People can generate internal representations in the absence of external stimulation, for example, by speaking or singing in their minds. This experience is referred to as mental imagery, which reflects a higher cognitive function of the human brain (Kosslyn et al., 2001). Speech mental imagery is among the most pervasive human experiences, and it has been estimated that people spend more than one-quarter of their lifetime engaged in such inner experiences (Heavey and Hurlburt, 2008). Many functional magnetic resonance imaging (fMRI) studies have reported distributed neural networks involving the left pars opercularis in the inferior frontal cortex (Broca’s area) (Aleman et al., 2005; Kleber et al., 2007; Papoutsi et al., 2009; Koelsch et al., 2009; Price, 2012; Tian et al., 2016), the premotor cortex (Price, 2012; Kleber et al., 2007; Koelsch et al., 2009; Tian et al., 2016; Aleman et al., 2005), the inferior parietal lobe (Kleber et al., 2007; Tian et al., 2016), the middle frontal cortex (Tian et al., 2016), the anterior part of the insula (Koelsch et al., 2009; Tian et al., 2016; Aleman et al., 2005; Ackermann and Riecker, 2004), and the superior temporal gyrus (Aleman et al., 2005) that can be activated by speech mental imagery. However, limited by the temporal resolution of the fMRI technique, the neural dynamics of speech mental imagery have been mostly overlooked.

Neuronal information processing is shaped by multiscale oscillatory activity (Buzsáki and Draguhn, 2004), in which subjective experience in consciousness involves ongoing neural dynamics on the time scale of hundreds of milliseconds (Dehaene and Changeux, 2011). Therefore, establishing a neurophysiological model of speech mental imagery is highly important for elucidating the spectral-temporal features of imagery-induced neural activity. However, in the experimental environment, tracking subjects’ internal subjective representations in milliseconds remains highly challenging due to the lack of external signals identifying imagery-related neural activity. Considering the limitations of the current methodologies, several neurophysiological studies have used the imagery-perception repetition paradigm to measure the sensory attenuation effect caused by speech mental imagery on subsequent auditory perception (Tian and Poeppel, 2013; Tian and Poeppel, 2015; Ylinen et al., 2015; Whitford et al., 2017; Tian et al., 2018). Preliminary magnetoencephalography (MEG) studies have shown that the modulatory effect of imagined speech on the amplitude of event-related responses to a subsequent auditory stimulus with matched pitch and/or content occurs as early as 100 to 200 ms (Tian and Poeppel, 2013; Tian and Poeppel, 2015; Ylinen et al., 2015), and in an electroencephalogram (EEG) study, N1 suppression was observed when a matched phoneme was generated covertly before an audible phoneme was presented (Whitford et al., 2017). Recently, Tian et al. (2018) examined the influence of imagined speech on the perceived loudness of a subsequent syllable and demonstrated the involvement of the sensory-driven cortex (i.e. the auditory cortex) in the early-stage interaction of speech imagery and perception. These neurophysiological findings implicate the early involvement of sensory-related neural substrates in the process of speech mental imagery, while in the adaptation paradigm, the neural pathway of top-down mental imagery cannot be disentangled from the bottom-up perception. To date, the neural dynamics of mental imagery independent of auditory perception remain puzzling.

Frequency-tagging is a promising approach for disentangling exogenous and endogenous processes when the corresponding neural activity changes periodically at different frequencies. Recently, frequency-tagging has been used to characterize higher-lever processes such as music rhythmic perception (Nozaradan et al., 2011; Tal et al., 2017; Lenc et al., 2018; Nozaradan et al., 2018) and speech linguistic construction (Ding et al., 2016; Sheng et al., 2019). For example, Nozaradan et al. (2011) asked eight musicians to imagine a binary/ternary meter when listening to a constant beat and found spectral responses not only to the beat stimulation but also to the imagined meters of this beat. Ding et al. (2016) reported cortical responses to internally constructed linguistic structures such as words, phrases and sentences at different frequencies during the comprehension of connected speech. Therefore, frequency-tagging is an excellent candidate as an approach for capturing the dynamic features during the internal construction of speech mental imagery by tagging the corresponding neural activity in the frequency domain.

