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Research Articles, Behavioral/Cognitive
β-Bursts Reveal the Trial-to-Trial Dynamics of Movement Initiation and Cancellation
Jan R. Wessel
Journal of Neuroscience 8 January 2020, 40 (2) 411-423; DOI: https://doi.org/10.1523/JNEUROSCI.1887-19.2019
1Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa 52245, and 2Department of Neurology, University of Iowa Hospital and Clinics, Iowa City, Iowa 52242
ORCID record for Jan R. Wessel
The neurophysiological basis of motor control is of substantial interest to basic researchers and clinicians alike. Motor processes are accompanied by prominent field potential changes in the β-frequency band (15–29 Hz): in trial-averages, movement initiation is accompanied by β-band desynchronization over sensorimotor areas, whereas movement cancellation is accompanied by β-power increases over (pre)frontal areas. However, averaging misrepresents the true nature of the β-signal. Unaveraged β-band activity is characterized by short-lasting, burst-like events, rather than by steady modulations. Therefore, averaging-based quantifications may miss important brain–behavior relationships. To investigate how β-bursts relate to movement in male and female humans (N = 234), we investigated scalp-recorded β-band activity during the stop-signal task, which operationalizes both movement initiation and cancellation. Both processes were indexed by systematic spatiotemporal changes in β-burst rates. Before movement initiation, β-bursting was prominent at bilateral sensorimotor sites. These burst-rates predicted reaction time (a relationship that was absent in trial-average data), suggesting that sensorimotor β-bursting signifies an inhibited motor system, which has to be overcome to initiate movements. Indeed, during movement initiation, sensorimotor burst-rates steadily decreased, lateralizing just before movement execution. In contrast, successful movement cancellation was signified by increased phasic β-bursting over fronto-central sites. Such β-bursts were followed by short-latency increases of bilateral sensorimotor β-burst rates, suggesting that motor inhibition can be rapidly re-instantiated by frontal areas when movements have to be rapidly cancelled. Together, these findings suggest that β-bursting is a fundamental signature of the motor system, used by both sensorimotor and frontal areas involved in the trial-by-trial control of behavior.
SIGNIFICANCE STATEMENT Movement-related β-frequency (15–29 Hz) changes are among the most prominent features of neural recordings across species, scales, and methods. However, standard averaging-based methods obscure the true dynamics of β-band activity, which is dominated by short-lived, burst-like events. Here, we demonstrate that both movement-initiation and cancellation in humans are characterized by unique trial-to-trial patterns of β-bursting. Movement initiation is characterized by steady reductions of β-bursting over bilateral sensorimotor sites. In contrast, during rapid movement cancellation, β-bursts first emerge over fronto-central sites typically associated with motor control, after which sensorimotor β-bursting re-initiates. These findings suggest a fundamentally novel, non-invasive measure of the neural interaction underlying movement-initiation and -cancellation, opening new avenues for the study of motor control in health and disease.
β-bursts
motor inhibition
response inhibition
stop-signal task
Activity in the β-frequency band (15–29 Hz) is a prominent constituent of the neural local field potential. It can be observed at spatial scales ranging from extracellular to scalp recordings, in species ranging from rodents to humans, and using methods ranging from intracranial recordings to magnetoencephalography (Murthy and Fetz, 1992; Sanes and Donoghue, 1993; Engel and Fries, 2010; Shin et al., 2017). The β-frequency plays a particularly important role in motor functions. In particular, during movement initiation, a prominent desynchronization of β-band activity is clearly observable over sensorimotor areas (McFarland et al., 2000; Pfurtscheller et al., 2003). In contrast, the rapid cancellation of movements is accompanied by β-power increases over (pre)frontal cortical areas typically implicated in cognitive control (Swann et al., 2009, 2011, 2012; Picazio et al., 2014; Wagner et al., 2018). Moreover, movement-related changes in β-power can also be observed in extrapyramidal parts of the motor system, including the basal ganglia (Kühn et al., 2004; Ray et al., 2012; Brittain and Brown, 2014; Wessel et al., 2016), where abnormal β-rhythms are prominently observed in movement disorders such as Parkinson's disease (Hammond et al., 2007; Bronte-Stewart et al., 2009; Jenkinson and Brown, 2011; Little and Brown, 2014; Quinn et al., 2015).
