The subjective inner experience of mental imagery is among the most ubiquitous human experiences in daily life. Elucidating the neural implementation underpinning the dynamic construction of mental imagery is critical to understanding high-order cognitive function in the human brain. Here, we applied a frequency-tagging method to isolate the top-down process of speech mental imagery from bottom-up sensory-driven activities and concurrently tracked the neural processing time scales corresponding to the two processes in human subjects. Notably, by estimating the source of the magnetoencephalography (MEG) signals, we identified isolated brain networks activated at the imagery-rate frequency. In contrast, more extensive brain regions in the auditory temporal cortex were activated at the stimulus-rate frequency. Furthermore, intracranial stereotactic electroencephalogram (sEEG) evidence confirmed the participation of the inferior frontal gyrus in generating speech mental imagery. Our results indicate that a disassociated neural network underlies the dynamic construction of speech mental imagery independent of auditory perception. 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 . Speech Neuronal information processing is shaped by multiscale oscillatory activity , in whicFrequency-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  and speeHere, 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 . 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.1, 2, 3, 4, 5, 2, 2, 3, 4, 5, 3, 2, 3, 4, 5…10, 2, 3, 4, 5) until the 50 pure tones terminated. As a result, when the subjects were performing the task, the spectral peaks were significant at the frequency of 0.8 Hz, corresponding to the grouping-rate rhythm of the mental imagery , and at the frequency of 4 Hz, corresponding to the rate of the auditory stimuli  . No significant peaks were observed at the harmonic frequencies of 2.4 Hz  and 3.2 Hz . In contrast, under the control (baseline) condition, in which the participant received identical auditory stimuli without the imagery task, a significant peak was observed only at the stimulus-rate frequency of 4 Hz . The topographic distribution of the grand averaged power at the imagery-rate frequency (0.8 Hz) and stimulus-rate frequency (4 Hz) displayed bilateral responses. Specifically, the two-tailed paired t test showed that the averaged power over the sensors in the right hemisphere was significantly stronger than that in the left hemisphere at 4 Hz under both the mental imagery condition  and the baseline condition  but not at the imagery-rate frequency of 0.8 Hz under the mental imagery condition .In the MEG study, we aimed to identify the cortical representations of speech mental imagery independent of stimulus-driven responses. We constructed a 4 Hz sequence of auditory stimuli that were mentally organized into 0.8 Hz groups by a speech mental imagery task . Specifi = 4.13) . An addit test showed that the peak power at the imagery-rate frequency of 0.8 Hz when the participants induced rhythmic mental imagery was significantly larger than that under the baseline condition . No significant difference was observed between the two conditions at 4 Hz . Interestingly, the power of the neural responses at an imagery rate of 0.8 Hz was negatively correlated with that at a stimulus rate of 4 Hz when the participants induced the rhythmic mental imagery following the sound sequence  but not when they passively listened to the auditory stimuli   .Using the frequency-tagging method, we identified the imagery-induced MEG manifestation in the frequency domain, but which neural networks generated such neural activity? Taking advantage of the previously developed minimum L1-norm source estimation methods , we addrt value among the three levels was identified, and the amplitude of the activation was extracted from all 20 subjects. Two-way repeated measures ANOVAs (condition × brain region) for ROI activation showed a significant main effect of condition , a significant main effect of brain region , and a significant interaction between the two variables . To untangle the interaction, we performed post hoc comparisons with Bonferroni correction for each brain region. The results showed that in the left hemisphere, the neural responses in the left IOG at 0.8 Hz under the mental imagery condition were significantly stronger than those at 4 Hz under the mental imagery condition and at 4 Hz under the baseline condition (both p<0.05). In addition, the neural activation at 4 Hz under the imagery and baseline conditions was stronger than that at 0.8 Hz under the imagery condition in the left HG , the left INS  and the left SMG . In the left STG, the activation amplitude at 4 Hz under the baseline condition was larger than that at 0.8 Hz under the imagery condition (p=0.006). In the right hemisphere, the activation observed at 4 Hz under the imagery and baseline conditions was stronger than that observed at 0.8 Hz under the imagery condition in most regions , reflecting robust auditory neural representations of the 4 Hz sequence of sound stimuli. No significant difference was observed in the brain activation levels at 4 Hz between the imagery condition and baseline condition, except in the right MFG, in which the activation at 4 Hz during mental imagery was stronger than that at baseline (p=0.014). These data indicate that the isolated neural networks underlying speech mental imagery involve the left central-frontal lobe, the left inferior occipital lobe and the right inferior parietal lobule, and that these networks are disassociated from the sensory-driven processes.Then, we compared the activation of regions of interest (ROIs) in different brain regions at the following three significant spectral peaks: (1) at 0.8 Hz under the mental imagery condition, (2) at 4 Hz under the mental imagery condition and (3) at 4 Hz under the baseline condition . For eact19 = 3.61, p=0.021, Cohen’s d = 0.81, paired one-sided t test, FDR corrected) , which is highly correlated with the neural firing rate . We founrrected) . The imarrected) . Unlike rrected) . Due to In addition, we found significant spectral responses at the stimulus-rate frequency of 4 Hz .t tests revealed significant 4 Hz peak powers under the baseline condition in several brain areas, including the left posterior INS , left anterior SMG , left HG , bilateral middle STG  and right middle MTG . Among these regions, the 4 Hz peaks were also significantly larger than zero under the mental imagery condition in the brain areas of the left posterior INS , left anterior SMG , left HG  and right middle STG .Subsequently, we combined all the trials at the contacts localized in the same brain regions across different subjects. t38 = −2.93, p=0.006, Cohen’s d = 0.93; right middle STG, t198 = −4.05, p<0.001, Cohen’s d = 0.57, two-tailed independent t test). Notably, the power at the 0.8 Hz peak in the operIFG during the imagery task was increased compared to that under the baseline condition . This result further supports the MEG results showing the reverse correlation between the power of the top-down neural activity induced by mental imagery and the power of the bottom-up stimulus-driven processes . 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 . Mental 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 , and theIn 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  and righNote 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 , which cDue 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 , but theIn 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.Twenty Mandarin-speaking young students  participated in the MEG experiment. The participants all had normal hearing abilities and reported having no history of mental disorders. Written informed consent was obtained from all participants. The participants were paid, and the experimental protocol was approved by the Peking University Institutional Review Board.The auditory signal was a 50 ms pure tone generated by Adobe Audition software . The frequency of the pure tone was 440 Hz, and the sampling rate was 1.6 kHz.1, 2, 3, 4, 5, 2, 2, 3, 4, 5, 3, 2, 3, 4, 5…10, 2, 3, 4, 5, following the rhythm of the sound until the sound is terminated’. In this counting task, the first number in each group increased from ‘1’ to ‘10’, and the remaining numbers in the group were always ‘2, 3, 4, 5’. For the baseline block, the instructions were ‘Please listen carefully to the sound until the sound is terminated’. The participants pressed the button when they were ready, and then a central fixation cross remained on the screen until the trial terminated. After a silent random interval ranging from 1 to 1.5 s, a pure tone was presented 50 times repeatedly with an onset-to-onset interval of 250 ms. Thus, the frequency of the presentation of the pure tones was 1/0.25 Hz, that is 4 Hz. More importantly, the frequency of the occurrence of the rhythmic imagery was 1/1.25 Hz, that is 0.8 Hz. The sound sequence in each trial lasted for 12.5 s.During the experiment, each participant was seated in a dimly lit and magnetically shielded room  with a distance of 1 m from a projected screen. The acoustic signals were delivered to the participants using a MEG-compatible inserted ear-phone. Two blocks  were performed, and each block included 15 trials. The presentation order of the blocks was counterbalanced across subjects. Within each trial, the instruction was first presented on the screen. For the imagery block, the instructions were “Please count loudly in mind with five numbers in a group, Before the formal experiment, the subjects received a training session in which they were asked to orally report their inner performance after each trial. The training session was supervised to ensure that each subject could perform the task correctly. After the training, all the participants were able to generate the mental imagery of 50 numbers with every five numbers in a group following the 50 pure tones.The continuous neuromagnetic signal was recorded using a 306-channel, whole-head MEG system  at Peking University. Before each block started, the head position of each subject was determined by four head-position indicator (HPI) coils. The electrooculogram (EOG) signal was simultaneously captured by two electrodes placed near the eyes as follows: one electrode was placed above the right eye, and one electrode was placed below the left eye. The continuous MEG data were on-line bandpass filtered at 0.1–330 Hz. The sampling rate was 1000 Hz.3 resolution T1-weighted anatomical images. We co-registered the MEG data with the MRI data based on the location of three fiducial marks (the nasion and two pre-auricular points) and approximately 150 digitalization points