Author response: Cortico-subcortical β burst dynamics underlying movement cancellation in humans

Field: Neuroscience (Life Sciences)

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Dominant neuroanatomical models hold that humans regulate their movements via loop-like cortico-subcortical networks, which include the subthalamic nucleus (STN), motor thalamus, and sensorimotor cortex (SMC). Inhibitory commands across these networks are purportedly sent via transient, burst-like signals in the β frequency (15–29 Hz). However, since human depth-recording studies are typically limited to one recording site, direct evidence for this proposition is hitherto lacking. Here, we present simultaneous multi-site recordings from SMC and either STN or motor thalamus in humans performing the stop-signal task. In line with their purported function as inhibitory signals, subcortical β-bursts were increased on successful stop-trials. STN bursts in particular were followed within 50 ms by increased β-bursting over SMC. Moreover, between-site comparisons (including in a patient with simultaneous recordings from SMC, thalamus, and STN) confirmed that β-bursts in STN temporally precede thalamic β-bursts. This highly unique set of recordings provides empirical evidence for the role of β-bursts in conveying inhibitory commands along long-proposed cortico-subcortical networks underlying movement regulation in humans. Editor's evaluation This work makes an important contribution to the literature and addresses timely and interesting questions relating to the role of transient beta oscillations in cancelling motor responses in a rare and valuable dataset. https://doi.org/10.7554/eLife.70270.sa0 Decision letter eLife's review process Introduction Movement cancellation – that is, the ability to stop ongoing or prepotent movements when necessary – allows humans to adapt their behavior quickly to changing environmental demands. A predominant paradigm used to investigate inhibitory control is the stop-signal task (SST), wherein participants are tasked with executing and sometimes cancelling movements (Logan et al., 1984; Verbruggen et al., 2019). This task allows for computation of the duration of the latent cancellation process (stop-signal reaction time, SSRT), although no overt response is made when participants successfully stop (Verbruggen,, 2008; Boucher et al., 2007.) The neural pathways underlying movement cancellation comprise a fronto-basal ganglia (FBg) network for inhibitory control (Wessel and Aron, 2017), which recruits known anti-kinetic basal ganglia pathways (Jahanshahi et al., 2015). When a stop-signal occurs, the right inferior frontal cortex (rIFC) purportedly excites the subthalamic nucleus (STN) via a monosynaptic 'hyperdirect' pathway between the two regions (Nambu et al., 2002; Aron, 2007; Chen et al., 2020). Subsequently, the STN broadly excites the internal segment of the globus pallidus (GPi; Parent and Hazrati, 1993; Gillies and Willshaw, 1998), the output nucleus of the basal ganglia. In turn, the GPi inhibits the ventral oral posterior (Vop) region of the motor thalamus (Inase and Tanji, 1995; Kuo and Carpenter, 1973). It has been proposed that the resultant net-inhibition of thalamocortical signaling loops (i.e., motoric loops between thalamus and sensorimotor cortex) enables the type of rapid movement cancellation found in tasks like the SST (Parent and Hazrati, 1993; Jahanshahi et al., 2015). Recordings from nodes of this FBg network have revealed that communication through these pathways likely occurs in the β frequency band. During movement execution, decreases in averaged β power are observed over sensorimotor cortex (SMC; both intracranially and on the scalp, Crone et al., 1998; Pfurtscheller and Lopes da Silva, 1999; Kühn et al., 2004; Takemi et al., 2013) and in subcortical motor regions such as the STN (Alegre et al., 2005) and the ventral intermediate (VIM) nucleus of the motor thalamus, a part of motor thalamus adjacent to Vop (Basha et al., 2014). In contrast, averaged β power is increased in SMC and STN when inhibitory control is required, both following stop-signals in the SST (Wessel et al., 2016 ; Swann et al., 2009; Swann et al., 2011; Ray et al., 2012; Alegre et al., 2013; Benis et al., 2014; Bastin et al., 2014) and during motor conflict more broadly (Brittain