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  1. Author Response:

    Evaluation Summary:

    Since DBS of the habenula is a new treatment, these are the first data of its kind and potentially of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap. This paper is of interest to neuroscientists studying emotions and clinicians treating psychiatric disorders. Specifically the paper shows that the habenula is involved in processing of negative emotions and that it is synchronized to the prefrontal cortex in the theta band. These are important insights into the electrophysiology of emotion processing in the human brain.

    The authors are very grateful for the reviewers’ positive comments on our study. We also thank all the reviewers for the comments which has helped to improve the manuscript.

    Reviewer #1 (Public Review):

    The study by Huang et al. report on direct recordings (using DBS electrodes) from the human habenula in conjunction with MEG recordings in 9 patients. Participants were shown emotional pictures. The key finding was a transient increase in theta/alpha activity with negative compared to positive stimuli. Furthermore, there was a later increase in oscillatory coupling in the same band. These are important data, as there are few reports of direct recordings from the habenula together with the MEG in humans performing cognitive tasks. The findings do provide novel insight into the network dynamics associated with the processing of emotional stimuli and particular the role of the habenula.

    Recommendations:

    How can we be sure that the recordings from the habenula are not contaminated by volume conduction; i.e. signals from neighbouring regions? I do understand that bipolar signals were considered for the DBS electrode leads. However, high-frequency power (gamma band and up) is often associated with spiking/MUA and considered less prone to volume conduction. I propose to also investigate that high-frequency gamma band activity recorded from the bipolar DBS electrodes and relate to the emotional faces. This will provide more certainty that the measured activity indeed stems from the habenula.

    We thank the reviewer for the comment. As the reviewer pointed out, bipolar macroelectrode can detect locally generated potentials, as demonstrated in the case of recordings from subthalamic nucleus and especially when the macroelectrodes are inside the subthalamic nucleus (Marmor et al., 2017). However, considering the size of the habenula and the size of the DBS electrode contacts, we have to acknowledge that we cannot completely exclude the possibility that the recordings are contaminated by volume conduction of activities from neighbouring areas, as shown in Bertone-Cueto et al. 2019. We have now added extra information about the size of the habenula and acknowledged the potential contamination of activities from neighbouring areas through volume conduction in the ‘Limitation’:

    "Another caveat we would like to acknowledge that the human habenula is a small region. Existing data from structural MRI scans reported combined habenula (the sum of the left and right hemispheres) volumes of ~ 30–36 mm3 (Savitz et al., 2011a; Savitz et al., 2011b) which means each habenula has the size of 2~3 mm in each dimension, which may be even smaller than the standard functional MRI voxel size (Lawson et al., 2013). The size of the habenula is also small relative to the standard DBS electrodes (as shown in Fig. 2A). The electrodes used in this study (Medtronic 3389) have electrode diameter of 1.27 mm with each contact length of 1.5 mm, and contact spacing of 0.5 mm. We have tried different ways to confirm the location of the electrode and to select the contacts that is within or closest to the habenula: 1.) the MRI was co-registered with a CT image (General Electric, Waukesha, WI, USA) with the Leksell stereotactic frame to obtain the coordinate values of the tip of the electrode; 2.) Post-operative CT was co-registered to pre-operative T1 MRI using a two-stage linear registration using Lead-DBS software. We used bipolar signals constructed from neighbouring macroelectrode recordings, which have been shown to detect locally generated potentials from subthalamic nucleus and especially when the macroelectrodes are inside the subthalamic nucleus (Marmor et al., 2017). Considering that not all contacts for bipolar LFP construction are in the habenula in this study, as shown in Fig. 2, we cannot exclude the possibility that the activities we measured are contaminated by activities from neighbouring areas through volume conduction. In particular, the human habenula is surrounded by thalamus and adjacent to the posterior end of the medial dorsal thalamus, so we may have captured activities from the medial dorsal thalamus. However, we also showed that those bipolar LFPs from contacts in the habenula tend to have a peak in the theta/alpha band in the power spectra density (PSD); whereas recordings from contacts outside the habenula tend to have extra peak in beta frequency band in the PSD. This supports the habenula origin of the emotional valence related changes in the theta/alpha activities reported here."

    We have also looked at gamma band oscillations or high frequency activities in the recordings. However, we didn’t observe any peak in high frequency band in the average power spectral density, or any consistent difference in the high frequency activities induced by the emotional stimuli (Fig. S1). We suspect that high frequency activities related to MUA/spiking are very local and have very small amplitude, so they are not picked up by the bipolar LFPs measured from contacts with both the contact area for each contact and the between-contact space quite large comparative to the size of the habenula.

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    Figure S1. (A) Power spectral density of habenula LFPs across all time period when emotional stimuli were presented. The bold blue line and shadowed region indicates the mean ± SEM across all recorded hemispheres and the thin grey lines show measurements from individual hemispheres. (B) Time-frequency representations of the power response relative to pre-stimulus baseline for different conditions showing habenula gamma and high frequency activity are not modulated by emotional

    References:

    Savitz JB, Bonne O, Nugent AC, Vythilingam M, Bogers W, Charney DS, et al. Habenula volume in post-traumatic stress disorder measured with high-resolution MRI. Biology of Mood & Anxiety Disorders 2011a; 1(1): 7.