on each subject’s scalp.The subjects’ structural MRI images were obtained with a 3T GE Discovery MR750 MRI scanner . A three-dimensional (3D) fast gradient-recalled acquisition in the steady state with a radiofrequency spoiling (FSPGR) sequence was used to obtain 1 × 1 × 1 mmhttp://www.martinos.org/mne/). Specifically, the ICA component with the highest correlation to the EOG signal was selected and rejected. Then, a bandpass filter (0.2 to 60 Hz) and a notch filter (50 Hz power-line interference) were applied to the de-noised data using the FieldTrip toolbox (http://www.fieldtriptoolbox.org), during which edge effects after filtering were eliminated by padding extra data on either side of each epoch. Finally, the event-related field (ERF) response was obtained by averaging the data with a time window of −0.5 to 12.5 s.The raw data were first processed using the temporal Signal Space Separation (tSSS) method  implantehttp://surfer.nmr.mgh.harvard.edu/). A realistic boundary element method (BEM) model was employed to solve the forward problem. A 5 mm cubic grid was prepared as the source space, resulting in approximately 10,000 nodes in the entire brain.The cortical reconstruction and volume-based segmentation were completed based on each subject’s T1-weighted image using Freesurfer . The average response power of all channels was calculated to compare the rhythmic response among the different conditions.m × t sensor waveform matrix, where m and t represent the number of MEG sensors and the number of time points, respectively, was transformed using the FFT into frequency domain matrix K, which included both real and imaginary parts. To focus on the peak response at a given frequency , we chose the single-frequency bin Kf for the source estimation, where f refers to the target frequency bin. Furthermore, a convex second-order cone programming (SOCP) method for solving the minimum L1-norm problem was applied to minimize the bias toward the coordinate axes  using singular value decomposition. The solution is the n × 1 complex vector Ωf.The present source estimation method was rooted in the previously developed minimum L1-norm estimation methods , which oate axes  as follot test was conducted to evaluate whether a significant spectral peak occurred at a frequency bin compared to the average of two neighboring frequency bins. This test was applied to all the frequency bins between 0.5 and 4.5 Hz, and the p values were adjusted by a false discovery rate (FDR) correction for multiple comparisons.For the sensor-level analysis, a one-tailed paired https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and spatially smoothed with a Gaussian kernel of 5 mm full-width at half-maximum (FWHM). The brain results were further log-transformed to reduce the skewness of the distribution of the minimum L1-norm solutions . Furthermore, several regions of interest (ROIs) were identified by finding the local maximum of the significant neural clusters, and a 5 mm cube was used to extract the activation data from each ROI. Then, the brain activation was quantitatively compared among the following three levels: (1) at the frequency of 0.8 Hz under the imagery condition; (2) at the frequency of 4 Hz under the imagery condition; and (3) at the frequency of 4 Hz under the baseline condition. Repeated measures ANOVAs were used to examine the differences among the three levels. A Greenhouse-Geisser correction was applied for violation of sphericity. Post hoc comparisons were conducted using Bonferroni corrections. Here, non-independent ROI selection criteria were chosen to quantitatively compare the source-level neural activation between conditions; thus, we interpreted the results with caution to avoid overestimation of the ROI data.For the source-level analysis, each subject’s ERF response ranging from 1.25 to 12.5 s was first transformed into the frequency domain for the source estimation. The brain activation to the target frequency bin was obtained by calculating the root mean square (RMS) of the real and imaginary parts. A control state was created by averaging the sources of two neighbor frequency bins adjacent to the target frequency. Before the group analysis, individual brain result was projected to the MNI-152 template using FSL software  who were undergoing clinical intracerebral evaluation for epilepsy participated in the sEEG experiment. All the participants were right-handed and had normal hearing abilities. Before the experiment, the participants gave their written consent for participation, including permission for scientific publication. As one subject was a minor, written informed consent from his/her parent was obtained. Intracerebral electrodes were stereotactically implanted within the participants’ brains solely for clinical purposes. All analyses were performed offline and did not interfere with the clinical management of the subjects. The sEEG study was approved by the Ethics Committee of the Sanbo Brain Hospital, Capital Medical University.http://brainsuite.org/). Then, the CT with the electrode localizations and the anatomical images were co-registered and normalized to the MNI-152 template. The contact locations were identified and visualized with MNI coordinates using the Brainstorm toolbox (http://neuroimgage.usc.edu/brainstorm/) and the BrainNetViewer toolbox (https://www.nitrc.org/projects/bnv/) in the MATLAB environment. The intracranial neural responses were collected with the Nicolet clinical amplifier. The sampling rate was 512 Hz in four subjects (subjects 1 to 4) and 2048 Hz in one subject (subject 5).Each 0.8 mm diameter intracerebral electrode contained 12–18 independent recording contacts of 2 mm in length and 1.5 mm apart from each other. The reference site was attached to the skin of each subject’s forehead. For each subject, the cortical reconstruction and segmentation were computed based on the T1-weighted image obtained before the electrode implantation using BrainSuite software  following a sequence of 50 pure tones (stimulus-rate frequency = 4 Hz). The auditory stimuli and trial structure were identical to those used in the MEG study, except that at the end of each trial under the mental imagery condition, the participants were instructed to orally report whether they had counted correctly following all the sounds. Then, the experimenter recorded their subjective report (correct/incorrect). Only correct trials were included in the subsequent analyses. In addition, the number of trials under each condition was dependent on each patient’s physical status, while a fixed number of trials was used in the MEG experiment. Furthermore, to reduce the confusion caused by the imagery task among the patients, the baseline condition was presented first, followed by the mental imagery condition. Before the formal mental imagery trials, the participants engaged in a practice session to ensure that they had fully understood the imagery task.t test among all the trials under the same condition. This test was applied to all frequency bins between 0.5 and 4.5 Hz, and the p values were FDR corrected for multiple comparisons. The spectral power at each contact under both conditions was transformed to the Z-score for visual presentation (Z-score was computed as the difference between the power response in a frequency bin and the mean power response between 0.5 Hz and 4.5 Hz excluding the frequency bins of 0.8 Hz and 4 Hz divided by the standard deviation (SD) of the power response in these frequency bins.The sEEG data from each contact were first analyzed separately, and the high gamma activity, which is related to the neural firing rate , was extentation . Specifitarget refers to the response power at a target frequency (i.e. 4 Hz), and Powerneighboring refers to the averaged response power at the two frequency bins neighboring the target frequency. For each brain region, a one-tailed paired t test was used to evaluate whether the normalized peak of all trials in that region was significantly larger than zero. Then, a two-tailed independent t test was used to identify the difference in the normalized peak responses between the imagery condition and the baseline condition. The significance level was set at p<0.05.To compare the responses among the contacts, the spectral peaks at each contact were first extracted and normalized as follows: In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Barbara Shinn-Cunningham as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Thomas Cope (Reviewer #2).Thank you for submitting your article "Neural tracking of speech mental imagery" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The reviewers found the work interesting in demonstrating 'tagged' neural correlates using MEG and invasive recording of cyclical counting in premotor cortex, temporoparietal junction and occipitotemporal transition that could not be attributable to the stimulus.Essential revisions:eLife does not encourage extensive further experiments or multiple cycles of revision that might address these components. What is required in a revision is a better justification of the term 'speech mental imagery' in the title and text or a reconsideration of this term.1) The single most important comment is the attribution of the basis for the cyclical brain activity corresponding to the counting. This has a number of cognitive components including, for example, attention, working memory, phonological analysis, and (possibly) articulatory planning. To what extent do the authors feel that their use of the term 'speech mental imagery' is justified? The reviewers considered whether a more focused term was more appropriate. 2) The ANOVA with MEG is biased because the MEG activity focus was first defined by the contrast between the respective condition and baseline.3) The independent repeated measures ANOVAs for each region is not the appropriate test here. Rather, an overall RM-ANOVA should be employed, with the test of interest being for a condition by region interaction. Post hoc tests should then be performed on marginal means, normalised for the overall effect of condition.4) The activation of sensory-motor cortex is intriguing, but the claim that this area is involved in imagery speech as also claimed in  is not very compelling. For such a claim, it is necessary to ensure there was no articulatory movement (e.g. with EMG sensors). Otherwise, it could simply reflect a hardly noticeable yet existing muscle movement in the articulators as the subjects count the numbers. Essential revisions:1) The single most important comment is the attribution of the basis for the cyclical brain activity corresponding to the counting. This has a number of cognitive components including, for example, attention, working memory, phonological analysis, and (possibly) articulatory planning. To what extent do the authors feel that their use of the term 'speech mental imagery' is justified? The reviewers considered whether a more focused term was more appropriate. eLife does not encourage extensive further experiments or multiple cycles of revision that might address these components. What is required in a revision is a better justification of the term 'speech mental imagery' in the title and text or a reconsideration of this term.We completely agree with the view that several cognitive components were involved in the rhythmic inner counting task when participants were asked to construct their speech mental imagery. Mental imagery has been proposed to be essentially an attention-guided memory retrieval process, for example, in the visual domain . 