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 Table of Contents  
Year : 2023  |  Volume : 2  |  Issue : 2  |  Page : 47-52

Neurophysiological isolation of individual rhythmic brain activity arising from auditory-speech load

1 Engineering Physics Institute of Biomedicine, National Research Nuclear University MEPhI; Federal Center for Brain and Neurotechnologies of FMBA of Russia, Moscow, Russia
2 Federal Center for Brain and Neurotechnologies of FMBA of Russia, Moscow, Russia

Date of Submission06-Apr-2023
Date of Decision01-Jun-2023
Date of Acceptance09-Jun-2023
Date of Web Publication28-Jun-2023

Correspondence Address:
Sergey Alexander Gulyaev
Engineering Physics Institute of Biomedicine, National Research Nuclear University MEPhI; Federal Center for Brain and Neurotechnologies of FMBA of Russia, Moscow
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2773-2398.379340

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Knowledge about the rhythmic activity of neural networks associated with the implementation of a particular brain function can be used to construct diagnostic systems for objective analyses of cognitive dysfunctions. The aim of this study was to identify specific frequency-based electroencephalogram phenomena associated with speech processing. The study included data from 40 clinically healthy volunteers aged 30 to 50 years (median 32.5 years), including 23 men and 17 women. While listening to a speech stimulus, changes in bioelectrical activity over the speech centers were recorded in 23 subjects (58%). During active speech production, similar changes were recorded in 12 subjects (30%). A pairwise comparison of electroencephalogram frequencies recorded during background recording and listening to the stimuli revealed statistically significant differences in changes in rhythmic activity over Broca’s area during listening and over Wernicke's area during active speech production, while changes in rhythmic activity over Broca’s area during active speech production and over Wernicke's area during listening were less significant. The most characteristic changes in the bioelectrical activity over the speech centers during listening and speaking were fluctuations with a frequency (on average) of 17.5–17.7 Hz. This may reflect a specific electroencephalogram rhythm associated with activity in the speech areas of the brain, which could allow these regions to be more accurately identified during auditory-verbal processing.

Keywords: brain; diagnostics; electroencephalography; rhythms; speech function

How to cite this article:
Gulyaev SA, Lelyuk VG. Neurophysiological isolation of individual rhythmic brain activity arising from auditory-speech load. Brain Netw Modulation 2023;2:47-52

How to cite this URL:
Gulyaev SA, Lelyuk VG. Neurophysiological isolation of individual rhythmic brain activity arising from auditory-speech load. Brain Netw Modulation [serial online] 2023 [cited 2023 Sep 22];2:47-52. Available from: http://www.bnmjournal.com/text.asp?2023/2/2/47/379340

  Introduction Top

One of the main goals of modern neuroscience is to collect objective data that describe the mental processes that take place in the human brain. At present, this goal is still far from being achieved (Pinti et al., 2020; Goodale et al., 2021), because existing technologies are only able to measure specific brain processes. This limits the degree to which we can understand the global brain activity that contributes to cognitive function.

Speech is an important element of social function. The neural underpinnings of speech can be studied using various technologies, such as functional magnetic resonance imaging (MRI) with various auditory and speech paradigms. However, MRI studies are limited in that they require the subject to maintain a specific position for a long period of time, require repeated repetition of the stimulating phrase (word), and are influenced by external noise generated by the MRI equipment, which creates multiple artifacts, requiring the use of special headphones and/or noise-canceling devices. As a result, MRI examinations often produce just a summary of the functional response of the brain structures involved in the process under study, i.e., they link the speech component and the response to various tonal and noise stimuli (Seeck et al., 2017).

A significant problem in neurophysiological studies is the use of classical electroencephalogram (EEG) methods to examine rhythmic activity produced by brain structures. This approach prioritizes the frequency analysis in the examination of the bioelectrical activity of brain structures. However, rhythmic brain activity is heterogeneous in nature, and so concepts of "excitation" and "activity" can be ambiguous when assessing the functional activity of neuronal structures in normal and pathological conditions (Lopes Da Silva and Storm Van Leeuwen, 1977; Klimesch, 1999, 2012; Klimesch et al., 2006, 2007; Bazanova, 2011; Stankova, 2017; Rusalova, 2021).