et al., 2012; Wessel et al., 2019). Similar increases in β power during movement cancellation are observed in cortical regions ostensibly upstream of the STN and thalamus, such as the pre-supplementary motor area (Swann et al., 2012; Picazio et al., 2014) and the rIFC (Swann et al., 2009). Together, these findings have established cortical and subthalamic β activity as an index of inhibitory control. However, cross-species research has revealed that these changes in β power do not reflect sustained β oscillations at the single-trial level (Feingold et al., 2015; Sherman et al., 2016; Shin et al., 2017; Tinkhauser et al., 2017; Maling et al., 2018; Cagnan et al., 2019). Unaveraged β activity has properties better characterized as intermittent bursting instead of slow-and-steady modulations of amplitude (van Ede et al., 2018). In line with this, β bursts are more predictive of behavior than fluctuations in averaged β power. For example, perceptual stimuli preceded closely by β bursts in somatosensory cortex are less likely to be detected (Shin et al., 2017) and β bursts in motor cortex closely preceding imperative stimuli are associated with slower responses (Little et al., 2019). Biophysical computational models suggest that these bursts in SMC relate to coincident proximal and distal excitatory drives to the synapses of neocortical pyramidal neurons (Sherman et al., 2016). Thus, not only do β bursts carry fine-grained information about behavior on the single-trial level, they also relate more closely to underlying mechanisms than averaged β. Notably, two recent studies have demonstrated that β bursts on the scalp relate to the inhibitory aspects of movement regulation. One study demonstrated reductions in β burst rates over SMC during go trials, as well as increases in burst rates over frontocentral and motor cortices during stop trials, and found that successful stop trials featured a greater number of frontocentral β bursts before SSRT on average than failed stop trials (Wessel, 2020). A subsequent study by Jana et al., 2020 demonstrated that β bursts over prefrontal cortex were followed within 20ms by broad skeleto-motor suppression and within 40ms by outright cancellation detectable at the motor effector. While these studies identify potential (pre)frontal cortical control signals associated with movement cancellation, they are uninformative regarding the downstream basal ganglia-thalamic dynamics through which inhibitory control of SMC is ostensibly implemented. Although transient β bursts are known to exist in the STN (Torrecillos et al., 2018; Lofredi et al., 2019), it is unclear what functional role subcortical β bursts play during movement regulation, and whether their dynamics conform to the dominant neurophysiological and neuroanatomical models of inhibitory control. Beyond generating basic knowledge about the neurophysiology of basal ganglia motor circuitry, elucidating these dynamics would also greatly inform therapeutic approaches that are already targeting the known pathological β bursting that occurs in these subcortical regions (Tinkhauser et al., 2017; Little and Brown, 2020). Our aims for the current study were twofold. Firstly, we investigated whether β bursts in subcortical regions of basal ganglia-thalamic inhibitory pathways are associated with movement cancellation. To this end, we investigated the relationship between SST performance and β burst rates in both STN and motor thalamus. Furthermore, we tested whether these subcortical bursts have reliable temporal relationships with movement-related β bursts in SMC, suggestive of an inhibitory influence of the subcortical regions on SMC. Secondly, we evaluated existing models of inhibitory control networks by assessing relative timing of bursts across subcortical recording sites. The dominant model of a fronto-basal ganglia circuit for inhibitory control suggests that movement cancellation is accomplished by net-inhibition of the motor thalamus by STN (Jahanshahi et al., 2015). Hence, in line with the proposition that STN is recruited before the thalamus during cancellation, we expected β bursts related to movement cancellation to emerge first in the STN, followed by bursts in the thalamus. We collected recordings of local field potentials (LFPs) simultaneously from SMC and a subcortical site (either the STN