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 individuals to have subjective experiences of their mental imagery . Therefore, these cognitive components cannot be definitely isolated from the term speech mental imagery.In our study, neural tracking of speech mental imagery refers to the detection of the cyclical neural signal induced by the task of rhythmic inner counting. This cyclical neural signal can be attributed to the top-down regulation of the generation of number imagery, specifically, the internal arrangement of numbers into groups. Such internal organization is primarily based on the details of the inner representation of number imagery. In this way, the imagery-rate neural activity is tagged in the frequency domain and can be captured using neurophysiological measurements. It is known that the activation patterns of the brain network underlying speech mental imagery can be modulated by the task requirements and contextual variation . Therefore, the most important point to note is the operational definition of the internal organization of mental imagery, which is the key factor that induces cyclical neural signals. Considering that the term “speech mental imagery” as used in the previous manuscript is not focused enough to provide sufficient information about the intention and methodology of our study, we have now revised the term by adding the operational definition of the internal organization of mental imagery, namely, the task of rhythmic inner counting. Correspondingly, in the revised manuscript, we have added relevant content to justify the use of “speech mental imagery” as follows:[Title] “Neural tracking of speech mental imagery during rhythmic inner counting”[Discussion] “Mental imagery has been proposed to be essentially an attention-guided memory retrieval process, for example, in the visual domain . […] 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.”Correspondingly, we have added the following references to the revised manuscript:Ishai, Haxby and Ungerleider, 2002; Tian and Poeppel, 2012.2) The ANOVA with MEG is biased because the MEG activity focus was first defined by the contrast between the respective condition and baseline.under each condition. These activation maps illustrate the neural sources behind the rhythmic signals induced by mental imagery and auditory perception, respectively. The null hypothesis of the ROI selection was that there were no significant differences between the source-level activation at the target frequency bins and their neighbouring controllers. Different from the ROI selection criteria, we applied ANOVA on the ROI activation among three levels  and 16 brain regions to identify the significant differences between conditions, which was done to quantitatively examine the activation difference in these brain regions between top-down and bottom-up processes. The null hypothesis for the ANOVA was that there were no significant differences in the neural activation among different conditions and brain regions, which differed from the assumption of the previous ROI selection criteria. In this way, we avoided circular analysis and reduced the distortion of our results caused by the ROI selection bias.ROI analysis between conditions can be biased when the ROIs are selected by contrasting between those conditions, leading to circular analysis that can distort the results . To control the influence of circular analysis in our study, different statistical comparisons based on different null hypotheses were applied in ROI selection and ROI analysis. Specifically, we selected the ROIs from the significant activation maps, which were obtained by contrasting the whole brain activity at the target frequency bin and the average of its two neighbouring frequency bins not overestimated, as no condition difference was observed in the regions of IFG and SMG, which were the main neural clusters responsive for the top-down imagery construction according to source estimation, and the crucial findings were not based on the results of ROI analysis. However, we urge caution in interpreting the ROI results in the revised manuscript by adding the relevant content:An important point to note is that, to some extent, the overlapping data subset applied for ROI selection and analysis might still cause bias due to the noise effect within the same data subset . From this view, we agree with the comment that selection bias might enter into the ANOVA, despite the different statistical methods we have applied for ROI selection and analysis. We note that this statistical bias is not restricted to the current study; it potentially applies to any ROI analysis that attempts to compare ROI activity after defining the ROIs in the same data subset using non-independent ROI selection instead of independent selection criteria such as the anatomy atlas. Selection bias may cause overestimation of the results when comparing ROI data between conditions . Critically, it should be noted that in our study, the results of ROI comparisons between conditions were [Materials and methods] “Here, non-independent ROI selection criteria were chosen to quantitatively compare the source-level neural activation between conditions; thus, we interpreted the results with caution to avoid overestimation of the ROI data.”3) The independent repeated measures ANOVAs for each region is not the appropriate test here. Rather, an overall RM-ANOVA should be employed, with the test of interest being for a condition by region interaction. Post hoc tests should then be performed on marginal means, normalised for the overall effect of condition.As per the suggestion, we have conducted an overall repeated measures ANOVA (condition by brain region) for the ROI data. As a result, we found significant main effects of both condition and brain region and a significant interaction between the two variables. Then, we performed post hoc comparisons with Bonferroni correction on estimated marginal means to illustrate the differences between conditions for each brain region. We have revised the relevant content in the Results section as listed below:2, 38 = 23.56, p < 0.001, ηp2 = 0.55), a significant main effect of brain region , and a significant interaction between the two variables . […] No significant difference was observed in the brain activation levels at 4 Hz between the imagery condition and baseline condition, except in the right MFG, in which the activation at 4 Hz during mental imagery was stronger than that at baseline (p = 0.014).”“Two-way repeated measures ANOVAs (condition × brain region) for ROI activation showed a significant main effect of condition (F4) The activation of sensory-motor cortex is intriguing, but the claim that this area is involved in imagery speech as also claimed in  is not very compelling. For such a claim, it is necessary to ensure there was no articulatory movement (e.g. with EMG sensors). Otherwise, it could simply reflect a hardly noticeable yet existing muscle movement in the articulators as the subjects count the numbers.We fully agree with the comment on the point that it is necessary to exclude the influence of muscle movements to reach a compelling conclusion on the role of the sensorimotor cortex in speech mental imagery. We respond to two aspects of this helpful comment:First, Bouchard et al. (2013) claimed that speech-articulator representations are arranged somatotopically in the ventral PrG and PoG during syllable production, while our study found that the ventral PrG and PoG were engaged in constructing speech imagery when participants were told to count numbers in their mind without moving their lips, jaw or tongue. Our result is consistent with previous findings concerning the involvement of the sensorimotor cortex in speech imagery. For example, Tian et al. (2016) reported that the sensorimotor cortex showed stronger activation in articulatory imagery in which participants generated kinaesthetic feelings of articulation than in hearing imagery in which motor-related imagery was discouraged, suggesting that speech mental imagery with motor-related feelings could activate the sensorimotor cortex. In addition, the neural signals directly recorded from the sensorimotor cortex contributed to the imagined-speech decoding in both hearing imagery without kinaesthetic representation  and silent reading of speech . We have now revised the statement and have drawn a more careful conclusion about the involvement of the sensorimotor cortex.Second, an earlier study by Kleber et al. (2007) attempted to record the EMG sensor placed on the throat and ensured that the muscle movements in imagined singing were significantly weaker than those in overt singing and did not differ from those during the resting state. After taking EMG data into consideration, Kleber et al. (2007) still found activation in the sensorimotor cortex in imagined singing, which could not be accounted for by muscle movements in articulators. However, to our knowledge, most imagery studies have not strictly controlled for articulator movement using EMG recording  but primarily based the results on the assumption that participants followed the instructions on how to generate mental imagery. This valuable comment raises the importance of controlling the articulatory movement effect, which should be noted in future studies, especially when the activation of the sensorimotor cortex is observed in mental imagery studies.Thus, to clarify the logic and to address the concerns about the effect of muscle movements, we have revised the relevant content in the Discussion section as follows:“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  and the direct neural signal from the sensorimotor cortex contributed to the decoding of imagined speech and silent reading . 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.”Correspondingly, we have added relevant references to the manuscript:Martin et al., 2016; Martin et al., 2014.