    Savitz JB, Nugent AC, Bogers W, Roiser JP, Bain EE, Neumeister A, et al. Habenula volume in bipolar disorder and major depressive disorder: a high-resolution magnetic resonance imaging study. Biological Psychiatry 2011b; 69(4): 336-43.

    Lawson RP, Drevets WC, Roiser JP. Defining the habenula in human neuroimaging studies. NeuroImage 2013; 64: 722-7.

    Marmor O, Valsky D, Joshua M, Bick AS, Arkadir D, Tamir I, et al. Local vs. volume conductance activity of field potentials in the human subthalamic nucleus. Journal of Neurophysiology 2017; 117(6): 2140-51.

    Bertone-Cueto NI, Makarova J, Mosqueira A, García-Violini D, Sánchez-Peña R, Herreras O, et al. Volume-Conducted Origin of the Field Potential at the Lateral Habenula. Frontiers in Systems Neuroscience 2019; 13:78.

    Figure 3: the alpha/theta band activity is very transient and not band-limited. Why refer to this as oscillatory? Can you exclude that the TFRs of power reflect the spectral power of ERPs rather than modulations of oscillations? I propose to also calculate the ERPs and perform the TFR of power on those. This might result in a re-interpretation of the early effects in theta/alpha band.

    We agree with the reviewer that the activity increase in the first time window with short latency after the stimuli onset is very transient and not band-limited. This raise the question that whether this is oscillatory or a transient evoked activity. We have now looked at this initial transient activity in different ways: 1.) We quantified the ERP in LFPs locked to the stimuli onset for each emotional valence condition and for each habenula. We investigated whether there was difference in the amplitude or latency of the ERP for different stimuli emotional valence conditions. As showing in the following figure, there is ERP with stimuli onset with a positive peak at 402 ± 27 ms (neutral stimuli), 407 ± 35 ms (positive stimuli), 399 ± 30 ms (negative stimuli). The flowing figure (Fig. 3–figure supplement 1) will be submitted as figure supplement related to Fig. 3. However, there was no significant difference in ERP latency or amplitude caused by different emotional valence stimuli. 2.) We have quantified the pure non-phase-locked (induced only) power spectra by calculating the time-frequency power spectrogram after subtracting the ERP (the time-domain trial average) from time-domain neural signal on each trial (Kalcher and Pfurtscheller, 1995; Cohen and Donner, 2013). This shows very similar results as we reported in the main manuscript, as shown in Fig. 3–figure supplement 2. These further analyses show that even though there were event related potential changes time locked around the stimuli onset, and this ERP did NOT contribute to the initial broad-band activity increase at the early time window shown in plot A-C in Figure 3. The figures of the new analyses and following have now been added in the main text:

    "In addition, we tested whether stimuli-related habenula LFP modulations primarily reflect a modulation of oscillations, which is not phase-locked to stimulus onset, or, alternatively, if they are attributed to evoked event-related potential (ERP). We quantified the ERP for each emotional valence condition for each habenula. There was no significant difference in ERP latency or amplitude caused by different emotional valence stimuli (Fig. 3–figure supplement 1). In addition, when only considering the non phase-locked activity by removing the ERP from the time series before frequency-time decomposition, the emotional valence effect (presented in Fig. 3–figure supplement 2) is very similar to those shown in Fig.3. These additional analyses demonstrated that the emotional valence effect in the LFP signal is more likely to be driven by non-phase-locked (induced only) activity."

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    Fig. 3–figure supplement 1. Event-related potential (ERP) in habenula LFP signals in different emotional valence (neutral, positive and negative) conditions. (A) Averaged ERP waveforms across patients for different conditions. (B) Peak latency and amplitude (Mean ± SEM) of the ERP components for different conditions.

    Fig. 3–figure supplement 2. Non-phase-locked activity in different emotional valence (neutral, positive and negative) conditions (N = 18). (A) Time-frequency representation of the power changes relative to pre-stimulus baseline for three conditions. Significant clusters (p < 0.05, non-parametric permutation test) are encircled with a solid black line. (B) Time-frequency representation of the power response difference between negative and positive valence stimuli, showing significant increased activity the theta/alpha band (5-10 Hz) at short latency (100-500 ms) and another increased theta activity (4-7 Hz) at long latencies (2700-3300 ms) with negative stimuli (p < 0.05, non-parametric permutation test). (C) Normalized power of the activities at theta/alpha (5-10 Hz) and theta (4-7 Hz) band over time. Significant difference between the negative and positive valence stimuli is marked by a shadowed bar (p < 0.05, corrected for multiple comparison).

    References:

    Kalcher J, Pfurtscheller G. Discrimination between phase-locked and non-phase-locked event-related EEG activity. Electroencephalography and Clinical Neurophysiology 1995; 94(5): 381-4.

    Cohen MX, Donner TH. Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. Journal of Neurophysiology 2013; 110(12): 2752-63.

    Figure 4D: can you exclude that the frontal activity is not due to saccade artifacts? Only eye blink artifacts were reduced by the ICA approach. Trials with saccades should be identified in the MEG traces and rejected prior to further analysis.