Recent studies of raw, unaveraged β-band activity, however, have led to a significant reappraisal of the nature of neural activity in the β-band. Whereas trial-averaging approaches have long suggested that movement-related changes in the β-band are expressed in steady (de)synchronizations that stretch over several hundred milliseconds, un-averaged β-band activity is primarily characterized by transient burst-like events, which typically last <∼150 ms (Leventhal et al., 2012; Feingold et al., 2015; Sherman et al., 2016). Although these burst-events look like slow-evolving (de)synchronizations when averaged across trials, analyses of single-trial data have found that the simple presence or absence of these β-bursts, rather than overall changes in β-power, is the most reliable predictor of trial-to-trial behavior (Shin et al., 2017).
Here, we therefore investigated the characteristics of single-trial β-bursting in humans during both the initiation and the rapid cancellation of movement. A large sample of healthy human participants (N = 234) performed the stop-signal task (Logan and Cowan, 1984; Verbruggen et al., 2019), a motor task that includes both instances of movement initiation (following a go-signal) and movement cancellation (on a subset of trials that include a subsequent stop-signal). We used non-invasive scalp-EEG recordings to investigate how β-bursting on individual trials indexes both processes, as well as their interaction. Specifically, we investigated four questions: (1) Is human cortical β-band activity during movement burst-like? (2) If so, do systematic spatiotemporal patterns of β-burst activity distinguish successful from unsuccessful movement cancellation, as would be expected based on the trial-average literature on β-power? (3) Are there systematic relationships between initiation and cancellation-related changes in β-bursting when movements have to be rapidly stopped? (4) Do β-bursts provide a more accurate representation of brain–behavior relationships compared with changes in overall β-amplitude?
Two hundred and thirty-four healthy adult humans (mean age: 22.7, SEM: 0.43, 137 female, 25 left-handed) from the Iowa City community participated in the study, either for course credit or for an hourly payment. The majority of these datasets have been previously published as part of other studies, none of which focused on β-bursting (Dutra et al., 2018; Wessel, 2018a,b; Dykstra et al., 2019; Waller et al., 2019; Wessel et al., 2019). All procedures were approved by the local ethics committee at the University of Iowa (IRB 201511709).
The task was identical to the one described by Wessel, (2018a,b), Dutra et al. (2018), and Waller et al. (2019). In short, trials began with a fixation cross (500 ms duration), followed by a white leftward or rightward arrow (Go-signal). Participants were instructed to respond as fast and accurately as possible to the arrow using their left or right index finger (the respective response buttons were q and p on a QWERTY keyboard). On one-third of trials, a Stop-signal occurred (the arrow turned from white to red) at a delay after the go-stimulus [stop-signal delay (SSD)]. The SSD, which was initially set to 200 ms, was dynamically adjusted in 50 ms increments to achieve a p(stop) of 0.5: after successful stops, the SSD was prolonged; after failed stops, it was shortened. This was done independently for leftward and rightward go-stimuli. Trial duration was fixed at 3000 ms. Six blocks of 50 trials were performed (200 Go, 100 Stop).
All data, procedures, and analysis routines can be downloaded on the Open Science Framework at https://osf.io/v3a78/.
EEG recording
Scalp-EEG was recorded using two different Brain Products systems; one active (actiChamp) and one passive (MR plus). In both cases, 62-channel electrode caps with two additional electrodes on the left canthus (over the lateral part of the orbital bone of the left eye) and over the part of the orbital bone directly below the left eye were used. The ground was placed at electrode Fz, and the reference was placed at electrode Pz. EEG was digitized at a sampling rate of 500 Hz, with hardware filters set to 10 s time-constant high-pass and 1000 Hz low-pass.
EEG data preprocessing
Data were preprocessed using custom routines in MATLAB, incorporating functions from the EEGLAB toolbox (Delorme and Makeig, 2004; RRID:SCR_007292). The electrode × time-series matrices for each task were imported into MATLAB and then filtered using symmetric two-way least-squares finite impulse response filters (high-pass cutoff: 0.3 Hz, low-pass cutoff: 30 Hz). Non-stereotyped artifacts were automatically removed from further analysis using segment statistics applied to each second-long segment of data (joint probability and joint kurtosis, with both cutoffs set to 5 SD, cf. Delorme et al., 2007). Trials that included a rejected data segment were excluded from further analysis. After removal of non-stereotypic artifacts, the data were re-referenced to common average and subjected to a temporal infomax ICA decomposition algorithm (Bell and Sejnowski, 1995), with extension to sub-Gaussian sources (Lee et al., 1999). The resulting component matrix was screened for components representing eye movement and electrode artifacts using outlier statistics and non-dipolar components (residual variance cutoff at 15%; Delorme et al., 2012), which were removed from the data using automated outlier-based statistics (Dutra et al., 2018; Wessel, 2018a,b; Waller et al., 2019). The remaining components were subjected to further analyses. For all statistical analyses and all plots (except the topographical plots in Figs. 1a, 2a), the data were subsequently transformed to a reference-free montage using the current-source density method (Perrin et al., 1989; Tenke and Kayser, 2005), which minimizes the effects of volume conduction on the scalp-measured activity. This was done to enable β-event detection at frontocentral electrode FCz and lateral sensorimotor electrodes C3 and C4 while reducing the chance of cross-contamination by either side.