or motor thalamus) during awake deep-brain stimulation (DBS) lead implantation surgery in two groups of patients: patients with Parkinson's disease (PD) undergoing STN implantation and essential tremor (ET) patients undergoing implantations in the motor thalamus. Moreover, data from one highly unique PD patient included simultaneous recordings from all three locations: SMC, STN, and motor thalamus. During the recordings, patients performed an auditory version of the SST to test their ability to rapidly cancel movements. This study leverages unique multi-site intracranial recordings and a cognitive paradigm to investigate the circuit dynamics of cortical and subcortical β bursts during movement cancellation. Results Behavior While LFPs were recorded from SMC and either STN or thalamus, 21 participants completed an auditory SST (see Figure 3—figure supplement 1). Behavioral results are shown in Table 1. To confirm that patients' diagnoses did not affect cognitive performance or task strategies in a way that would preclude comparing the two participant groups, we compared behavioral performance between the STN and thalamic groups. Go accuracy was the only behavioral metric which significantly differed between the STN and thalamic groups, with thalamic patients responding more accurately on go-trials (94% vs 83% accuracy, T(19) = 5.22, p < 0.0001, d = 2.27). Numerically, the thalamic implant group also responded faster during correct go and failed stop trials and cancelled movements more quickly than STN DBS patients, as indicated by SSRT (which was in the typical elongated range for movement disorder patients: Gauggel et al., 2004; Obeso et al., 2011; Hughes et al., 2019). However, none of these results were significant, indicating comparable task performance between both groups. (Go RT: T(19) = –0.19, p = 0.85, d = 0.08; Failed stop RT: T(19) = –0.58, p = 0.57, d = 0.25; SSRT: T(19) = –1.11, P = 0.28, d = 0.48; Stop accuracy: T(19) = 1.59, P = 0.13, d = 0.68.) Table 1 Means of stop-signal task behavioral performance metrics. * Indicates significant difference between thalamic and STN groups at p < 0.0001. SD = standard deviation. Reaction times (ms)AccuracyGoFailed stopSSRTGo trials*Stop trialsAll participants943 (SD: 213)763 (SD: 174)474 (SD: 232)0.89 (SD: 0.07)0.61 (SD: 0.16)STN DBS952 (SD: 234)786 (SD: 213)533 (SD: 296)0.83 (SD: 0.05)0.55 (SD: 0.18)Thalamic DBS934 (SD: 203)741 (SD: 136)421 (SD: 150)0.94 (SD: 0.04)0.65 (SD: 0.11) Averaged event-related spectral perturbation (ERSP) analysis To confirm accurate electrode placement over hand-related areas of SMC, event-related spectral perturbation (ERSP) was quantified from –100 preceding to 1500 ms following the go-signals in a go-only localizer task. This short, 40-trial block was identical to the main stop-signal task but did not contain stop signals (in other words, it was a speeded two-choice response task) and was administered before the subcortical lead placement. While in the OR, we then visually checked the ERSPs for a decrease in average β band amplitude, a known signature of movement-related activity in SMC (cf., Crone et al., 1998; Pfurtscheller and Lopes da Silva, 1999; Kühn et al., 2004; Takemi et al., 2013), to confirm SMC electrode placement for the main experiment. More information about the localization process can be found in the Methods section. We then quantitatively investigated these relationships after surgery and found decreases in averaged β band (15–29 Hz) power observed at contralateral (to the response) SMC sites following go signals (see Figure 1A). This pattern was evident in both the localizer task and during the main SST. Figure 1 with 1 supplement see all Download asset Open asset Averaged β power and β burst rates decreased across recording sites during movement execution. (A) Broadband ERSPs shown were computed from 100ms preceding to 1500ms following the go signal. The two left-most plots include ERSPs from the localizer block and the right-most plots include ERSPs during the main block. In averaged ERSPs during movement on go-trials, both localizer and main task sessions show clearly visible β power decreases following the go signal. (B) Average burst rates at each recording location following the go-signal and surrounding response execution (for go and failed stop trials) are depicted in time