Here, we apply the frequency-tagging method in a behavioral task involving speech mental imagery to experimentally isolate the neural manifestations of internally constructed speech from the stimulus-driven processes. In this way, we are able to directly track the neural processing time scales of speech mental imagery in the frequency domain (in our experiment, we manipulated the imagery-rate frequency to 0.8 Hz). Furthermore, we used the MEG source estimation method to localize the neural underpinnings of the isolated processes of speech imagery independent of the bottom-up auditory perception and confirmed the localization of the neural sources by intracranial stereotactic electroencephalogram (sEEG) recordings. We hypothesized that in addition to the sensory-driven auditory cortex, distinctive neural networks are responsible for generating speech mental imagery.

Our data show that the neural activity induced by speech mental imagery can be tracked in the frequency domain and distinguished from stimulus-driven processing. The frequency-tagging method we applied in the imagery task casts light on the neural dynamics of the internally generated representations (i.e. speech mental imagery). More specifically, the left central-frontal lobe, the left inferior occipital lobe and the right inferior parietal lobe contribute to the spectral responses generated by speech mental imagery. Furthermore, the detection of imagery-induced neural signals in the left central-frontal lobe, particularly the region of the operIFG, in rare intracranial cases confirms the reliability of the minimum L1-norm estimation methods in solving the inverse problem for MEG data in our study.

Mental imagery has been proposed to be essentially an attention-guided memory retrieval process, for example, in the visual domain (Ishai et al., 2002). Mental imagery of speech not only recruits the pathway by which phonological, lexical, or semantic details are retrieved from the memory system but also requires planned articulation that is simulated internally to enable the individuals to have the subjective experiences of their mental imagery (Tian and Poeppel, 2012; Tian et al., 2016). In our study, the task of rhythmic inner counting produced cyclical neural signals that were captured in the frequency domain, reflecting the top-down regulation of the internally constructed mental imagery. The operational definition of speech mental imagery condition, namely, the rhythmic inner counting task, is not only related to the internal representations of speech but also requires high levels of working memory. To count following a rapid sequence of sounds presented at a 250 ms onset-to-onset interval, the brain must maintain the mental representations of the last group of 5 numbers (or at least of the beginning number) and construct the ongoing imagery of the subsequent group of 5 numbers concurrently with the sound stimuli. Additionally, the participants needed to verbally name the numbers and count ‘loudly’ in their minds. It is worth mentioning that the rhythmic inner counting task in our study eliminated the influence of bottom-up sensory-driven processes by using the same pure tones as the stimuli and investigated the pure top-down processes of speech mental imagery independent of bottom-up perception. This is different from the typical silent counting task used in evoking P300 responses, which largely depends on the sensory perception and anticipation of the bottom-up physical features of the stimulus. The 0.8 Hz rhythmic mental operation activated the region of the left IOG, which is reported to be associated with verbal representation (Saito et al., 2012) and memory maintenance in numeral processing (Daitch et al., 2016), and the region of the right IPL, which is associated with auditory working memory (Paulesu et al., 1993) and has also been proposed to be activated in imagined singing and imagined movement (Kleber et al., 2007; Lebon et al., 2018). In addition, our study shows that the ventral PrG and PoG are also activated during the construction of speech imagery, which is consistent with previous findings that stronger activation of the sensorimotor cortex induced by articulatory imagery than hearing imagery (Tian et al., 2016) and the direct neural signal from the sensorimotor cortex contributed to the decoding of imagined speech and silent reading (Martin et al., 2014; Martin et al., 2016). Nevertheless, a firm conclusion on the contribution of the sensorimotor cortex to the construction of speech mental imagery will only be possible after controlling for any articulator movement in future studies. Furthermore, consistent with previous findings in fMRI studies suggesting that Broca’s area (operIFG) is activated during the silent articulation of speech (Aleman et al., 2005; Kleber et al., 2007; Papoutsi et al., 2009; Koelsch et al., 2009; Price, 2012; Tian et al., 2016), the imagery-enhanced spectral responses in the region of the operIFG are not only identified in the MEG neural sources but also detected in the intracranial focal recordings, confirming the involvement of the higher order frontal cortex in the dynamic construction of speech mental imagery. Taken together, these results show for the first time that the disassociated neural networks underlying the internal organization of speech mental imagery are independent of auditory perception. Because we identified only the operIFG as a neural source in the speech mental imagery due to the limited intracranial cases in the present sEEG experiment, more work is still needed to investigate the role of the left IOG and right IPL in the neural pathways underlying speech mental imagery.