    We understand and appreciate the reviewer’s concern on the source of the activity modulations shown in Fig. 4D. We tried to minimise the eye movement or saccade in the recording by presenting all figures at the centre of the screen, scaling all presented figures to similar size, and presenting a white cross at the centre of the screen preparing the participants for the onset of the stimuli. Despite this, participants my still make eye movements and saccade in the recording. We used ICA to exclude the low frequency large amplitude artefacts which can be related to either eye blink or other large eye movements. However, this may not be able to exclude artefacts related to miniature saccades. As shown in Fig. 4D, on the sensor level, the sensors with significant difference between the negative vs. positive emotional valence condition clustered around frontal cortex, close to the eye area. However, we think this is not dominated by saccades because of the following two reasons:

    1.) The power spectrum of the saccadic spike artifact in MEG is characterized by a broadband peak in the gamma band from roughly 30 to 120 Hz (Yuval-Greenberg et al., 2008; Keren et al., 2010). In this study the activity modulation we observed in the frontal sensors are limited to the theta/alpha frequency band, so it is different from the power spectra of the saccadic spike artefact.

    2.) The source of the saccadic spike artefacts in MEG measurement tend to be localized to the region of the extraocular muscles of both eyes (Carl et al., 2012).We used beamforming source localisation to identify the source of the activity modulation reported in Fig. 4D. This beamforming analysis identified the source to be in the Broadmann area 9 and 10 (shown in Fig. 5). This excludes the possibility that the activity modulation in the sensor level reported in Fig. 4D is due to saccades. In addition, Broadman area 9 and 10, have previously been associated with emotional stimulus processing (Bermpohl et al., 2006), Broadman area 9 in the left hemisphere has also been used as the target for repetitive transcranial magnetic stimulation (rTMS) as a treatment for drug-resistant depression (Cash et al., 2020). The source localisation results, together with previous literature on the function of the identified source area suggest that the activity modulation we observed in the frontal cortex is very likely to be related to emotional stimuli processing.

    References:

    Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, Deouell LY. Transient induced gamma-band response in EEG as a manifestation of miniature saccades. Neuron 2008; 58(3): 429-41.

    Keren AS, Yuval-Greenberg S, Deouell LY. Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression. NeuroImage 2010; 49(3): 2248-63.

    Carl C, Acik A, Konig P, Engel AK, Hipp JF. The saccadic spike artifact in MEG. NeuroImage 2012; 59(2): 1657-67.

    Bermpohl F, Pascual-Leone A, Amedi A, Merabet LB, Fregni F, Gaab N, et al. Attentional modulation of emotional stimulus processing: an fMRI study using emotional expectancy. Human Brain Mapping 2006; 27(8): 662-77.

    Cash RFH, Weigand A, Zalesky A, Siddiqi SH, Downar J, Fitzgerald PB, et al. Using Brain Imaging to Improve Spatial Targeting of Transcranial Magnetic Stimulation for Depression. Biological Psychiatry 2020.

    The coherence modulations in Fig 5 occur quite late in time compared to the power modulations in Fig 3 and 4. When discussing the results (in e.g. the abstract) it reads as if these findings are reflecting the same process. How can the two effect reflect the same process if the timing is so different?

    As the reviewer pointed out correctly, the time window where we observed the coherence modulations happened quite late in time compared to the initial power modulations in the frontal cortex and the habenula (Fig. 4). And there was another increase in the theta band activities in the habenula area even later, at around 3 second after stimuli onset when the emotional figure has already disappeared. Emotional response is composed of a number of factors, two of which are the initial reactivity to an emotional stimulus and the subsequent recovery once the stimulus terminates or ceases to be relevant (Schuyler et al., 2014). We think these neural effects we observed in the three different time windows may reflect different underlying processes. We have discussed this in the ‘Discussion’:

    "These activity changes at different time windows may reflect the different neuropsychological processes underlying emotion perception including identification and appraisal of emotional material, production of affective states, and autonomic response regulation and recovery (Phillips et al., 2003a). The later effects of increased theta activities in the habenula when the stimuli disappeared were also supported by other literature showing that, there can be prolonged effects of negative stimuli in the neural structure involved in emotional processing (Haas et al., 2008; Puccetti et al., 2021). In particular, greater sustained patterns of brain activity in the medial prefrontal cortex when responding to blocks of negative facial expressions was associated with higher scores of neuroticism across participants (Haas et al., 2008). Slower amygdala recovery from negative images also predicts greater trait neuroticism, lower levels of likability of a set of social stimuli (neutral faces), and declined day-to-day psychological wellbeing (Schuyler et al., 2014; Puccetti et al., 2021)."

    References:

    Schuyler BS, Kral TR, Jacquart J, Burghy CA, Weng HY, Perlman DM, et al. Temporal dynamics of emotional responding: amygdala recovery predicts emotional traits. Social Cognitive and Affective Neuroscience 2014; 9(2): 176-81.

    Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biological Psychiatry 2003a; 54(5): 504-14.

    Haas BW, Constable RT, Canli T. Stop the sadness: Neuroticism is associated with sustained medial prefrontal cortex response to emotional facial expressions. NeuroImage 2008; 42(1): 385-92.

