; Wang and Buzsáki 1996; Colgin et al. 2009; Belluscio et al. 2012), olfactory bulb (Adrian 1942, 1950; Neville and Haberly 2003), auditory cortex (Lakatos et al. 2005; Fujioka et al. 2009; Vianney-Rodrigues et al. 2011; Gross et al. 2013), parietal cortex (Bouyer et al. 1981; Pesaran et al. 2002; Hawellek et al. 2016), prefrontal cortex (Gregoriou et al. 2009; Benchenane et al. 2011; Colgin 2011; Kim et al. 2016), and visual cortex (Eckhorn et al. 1988; Gray and Singer 1989; Gray et al. 1989; Frien et al. 1994; Kreiter and Singer 1996; Friedman-Hill et al. 2000; Maldonado et al. 2000; Hermes et al. 2015). Our results using 40 Hz auditory stimulation found stronger neural activities in the parietal and prefrontal region where the Default Mode Network (DMN; Raichle and Raichle 2001; Raichle et al. 2001) is mainly located, suggesting that the neural response during 40 Hz entrainment may be related to higher-level brain functions, such as learning, attention, or memory.

Neural mechanism of the alpha suppression during 40 Hz entrainment

Alpha rhythm (8–13 Hz) was prominent in the human EEG signals and is deemed a reflection of brain state. Alpha power increases when eyes are closed but attenuates when eyes are open (Berger 1929; Niedermeyer 1999; Draguhn and Buzsáki 2004), indicating that it is also modulated by attention (Hanslmayr et al. 2011; Klimesch 2012; Weisz et al. 2014; Snyder et al. 2015; Capotosto et al. 2017) and memory load (Palva and Palva 2007; Freunberger et al. 2009; Sauseng et al. 2010; Foster et al. 2017). In our study, we explored how alpha-band activity was modulated by different sound types in different states. Our results revealed that, compared with the sinusoidal wave, the suppression of the alpha rhythms is stronger under the 40 Hz square wave. Additionally, the suppression effect is more robust when eyes are closed rather than open. As noted earlier, square sound is more salient than continuously varying sinusoidal wave. Since alpha rhythm plays a critical role in attention selection and suppression (Klimesch 2012), we infer that the observed difference may be caused by the type of the sound wave. The unnatural square sound wave may attract more attention due to it being unusual and may therefore stand out, especially when eyes are closed. When eyes are closed, participants may also have been more attentive to other sensory inputs. These reasons may have contributed to the strength of the alpha-band activity.

Potential application to the clinical intervention

Previous studies have found that auditory gamma entrainment could reduce amyloid load in the auditory cortex (Iaccarino et al. 2016), by improving the expression of genes and proteins related to synaptic function and vesicular trafficking. In so doing, it would contribute to improved cognitive processes (Chan et al. 2021). We showed that the neural response by 40 Hz entrainment was modulated by different sound types and eye states, which might have different ameliorating effects on pathology. The stronger gamma response induced by sinusoidal sound with eyes closed, and inhibition of alpha response by square wave sound with eyes closed, may have a better effect on the behavior improvement (e.g., higher accuracy and faster reaction time) of patients with early AD. It might provide a new and feasible way to improve clinical symptoms in the future.

Limitations and future work

40 Hz auditory sensory stimulation can safely and efficiently induce entrainment of neural oscillations in the prefrontal region, especially when using sinusoidal sound waves when eyes are closed. Our new paradigm of GENUS at 40 Hz may become a novel preventive approach to protect against possible deterioration or mitigate ongoing brain atrophy. One limitation of this study is the small sample size. In the future, an updated experiment with more subjects can be conducted. Another limitation is that we did not test the intervention effect and transfer effect after the stimulation. Whether the sinusoidal auditory entrainment in a closed-eye state shows favorable effects on the functional and structural connectivity in the brain and cognitive performance (such as less ventricular dilation and better performance) is still to be explored.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 60 kb) (60KB, docx)

Acknowledgements

This study was sponsored by the Beijing Municipal Hospital Clinical Technology Innovation and Research Plan (XMLX201805), Beijing Municipal Hospital Research and Development Project (PX2021068), Advanced Innovation Center for Human Brain Protection Project (3500-12020137). We also acknowledge the great support from the WM Therapeutics Ltd.