Mixed evidence exists regarding the relationship between imagery and perception. One line of evidence suggests that mental imagery and sensory perception share common processes; for instance, the visual cortex can also be activated by visual imagery (Kosslyn et al., 1999; Stokes et al., 2009; Dijkstra et al., 2017), and the auditory cortex is recruited during the imagination of music (Zatorre and Halpern, 2005; Kraemer et al., 2005). However, other studies have suggested that differentiated neural substrates underlie these two processes; for example, brain-damaged patients with impaired object recognition have normal visual imagery (Behrmann et al., 1992), and the primary auditory cortex is activated by overt singing but not imagined singing (Kleber et al., 2007). Tian et al. (2018) shed light on this issue by investigating the imagery-perception adaptation effect in speech mental imagery and found that imagined speech modulates the perceived loudness of a subsequent speech stimulus, demonstrating the overlapping neural underpinnings of the auditory cortex underlying the two processes of imagery and perception. Limited by the imagery-perception repetition paradigm, Tian et al. did not illustrate the role of non-overlapping brain regions that might be involved in the modulatory effect of imagery on perceived loudness. In our study, we address this gap by disassociating the two processes of imagery and perception, which were precisely frequency-tagged, and identifying the specific neural sources of speech mental imagery independent of auditory perception. We show that the auditory cortex was not engaged in neural activation at an imagery-rate frequency of 0.8 Hz. Instead, some isolated neural clusters, including the higher order frontal cortex, were recruited at the imagery-rate frequency, reflecting the non-overlapping neural populations underlying the two distinctive processes of speech imagery and auditory perception. Notably, during the imagery task, the spectral power at the stimulus-rate frequency was weakened at the local contacts of the middle STG according to the sEEG recordings, indicating a potential imagery-driven suppression of auditory perception that fits well with the results of previous adaptation studies (Tian and Poeppel, 2013; Tian and Poeppel, 2015; Ylinen et al., 2015; Whitford et al., 2017; Tian et al., 2018). In addition, we uncovered a reverse correlation between the grand averaged power at the imagery-rate frequency and the stimulus-rate frequency (Figure 2b), which might have resulted from the reallocation of limited cognitive resources between the low-level auditory representation and high-level imagery construction. The crucial role of the (left-lateralized) frontal cortex in the top-down control mechanism has been well demonstrated in the speech perception literature with the account of predictive coding (Park et al., 2015; Cope et al., 2017). Interestingly, Sohoglu et al. (2012) also found opposite effects of top-down expectations and bottom-up sensory details on the evoked response in the STG. Based on these findings, we propose that the higher order cortex (i.e. the inferior frontal cortex) is engaged in the dynamic organization of speech mental imagery, which might modulate neural activity in the lower-order auditory cortex. Determining the functional connectivity between the higher-order cortex and auditory cortex during the construction of speech mental imagery may be an important direction in future studies.

In our study, the same auditory stimuli (a 4 Hz sequence of pure tones) were played under both the mental imagery condition and baseline condition. As a result, strong spectral peaks with similar power amplitudes (Figure 2a) and right-lateralized topographic distributions (Figure 1b) at the sensor level were observed at a frequency of 4 Hz, reflecting robust stimulus-driven auditory responses. Therefore, we mainly refer to 4 Hz as the ‘stimulus-rate’ frequency, but we should be aware that the mental imagery of each number during the counting task was first produced at 4 Hz and then internally organized into groups at 0.8 Hz. When comparing the 4 Hz responses between conditions at the source level, we found greater activation of the right MFG in the mental imagery condition (Figure 4), which might reflect the additive effect of generating the speech mental imagery of each number during the counting task. Previous fMRI findings have suggested that the MFG is activated by the auditory imagery of music (Zvyagintsev et al., 2013; Lima et al., 2015) and can be activated by hearing imagery (HI) of speech compared to articulatory imagery (AI) (Tian et al., 2016), but the results of our MEG experiment showed that the MFG also contributes to the processing of articulation imagery. From the perspective of inner speech theories (Alderson-Day and Fernyhough, 2015), HI, which refers to the inner representation of another’s voice, is distinct from articulatory imagery, which refers to covert speech production, including motor preparation and verbal working memory maintenance (Acheson et al., 2011; Tian and Poeppel, 2013; Tian et al., 2016). To ensure that the imagery task appeared to be natural and reflected subjects’ daily experience, we set the imagery task in our study as the mental operation of counting silently, which can be treated as a type of articulatory imagery, without asking the participants to strictly distinguish between HI and articulatory imagery. Therefore, care should be taken when comparing our results with other neuroimaging findings in the research area of HI of speech or auditory imagery of music. In the future, whether the neural mechanism of speech mental imagery uncovered in our paradigm can be extended to a richer account of inner speech must be considered.