    Puccetti NA, Schaefer SM, van Reekum CM, Ong AD, Almeida DM, Ryff CD, et al. Linking Amygdala Persistence to Real-World Emotional Experience and Psychological Well-Being. Journal of Neuroscience 2021: JN-RM-1637-20.

    Be explicit on the degrees of freedom in the statistical tests given that one subject was excluded from some of the tests.

    We thank the reviewers for the comment. The number of samples used for each statistics analysis are stated in the title of the figures. We have now also added the degree of freedom in the main text when parametric statistical tests such as t-test or ANOVAs have been used. When permutation tests (which do not have any degrees of freedom associated with it) are used, we have now added the number of samples for the permutation test.

    Reviewer #2 (Public Review):

    In this study, Huang and colleagues recorded local field potentials from the lateral habenula in patients with psychiatric disorders who recently underwent surgery for deep brain stimulation (DBS). The authors combined these invasive measurements with non-invasive whole-head MEG recordings to study functional connectivity between the habenula and cortical areas. Since the lateral habenula is believed to be involved in the processing of emotions, and negative emotions in particular, the authors investigated whether brain activity in this region is related to emotional valence. They presented pictures inducing negative and positive emotions to the patients and found that theta and alpha activity in the habenula and frontal cortex increases when patients experience negative emotions. Functional connectivity between the habenula and the cortex was likewise increased in this band. The authors conclude that theta/alpha oscillations in the habenula-cortex network are involved in the processing of negative emotions in humans.

    Because DBS of the habenula is a new treatment tested in this cohort in the framework of a clinical trial, these are the first data of its kind. Accordingly, they are of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap.

    In terms of community impact, I see the strengths of this paper in basic science rather than the clinical field. The authors demonstrate the involvement of theta oscillations in the habenula-prefrontal cortex network in emotion processing in the human brain. The potential of theta oscillations to serve as a marker in closed-loop DBS, as put forward by the authors, appears less relevant to me at this stage, given that the clinical effects and side-effects of habenula DBS are not known yet.

    We thank the reviewers for the favourable comments about the implication of our study in basic science and about the value of our study in closing a knowledge gap. We agree that further studies would be required to make conclusions about the clinical effects and side-effects of habenula DBS.

    Detailed comments:

    The group-average MEG power spectrum (Fig. 4B) suggests that negative emotions lead to a sustained theta power increase and a similar effect, though possibly masked by a visual ERP, can be seen in the habenula (Fig. 3C). Yet the statistics identify brief elevations of habenula theta power at around 3s (which is very late), a brief elevation of prefrontal power a time 0 or even before (Fig. 4C) and a brief elevation of Habenula-MEG theta coherence around 1 s. It seems possible that this lack of consistency arises from a low signal-to-noise ratio. The data contain only 27 trails per condition on average and are contaminated by artifacts caused by the extension wires.

    With regard to the nature of the activity modulation with short latency after stimuli onset: whether this is an ERP or oscillation? We have now investigated this. In summary, by analysing the ERP and removing the influence of the ERP from the total power spectra, we didn’t observe stimulus emotional valence related modulation in the ERP, and the modulation related to emotional valence in the pure induced (non-phase-locked) power spectra was similar to what we have observed in the total power shown in Fig. 3. Therefore, we argue that the theta/alpha increase with negative emotional stimuli we observed in both habenula and prefrontal cortex 0-500 ms after stimuli onset are not dominated by visual or other ERP.

    With regard to the signal-to-noise ratio from only 27 trials per condition on average per participant: We have tried to clean the data by removing the trials with obvious artefacts characterised by increased measurements in the time domain over 5 times the standard deviation and increased activities across all frequency bands in the frequency domain. After removing the trials with artefacts, we have 27 trials per condition per subject on average. We agree that 27 trials per condition on average is not a high number, and increasing the number of trials would further increase the signal-to-noise ratio. However, our studies with EEG recordings and LFP recordings from externalised patients have shown that 30 trials was enough to identify reduction in the amplitude of post-movement beta oscillations at the beginning of visuomotor adaption in the motor cortex and STN (Tan et al., 2014a; Tan et al., 2014b). These results of motor error related modulation in the post-movement beta have been repeated by other studies from other groups. In Tan et al. 2014b, with simultaneous EEG and STN LFP measurements and a similar number of trials (around 30), we also quantified the time-course of STN-motor cortex coherence during voluntary movements. This pattern has also been repeated in a separate study from another group with around 50 trials per participant (Talakoub et al., 2016). In addition, similar behavioural paradigm (passive figure viewing paradigm) has been used in two previous studies with LFP recordings from STN from different patient groups (Brucke et al., 2007; Huebl et al., 2014). In both studies, a similar number of trials per condition around 27 was used. The authors have identified meaningful activity modulation in the STN by emotional stimuli. Therefore, we think the number of trials per condition was sufficient to identify emotional valence induced difference in the LFPs in the paradigm.