Authors Contribution

MG, CH, ML, XZ conceived and designed the study. MG, CH, ML, NH, JT, SL contributed to the literature search, MG, CH, JT, SL contributed to data collection. CH, MG, XF, JQ contributed to the data analysis, and the interpretation of results. All authors contributed to writing the paper.

Declarations

Conflict of interest

The authors declare no competing conflicts of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Chuanliang Han and Xixi Zhao have contributed equally to this work.

Contributor Information

Chuanliang Han, Email: cl.han@siat.ac.cn.

Michel Gao, Email: gaomichel@brainwm.com.

References

  1. Adaikkan C, Middleton SJ, Marco A, et al. Gamma entrainment binds higher-order brain regions and offers neuroprotection. Neuron. 2019;102:929–943.e8. doi: 10.1016/j.neuron.2019.04.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Adaikkan C, Tsai LH. Gamma entrainment: impact on neurocircuits, glia, and therapeutic opportunities. Trends Neurosci. 2020;43:24–41. doi: 10.1016/j.tins.2019.11.001. [DOI] [PubMed] [Google Scholar]
  3. Adrian ED. Olfactory reactions in the brain of the hedgehog. J Physiol. 1942;100:459–473. doi: 10.1113/jphysiol.1942.sp003955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Adrian ED. The electrical activity of the mammalian olfactory bulb. Electroencephalogr Clin Neurophysiol. 1950;2(1–4):377–388. doi: 10.1016/0013-4694(50)90075-7. [DOI] [PubMed] [Google Scholar]
  5. An KM, Ikeda T, Yoshimura Y, et al. Altered Gamma oscillations during motor control in children with Autism spectrum disorder. J Neurosci. 2018 doi: 10.1523/JNEUROSCI.1229-18.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barry RJ, Clarke AR, Johnstone SJ, et al. EEG differences between eyes-closed and eyes-open resting conditions. Clin Neurophysiol. 2007;118:2765–2773. doi: 10.1016/j.clinph.2007.07.028. [DOI] [PubMed] [Google Scholar]
  7. Bauer EP, Paz R, Paré D. Gamma oscillations coordinate amygdalo-rhinal interactions during learning. J Neurosci. 2007;27:9369–9379. doi: 10.1523/JNEUROSCI.2153-07.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bell B, Percival DB, Walden AT. Calculating Thomson’s spectral multitapers by inverse iteration. J Comput Graph Stat. 1993;2(1):119–130. [Google Scholar]
  9. Belluscio MA, Mizuseki K, Schmidt R, et al. Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus. J Neurosci. 2012;32:423–435. doi: 10.1523/JNEUROSCI.4122-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Benchenane K, Tiesinga PH, Battaglia FP. Oscillations in the prefrontal cortex: a gateway to memory and attention. Curr Opin Neurobiol. 2011;21(3):475–485. doi: 10.1016/j.conb.2011.01.004. [DOI] [PubMed] [Google Scholar]
  11. Berger H. Über das Elektrenkephalogramm des Menschen. Arch Psychiatr Nervenkr. 1929 doi: 10.1007/BF01797193. [DOI] [Google Scholar]
  12. Bouyer JJ, Montaron MF, Rougeul A. Fast fronto-parietal rhythms during combined focused attentive behaviour and immobility in cat: cortical and thalamic localizations. Electroencephalogr Clin Neurophysiol. 1981;51(3):244–252. doi: 10.1016/0013-4694(81)90138-3. [DOI] [PubMed] [Google Scholar]
  13. Bragin A, Jandó G, Nádasdy Z, et al. Gamma (40–100 Hz) oscillation in the hippocampus of the behaving rat. J Neurosci. 1995;15:47–60. doi: 10.1523/JNEUROSCI.15-01-00047.1995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Buzsáki G. Rhythms of the Brain. Oxford: Oxford Univerisity Press; 2009. [Google Scholar]