The present study examined whether these rhythms also play a role in non-temporal aspects of musical imagery including musical pitch. Brain function was measured with MEG from 19 healthy adults while they performed a validated musical pitch imagery task and two non-imagery control tasks with identical temporal characteristics. A 4-dipole source model probed activity in bilateral auditory and sensorimotor cortices. Significantly greater β-band modulation was found during imagery compared to control tasks of auditory perception and mental arithmetic. Imagery-induced β-modulation showed no significant differences between auditory and sensorimotor regions, which may reflect a tightly coordinated mode of communication between these areas. Directed connectivity analysis in the θ-band revealed that the left sensorimotor region drove left auditory region during imagery onset. These results add to the growing evidence that motor regions of the brain are involved in the top-down generation of musical imagery, and that imagery-like processes may be involved in musical perception.Recent magnetoencephalography (MEG) studies have established that sensorimotor brain rhythms are strongly modulated during mental imagery of musical  The pianist Glenn Gould had unimpaired hearing but preferred to study music by reading it rather than playing it, indicating that musical imagery can be a powerful strategy in and of itself, and not merely a backup strategy necessitated by deafness3. Among contemporary composers, functional magnetic resonance imaging (fMRI) measurements show that Sting uses highly similar brain regions when listening to or imagining music4. However, musical imagery is not restricted to the musical elite; the mental replaying of music is an everyday experience for non-musicians, and involuntary musical imagery or “earworms” is common6.The silent generation of music in one’s own mind is a common human experience. Musical imagery is well developed in musicians: Mozart reportedly experienced his compositions through polyphonic imagery7, and auditory brain regions, particularly in the right hemisphere, are active during musical imagery10. Recent work has also pointed to an important role for motor regions of the brain in auditory perception and imagery. Several fMRI studies have reported increased BOLD responses in motor areas during musical imagery including primary motor, pre-motor, parietal and inferior frontal cortex14. Supplementary motor area (SMA) and pre-SMA become active both during motor sequence learning and during anticipation of sound sequences, suggesting the use of motoric predictive mechanisms in both domains15. There is also evidence that individual differences in vividness of auditory imagery are correlated with grey matter volume in the SMA, parietal and prefrontal regions, suggesting that the generation of auditory images requires access to auditory-motor representations16. Moore17 and Schaefer18 have suggested that this overlap in imagery and perception may reflect a type of “constructive imagery”, invoking temporal prediction as an aid to auditory perception. Taken together, these lines of evidence suggest that music perception and imagery require the coordination of both auditory and motor regions of the brain, even when no overt actions are required. Such findings are consistent with current neurophysiological frameworks that posit ventral and dorsal streams for processing auditory information comparable to those of the visual system21.Imagery and perception are thought to use overlapping neural mechanisms23. Recent work has implicated the β-rhythm in temporal predictions of sounds and sound sequences28. Beta-band responses to sounds show a characteristic event related desynchronisation (ERD) at a latency of about 200 ms after sound onset, followed by a rebound event related synchronisation (ERS). Fujioka et al.26 found that the slope of the β-ERS peaks just before an expected tone. The β-ERD also varies as a function of a listener’s metrical interpretation of a simple rhythmic pattern27. Recent evidence has also confirmed that β-band modulation reflects predictability of pitch (that is ‘what’) and not just timing (or ‘when’ an event will occur), since greater trial-by-trial ERD prior to a predictable tone is related to a reduced P3a amplitude after the tone30. Finally, both physical and imagined accents on a downbeat modulate the β-band response, suggesting that the β-band plays a role in the temporal coordination of auditory and motor operations in music perception and imagery27. Oscillations in the μ-band have been found to increase in parieto-occipital regions more in imagery than perception31, but decrease in bilateral auditory and right precentral regions during imagery of familiar song occurring white noise32. Better auditory working memory ability is associated with greater ERD across 8–18 Hz33. Thus, converging evidence suggests that β-band oscillations, and possibly also μ-band oscillations, are functionally associated with the temporal and spectral features of music perception, as well as the temporal features of musical imagery including rhythm, accent and beat.Beta-band (β: ~13–30 Hz) and mu-band (μ: ~8–12 Hz) oscillations provide robust neurophysiological markers of motor cortical function that can be measured noninvasively with MEG34, temporal prediction35 and passive listening36. Results from these studies suggest the right auditory cortex is primarily involved in beat perception34. Using directed Phase Transfer Entropy (dPTE) connectivity analysis for measuring the direction of communication between regions at different frequency bands, Morillon and Baillet35 showed that in the β-band (18–24 Hz), the bilateral sensorimotor regions drive activity in the auditory cortex during temporal prediction, whilst in lower frequencies (2–4 Hz) the auditory regions drive the sensorimotor regions. Enhanced functional connectivity in the θ-band between the left sensorimotor and the bilateral auditory regions has also been found during passive listening to an unfamiliar instrument, after short term motor training in the action required to play the instrument36. Theta-band connectivity is thought to reflect long range communication between brain regions37, and brain stimulation of the left intraparietal sulcus at the theta frequency has also been shown to improve performance of imagery manipulation  but not maintenance of melodies38. Given the overlapping activation in these areas during perception and imagery, investigating the coordination and connectivity of the primary auditory and sensorimotor regions in the θ, μ and β frequencies could prove to be fruitful in the exploration of similarities and differences between musical imagery and perception.Recent approaches using magnetoencephalography (MEG) have isolated the primary auditory and sensorimotor regions to investigate how they coordinate in perceptual activities, specifically in beat perceptionHence the aim of the present study was to investigate oscillatory activity in bilateral auditory and sensorimotor regions during musical imagery. Two separate conditions with similar visual and temporal attributes were used: perception condition – identical to imagery but with all tones sounded – and mental arithmetic – a control for working memory. In the imagery task, participants were prompted with visual cues of up and down arrows to silently imagine successive changes in the pitch of a piano note up and down the major scale. Performance on this imagery task was then objectively measured by requiring participants to decide if a probe note did or did not match to the result of the specified manipulations.26; (2) greater modulation would be seen in the right than the left hemisphere39; (3) there would be greater sensorimotor to auditory directed connectivity in musical imagery than perception35.Our experiment was designed to address three main questions: (1) Does musical imagery differentially modulate β-band oscillations relative to other mental operations  with identical temporal features? (2) Are β-band oscillations differentially modulated by musical imagery in different brain regions (sensorimotor versus auditory) or hemispheres (right versus left)? (3) Do musical imagery and perception differ in how they direct functional connections between bilateral sensorimotor and auditory regions? We hypothesised that (1) musical imagery would require greater coordination between auditory and sensorimotor regions compared to perception and mental arithmetic, and therefore would show greater β-band modulationMethods). Individually fitted 4-dipole source models probed activity in bilateral auditory and sensorimotor cortices  = 16.18; p < 0.001; η2 = 0.037), reflecting greater difficulty of the Imagery task. Mean hit reaction time (ms) was significantly slower for Imagery trials  than for Perception  or Maths trials   = 7.36; p = 0.002; η2 = 0.22).Mean accuracy on the Imagery task was 80.2% (SD = 14.9%), significantly lower than performance on Perception  and Maths  conditions . In contrast to the results of Gelding et al.40, accuracy on the Perception task was not significantly related to MEI, due to a ceiling effect in which all but 3 participants obtained 100% accuracy on the Perception trials.Accuracy on the Imagery and Maths tasks was significantly correlated with musical experience index  and MEI , and negatively correlated with Imagery reaction time  and Perception reaction time .Debrief vividness ratings (a 1–5 self-rated score of vividness experienced during task completion obtained at the end of the experiment) were significantly positively correlated with Imagery accuracy  and control (BAIS-C)  subscales of the BAIS, consistent with the results of the previous behavioural study40. Neither subscale showed a significant correlation with accuracy in the Perception or Maths condition. The BAIS-C subscale showed a significant negative correlation with mean hit reaction times in both the Imagery  and Perception  conditions.Imagery accuracy (percent correct) was positively correlated with both the vividness (BAIS-V) ; and all participants confirmed the use of a musical imagery to complete the task (i.e. hearing the sounds in their head or singing them in their head) in post-experimental debriefings.The mean induced β-band time course over 0–3 seconds, corresponding to the last 3 arrows presented in each condition, were calculated, normalised and plotted Fig. . The absGiven the isochronous presentation of the arrows at 1 per second in all conditions, the 1 Hz power of the average time courses in three oscillatory bands of interest (evoked response (3–10 Hz), induced μ-band (8–12 Hz) and induced β-band (15–25 Hz)) for each condition and source were calculated