    We agree that the measurement of coherence can be more susceptible to noise and suffer from the reduced signal-to-noise ratio in MEG recording. In Hirschmann et al. 2013, 5 minutes of resting recording and 5 minutes of movement recording from 10 PD patients were used to quantify movement related changes in STN-cortical coherence and how this was modulated by levodopa (Hirschmann et al., 2013). Litvak et al. (2012) have identified movement-related changes in the coherence between STN LFP and motor cortex with recording with simultaneous STN LFP and MEG recordings from 17 PD patients and 20 trials in average per participant per condition (Litvak et al., 2012). With similar methods, van Wijk et al. (2017) used recordings from 9 patients and around on average in 29 trials per hand per condition, and they identified reduced cortico-pallidal coherence in the low-beta decreases during movement (van Wijk et al., 2017). So the trial number per condition participant we used in this study are comparable to previous studies.

    The DBS extension wires do reduce signal-to-noise ratio in the MEG recording. therefore the spatiotemporal Signal Space Separation (tSSS) method (Taulu and Simola, 2006) implemented in the MaxFilter software (Elekta Oy, Helsinki, Finland) has been applied in this study to suppress strong magnetic artifacts caused by extension wires. This method has been proved to work well in de-noising the magnetic artifacts and movement artifacts in MEG data in our previous studies (Cao et al., 2019; Cao et al., 2020). In addition, the beamforming method proposed by several studies (Litvak et al., 2010; Hirschmann et al., 2011; Litvak et al., 2011) has been used in this study. In Litvak et al., 2010, the artifacts caused by DBS extension wires was detailed described and the beamforming was demonstrated to effectively suppress artifacts and thereby enable both localization of cortical sources coherent with the deep brain nucleus. We have now added more details and these references about the data cleaning and the beamforming method in the main text. With the beamforming method, we did observe the standard movement-related modulation in the beta frequency band in the motor cortex with 9 trials of figure pressing movements, shown in the following figure for one patient as an example (Figure 5–figure supplement 1). This suggests that the beamforming method did work well to suppress the artefacts and help to localise the source with a low number of trials. The figure on movement-related modulation in the motor cortex in the MEG signals have now been added as a supplementary figure to demonstrate the effect of the beamforming.

    Figure 5–figure supplement 1. (A) Time-frequency maps of MEG activity for right hand button press at sensor level from one participant (Case 8). (B) DICS beamforming source reconstruction of the areas with movement-related oscillation changes in the range of 12-30 Hz. The peak power was located in the left M1 area, MNI coordinate [-37, -12, 43].

    References:

    Tan H, Jenkinson N, Brown P. Dynamic neural correlates of motor error monitoring and adaptation during trial-to-trial learning. Journal of Neuroscience 2014a; 34(16): 5678-88.

    Tan H, Zavala B, Pogosyan A, Ashkan K, Zrinzo L, Foltynie T, et al. Human subthalamic nucleus in movement error detection and its evaluation during visuomotor adaptation. Journal of Neuroscience 2014b; 34(50): 16744-54.

    Talakoub O, Neagu B, Udupa K, Tsang E, Chen R, Popovic MR, et al. Time-course of coherence in the human basal ganglia during voluntary movements. Scientific Reports 2016; 6: 34930.

    Brucke C, Kupsch A, Schneider GH, Hariz MI, Nuttin B, Kopp U, et al. The subthalamic region is activated during valence-related emotional processing in patients with Parkinson's disease. European Journal of Neuroscience 2007; 26(3): 767-74.

    Huebl J, Spitzer B, Brucke C, Schonecker T, Kupsch A, Alesch F, et al. Oscillatory subthalamic nucleus activity is modulated by dopamine during emotional processing in Parkinson's disease. Cortex 2014; 60: 69-81.

    Hirschmann J, Ozkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, et al. Differential modulation of STN-cortical and cortico-muscular coherence by movement and levodopa in Parkinson's disease. NeuroImage 2013; 68: 203-13.

    Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes G, Foltynie T, et al. Movement-related changes in local and long-range synchronization in Parkinson's disease revealed by simultaneous magnetoencephalography and intracranial recordings. Journal of Neuroscience 2012; 32(31): 10541-53.

    van Wijk BCM, Neumann WJ, Schneider GH, Sander TH, Litvak V, Kuhn AA. Low-beta cortico-pallidal coherence decreases during movement and correlates with overall reaction time. NeuroImage 2017; 159: 1-8.

    Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology 2006; 51(7): 1759-68.

    Cao C, Huang P, Wang T, Zhan S, Liu W, Pan Y, et al. Cortico-subthalamic Coherence in a Patient With Dystonia Induced by Chorea-Acanthocytosis: A Case Report. Frontiers in Human Neuroscience 2019; 13: 163.

    Cao C, Li D, Zhan S, Zhang C, Sun B, Litvak V. L-dopa treatment increases oscillatory power in the motor cortex of Parkinson's disease patients. NeuroImage Clinical 2020; 26: 102255.

    Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes GR, Penny WD, et al. Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients. NeuroImage 2010; 50(4): 1578-88.

    Litvak V, Jha A, Eusebio A, Oostenveld R, Foltynie T, Limousin P, et al. Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson's disease. Brain 2011; 134(Pt 2): 359-74.

    Hirschmann J, Ozkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, et al. Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson's disease. NeuroImage 2011; 55(3): 1159-68.

    I doubt that the correlation between habenula power and habenula-MEG coherence (Fig. 6C) is informative of emotion processing. First, power and coherence in close-by time windows are likely to to be correlated irrespective of the task/stimuli. Second, if meaningful, one would expect the strongest correlation for the negative condition, as this is the only condition with an increase of theta coherence and a subsequent increase of theta power in the habenula. This, however, does not appear to be the case.