  15. Buzśaki G, Wang XJ. Mechanisms of gamma oscillations. Annu Rev Neurosci. 2012;35:203–225. doi: 10.1146/annurev-neuro-062111-150444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Capotosto P, Baldassarre A, Sestieri C, et al. Task and regions specific top-down modulation of alpha rhythms in parietal cortex. Cereb Cortex. 2017 doi: 10.1093/cercor/bhw278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Cardin JA, Carlén M, Meletis K, et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459:663–667. doi: 10.1038/nature08002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Carr MF, Karlsson MP, Frank LM. Transient slow gamma synchrony underlies hippocampal memory replay. Neuron. 2012;75:700–713. doi: 10.1016/j.neuron.2012.06.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chan D, Suk H-J, Jackson B, et al (2021) 40 Hz sensory stimulation induces gamma entrainment and affects brain structure, sleep and cognition in patients with Alzheimer’s dementia. medRxiv
  20. Chrobak JJ, Buzsáki G. Gamma oscillations in the entorhinal cortex of the freely behaving rat. J Neurosci. 1998 doi: 10.1523/jneurosci.18-01-00388.1998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Colgin LL. Oscillations and hippocampal-prefrontal synchrony. Curr Opin Neurobiol. 2011;21(3):467–474. doi: 10.1016/j.conb.2011.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Colgin LL, Denninger T, Fyhn M, et al. Frequency of gamma oscillations routes flow of information in the hippocampus. Nature. 2009;462:353–357. doi: 10.1038/nature08573. [DOI] [PubMed] [Google Scholar]
  23. Draguhn A, Buzsáki G. Neuronal oscillations in cortical networks. Science (80-) 2004;304:1926–1930. doi: 10.1126/science.1099745. [DOI] [PubMed] [Google Scholar]
  24. Eckhorn R, Bauer R, Jordan W, et al. Coherent oscillations: A mechanism of feature linking in the visual cortex? - Multiple electrode and correlation analyses in the cat. Biol Cybern. 1988;60(2):121–130. doi: 10.1007/BF00202899. [DOI] [PubMed] [Google Scholar]
  25. Foster JJ, Bsales EM, Jaffe RJ, Awh E. Alpha-band activity reveals spontaneous representations of spatial position in visual working memory. Curr Biol. 2017 doi: 10.1016/j.cub.2017.09.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Freunberger R, Fellinger R, Sauseng P, et al. Dissociation between phase-locked and nonphase-locked alpha oscillations in a working memory task. Hum Brain Mapp. 2009 doi: 10.1002/hbm.20766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Friedman-Hill S, Maldonado PE, Gray CM. Dynamics of striate cortical activity in the alert macaque: I. Incidence and stimulus-dependence of gamma-band neuronal oscillations. Cereb Cortex. 2000;10(11):1105–1116. doi: 10.1093/cercor/10.11.1105. [DOI] [PubMed] [Google Scholar]
  28. Frien A, Eckhorn R, Bauer R, et al. Stimulus-specific fast oscillations at zero phase between visual areas V1 and V2 of awake monkey. NeuroReport. 1994;5(17):2273–2277. doi: 10.1097/00001756-199411000-00017. [DOI] [PubMed] [Google Scholar]
  29. Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci. 2005;9:474–480. doi: 10.1016/j.tics.2005.08.011. [DOI] [PubMed] [Google Scholar]
  30. Fries P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci. 2009;32(1):209–224. doi: 10.1146/annurev.neuro.051508.135603. [DOI] [PubMed] [Google Scholar]
  31. Fries P, Reynolds JH, Rorie AE, Desimone R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science (80-) 2001;291:1560–1563. doi: 10.1126/science.1055465. [DOI] [PubMed] [Google Scholar]
  32. Fujioka T, Trainor LJ, Large EW, Ross B (2009) Beta and gamma rhythms in human auditory cortex during musical beat processing. In: Annals of the New York Academy of Sciences [DOI] [PubMed]
  33. Galambos R, Makeig S, Talmachoff PJ. A 40-Hz auditory potential recorded from the human scalp. Proc Natl Acad Sci U S A. 1981;78:2643–2647. doi: 10.1073/pnas.78.4.2643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gray CM, König P, Engel AK, Singer W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature. 1989;338(6213):334–337. doi: 10.1038/338334a0. [DOI] [PubMed] [Google Scholar]