Under physiologically normal conditions, the recorded rhythmic components of an EEG signal do not represent areas of excitation of the nervous tissue, but instead, zones that are in a state of preparation for the realization of excitation. When comparing brain areas of rhythmic activity determined using EEG and "hot" areas identified using functional MRI (fMRI), a spatial mismatch is observed. At the same time, pathological EEG rhythms can form as a result of neurological damage, resulting from the high-amplitude total potential of synchronous activity among damaged neurons (Klimesch et al., 2007; Stankova, 2017; Rusalova, 2021).

Thus, under physiologically normal conditions, the main pattern of excitation measured via EEG is often the suppression of rhythmic activity, while neural stimulation leads to the formation of a pathological rhythm.

This creates significant difficulties in the use of modern EEG diagnostic tools (Lopes Da Silva and Storm Van Leeuwen, 1977; Klimesch et al., 2007; Pinti et al., 2020; Rusalova, 2021). The phenomenon of neurophysiological excitation was observed for the first time in humans in 1929 by Berger (Berger, 1929; Kugler, 1991), as in, the suppression of rhythmic alpha activity that occurred when opening the eyes. It was studied in detail by Penfield et al. (Penfield and Jasper, 1948; Penfield and Rasmussen, 1949), and then additional brain rhythms were described, such as mu (Gastaut) and kappa (Kennedy) (Kennedy et al., 1948; Gastaut, 1952; Näätänen and Michie, 1979; Kruchinina, 2020). These studies have shown that individual brain structures associated with the implementation of various brain functions are likely to be characterized by unique and specific rhythmic phenomena that can be detected and recorded using EEG research technology. Functional excitation associated with the initiation of a cognitive process appears to lead to inhibition of this rhythmic activity, without affecting other brain rhythms associated with activity in other brain structures.

At present, the establishment of such a connection (Klimesch et al., 2006; Bazanova, 2011; Emmendorfer et al., 2020) has led researchers to question of the presence of specific rhythmic activity in individual neural networks that could function as individual markers of the implementation of particular brain functions (Michel and Koenig, 2018). Therefore, investigating the uniqueness of rhythmic phenomena produced by various brain structures is important for developing neurophysiological technologies for studying the functional activity of the human brain.

The identification of specific frequency ranges could facilitate the study of brain activity in terms of analyzing individual brain rhythms (Hlinka et al., 2010; Al-Ezzi et al., 2021; Das et al., 2022) and increasing the spatial specificity of recording for individual groups of neurons and individual EEG microstates (Milz et al., 2016; Mishra et al., 2020). Moreover, the identification of separate frequency bands could greatly facilitate the development of technologies based on image resolution systems (Grech et al., 2008; Neuner et al., 2014; Milz et al., 2016).

Thus, the search for specific EEG rhythms has important consequences for modern EEG diagnostics, which underlies many promising applications in the study of human cognition.

The aim of this study was to search for specific continuous-frequency EEG phenomena associated with the implementation of the speech function.

  Materials and methods Top


TThis comparative study included data from 40 clinically healthy volunteers who were employees of the Federal State Budgetary Institution Scientific Center for Brain and Neurotechnologies of the Federal Medical and Biological Agency of Russia. They were 30–50 years of age (median 32.5 years), included 23 men and 17 women, and were examined from 2021–2022. All employees regularly underwent scheduled medical examinations and were in good health. All studies were conducted in accordance with the principles of biomedical ethics formulated in the Helsinki Declaration of 1964 and its subsequent updates. Study approval was granted by the local ethics committee of the Federal Medical Biologic Agency of Russia and Moscow Engineering Physics Institute of Russia (Moscow) (approval No. 04/10–21) on October 21, 2021. Each study participant voluntarily signed an informed consent form after receiving an explanation of the potential risks and benefits, as well as the nature of the upcoming study.