Note that the auditory cortex was not significantly activated at the imagery-rate frequency. This result is different from the previous finding that similar neural responses were observed for overt and covert hearing tasks (Tian and Poeppel, 2010), which can be explained from two aspects. Firstly, the neural pathways underlying speech mental imagery are strongly modulated by the specific imagery requirement (i.e. articulatory imagery or HI) and task demand (Tian and Poeppel, 2010; Tian and Poeppel, 2012; Tian and Poeppel, 2013; Tian et al., 2016; Tian et al., 2018). Unlike the detailed requirements for imagery used in Tian and Poeppel (2010), in our study, we instructed the participants to count in their minds without asking them to strictly distinguish between articulatory imagery and HI. Secondly, and most importantly, we for the first time applied frequency-tagging paradigm and disassociated the neural activity induced by rhythmic inner counting from that elicited by bottom-up auditory perception. The activation of auditory cortex was tagged and detected at the stimulus-rate frequency instead of imagery-rate frequency. These differences in the task requirements and experimental paradigm might have led to the different findings concerning the involvement of the auditory cortex (Tian and Poeppel, 2010) in forming speech mental imagery.

Due to the absence of overt behavioral outcomes in mental imagery tasks, it is difficult or impossible to directly correlate the neural signals to the corresponding behavioral performance during the imagery task. To overcome this obstacle, some behavioral measurements have been developed in previous imagery studies, such as participant self-report assessments and judgement tasks regarding the content and nature of inner speech (Hurlburt et al., 2013; Hurlburt and Heavey, 2015; Lima et al., 2015), but these approaches still largely rely on subjective experience without objective verification. Our study did not obtain behavioral data from the participants during the task but controlled for their behavioral performance via a preceding training session in which the participants were asked to report their performance of inner number generation and ensured that after training, they were able to perform the imagery task correctly. In addition, in the sEEG experiment, we recorded the participants’ subjective reports to exclude the incorrect trials from the analysis. A key issue that should be further addressed is advancing the paradigm to study speech imagery using behavioral measurements. Recently, an enlightening study conducted by Martin et al. (2018) proposed a novel task that recorded both the output of a keyboard (without producing real sounds) and the neural activity when a musician imagined the music he or she was playing. Thus, the objective data from the recorded sound of the keyboard were simultaneously obtained and could be analyzed with the neural representations of the music imagery. In addition, intracranial neural signals from the auditory cortex and sensorimotor cortex can be used to reconstruct perceived and imagined speech (Martin et al., 2014; Akbari et al., 2019), providing a new account for decoding the neural signals induced by speech imagery. Inspired by these studies, more work needs to be performed to examine the correlations between possible behavioral manifestations and the neural substrates underlying mental imagery to deepen the understanding of speech mental imagery and advance its application in brain-machine interfaces.

In summary, our study applied a frequency-tagging paradigm to track the neural processing time scales in line with the generation of speech mental imagery and the stimulus-driven activity using both the neuronal measures (MEG) and direct recordings from the cortex (sEEG). We uncovered a distinctive neural network underlying the dynamic construction of speech mental imagery that was disentangled from sensory-driven auditory perception.

Source data files have been provided for Figures 1, 2, 4 and 5. MEG and sEEG data have been provided on the Open Science Framework under the identifier bacge (https://osf.io/bacge/).

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Author details

1. Lingxi Lu

   1. PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
   2. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

2. Qian Wang

   Department of Clinical Neuropsychology, Sanbo Brain Hospital, Capital Medical University, Beijing, China

   Contribution

   Resources, Data curation, Formal analysis, Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Jingwei Sheng

   Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

   Contribution

   Software, Formal analysis, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Zhaowei Liu

   Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

   Contribution

   Data curation, Software, Writing—review and editing

   Competing interests

   No competing interests declared

5. Lang Qin

   1. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
   2. Department of Linguistics, The University of Hong Kong, Hong Kong, China

   Contribution

   Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

6. Liang Li

   Speech and Hearing Research Center, School of Psychological and Cognitive Sciences, Peking University, Beijing, China

   Contribution

   Conceptualization, Methodology

   Competing interests

   No competing interests declared

7. Jia-Hong Gao

   1. PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
   2. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
   3. Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   jgao@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9311-0297

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