    The authors included the factors valence and arousal in their linear model and found that only valence correlated with electrophysiological effects. I suspect that arousal and valence scores are highly correlated. When fed with informative yet highly correlated variables, the significance of individual input variables becomes difficult to assess in many statistical models. Hence, I am not convinced that valence matters but arousal not.

    For the correlation shown in Fig. 6C, we used a linear mixed-effect modelling (‘fitlme’ in Matlab) with different recorded subjects as random effects to investigate the correlations between the habenula power and habenula-MEG coherence at an earlier window, while considering all trials together. Therefore the reported value in the main text and in the figure (k = 0.2434 ± 0.1031, p = 0.0226, R2 = 0.104) show the within subjects correlation that are consistent across all measured subjects. The correlation is likely to be mediated by emotional valence condition, as negative emotional stimuli tend to be associated with both high habenula-MEG coherence and high theta power in the later time window tend to happen in the trials with.

    The arousal scores are significantly different for the three valence conditions as shown in Fig. 1B. However, the arousal scores and the valence scores are not monotonically correlated, as shown in the following figure (Fig. S2). The emotional neutral figures have the lowest arousal value, but have the valence value sitting between the negative figures and the positive figures. We have now added the following sentence in the main text:

    "This nonlinear and non-monotonic relationship between arousal scores and the emotional valence scores allowed us to differentiate the effect of the valence from arousal."

    Table 2 in the main text show the results of the linear mixed-effect modelling with the neural signal as the dependent variable and the valence and arousal scores as independent variables. Because of the non-linear and non-monotonic relationship between the valence and arousal scores, we think the significance of individual input variables is valid in this statistical model. We have now added a new figure (shown below, Fig. 7) with scatter plots showing the relationship between the electrophysiological signal and the arousal and emotional valence scores separately using Spearman’s partial correlation analysis. In each scatter plot, each dot indicates the average measurement from one participant in one emotional valence condition. As shown in the following figure, the electrophysiological measurements linearly correlated with the valence score, but not with the arousal scores. However, the statistics reported in this figure considered all the dots together. The linear mixed effect modelling taking into account the interdependency of the measurements from the same participant. So the results reported in the main text using linear mixed effect modelling are statistically more valid, but supplementary figure here below illustrate the relationship.

    Figure S2. Averaged valence and arousal ratings (mean ± SD) for figures of the three emotional condition. (B) Scatter plots showing the relationship between arousal and valence scores for each emotional condition for each participant.

    Figure 7. Scatter plots showing how early theta/alpha band power increase in the frontal cortex (A), theta/alpha band frontal cortex-habenula coherence (B) and theta band power increase in habenula stimuli (C) changed with emotional valence (left column) and arousal (right column). Each dot shows the average of one participant in each categorical valence condition, which are also the source data of the multilevel modelling results presented in Table 2. The R and p value in the figure are the results of partial correlation considering all data points together.

    Page 8: "The time-varying coherence was calculated for each trial". This is confusing because coherence quantifies the stability of a phase difference over time, i.e. it is a temporal average, not defined for individual trials. It has also been used to describe the phase difference stability over trials rather than time, and I assume this is the method applied here. Typically, the greatest coherence values coincide with event-related power increases, which is why I am surprised to see maximum coherence at 1s rather than immediately post-stimulus.

    We thank the reviewer for pointing out this incorrect description. As the reviewer pointed out correctly, the method we used describe the phase difference stability over trials rather than time. We have now clarified how coherence was calculated and added more details in the methods:

    "The time-varying cross trial coherence between each MEG sensor and the habenula LFP was first calculated for each emotional valence condition. For this, time-frequency auto- and cross-spectral densities in the theta/alpha frequency band (5-10 Hz) between the habenula LFP and each MEG channel at sensor level were calculated using the wavelet transform-based approach from -2000 to 4000 ms for each trial with 1 Hz steps using the Morlet wavelet and cycle number of 6. Cross-trial coherence spectra for each LFP-MEG channel combination was calculated for each emotional valence condition for each habenula using the function ‘ft_connectivityanalysis’ in Fieldtrip (version 20170628). Stimulus-related changes in coherence were assessed by expressing the time-resolved coherence spectra as a percentage change compared to the average value in the -2000 to -200 ms (pre-stimulus) time window for each frequency."

    In the Morlet wavelet analysis we used here, the cycle number (C) determines the temporal resolution and frequency resolution for each frequency (F). The spectral bandwidth at a given frequency F is equal to 2F/C while the wavelet duration is equal to C/F/pi. We used a cycle number of 6. For theta band activities around 5 Hz, we will have the spectral bandwidth of 25/6 = 1.7 Hz and the wavelet duration of 6/5/pi = 0.38s = 380ms.

    As the reviewer noticed, we observed increased activities across a wide frequency band in both habenula and the prefrontal cortex within 500 ms after stimuli onset. But the increase of cross-trial coherence starts at around 300 ms. The increase of coherence in a time window without increase of power in either of the two structures indicates a phase difference stability across trials in the oscillatory activities from the two regions, and this phase difference stability across trials was not secondary to power increase.