and compared.Repeated measures ANOVAs  x 2 Hemispheres  computed for 1 Hz power over time window of 0–2.95 s were calculated for Auditory and Sensorimotor sources separately. Greenhouse-Geisser corrections were applied where appropriate. No significant main effects or interactions were found. Figure The 3 × 2 repeated measures ANOVAs for Auditory and Sensorimotor sources also showed no significant main effects or interactions. This is seen in Fig. F = 3.788, p = 0.0045, GG ε = 0.770). Post hoc comparisons  showed that Imagery power was significantly greater than Perception (p = 0.0042), and there was no significant difference between Perception and Maths (p = 0.0635) or Imagery and Maths (p = 0.0279). The 3 × 2 repeated measures ANOVA for the induced β-band 1 Hz power from Auditory sources also showed a significant main effect of Condition, however after correcting for sphericity using Greenhouse-Geisser this was no longer significant at p < 0.005  = 3.444, p = 0.0051, GG ε = 0.857). This suggests that β-band magnitude was modulated significantly more during auditory Imagery than during auditory Perception, as seen in Fig. The 3 × 2 repeated measures ANOVA for the induced β-band 1 Hz power from Motor sources showed a significant main effect of Condition after correcting for sphericity using Greenhouse-Geisser  and Maximum β-ERS , and the difference between them (Time Difference and Magnitude Difference) (Method). All ANOVAs were corrected for Sphericity where Mauchly’s test was significant, as well as False Discovery Rate.Additional analyses explored the sensitivity and power of additional metrics of β-power modulation: timing, and magnitude of Maximum β-ERD  = 20.26, p = 0.0001, GG ε = 0.866, FDR corrected). Post hoc Tukey HSD comparisons revealed that the Max ERDTIME in Perception  occurred significantly earlier than both Imagery  (p = 0.003) and Maths  (p < 0.0001), and Imagery was earlier than Maths (p < 0.0001).A significant main effect of Condition was found for Max ERDTIME  = 12.51, p = 0.003, GG ε = 0.839, FDR corrected). Post hoc Tukey HSD comparisons showed that Max ERSTIME in Perception  occurred again significantly earlier than both Imagery  (p = 0.0002) and Maths  (p < 0.0001), but there was no significant difference in the timing of the rebound for Imagery and Maths (p = 0.0083). Figure TIME and Max ERSTIME, with Perception β-ERD and β-ERS occurring earlier than the other conditions.A significant main effect of Condition was also found for Max ERSME  = 6.43, p = 0.0338, FDR corrected), that is the difference between the maximum ERS and maximum ERD. Post hoc Tukey HSD comparisons showed that Imagery  had a significantly larger Magnitude Difference (i.e. greater difference between Max ERSAMP and Max ERDAMP) than Perception  (p = 0.029) and Maths  (p = 0.019). There was no difference between Perception and Maths.A main effect of Condition was also found in Magnitude Difference . This finding confirms that both metrics provide similar information about the modulation of oscillations occurring during the last three arrows across conditions.In addition, to validate the use of the 1 Hz Power FFT as a measure of modulation, the correlation between Magnitude Difference and 1 Hz Power in β-band was calculated, and found to be significant  and band pass filtered into three frequency bands of interest θ (4–8 Hz), μ (8–12 Hz) and β (15–25 Hz), for each condition. (Methods). Imagery and Perception dPTE values revealed several significant differences after FDR correction for the θ-band only. The mean and standard error of the dPTE values for Imagery and Perception are plotted for the right auditory source connections  = −3.575, p = 0.0194; rAUD to rSMr: t(18) = −3.175, p = 0.0315; lAUD to lSM: t(18) = −3.772, p = 0.0195, FDR corrected). In order to confirm that Imagery showed an opposite direction of information flow, and not just a difference in information flow, t-tests were conducted against 0 for each of these epoch/connections. Only the left sensorimotor to left auditory region connection showed a dPTE value that was significantly above zero (rAUD to lSM: t(18) = 1.971, p = 0.064; rAUD to rSM: t(18) = 0.786, p = 0.442; lAUD to lSM: t(18) = 2.259, p = 0.0365). This suggests that at the onset of the arrow, when a tone was heard (perception) information flowed from left auditory to left sensorimotor, however the opposite direction of information flow occurred when the tone was imagined. Interestingly, this pattern is reversed during the last epoch (666–999 ms), for the left sensorimotor to right auditory connection, where the left sensorimotor region leads the auditory in the Perception condition, but the right auditory leads the left sensorimotor in the Imagery. However, this difference was not significant after FDR correction (p = 0.051).Directed phase transfer entropy (dPTE) is a method that has recently been applied to measuring the effective connectivity between regions of interestons Fig. , the lefons Fig.  and for ons Fig. . At the st row); D (4th row)), whilst the left sensorimotor regions also leads both the right auditory region ) and the right sensorimotor region , when the image of the tone was being generated or updated, following the visual perception of the arrow, the left auditory regions leads the θ-band connectivity to all other regions  sensorimotor cortex drives the bilateral auditory regions during the anticipation of a sound. The latter pattern, is also seen at the onset of silent arrows for Imagery condition, suggesting the invocation of motorically-driven predictive mechanisms. Further, the left auditory source driving the bilateral sensorimotor and the right auditory activity, along with the left sensorimotor source also driving both right hemisphere sources, during the middle epoch of Imagery, supports an important role for the left hemisphere in pitch memory18, we explored the nature of this relationship further through regression modelling to determine if imagery ability is facilitated by sensorimotor processing that precedes auditory processing. Linear regression was calculated using the dPTE value of the right auditory to the left sensorimotor connection in Perception condition in the last epoch (666–999 ms) as the criterion variable. The predictor variable in the initial regression was Imagery accuracy (percent correct Imagery condition) alone. The result was significant  = 6.951, p = 0.017), with Imagery accuracy alone accounting for 29% of the variance in dPTE value  , our analyses showed no significant hemispheric in β-band modulation for any type of cognitive operation. In relation to hypothesis (3), there was no evidence for a differential connectivity during perception and imagery in the β-band or μ -band. However, in the θ-band greater auditory to sensorimotor connectivity was seen during perception than imagery, and imagery ability was related to increased sensorimotor to auditory directed connectivity in perceptual temporal prediction. Our results may have implications for our understanding of how sensorimotor brain regions interact to support musical imagery, and how imagery-like processes may support perception.The results of this investigation support hypothesis (1): greater β-band modulation occurred during pitch imagery than pitch perception or maths. Whilst the β-band has previously been shown to be involved in predictive timing of ‘when’ events will occur in both perceptionIn any study of a subjective phenomenon such as mental imagery, it is necessary to ensure that the activity of interest is taking place. Our results support the assertion that participants were using pitch imagery to complete the task. Firstly, objective behavioural measures of accuracy and reaction time measured performance and were significantly correlated with an independent measure of individual imagery ability (BAIS). Secondly, all participants confirmed the use of a musical imagery strategy to complete the task (i.e. hearing the sounds in their head or singing them in their head).Greater β-band power modulation was seen during pitch Imagery than the Perception condition, as evidenced both in the initial normalised timecourse plotting Fig.  and in t36. Much attention has been given to latter, suggesting a role of the β-band rebound in predictive timing and rhythm perception over intervals of less than 1 second45, with the same predictive effect not seen in longer inter stimulus intervals46. In addition, auditory β-band activity tracks the location in time of imagined beats during a presentation of a syncopated rhythm47.The dynamics of the β-band in response to sound has been argued to contain both exogenous components, seen in the initial β-ERD, and endogenous components, seen in the subsequent rebound, β-ERS48 and was a slower one than the human preferred tempo28. Flashing visual metronomes are much less effective for synchronising than auditory metronomes at the same tempos49 and do not induce a strong sense of beat50.In this task the ‘when’ of arrow presentation was kept constant, while the ‘what’ varied . Hence the emphasis during imagery was on pitch manipulations, and not the predictive timing of events. This is due to the arrows providing a visual isochronous metronome at a rate of 1 per second which minimised the anticipatory timing30. Chang et al.29 used a mismatch negativity (MMN) paradigm, with either 10% or 20% deviant tones and two different presentation rates . Discrete Fourier Transforms were calculated for the resulting β-band time courses and peaks were found at the presentation rate of 2 Hz and 1.6 Hz respectively. They use this as evidence that the β-band entrains to the different inter-onset intervals in isochronous sequences29. Though not compared statistically, the left auditory 2 Hz power in the 20% deviant appears larger than the 10% deviant condition, suggesting greater entrainment when a deviant is more predictable. This is confirmed in a follow-up study, that showed greater β-band modulation was seen prior to predictable than un-predictable tones30. Further, a trial-by-trial analysis revealed that greater β-ERD prior to a predictable deviant tone was related to reduced P3a amplitude after that tone, suggesting a reduced attention-prediction error response30. Our study has shown greater modulation of the β-band during pitch imagery in a task that crucially has constant presentation rate across all conditions, thereby minimising uncertainty about timing. This confirms that β-band is involved not only in ‘when’ but also ‘what’ is to occur. Even in the absence of a sound, the internal generation or manipulation of a sound image corresponded to greater modulation of β-band than for perception.Whilst β-band dynamics have been extensively studied in the role of anticipation