  35. Gray CM, Singer W. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci U S A. 1989;86:1698–1702. doi: 10.1073/pnas.86.5.1698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Gregoriou GG, Gotts SJ, Zhou H, Desimone R. High-Frequency, long-range coupling between prefrontal and visual cortex during attention. Science (80-) 2009;324(5931):1207–1210. doi: 10.1126/science.1171402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Gross J, Hoogenboom N, Thut G, et al. Speech rhythms and multiplexed oscillatory sensory coding in the human brain. PLoS Biol. 2013;11(12):e1001752. doi: 10.1371/journal.pbio.1001752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Han C, Shapley R, Xing D. Gamma rhythms in the visual cortex: functions and mechanisms. Cogn Neurodyn. 2021 doi: 10.1007/s11571-021-09767-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Han C, Wang B, Yang G, et al. Neural mechanism of orientation selectivity for distinct gamma oscillations in cat V1. J vis. 2020;20:1116. doi: 10.1167/jov.20.11.1116. [DOI] [Google Scholar]
  40. Han C, Wang T, Wu Y, et al. The Generation and modulation of distinct gamma oscillations with local, horizontal, and feedback connections in the primary visual cortex: a model study on large-scale networks. Neural Plast. 2021;2021:8874516. doi: 10.1155/2021/8874516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Han C, Wang T, Yang Y, et al. Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex. PLoS Biol. 2021 doi: 10.1371/journal.pbio.3001466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Hanslmayr S, Gross J, Klimesch W, Shapiro KL. The role of alpha oscillations in temporal attention. Brain Res Rev. 2011;67(1–2):331–343. doi: 10.1016/j.brainresrev.2011.04.002. [DOI] [PubMed] [Google Scholar]
  43. Hawellek DJ, Wong YT, Pesaran B. Temporal coding of reward-guided choice in the posterior parietal cortex. Proc Natl Acad Sci U S A. 2016;113(47):13492–13497. doi: 10.1073/pnas.1606479113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Hermes D, Kasteleijn-Nolst Trenité DGA, Winawer J. Gamma oscillations and photosensitive epilepsy. Curr Biol. 2017;27:R336–R338. doi: 10.1016/j.cub.2017.03.076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Hermes D, Miller KJ, Wandell BA, Winawer J. Stimulus dependence of gamma oscillations in human visual cortex. Cereb Cortex. 2015;25:2951–2959. doi: 10.1093/cercor/bhu091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Hirano Y, Oribe N, Kanba S, et al. Spontaneous gamma activity in schizophrenia. JAMA Psychiat. 2015 doi: 10.1001/jamapsychiatry.2014.2642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Ho J, Kyoung H, Nam W, et al. Effects of degree and symmetricity of bilateral spectral smearing, carrier frequency, and subject sex on amplitude of evoked auditory steady-state response signal. Cogn Neurodyn. 2019;13:151–160. doi: 10.1007/s11571-018-9512-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Iaccarino HF, Singer AC, Martorell AJ, et al. Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature. 2016;540:230–235. doi: 10.1038/nature20587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Jensen O, Kaiser J, Lachaux JP. Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 2007;30(7):317–324. doi: 10.1016/j.tins.2007.05.001. [DOI] [PubMed] [Google Scholar]
  50. Jia X, Tanabe S, Kohn A. Gamma and the coordination of spiking activity in early visual cortex. Neuron. 2013;77:762–774. doi: 10.1016/j.neuron.2012.12.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Jia X, Xing D, Kohn A. No consistent relationship between gamma power and peak frequency in macaque primary visual cortex. J Neurosci. 2013;33:17–25. doi: 10.1523/JNEUROSCI.1687-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kim H, Ährlund-Richter S, Wang X, et al. Prefrontal parvalbumin neurons in control of attention. Cell. 2016;164(1–2):208–218. doi: 10.1016/j.cell.2015.11.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Klimesch W. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci. 2012;16:606–617. doi: 10.1016/j.tics.2012.10.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. 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. doi: 10.1007/s00221-015-4494-3. [DOI] [PubMed] [Google Scholar]