All participants were native Russian-speaking individuals with left-hemispheric dominance and right-handedness (confirmed by clinical studies, including tests of the leading eye and leading hand, and a posture selection test). Retrained left-handers were excluded from the study. The included volunteers had a comparable level of education (higher specialized).


Before the start of EEG testing, the participants underwent a high-field MRI study to exclude focal and (or) diffuse brain damage, as well as event-related fMRI with an auditory-speech paradigm (included in the basic study protocol) to determine the basic localization of the main speech centers (Broca’s and Wernicke’s areas, and the lateral structure of the arcuate fasciculus). We did this to better understand the spatial organization of the speech network and to enable matching of the obtained EEG data with the activity of these formations.

We developed a controlled experiment to assess changes in the bioelectrical activity of the brain under conditions of auditory and speech loading. First, we studied brain activity in a state of relaxed wakefulness, without the presentation of auditory-speech stimulation. Then, we studied activity with an auditory load, and finally, we examined activity during active speech production.

We used the auditory-speech paradigm to examine changes in the rhythmic characteristics of brain activity during an objectively oriented action (Fox et al., 2016). To induce a cognitive load, we asked the subject to listen to a short story in Russian, which including standard vocabulary and speech patterns used in everyday speech, and then asked them to retell the text aloud. We chose this load variant to exclude 1) activation associated with visual processing, which occurs in tests that require the naming of visible objects, 2) the effect of the rhythm of speech that is observed during the rhythmic repetition of sounds, and 3) activation associated with processing written language, which usually accompanies tasks with verbalization. All actions were performed with the eyes closed.

The duration of each functional EEG test was at least 3 minutes, and the test lasted from 2 to 4 minutes when playing the speech stimulus, depending on the rate of speech. EEG was recorded in a darkened room with relative sound insulation.

All studies were carried out in the first half of the day, from 10 a.m. to 2 p.m. The day before, the aims and objectives of the experiment were explained to the patient, and the participant was asked to avoid the intake of hypnotics or stimulants. The participant was asked to sleep for at least 8 hours on the night before the study. If these conditions were violated, the study was postponed to another day.

EEG test

EEG was recorded using the original 128-channel HydroCel-128 system (Magstim, Roseville, CA, USA) with an averaged reference. Recording, switching, and hardware filtering of the bioelectrical EEG signal was carried out using an EGI-GES-300 bioamplifier (Magstim). The received signal was digitized via sampling at a frequency of 500 Hz, and the signal bandwidth was from 0.5 to 70 Hz with a 50 Hz notch filter. The impedance did not exceed 10 kΩ, and this was controlled during the entire study according to the recommendations of the manufacturer. In addition, third-party electrical devices that create parasitic electromagnetic fields were turned off, the temperature in the room was regulated, and artifact muscle movements were minimized by asking the participant to assume a comfortable posture.

The subsequent processing and analysis of the obtained results included primary filtering using a 1–70 Hz broadband filter and standardization of the basic mounting into a single electrode space. We extracted the independent signal components using the EEGLAB R2020a (v.98) software package (SCCN, San Diego, CA, USA). This procedure made it possible to remove various physical and biological artifacts that had frequency responses within the prefilter window (Delorme et al., 2007). As a result, we were able to conduct a sequential analysis of the dynamics of changes in the spectral density of the EEG signal with the construction of individual scalp maps, combined with an individual head model obtained by recalculating T1-weighted MRI images. To search for unique harmonic components, the calculations were carried out in a continuous sequence of frequencies from 1 to 40 Hz.

We selected individual harmonics associated with the implementation of speech processing by analyzing the total volume of the spectral power with a smooth change in the narrow-band frequency window.