of auditory beats, recently the role of β-band in predicting not only ‘when’ but ‘what’ a pitch will be, have been explored51. Our results are consistent with the concept of β-band as an “open line” of communication between the regions, as predicted by forward and inverse feedback loops between the regions19. This open line of communication appears to be present during perception, imagery as well as mental arithmetic.Our neurophysiological measures were analysed to provide sufficient spatial sensitivity to discriminate broadly between neural activity in the two hemispheres, and between auditory and sensorimotor regions within hemispheres. Within each of the three conditions, similar time courses were seen between the auditory and sensorimotor sources in the β-band. Evidence for this is seen in the finding of no source or hemisphere differences in 1 Hz power in the β-band time series Fig. . In addi17 and Schaefer18 who posited two different types of imagery: an obligatory form that is invoked to aid perception (“constructive imagery”); and a form of “sensory imagery” that can be invoked voluntarily in the absence of sensory input. To our knowledge, this is the first empirical evidence for this proposition.Our connectivity analysis revealed that the left sensorimotor cortex leads activity in the auditory regions early in the imagery onset, and the opposite directionality holds for perception. Interestingly, these directions are reversed in the latter part of the trials. Further, accuracy of imagery predicted a significant portion of variance in the connectivity in the perception condition. This finding is relevant to the suggestions by Moore52, most previous studies have not isolated pitch imagery from other aspects of the musical image . The present results are however consistent with those of brain stimulation studies, showing a causal role in pitch memory for the left but not the right supramarginal gyrus53. Further, θ-frequency stimulation to the left intraparietal sulcus has been shown to boost performance for mental manipulation of melodies38.In addition, our connectivity results suggest that the left hemisphere plays an important role in pitch imagery during the mid-portion of the imagery trial. While the right hemisphere has been repeatedly implicated in musical imagery35, who reported top-down directed connectivity during temporal prediction of a melody. In the present study, this top-down pattern was observed in the θ-band. However, given the sample of participants contains a majority of musicians it could be that musicians rely more on motor regions during imagery than non-musicians. Future studies could address whether a sample of only non-musicians would show the same patterns of activity.Finally, we found no evidence for any regional patterning of β-band connectivity during imagery or perception. For all connections, the normalised dPTE values in both conditions remained around 0. This finding stands in contrast to the results of Morillon and Bailletdegree of connectivity directed from sensorimotor to auditory regions during temporal prediction in perception.In summary, this is the first study to show that β-band sensorimotor oscillations are modulated more by pitch imagery manipulation than by perception, and the first to show oppositely-directed θ-band connectivity in imagery and perception, particularly in the left hemisphere. Further, our data provide evidence that imagery performance is associated with the 40, and who reported using musical imagery to complete the task. 31 participants met the screening criteria and of these 19 participants  were recruited for the MEG study. All participants provided written informed consent and all procedures were approved by and performed in accordance with the relevant guidelines and regulations of the Macquarie University Human Research Ethics Committee. Participants ranged in musical training from novice  to professional musicians. On average, participants had spent 63% of their life’s years actively engaging in musical activities, and 2 out of 19 had negligible musical training.64 participants were trained and screened for imagery performance in a separate behavioural testing session. We selected those who obtained a score of greater than 70% in imagining 3 successive pitch transformations in the Pitch Imagery Arrow Task (PIAT)Presentation® software  was used to control the experiment and to record responses. Acoustic stimuli were generated from the ‘Piano’ instrument sound by Finale 2012 software  and exported as .wav files for use in Presentation®.40, and was combined with two control conditions: Perception and Mental Arithmetic (Maths). Musical trials began with an ascending major scale to provide a tonal context. A start note  was then presented simultaneously with the visual presentation of a dot on the screen. A variable number, between 3–5, of up/down arrows was next displayed in random order, with each arrow accompanied by a corresponding pitch that moved up/down the scale in stepwise motion. For Imagery trials participants were required to imagine pitch steps indicated by the arrow direction, when arrows were presented in silence. A pre-probe screen appeared 1 second before an audible probe pitch, and participants indicated whether the probe matched the final imagined tone, or in the case of Perception trials, then last tone heard. The probe was correct 50% of the time, and when incorrect was within the key signature and a maximum of 2 steps away from correct answer. Participants did not know if they were performing a perception or imagery trial until the pre-probe screen appeared.The Pitch Imagery Arrow Task (PIAT) was designed to reliably induce pitch imagery in individuals with a range of musical training40.Maths trials began with the instruction “Begin Mental Arithmetic” in silence. A starting number then displayed on the screen, followed by a series of the same visual presentation of arrows, but this time in silence, with numbers above/below the point of the arrows corresponding to addition (up arrow) and subtraction (down arrow) of an ongoing mental calculation. The final probe was a visual number that corresponded to a correct or incorrect answer to this calculation. For more details seeThe PIAT was modified in several respects for the MEG study. All Imagery trials required 3 imagined tones , all conditions had an equal number of trials  and were randomly interleaved rather than blocked. No feedback was provided to participants during the task. Figure 54. The 7-point Likert scale includes two sub scales, for vividness (BAIS-V) and control (BAIS-C). The scale items have high reliability 54.Participants completed the Bucknell Auditory Imagery Scale BAISBrain activity was recorded with a whole-head MEG system  consisting of 160 coaxial first-order gradiometers with a 50 mm baseline. Prior to MEG measurements, five marker coils were placed on the participant’s head and their positions and the participant’s head shape were measured with a pen digitizer . Head position was measured by energizing the marker coils in the MEG immediately before and after each block within the recording session. During acquisition MEG was sampled at 1 kHz and band-pass filtered between 0.03 and 200 Hz. Individual structural magnetic resonance images were not available for the present experiment so the adult template brain in BESA Research 6.1  was used for all participants, using a spherical head model.There were 80 trials in each of the three conditions. Approximately every 15 trials participants received rest time consisting of a blank screen for several seconds. Participants were asked to remain as still as possible and to limit eye blinks to the inter trial period or beginning of each trial. Button box  responses were made with the right index finger. Practice trials with feedback were provided prior to the start of data acquisition. The average time taken to complete the task in the MEG was approximately 56 minutes.Surface electromyography (EMG) using BrainAmp ExG MR 16 P  was also measured using two pairs of bipolar electrodes attached to the orbicularis oris and laryngeal muscle. EMG was viewed in real-time during the experiment to rule out any systematic muscle activities during the tasks that would indicate vocalization of the imagined notes or mental arithmetic.MEG analyses were carried out in BESA Research 6.1 . Our spatial filtering approach used four dipole sources modelled in bilateral auditory cortices and bilateral sensorimotor cortices. Auditory sources were fit to each participant’s data using the rising half of the averaged M100 response to the onset of each (sounded) arrow in both Imagery and Perception trials . Locations of sensorimotor sources were obtained from beamformer analysis of the β-band ERD to all button press responses  for each individual participant. Bilateral sources were obtained for 17 out of 19 participants, while for 2 participants the right sensorimotor source was taken as the mirror opposite of the left. The auditory and sensorimotor sources were then combined into a single model for each participant and time frequency analysis was conducted for each source, and orientations of the sensorimotor sources were optimised in BESA Research 6.1.Source modelling reduced the MEG data to a 4-source montage and provided spatial filters in close spatial proximity to bilateral auditory and sensorimotor cortices Fig. . SubsequAverage Talairach coordinates of the auditory sources were x = 52.3 (right), y = −21.1 (posterior), and z = −1.6 (superior) in the right hemisphere  and x = −52.1, y = −23.3, and z = 2.3 in the left hemisphere . Talairach coordinates for sensorimotor sources were: x = 38.5 (right), y = −25.4 (posterior), and z = 45.4 (superior) in the right hemisphere  and x = −40, y = −29.4, and z = 47.6 in the left hemisphere .A 5 second analysis epoch was defined with t = 0 s aligning with the onset of the first of the last three arrow cues in the trial. Hence the epoch consisted of: the last three arrow presentations (0–3 s), the retention period (3–4 s) and the probe and response period (4–5 s). The baseline for each trial was defined as the last 900 ms of the inter-trial interval before the trial. Trials with MEG artefacts including blinks and eye-movements during 0–5000 ms were rejected from the time-frequency calculations using the artefact scan tool in BESA Research 6.1.t, relative to the activity during the baseline epoch. The evoked (averaged) signal was subtracted from all trials prior to computing the mean time-frequency transform. Average time courses for the evoked response (3–10 Hz), induced β-band (15–25 Hz), and induced mu (μ) band (8–12 Hz) were calculated in MATLAB 8.2 .Time-frequency plots were generated with