bins of 100ms. During the main task session, β burst rates decrease quickly following the go signal in STN, thalamus (both ventral and dorsal contacts), and SMC until a response is made. β is burst-like in subcortex and cortex To determine whether β was indeed burst-like in our data, as opposed to an ongoing, oscillatory signature, we used a lagged coherence analysis (as in Wessel, 2020). Lagged coherence describes to what degree the current phase of a signal predicts its own phase in the future (Fransen et al., 2015). Signals that are oscillatory in nature can be expected to predict their own activity many cycles on, while phase of a transient signal will be less predictive of future activity in the same frequency band. In line with the assertion that β is a transient and not an oscillatory signal, we observed decreasing levels of lagged coherence in the β band as the number of cycles increased (Figure 2A). Moreover, at three cycles, a trough in lagged coherence is observed in the search band for β bursts (15–29 Hz) compared to surrounding frequencies at all recording locations (Figure 2B). This lagged coherence trough was most pronounced in the SMC and least pronounced in compared to neighboring frequencies in the thalamus, suggesting that SMC β is most burst-like and thalamic β more sustained in nature. STN β was slightly less burst-like compared to SMC β, but we note this could be an artifact of pathologically long STN β bursts characterized in movement disorders (Little et al., 2013; Anidi et al., 2018). Figure 2 Download asset Open asset Results from a lagged coherence analysis on epoched data including go and stop trials. (A) While coherence is relatively high at one cycle (current phase predicts phase in one cycle well), lagged coherence decreases over time so that lagged coherence in the β band is relatively low at three cycles across recording locations. This supports the claim that β is transient and not oscillatory in nature. (B) Coherence shown is at three cycles. The β burst search band (15–29 Hz) represents a relative trough compared to below- and above-β frequencies, indicating that β signals are more transient and less oscillatory than signals in neighboring frequency bands. β is most burst-like in the SMC, slightly less burst-like in STN, and least burst-like compared to neighboring frequencies in the thalamus. Having confirmed that β is indeed burst-like in our recording, we quantified several qualities of the observed β bursts and found that, on average, peak β burst frequency during the burst search window across the entire recording was 22 Hz in STN, 20 Hz in ventral thalamus, 21 Hz in dorsal thalamus, and 22 Hz in SMC. Average β burst duration was 127 ms in STN, 108 ms in ventral thalamus, 111 ms in dorsal thalamus, and 126 ms in SMC. The subsequent analyses described herein address β burst rates and counts, which have been found to be highly predictive of behavior (Sherman et al., 2016; Shin et al., 2017; Little et al., 2019; Wessel, 2020; Jana et al., 2020; Enz et al., 2021). β bursts decrease during movement initiation We first investigated whether the SMC and subcortical sites showed corresponding movement-related reductions in β burst rates leading up to the response (Wessel, 2020; Soh et al., 2021). Implantation in the motor thalamus specifically targeted the ventral intermediate nucleus (VIM, Benabid et al., 1993; Lozano, 2000). Neurosurgical implantation of VIM DBS multi-electrode leads positions electrodes on the border of the VIM and the ventral oral posterior (Vop) nuclei of the thalamus, which receive cerebellar (Na et al., 1997) and pallidal inputs (Inase and Tanji, 1995; Kuo and Carpenter, 1973), respectively. Both VIM and Vop are prominently involved in motor function. Due to volume conduction and the small size of these nuclei, we expected the thalamic depth-electrode recordings to be summative recordings from multiple nuclei within motor thalamus (VIM, Vop, and potentially the ventral oral anterior nucleus). To investigate whether contributions of different thalamic nuclei could be parsed in our thalamic recordings, we analyzed the most ventral and most dorsal contact pairs within motor thalamus separately. Notably, the results from both contact pairs were remarkably similar for most statistical comparisons, suggesting that all thalamic contacts