After signal processing, we obtained the spectral distribution of the activity, which was combined with T1-weighted MRI tomograms in the Brainstorm program (University of Southern California, Los Angeles, LA, USA and McGill University, Montreal, Canada) according to a method for spatial combination of MRI and EEG data (Tadel et al., 2011). This enabled us to clarify the localization of the observed changes and to establish their connection with certain areas of the cortex. Subsequently, we verified the relationship between the areas with altered bioelectrical activity during the EEG study and the results of event-related fMRI using an algorithm for solving the inverse EEG problem, performed via the sLORETA software package (v-20210701; Institute for Brain-Mind Research, University of Zurich, Zurich, Switzerland) (Pascual-Marqui et al., 2002).

Statistical analysis

Statistical analysis was performed using the SPSS Statistics 23.0 software package (IBM Corp., Armonk, NY, USA). The null hypothesis was rejected at a significance level of P < 0.05. The relationship among variables for one sample was estimated using the Pearson correlation analysis. To compare the frequencies of EEG rhythms recorded over Broca’s and Wernicke’s areas during the trials, we used a paired t-test.

  Results Top

In the state of relaxed wakefulness, all participants had a typical EEG pattern, characterized by the predominance of an alpha rhythm in the occipital leads and fast variants of activity in the anterior leads with the preservation of zonal differences in rhythms and the absence of interhemispheric asymmetry.

During the listening task, changes in bioelectrical activity over the speech centers were recorded in 23 people (58%). During active speech production, similar changes were recorded only in 12 people (30%). In comparison with the data obtained using event-related fMRI (speech paradigm), speech centers were identified in 25 people during listening, which was comparable with the indicators obtained during the EEG study (r = 0.65). The results of the fMRI during active speech production made it possible to identify speech centers in 24 people (60% of all examinations), which was directly correlated with the results of the EEG study (r = 0.0l) [Table 1].
Table 1: Comparison of localization of speech centers according to fMRI and EEG studies

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A subsequent study of the EEG results, carried out using the algorithm for solving the inverse EEG problem in the sLORETA software package, showed that the changes recorded during the EEG study had “inverse” characteristics compared with the rhythmical activity. Overall, during passive relaxed wakefulness (rest state), Wernicke’s area and Broca’s area were not sources of rhythmic activity. When listening, Broca’s area was detected as a source of rhythmic activity more often (40%) than Wernicke’s area (25%). During active speech production, Broca’s area was not a source of rhythmic activity, while Wernicke’s area (Brodmann area 39) was a source in 30% of cases [Table 2].
Table 2: Investigation of the localization of the EEG signal source by solving the inverse EEG problem

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Examination of the characteristics of the rhythmic activity recorded over the speech centers produced the following results, presented in [Table 3].
Table 3: Frequency characteristics of bioelectrical activity over Wernicke’s and Broca’s areas under conditions of rest and auditory-speech load

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In accordance with the obtained data, during active listening and the excitation of Wernicke’s area, a 17.7 Hz rhythmic phenomenon was recorded over Broca’s area. During a state of relaxed wakefulness, the rhythm in this area was 22.3 Hz. During active speech production (retelling of the text), the frequency characteristics of the bioelectrical activity above Broca’s area returned to their original values.

Similar changes took place over Wernicke’s area, but 17.5 Hz activity was observed during active speech production. During active listening, the frequency characteristics over this region did not differ from the activity observed during background recording.

A paired t-test to compare the EEG frequencies observed during background recording and those observed during active listening revealed statistically significant differences in changes in rhythmic activity over Broca’s area and over Wernicke’s area, while we found changes in rhythmic activity over Broca’s area during active speech production and over Wernicke’s area during listening (P = 0.06; [Table 4]).
Table 4: Results of paired comparisons of changes In electroencephalogram rhythms recorded over Broca’s and Wernicke’s areas during the auditory-speech test

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  Discussion Top

In this study, we used a special technique to record and process EEG at rest and during functional stimulation. First, we used a 128-channel system with a high density of electrodes on the scalp, which made it possible to reduce the information loss that is characteristic of standard (clinical) EEG studies and to increase the overall spatial resolution of the method to the level of localization of a separate Brodmann’s field. Second, we analyzed the spectral density of the EEG in a continuous sequence of frequencies from 1 to 40 Hz, which made it possible to identify the potentials associated with the processes of speech production through a smooth change in the frequency window. According to Andrew and Pfurtscheller, this approach is relatively sensitive for determining individual rhythmic harmonics (Andrew and Pfurtscheller, 1997).