a frequency range of 2 to 80 Hz, a frequency sampling of 1 Hz and a time sampling of 50 ms. The plots show the amplitude for each time point normalised to the mean amplitude of the baseline epoch for that frequency. A value of the time-frequency plot describes the spectral change of activity at time 55.The 1 Hz power of during the last three arrows (0–2.95 secs) for each condition and source, were calculated using a Fast Fourier Transform, that was zero padded with a frequency resolution of 0.2 Hz. ANOVAs then compared the 1 Hz Power in the three oscillatory bands of interest  across conditions and sources. To further investigate this modulation, the Maximum ERD and ERS in the β-band, relative to the arrow cue, was calculated. The timing and amount of Maximum ERD was measured at 50–450 ms after each of the last three arrow cues, and averaged. Similarly, the timing and amount of Maximum ERS was measured between 500–950 ms after each of the last three arrows, and averaged. The difference in both timing and amount of Maximum ERS and ERD were also calculated per arrow, and averaged, to measure the range of modulation as the average time from maximum desynchronisation to reach maximum rebound, as well as the range of amplitude change over this time. ANOVAs were then conducted  x 2 Sources  x 2 Hemispheres ) for timing and amount of Maximum ERD and ERS, as well as difference in timing and amount between Maximum ERD and Maximum ERS, separately for β-band. Whenever Mauchley’s test of sphericity was significant a Greenhouse-Geisser correction was made. Multiple comparisons were also controlled for using False Discovery Rate41. The advantage of this type of connectivity analysis is that it is model free, computationally straightforward and robust for various time windows and trial numbers41. To calculate dPTE values, data for each participant was split into 3 equal epochs across the 1 second interval between arrow cues . Epochs were concatenated, and band pass filtered into three frequency bands of interest θ (4–8 Hz), μ (8–12 Hz) and β (15–25 Hz). Using epochs of greater than 250 ms ensures that the subsequent concatenation of epochs, and then filtering, is not impacted by edge effects of concatenation in the 4–8 Hz range. Theta was chosen due to evidence of increased phase connectivity in this frequency band36. dTPE was calculated using PhaseTE_MF.m56, using the bin size (h) of Scott57: h = 3.49σn−1/3 where σ is the standard deviation of the phase data calculated from the Hilbert transform for the time series, and n is the length (number of samples) of the time series. The default time delay was used, as determined by the frequency content of the data. The results were then normalised between −0.5 and 0.5 with the sign indicating the direction of the connectivity, as per Morillon and Baillet35. dPTE values for Imagery and Perception for each connection and each of the three epochs were then compared in t-tests (FDR corrected), to identify significant epochs and connections.This directed functional connectivity method calculates the instantaneous phase of the time series from each region of interest, and, like Granger causality, determines the direction of information based on temporal precedence and influence of one region on anotherSupplementary Information


Panulirus argus). We genotyped newly settled postlarvae in the Florida Keys and adults of spiny lobster from the Florida Keys and throughout the Caribbean Sea. We identified strong larval connectivity between Dominican Republic, Belize, Nicaragua, the Florida Keys, and West-Florida. The larval dispersal modeling suggests that Florida’s lobster population could receive recruits from within and from other areas outside its state and national maritime boundaries. The genetic analyses refine the oceanographic model indicating that the connectivity patterns described could also result from unknown parental populations sourcing adults and postlarvae in different spawning seasons to the Florida Keys. We discuss the importance of small temporal scales to identify patterns in larval export. Our findings are significant on two levels. From the larval dispersal perspective, genetic results and biophysical modeling identify patterns of gene flow enhancing persistence of local populations. From an economic and fishery perspective, P. argus is the most important commercial species in the Caribbean and our results inform how considering larval source and sink dynamics across international boundaries could improve management plans at local, national, and regional levels.The assessment of the mechanisms and patterns of larval connectivity between geographically separated populations leads to a better understanding of benthic marine population dynamics, especially in commercially valuable species. This study investigated for the first time the fine-scale temporal genetic variability of new settlers and their origins in a benthic marine organism with one of the longest pelagic larval phases, the Caribbean spiny lobster ( Fisheries data are used to inform how much is removed from the ecosystem, but knowing how much is recruited, the origin of those recruits, and the spatiotemporal variability in those patterns is further information required to assess population dynamics and to determine the suitable spatial scales to manage fished populations. This is especially critical for commercially important species with a broad distribution, complex life cycles, and that suffer high removal rates through chronic fishing pressure4.The increasing number of fisheries categorized as fully or overexploitedPanulirus argus , occurs throughout the Caribbean basin and north to Bermuda and is the most important commercial fishery in the Caribbean Sea6. Amongst marine invertebrates, P. argus has one of the longest larval dispersal phase, lasting up to nine months in the plankton8. Spiny lobster planktonic larvae have limited horizontal swimming ability and are therefore subject to widespread dispersal by currents9, but also undertake ontogenetic vertical migration during their dispersal phase10. The final larval stage metamorphoses into a non-feeding postlarva that actively swims from oceanic waters to the coast wherein they settle in shallow protective habitats9. The complex pelagic phase in the life cycle of P. argus has made it a species difficult to study for population dynamics.The Caribbean spiny lobster, 13, pink abalone14, California red rock lobster15, and demersal fish16). Nonetheless, larval transport and recruitment are known to be affected by the variability of oceanographic currents and biological factors, such as the larval dispersal duration, spawning abundance and characteristics, and larval behaviours. Hence, complicating the full assessment of benthic population dynamics. For example, in the emperor fish, Lethrinus nebulosus, extensive gene flow was predicted in biophysical modelling of passive particles, however, common history of spawning and cohesiveness of larval transport pathways resulted in a non-random genetic structure17. In P. argus, the predicted average distance a larva settles from its release location is reduced by over 60% when larval diel vertical migration is incorporated into a biophysical transport model10. Another factor affecting the assessment of larval dispersal is the effect of climate change; in the European lobster, Homarus gammarus, the increase of water temperature is expected to increase temperature-dependent mortality of larvae, as a result of a forward shift in hatching and poor quality and abundance of food18. The effects of climate change on the larval development and therefore recruitment to fishery strongly depend on the biology and phenology of the species and its vulnerability to environmental stressors19.Larval dispersal knowledge has benefited from the use of direct genetic methods, which have provided new evidence of different patterns of local retention, self-recruitment, or asymmetrical gene flow in marine organisms  were consistent in both adult and postlarvae databases for several loci, thus genetic diversity estimates for combined postlarvae and adult data were estimated and summarized Table . All locFST values ranged from −0.004 to 0.0079, but showed a complex pattern of genetic differentiation where some months were slightly less differentiated than others ; of these, 28.3% , were assigned to the Florida Keys adult population  locations for the postlarvae which settled at the two collection sites . The sources of larvae were particularly important in the west coast of Florida, northwest Cuba, the Caribbean coast of Mexico, Belize, and Honduras Fig. . The relThe present study is the first to investigate fine-scale genetic relatedness between adults and new settlers of spiny lobster using a parentage analysis as a proxy to assess the reproductive connectivity of postlarvae in the Florida Keys with adult populations across the wider Caribbean.et al.23 . Although including all the potential sources of postlarvae in our analysis could better resolve the larval origins and connectivity of the Florida Keys recruits, it remains difficult to achieve given the broad distribution of the species.The parental assignment based on the extent of alleles shared between Florida Keys postlarvae and adult spiny lobsters, indicated a high degree of connectivity to Dominican Republic, Belize, Nicaragua, West Florida and the Florida Keys. The backward larval dispersal modeling conducted at both Caribbean wide and Florida spatial scales predicted comparable results to the genetic analyses performed. Larval dispersal between the Florida Keys and Belize, Western Florida, Gulf of Mexico, and within the Florida Keys itself was indeed predicted by the models. These results are overall consistent with the major larval exporter countries previously suggested by Kough 20. In this regard, high connectivity between Florida, southwest Cuba, and the Mexican Caribbean might be expected considering the proximity of these sites to the Yucatan Current where advection is likely to occur despite of the occurrence of persisting recirculation observed in southwest Cuba and the Campeche Bank . Similarly, Zeng et al.’s26 dispersal model for bonefish larvae (53 days of pelagic phase duration) showed high connectivity between Belize, Mexico, Cuba, and the Florida Keys, but also showed that the Lower and Middle Key have the highest retention rate as seen in our local model. In a recent study, Garavelli et al.27 showed that the highest values of local retention and self-recruitment in the Caribbean wide region were observed in Mexico and Florida. These high retention rates were driven by the availability of favorable habitat and the populations in each region were found self-persistent, meaning