    Reviewer #3 (Public Review):

    This paper describes the oscillatory activity of the habenula using local field potentials, both within the region and, through the use of MEG, in connection to the prefrontal cortex. The characteristics of this activity were found to vary with the emotional valence but not with arousal. Sheding light on this is relevant, because the habenula is a promising target for deep brain stimulation.

    In general, because I am not much on top of the literature on the habenula, I find difficult to judge about the novelty and the impact of this study. What I can say is that I do find the paper is well-written and very clear; and the methods, although quite basic (which is not bad), are sound and rigourous.

    We thank the reviewer for the positive comments about the potential implication of our study and on the methods we used.

    On the less positive side, even though I am aware that in this type of studies it is difficult to have high N, the very low N in this case makes me worry about the robustness and replicability of the results. I'm sure I have missed it and it's specified somewhere, but why is N different for the different figures? Is it because only 8 people had MEG? The number of trials seems also a somewhat low. Therefore, I feel the authors perhaps need to make an effort to make up for the short number of subjects in order to add confidence to the results. I would strongly recommend to bootstrap the statistical analysis and extract non-parametric confidence intervals instead of showing parametric standard errors whenever is appropriate. When doing that, it must be taken into account that each two of the habenula belong to the same person; i.e. one bootstraps the subjects not the habenula.

    We do understand and appreciate the concern of the reviewer on the low sample numbers due to the strict recruitment criteria for this very early stage clinical trial: 9 patients for bilateral habenula LFPs, and 8 patients with good quality MEGs. Some information to justify the number of trials per condition for each participant has been provided in the reply to the Detailed Comments 1 from Reviewer 2. The sample number used in each analysis was included in the figures and in the main text.

    We have used non-parametric cluster-based permutation approach (Maris and Oostenveld, 2007) for all the main results as shown in Fig. 3-5. Once the clusters (time window and frequency band) with significant differences for different emotional valence conditions have been identified, parametric statistical test was applied to the average values of the clusters to show the direction of the difference. These parametric statistics are secondary to the main non-parametric permutation test.

    In addition, the DICS beamforming method was applied to localize cortical sources exhibiting stimuli-related power changes and cortical sources coherent with deep brain LFPs for each subject for positive and negative emotional valence conditions respectively. After source analysis, source statistics over subjects was performed. Non-parametric permutation testing with or without cluster-based correction for multiple comparisons was applied to statistically quantify the differences in cortical power source or coherence source between negative and positive emotional stimuli.

    References:

    Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods 2007; 164(1): 177-90.

    Related to this point, the results in Figure 6 seem quite noisy, because interactions (i.e. coherence) are harder to estimate and N is low. For example, I have to make an effort of optimism to believe that Fig 6A is not just noise, and the result in Fig 6C is also a bit weak and perhaps driven by the blue point at the bottom. My read is that the authors didn't do permutation testing here, and just a parametric linear-mixed effect testing. I believe the authors should embed this into permutation testing to make sure that the extremes are not driving the current p-value.

    We have now quantified the coherence between frontal cortex-habenula and occipital cortex-habenula separately (please see more details in the reply to Reviewer 2 (Recommendations for the authors 6). The new analysis showed that the increase in the theta/alpha band coherence around 1 s after the negative stimuli was only observed between prefrontal cortex-habenula and not between occipital cortex-habenula. This supports the argument that Fig. 6A is not just noise.

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  2. Reviewer #3 (Public Review):

    This paper describes the oscillatory activity of the habenula using local field potentials, both within the region and, through the use of MEG, in connection to the prefrontal cortex. The characteristics of this activity were found to vary with the emotional valence but not with arousal. Sheding light on this is relevant, because the habenula is a promising target for deep brain stimulation.

    In general, because I am not much on top of the literature on the habenula, I find difficult to judge about the novelty and the impact of this study. What I can say is that I do find the paper is well-written and very clear; and the methods, although quite basic (which is not bad), are sound and rigourous.

    On the less positive side, even though I am aware that in this type of studies it is difficult to have high N, the very low N in this case makes me worry about the robustness and replicability of the results. I'm sure I have missed it and it's specified somewhere, but why is N different for the different figures? Is it because only 8 people had MEG? The number of trials seems also a somewhat low. Therefore, I feel the authors perhaps need to make an effort to make up for the short number of subjects in order to add confidence to the results. I would strongly recommend to bootstrap the statistical analysis and extract non-parametric confidence intervals instead of showing parametric standard errors whenever is appropriate. When doing that, it must be taken into account that each two of the habenula belong to the same person; i.e. one bootstraps the subjects not the habenula.

    Related to this point, the results in Figure 6 seem quite noisy, because interactions (i.e. coherence) are harder to estimate and N is low. For example, I have to make an effort of optimism to believe that Fig 6A is not just noise, and the result in Fig 6C is also a bit weak and perhaps driven by the blue point at the bottom. My read is that the authors didn't do permutation testing here, and just a parametric linear-mixed effect testing. I believe the authors should embed this into permutation testing to make sure that the extremes are not driving the current p-value.