β-burst detection
β-burst detection was performed exactly as described by Shin et al. (2017). The description is adapted from therein.
First, each electrode's data were convolved with a complex Morlet wavelet of the form:
, and m = 7 (cycles) for each of 15 evenly spaced frequencies spanning the β-band (15–29 Hz). Time-frequency power estimates were extracted by calculating the squared magnitude of the complex wavelet-convolved data. These power estimates were then epoched relative to the events in question (ranging from −500 to +1000 ms with respect to Stop-/Go-signals). Individual β-bursts were defined as local maxima in the trial-by-trial β-band time-frequency power matrix for which the power exceeded a set cutoff of 6× the median power of the entire time-frequency power matrix for that electrode. Local maxima were identified using the MATLAB function imregional().
Experimental design and statistical analysis
Behavioral analysis.
Means were extracted for each subject for the following measures: Go-trial reaction time, failed Stop-trial reaction time, Stop-signal delay, Stopping accuracy, and Stop-signal reaction time, which was calculated via the integration method with replacement of miss-trial reaction times (Verbruggen et al., 2019). Failed Stop-trial reaction times and Go-trial reaction times were compared with a paired samples t test.
Topographical distribution of β-bursts.
To visualize the topographical distribution of β-bursts with respect to Stop- and Go-signals, 12 windows of 25 ms length, starting at 25 ms after the event, were defined. For each subject, the number of β-bursts in each window at each electrode following the respective stimulus (Go/Stop) was counted. The average number of β-bursts in each time window for each of the three trial types (Correct Go, Successful Stop, Failed Stop) was then plotted in a topographical grid representing the scalp surface (see Figs. 1a, 2a). Figure 3 depicts the difference between the number of β-bursts on Go- and Stop-trials in each window and at each electrode, tested for significance using paired-samples t tests. The resulting electrode × time window matrix of p values was corrected for multiple comparisons to a significance level of p = 0.0001 using the false-discovery rate (FDR) procedure (Benjamini et al., 2006).
Temporal development of β-bursts.
To visualize the temporal development of β-bursts on Go-trials at the two electrodes of interest (C3 and C4), 11 windows of 50 ms length ranging from 25 ms post-event to 575 ms post-event were defined. This time range spanned the entire post-Go-signal period leading up to (and including) mean reaction time. To test the linear decreasing trend observed at C3/C4 during movement initiation for significance, we submitted the means for left- and right-hand responses to the Mann–Kendall test. To test the lateralization of the linear trend toward the end of the response period (i.e., before mean RT), we compared the means for left- and right-hand responses at both electrodes using paired-samples t tests, again corrected for multiple comparisons to a significance level of p = 0.0001 using the FDR procedure. To visualize the temporal development of β-bursts on Stop-trials at the electrode of interest (FCz), six windows of 50 ms length ranging from 25 ms post-event to 325 ms post-event were defined. This time range spanned the entire post-Stop-signal period leading up to (and including) Stop-signal reaction time (SSRT; see Figs. 1b, 2b,d).
Correlation between lateral sensorimotor β-bursts and behavior.
The relative rates of β-bursts over lateral sensorimotor sites C3 and C4 for each subject and in each time-window following the Go-signal (see Fig. 1b,d) were then correlated with each subject's SSRT and Go-trial reaction time using Pearson's correlation coefficient. The resulting correlations were tested for significance and the respective p values were corrected for multiple comparisons using the FDR procedure (12 tests each, critical p < 0.05).
Comparison of pre-SSRT β-events.