captured contributions from a similar set of thalamic motor nuclei. While the same dorsal-ventral distinction is not typically used in analyses of STN (where one specific electrode contact pair can typically be identified as localized in STN by a clear-cut peak in the β activity spectrum), we also performed STN analyses split by dorsal-ventral pairs. However, these analyses are presented in the Figure supplements only. In the main analyses, we follow the convention of selecting one STN contact pair based on the overall amount of detected β bursts throughout the entire recording. Burst rates were quantified in non-overlapping bins of 100ms starting from the go signal. Indeed, we observed reductions in β burst rates during movement execution in all recording locations. 3 × 2 ANOVAs revealed significant effects of TIMEPOINT on burst rate in STN (F(2,8) = 8.78, p < 0.0001, η2 = 0.31), ventral thalamus (F(2,10) = 6.79, p < 0.0001, η2 = 0.25), dorsal thalamus (F(2,10) = 15.85, p < 0.0001, η2 = 0.37), and SMC (F(2,19) = 8.04, p < 0.0001, η2 = 0.22; see Figure 1B). In the STN group, there was also a significant effect of (F(2,8) = p = η2 = on burst but no TIMEPOINT (F(2,8) = p = η2 = the other for the ventral thalamic there was no significant effect of (F(2,10) = p = η2 = on burst but there was a significant TIMEPOINT = p = η2 = thalamic electrodes did not significant effects of (F(2,10) = p = η2 = or a TIMEPOINT (F(2,10) = p = η2 = For burst ANOVAs revealed significant effects of TIMEPOINT on burst rate in STN (F(2,8) = p = η2 = ventral thalamus (F(2,10) = p = η2 = dorsal thalamus (F(2,10) = p = η2 = and SMC (F(2,19) = p = η2 = see Figure 1B). For ventral thalamic there were also significant effects of (F(2,10) = p = η2 = and a TIMEPOINT (F(2,10) = p = η2 = For dorsal thalamus, there was a significant main effect of (F(2,10) = p = η2 = but no TIMEPOINT (F(2,10) = p = η2 = findings are in line with the proposition that β bursts are related to an of the motor which be to a of the cortico-subcortical motor and movement Soh et al., 2021). β bursts during movement cancellation the basal ganglia movement through of Furthermore, it has been demonstrated that β bursts are inhibitory with to movement (Little et al., 2019; Soh et al., 2021). the main of our study was that after stop-signals in the motor is by a rapid of an in SMC, preceded by β burst signaling from the subcortical nuclei. To test this we first investigated whether successful were by an in subcortical β bursting compared to go-trials and failed stop-trials. Indeed, during the time between the of the stop signal and the of a significant main effect of failed and on burst was found in STN and thalamus = p = η2 = ventral = p = η2 = dorsal = p = η2 = revealed that this was to a significant in bursts in the stop signal for successful stop trials compared to go trials P = ventral p = dorsal p = and successful stop trials compared to failed stop trials p = ventral p = see Figure the other failed stop trials did not contain significantly more bursts in the window compared to go trials p = ventral p = dorsal p = findings are in line with the that subcortical β bursting a rapid of inhibitory control after a Figure 3 with supplements see all Download asset Open asset β burst rates following stop (A) in average β band activity are observed at all subcortical recording sites during stop trials shown is between successful and correct go (B) The number of β bursts was quantified between stop-signal and For go-trials, this window at current in the represents the average burst for one β bursts increased at in STN and thalamus during successful cancellation quantified between and and at in both thalamus and STN during stop trials The in burst and rates in and could not be for by in or burst quantified in the 100ms preceding or stop-signal Average burst rates at each subcortical recording location to the stop-signal or SSRT are depicted in time bins of 100ms. The on the plots show the SSRT average for STN and thalamic DBS patient groups. comparisons = between successful stop and go trials, = between successful and failed stop * p < p < p < effects in the are effects of on burst (B) or on burst rates at time a control we also compared burst rates across in the before the stop signal and go signal. 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