  55. Kreiter AK, Singer W. Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey. J Neurosci. 1996;16(7):2381–2396. doi: 10.1523/JNEUROSCI.16-07-02381.1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Kucewicz MT, Berry BM, Kremen V, et al. Dissecting gamma frequency activity during human memory processing. Brain. 2017;140:1337–1350. doi: 10.1093/brain/awx043. [DOI] [PubMed] [Google Scholar]
  57. Lakatos P, Shah AS, Knuth KH, et al. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J Neurophysiol. 2005;94(3):1904–1911. doi: 10.1152/jn.00263.2005. [DOI] [PubMed] [Google Scholar]
  58. Lewis DA, Hashimoto T, Volk DW. Cortical inhibitory neurons and schizophrenia. Nat Rev Neurosci. 2005;6(4):312–324. doi: 10.1038/nrn1648. [DOI] [PubMed] [Google Scholar]
  59. Mably AJ, Colgin LL. Gamma oscillations in cognitive disorders. Curr Opin Neurobiol. 2018;52:182–187. doi: 10.1016/j.conb.2018.07.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Maldonado PE, Friedman-Hill S, Gray CM. Dynamics of striate cortical activity in the alert macaque: II. Fast time scale synchronization. Cereb Cortex. 2000;10(11):1105–1116. doi: 10.1093/cercor/10.11.1117. [DOI] [PubMed] [Google Scholar]
  61. Martorell AJ, Paulson AL, Suk HJ, et al. Multi-sensory gamma stimulation ameliorates alzheimer’s-associated pathology and improves cognition. Cell. 2019;177:256–271.e22. doi: 10.1016/j.cell.2019.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. McFadden KL, Steinmetz SE, Carroll AM, et al. Test-retest reliability of the 40 Hz EEG auditory steady-state response. PLoS ONE. 2014;9:59–61. doi: 10.1371/journal.pone.0085748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Neville KR, Haberly LB. Beta and gamma oscillations in the olfactory system of the urethane-anesthetized rat. J Neurophysiol. 2003;90(6):3921–3930. doi: 10.1152/jn.00475.2003. [DOI] [PubMed] [Google Scholar]
  64. Niedermeyer E (1999) The Normal EEG of the Waking Adult. Electroencephalogr Basic Princ Clin Appl Relat Fields
  65. Obleser J, Kayser C. Neural entrainment and attentional selection in the listening brain. Trends Cogn Sci. 2019;23(11):913–926. doi: 10.1016/j.tics.2019.08.004. [DOI] [PubMed] [Google Scholar]
  66. Palop JJ, Mucke L. Network abnormalities and interneuron dysfunction in Alzheimer disease. Nat Rev Neurosci. 2016;17:777–792. doi: 10.1038/nrn.2016.141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Palva S, Palva JM. New vistas for α-frequency band oscillations. Trends Neurosci. 2007;30:150–158. doi: 10.1016/j.tins.2007.02.001. [DOI] [PubMed] [Google Scholar]
  68. Percival DB, Walden AT. Spectral Analysis for Physical Applications. Cambridge: Cambridge University Press; 1993. [Google Scholar]
  69. Pesaran B, Pezaris JS, Sahani M, et al. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat Neurosci. 2002;5(8):805–811. doi: 10.1038/nn890. [DOI] [PubMed] [Google Scholar]
  70. Quilichini P, Sirota A, Buzsáki G. Intrinsic circuit organization and theta-gamma oscillation dynamics in the entorhinal cortex of the rat. J Neurosci. 2010 doi: 10.1523/JNEUROSCI.1327-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Raichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001 doi: 10.1073/pnas.98.2.676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Raichle ME, Raichle ME. Searching for a baseline: Functional imaging and the resting human brain. Nat Rev Neurosci. 2001 doi: 10.1038/35094500. [DOI] [PubMed] [Google Scholar]
  73. Ross B, Herdman AT, Pantev C. Stimulus induced desynchronization of human auditory 40-Hz steady-state responses. J Neurophysiol. 2005;94:4082–4093. doi: 10.1152/jn.00469.2005. [DOI] [PubMed] [Google Scholar]
  74. Roth C, Navin Gupta C, Plis SM, et al. The Influence of visuospatial attention on unattended auditory 40 Hz responses. Front Hum Neurosci. 2013;7:1–7. doi: 10.3389/fnhum.2013.00370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Sauseng P, Griesmayr B, Freunberger R, Klimesch W. Control mechanisms in working memory: A possible function of EEG theta oscillations. Neurosci Biobehav Rev. 2010;34:1015–1022. doi: 10.1016/j.neubiorev.2009.12.006. [DOI] [PubMed] [Google Scholar]