In this study, we did not record abnormally located speech areas or inconsistencies in their localization, which were assumed according to the neurophysiological model of two brain streams (Ansaldo and Saidi, 2014). In our opinion, the study participants were a homogeneous group.

However, the rhythmic phenomena were inconsistent, and we identified the areas containing the speech centers in only 58% of cases in the listening condition and in 30% of cases during active speech production. At the same time, event-related fMRI revealed metabolic changes in these areas in 62% of the participants during listening and in 60% during active speech. This gap could be associated with the peculiarities of the EEG method itself, as well as with the presence of myographic artifacts that occurred during articulation, as they had a similar frequency range to the rhythmic activity of speech centers. This finding could also be explained from the standpoint of genetic and age-dependent influences on the formation of brain rhythms (Kennedy et al., 1948; Gastaut, 1952).

Our experiment showed that the introduction of an auditory-speech destabilizing component in the sequence of an EEG study led to a change in the frequency characteristics, which was based either on the phenomenon of activity mismatch or excessive synchronization (Andrew and Pfurtscheller, 1997).

The most pronounced changes in the bioelectrical activity of the brain during speech production were recorded in a relatively narrow frequency band (17 ± 3 Hz). These results do not correspond with the characteristics of the brain rhythms described earlier, and may represent specific activity characteristic of the speech centers. This is indirectly confirmed by the finding that the recorded phenomena were associated with the times of the transitions of cortical structures to the state of “waiting” for functional activity, while when the corresponding area was included in the process of function realization, rhythmic activity disappeared, forming a picture opposite to the results obtained when using event-linked fMRI.

Based on the obtained results, it can be assumed that the general two-stream structure of speech remains inactive during the performance of habitual actions. Only a specific brain region was involved in the implementation of the speech function, and the involvement of other structures may be associated either with the need to expand functional activity (for example, when learning a foreign language) or with compensation for damaged speech centers as a result of injury or disease. It is possible that the well-documented differences in the recovery of speech function in stroke survivors who speak one or more languages are associated with this phenomenon (Ansaldo and Saidi, 2014; Goral et al., 2019).

Although the electroencephalographic investigation had pronounced methodological limitations in terms of spatial localization, the introduction of mathematical methods for clarifying the position of electrodes on the scalp surface and localizing sources of rhythmic activity made it possible to increase the accuracy of the method to the level of one Brodmann area. Therefore, we made the following general conclusions:

  1. Electroencephalography, especially as a component of high-density recording systems, is a very sensitive method, and the results can be compared with those obtained during event-related fMRI.

  2. In a physiological study, it is not possible to make a direct connection between the registration of metabolic changes recorded using fMRI technology and the registration of rhythmic activity recorded during an EEG study, because of the different nature of these processes. This observation calls into question the feasibility of combined EEG-fMRI studies of cerebral functions in healthy people, without appropriate accommodations.

  3. The most characteristic changes in the bioelectrical activity of speech-related brain centers during listening and speech were fluctuations with a frequency (on average) of 17.5–7.7 Hz. It is possible that this reflects a specific EEG rhythm along with the already established alpha, mu, and kappa oscillations.

  4. The present experimental conditions likely stimulated only part of the general structure of the speech network and only those elements that correspond to the dorsal stream of activity related to speech function.

Author contributions

Both authors made a significant contribution to the development of the methodology, the collection and processing of research material, as well as to the preparation of the article for publication.

Conflicts of interest

The author declares that there are no apparent or potential conflicts of interest related to the publication of this article.

Data availability statement

No additional data are available.

Open access statement

This is an open access journal, and articles are distributed under the terms of the Creative Commons AttributionNonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.[35]

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  [Table 1], [Table 2], [Table 3], [Table 4]


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