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  3. Reviewer #2 (Public Review):

    In this study, Huang and colleagues recorded local field potentials from the lateral habenula in patients with psychiatric disorders who recently underwent surgery for deep brain stimulation (DBS). The authors combined these invasive measurements with non-invasive whole-head MEG recordings to study functional connectivity between the habenula and cortical areas. Since the lateral habenula is believed to be involved in the processing of emotions, and negative emotions in particular, the authors investigated whether brain activity in this region is related to emotional valence. They presented pictures inducing negative and positive emotions to the patients and found that theta and alpha activity in the habenula and frontal cortex increases when patients experience negative emotions. Functional connectivity between the habenula and the cortex was likewise increased in this band. The authors conclude that theta/alpha oscillations in the habenula-cortex network are involved in the processing of negative emotions in humans.

    Because DBS of the habenula is a new treatment tested in this cohort in the framework of a clinical trial, these are the first data of its kind. Accordingly, they are of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap.

    In terms of community impact, I see the strengths of this paper in basic science rather than the clinical field. The authors demonstrate the involvement of theta oscillations in the habenula-prefrontal cortex network in emotion processing in the human brain. The potential of theta oscillations to serve as a marker in closed-loop DBS, as put forward by the authors, appears less relevant to me at this stage, given that the clinical effects and side-effects of habenula DBS are not known yet.

    Detailed comments:

    The group-average MEG power spectrum (Fig. 4B) suggests that negative emotions lead to a sustained theta power increase and a similar effect, though possibly masked by a visual ERP, can be seen in the habenula (Fig. 3C). Yet the statistics identify brief elevations of habenula theta power at around 3s (which is very late), a brief elevation of prefrontal power a time 0 or even before (Fig. 4C) and a brief elevation of Habenula-MEG theta coherence around 1 s. It seems possible that this lack of consistency arises from a low signal-to-noise ratio. The data contain only 27 trails per condition on average and are contaminated by artifacts caused by the extension wires.

    I doubt that the correlation between habenula power and habenula-MEG coherence (Fig. 6C) is informative of emotion processing. First, power and coherence in close-by time windows are likely to to be correlated irrespective of the task/stimuli. Second, if meaningful, one would expect the strongest correlation for the negative condition, as this is the only condition with an increase of theta coherence and a subsequent increase of theta power in the habenula. This, however, does not appear to be the case.

    The authors included the factors valence and arousal in their linear model and found that only valence correlated with electrophysiological effects. I suspect that arousal and valence scores are highly correlated. When fed with informative yet highly correlated variables, the significance of individual input variables becomes difficult to assess in many statistical models. Hence, I am not convinced that valence matters but arousal not.

    Page 8: "The time-varying coherence was calculated for each trial". This is confusing because coherence quantifies the stability of a phase difference over time, i.e. it is a temporal average, not defined for individual trials. It has also been used to describe the phase difference stability over trials rather than time, and I assume this is the method applied here. Typically, the greatest coherence values coincide with event-related power increases, which is why I am surprised to see maximum coherence at 1s rather than immediately post-stimulus.

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  4. Reviewer #1 (Public Review):

    The study by Huang et al. report on direct recordings (using DBS electrodes) from the human habenula in conjunction with MEG recordings in 9 patients. Participants were shown emotional pictures. The key finding was a transient increase in theta/alpha activity with negative compared to positive stimuli. Furthermore, there was a later increase in oscillatory coupling in the same band. These are important data, as there are few reports of direct recordings from the habenula together with the MEG in humans performing cognitive tasks. The findings do provide novel insight into the network dynamics associated with the processing of emotional stimuli and particular the role of the habenula.

    Recommendations:

    How can we be sure that the recordings from the habenula are not contaminated by volume conduction; i.e. signals from neighbouring regions? I do understand that bipolar signals were considered for the DBS electrode leads. However, high-frequency power (gamma band and up) is often associated with spiking/MUA and considered less prone to volume conduction. I propose to also investigate that high-frequency gamma band activity recorded from the bipolar DBS electrodes and relate to the emotional faces. This will provide more certainty that the measured activity indeed stems from the habenula.

    Figure 3: the alpha/theta band activity is very transient and not band-limited. Why refer to this as oscillatory? Can you exclude that the TFRs of power reflect the spectral power of ERPs rather than modulations of oscillations? I propose to also calculate the ERPs and perform the TFR of power on those. This might result in a re-interpretation of the early effects in theta/alpha band.

    Figure 4D: can you exclude that the frontal activity is not due to saccade artifacts? Only eye blink artifacts were reduced by the ICA approach. Trials with saccades should be identified in the MEG traces and rejected prior to further analysis.

    The coherence modulations in Fig 5 occur quite late in time compared to the power modulations in Fig 3 and 4. When discussing the results (in e.g. the abstract) it reads as if these findings are reflecting the same process. How can the two effect reflect the same process if the timing is so different?

    Be explicit on the degrees of freedom in the statistical tests given that one subject was excluded from some of the tests.

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  5. Evaluation Summary:

    Since DBS of the habenula is a new treatment, these are the first data of its kind and potentially of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap. This paper is of interest to neuroscientists studying emotions and clinicians treating psychiatric disorders. Specifically the paper shows that the habenula is involved in processing of negative emotions and that it is synchronized to the prefrontal cortex in the theta band. These are important insights into the electrophysiology of emotion processing in the human brain.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

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