  76. Singer W. Neuronal synchrony: a versatile code for the definition of relations? Neuron. 1999;24(1):49–65. doi: 10.1016/S0896-6273(00)80821-1. [DOI] [PubMed] [Google Scholar]
  77. Singer W, Gray CM. Visual feature integration and the temporal correlation hypothesis. Annu Rev Neurosci. 1995;18:555–586. doi: 10.1146/annurev.ne.18.030195.003011. [DOI] [PubMed] [Google Scholar]
  78. Snyder AC, Morais MJ, Willis CM, Smith MA. Global network influences on local functional connectivity. Nat Neurosci. 2015 doi: 10.1038/nn.3979. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Tada M, Nagai T, Kirihara K, et al. Differential alterations of auditory gamma oscillatory responses between pre-onset high-risk individuals and first-episode schizophrenia. Cereb Cortex. 2016 doi: 10.1093/cercor/bhu278. [DOI] [PubMed] [Google Scholar]
  80. Uhlhaas PJ, Singer W. Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks. Neuron. 2012;75:963–980. doi: 10.1016/j.neuron.2012.09.004. [DOI] [PubMed] [Google Scholar]
  81. Uhlhaas PJ, Singer W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron. 2006;52(1):155–168. doi: 10.1016/j.neuron.2006.09.020. [DOI] [PubMed] [Google Scholar]
  82. Uhlhaas PJ, Singer W (2007) What Do Disturbances in Neural Synchrony Tell Us About Autism? Biol Psychiatry [DOI] [PubMed]
  83. Uhlhaas PJ, Singer W. Abnormal neural oscillations and synchrony in schizophrenia. Nat Rev Neurosci. 2010;11(2):100–113. doi: 10.1038/nrn2774. [DOI] [PubMed] [Google Scholar]
  84. Vianney-Rodrigues P, Iancu OD, Welsh JP. Gamma oscillations in the auditory cortex of awake rats. Eur J Neurosci. 2011;33(1):119–129. doi: 10.1111/j.1460-9568.2010.07487.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Vinck M, Womelsdorf T, Buffalo EA, et al. Attentional modulation of cell-class-specific gamma-band synchronization in awake monkey area V4. Neuron. 2013;80:1077–1089. doi: 10.1016/j.neuron.2013.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Wang XJ. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev. 2010;90:1195–1268. doi: 10.1152/physrev.00035.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Wang XJ, Buzsáki G. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci. 1996;16:6402–6413. doi: 10.1523/JNEUROSCI.16-20-06402.1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Weisz N, Müller N, Jatzev S, Bertrand O. Oscillatory alpha modulations in right auditory regions reflect the validity of acoustic cues in an auditory spatial attention task. Cereb Cortex. 2014 doi: 10.1093/cercor/bht113. [DOI] [PubMed] [Google Scholar]
  89. Wilson TW, Rojas DC, Reite ML, et al. Children and adolescents with autism exhibit reduced MEG steady-state gamma responses. Biol Psychiatry. 2007 doi: 10.1016/j.biopsych.2006.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Xing D, Shen Y, Burns S, et al. Stochastic generation of gamma-band activity in primary visual cortex of awake and anesthetized monkeys. J Neurosci. 2012;32:13873–13880. doi: 10.1523/JNEUROSCI.5644-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Xing D, Yeh CI, Burns S, Shapley RM. Laminar analysis of visually evoked activity in the primary visual cortex. Proc Natl Acad Sci U S A. 2012;109:13871–13876. doi: 10.1073/pnas.1201478109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Xing D, Yeh CI, Gordon J, Shapley RM. Cortical brightness adaptation when darkness and brightness produce different dynamical states in the visual cortex. Proc Natl Acad Sci U S A. 2014;111:1210–1215. doi: 10.1073/pnas.1314690111. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 60 kb) (60KB, docx)

Articles from Cognitive Neurodynamics are provided here courtesy of Springer Science+Business Media B.V.

RESOURCES