that they did not rely on external larval supply to maintain their population27. Therefore, despite potential significant influx from other geographically close sources, the estimated percentage of postlarvae assigned to the Florida Keys could remain the same.Previous modeling studies have suggested that larval recruitment in the northern regions of the Caribbean are highly dependent on distant sources supplied by strong northward currents, while recruitment dynamics in the southern regions of the Caribbean are more influenced by retentive oceanographic structures, dominated by persistent gyres29. The strong genetic relatedness between postlarvae and adults from the Florida Keys supports the hypothesis that the southwest Florida shelf area plays an important role in the local retention and self-recruitment of larvae originated in the Florida Keys. The local retention and self-recruitment are also assisted by elevated nutrient levels from the upwelling in the interior and fronts of the eddies and gyres that surround the Florida Keys30. This upwelling enhances larval food supply and potentially may increase larval survival28. The Florida regional model simulation confirmed the retention role of the southwest Florida shelf  on both sides of the Florida Keys , North Carolina (0.0110), and Bermuda (0.0118) support this hypothesis, most likely due to the lack of significant reverse flow near the Gulf Stream, which is incompatible with the concept of a parental relationship.P. interruptus, in the Mexican Pacific coast where a high proportion of kinship among adult lobsters was found15. However, for P. argus in Florida, the strong spawner vs. recruit correlation suggestive of self-recruitment33, was reassessed and patterns observed seem to represent broader adult abundance from Caribbean wide origin34. Thus, the contribution of local Florida spawning stock to local post-larval recruitment remains unresolved by using adult lobster abundance and postlarvae recruitment patterns alone.Self-recruitment has also been suggested in a congeneric species, P. cygnus, the combination of temporal genetic variation and dissimilar recruitment patterns at two sites resulted in genetically different cohorts, but was also different from following time replicates35. Similarly, our results from the DAPC clustering analyses showed temporal variation of the inferred clusters and that clusters 7–10 were more represented than cluster 1–6 throughout the two years of the study  and south (Gulf of Maine) regions and among populations within these regions39.The use of direct genetic methods at fine temporal scales allowed the identification of complex genetic variability in recruits Table , Fig. S3udy Fig. . This teFST values are typically very low (<0.05), which challenges the inference of genetic structure40. The low genetic differentiation as estimated by FST may have resulted from significant gene flow or larval connectivity over evolutionary time scales that homogenize allele frequencies but, does not necessarily mean that populations are well mixed on ecological time scales. This supports the hypothesis that stepping-stone import of foreign genotypes will result in panmixia over an evolutionary time scale41. However, to correctly describe the ecological processes at work to inform management decision making, it is crucial to detect the fine-scale temporal variability of settlement and identify the consistent and key sources of recruits into local populations which may vary through time.In marine invertebrates with pelagic larval phase, P. argus in Honduras suggested that a reserve network at country scale will result in a significant increase of both persistence and yield of the species42. This is consistent with the hypothesis that Caribbean spiny lobster populations are influenced by both open and closed recruitment dynamics. Thus, management actions at national level to conserve spawning stock biomass may deliver improvements at a country scale given the correct oceanographic conditions even for species with long pelagic larval phases. Including the study of genetic variability at these shorter temporal and finer spatial scales is key to elucidating patterns in larval export and determining the importance of self-recruitment which may otherwise be masked in larger scale studies. Understanding these patterns can help assess the potential contribution from different levels of management intervention, from local to international, for marine species with a broad distribution and complex dispersal patterns.Population modeling studies on n = 50 targeted per lunar month), using five artificial collectors placed parallel to the shore near inter-island channels and anchored near shore in shallow waters (<2 m) from two long-term monitoring sites near Big Munson, in the Lower Florida Keys , and the south side of Long Key, in the Middle Keys  , Panama City (West Florida), Bermuda, the Bahamas, Belize, Nicaragua, St. Kitts, St. Croix (U.S.Virgin Islands), the Dominican Republic, and Venezuela Fig. . Approxi44. PCR conditions were as follow: 94 °C for 2 min, 32 cycles of 94 °C for 40 s, 58 °C for 40 s, 72 °C for 45 s, followed by 72 °C for 7 min. PCR products were visualized on an ABI 3130 genetic analyser and genotyped by using GeneMapper® software v3.7 . Genotypes across all 14 microsatellite loci were tested for the presence of allelic dropout and null alleles using the program MICRO-CHECKER45. Genetic diversity within each location and monthly collections were assessed using standard measures  estimated in FSTAT 2.9.346. Observed heterozygosity (Ho), expected heterozygosity (He), departures from Hardy-Weinberg equilibrium (HWE) and the power of resolution of the microsatellite loci (PIC) were examined in CERVUS 3.0.747.Total genomic DNA was extracted and purified from tissue samples using PUREGENE® DNA Isolation Kit  following manufacturer guidelines. Individual lobsters (postlarvae and adults) were genotyped for 14 bi-parentally inherited microsatellite DNA markers developed for this speciesFST using ARLEQUIN 3.548. Pairwise genetic differentiation was also estimated between postlarvae and adult population samples using convectional pairwise FST using ARLEQUIN 3.548. To determine genetic differentiation between postlarvae samples, we used a discriminant analysis of principal components (DAPC)49, a multivariate method implemented in the adegenet package50 for R51. The postlarvae collection date was used as a prior. The DAPC approach proposes a distribution of postlarvae into predefined groups in relation to the discriminant function of principal components. The optimum number of clusters was defined by k-means algorithm that uses the Bayesian information criterion. This approach is preferred for species exhibiting potentially high gene flow, as DAPC maximizes genetic separation among groups and minimizes variation within groups49.Temporal genetic differentiation among monthly postlarvae samples was examined based on differences in allele frequency to estimate convectional pairwise 47. The probability for the most likely parent for each postlarva was defined by taking the natural log (log base e) of the overall likelihood ratio (LOD scores). The critical LOD value was determined with a 95% confidence level running parentage simulations for 10,000 offspring while considering 98 candidate parents  and a proportion of candidate mothers estimated at 0.016, based on the female spawning stock assessment for the Florida Keys in 200433. To increase the strictness of the parentage analyses, we only considered genotype comparisons of more than 10 microsatellite loci and a maximum two pair loci mismatched. The critical LOD score estimated (LOD = 2.5) was the cut-off for assigning the single parent, which is a conservative approach that accounts for some unsampled putative parents in the population52. The proportion of postlarvae assigned to different source populations were weighted by the total number of postlarvae and the corresponding adult sample size.A single-parent parentage analysis was conducted to investigate the extent of connectivity between postlarvae settled in the Florida Keys and adult lobsters from other locations, both local within Florida and from other countries internationally. This direct method compared multi-loci genotypes of adults with multi-loci postlarvae genotypes to assign individual postlarvae to a candidate parent. This analysis is a proxy to infer the potential sourcing population, rather than an actual parentage analyses to assign postlarvae to parent populations (recruit-spawner). Given that lobsters are long-lived animals, there is potential for generation overlap that might make it difficult to distinguish between parent-offspring and siblings from a different year class. The parentage analysis was conducted as implemented in CERVUS 3.0.7P. argus larvae settling in the Florida Keys, we modeled the larval dispersal of the species using the offline Lagrangian tool Ichthyop v3.253. Ichthyop v3.2 allows the coupling between a hydrodynamic model and an individual-based model and is based on an Euler advection scheme. In the model, each particle represents a virtual larva and is characterized by its longitude, latitude, and depth in three dimensions. Two simulations were performed: the first one to investigate the transport patterns at large spatial scale in the Caribbean region and the second to investigate the retention patterns around Florida by using a higher spatial resolution ocean model. The large scale simulation used the 8-km horizontal resolution HYCOM consortium global model54. The HYCOM model fields were extracted daily for the intra America Seas region and converted to Ichthyop’s input format. At the Florida scale, we used the Regional Oceanic Modeling System (ROMS) high-resolution (~2.8 km) simulation of the south Florida shelf, which includes tides and is described in Criales et al.55. Two polygons, encompassing the postlarvae collection sites , i.e. settlement locations, were defined. We adapted the size of the polygons depending on the resolution of each model used. In the large-scale model, each polygon was 64 km2 and 25 km2 in the Florida scale model. Ten thousand particles were released from each polygon at 5 m depth once a month from July to December 2007. The dispersal duration was set to 196 days with 152 days of pre-competency period8. An ontogenetic vertical migration behaviour was included in the model following Callwood56. The P. argus virtual larvae were tracked backward in time from their settlement locations to estimate their origin and the percentage of larvae was averaged over the number of polygon per nation in the Caribbean region.To estimate the spawning locations of Supplementary information