To test the difference in the amount of β-bursts at fronto-central electrode FCz for significance, we counted the number of β-bursts in the time period ranging from the Stop-signal to each individual participant's SSRT estimate, separately for successful and failed Stop-trials. Moreover, we counted the number of β-bursts in a time period of identical length on Go-trials. The time period of interest on Go-trials was the time period ranging from the current Stop-signal delay on that trial and the participants' SSRT estimate. In other words, it corresponded to the Stop-signal-to-SSRT time period had there been a Stop-signal on that trial. The mean number of β-bursts for these periods were compared across subjects using a paired-samples t test.
Lateralized β-bursting after fronto-central β-bursts.
To investigate the pattern of β-bursting over bilateral electrodes C3/4 following fronto-central β-bursts in the Stop-Signal-to-SSRT period on successful Stop-trials, we identified each succesful Stop-trial in which such a fronto-central β-burst event occurred, and counted the amount of β-bursts at electrodes C3/4 (both contralateral and ipsilateral to the to-be-stopped response) in eight time windows of 25 ms duration ranging from −100 to +100 ms around the fronto-central β-burst event (see Fig. 4) In case more than one β-burst event was found, we chose the latency of the first of those events. To compare these β-burst counts to trials in which no fronto-central β-burst was found, a random time point in the Stop-signal-to-SSRT interval was chosen from a uniform distribution and C3/4 β-bursts were quantified in an identical time window around that random time point in the pre-SSRT period. The distribution of randomly selected time points did not significantly deviate from the distribution of β-burst onset times on trials with such bursts. We then compared the mean burst-counts for the two conditions in the four time-windows following the event (or the "pseudo-event" in the case of the trial without an actual fronto-central burst) using signed-rank tests (the non-parametrical equivalent of the paired-samples t test, chosen because of the large skew of the means toward 0 in these samples), corrected for multiple comparisons to a critical p value of p < 0.0001 using the FDR method.
β-power amplitude analysis.
To conduct the analyses presented in Figure 5, the time-frequency power estimates that were used for β-burst detection (see section "Beta burst detection") were converted to decibels using a 250 ms baseline before the event in question (Go-signal/Stop-signal). Activity in a specific post-event time period was then defined not by the amount of individual β-bursts within in the period, but instead by the average amplitude in that same period. All other analyses were then performed in the same manner as the β-burst analyses described.
Lagged phase coherence analysis.
Lagged phase coherence was quantified as described by Fransen et al. (2015). Computations were made in a beta version of the FieldTrip software package (Oostenveld et al., 2011; RRID:SCR_004849). Cycle number was set to 3 and frequencies between 8 and 35 Hz were chosen to cover both the β band and the surrounding frequencies. Lagged coherence was computed on the epoched Stop-trial data for electrode FCz and on the epoched Go-trial data for electrodes C3 and C4.
Both movement initiation and cancellation are accompanied by systematic spatiotemporal patterns of β-bursts
Figure 1a shows that after Go-signals (which prompt the start of movement initiation), bilateral sensorimotor sites (peaking over electrodes C3 and C4) initially showed localized β-bursting, which subsequently decreased in the time period leading up to movement execution. This resulted in a significant downward linear trend across the entire time period depicted in Figure 1, b and d (linear trend for left-hand responses at C4: Z = −3.63, p = 0.00028, left-hand responses at C3: Z = −3.22, p = 0.001, right-hand responses at C4: Z = −2.81, p = 0.005, right-hand responses at C3: Z = −3.23, p = 0.0013). Furthermore, this pattern lateralized toward the end of the response period, with sites contralateral to the response hand showing a stronger sustained reduction in β-bursting (significant lateralization at p < 0.0001, FDR-corrected, at time five consecutive windows from 325 to 575 ms for electrode C4, t(233) = −4.91, p = 1.75 × 10−06, d = 0.38, t(233) = −8.25, p = 1.2 × 10−14, d = 0.63, t(233) = −6.74, p = 1.24 × 10−10, d = 0.52, t(233) = −6.83, p = 7.37 × 10−11, d = 0.61, t(233) = −5.75, p = 2.81 × 10−08, d = 0.5; and at three consecutive time windows from 325 to 475 ms for electrode C3, t(233) = 5.28, p = 2.92 × 10−07, d = 0.41, t(233) = 6.02, p = 6.6 × 10−09, d = 0.49, t(233) = 4.81, p = 2.65 × 10−06, d = 0.37). These findings parallel reports from trial-averaged power-based quantifications of sensorimotor β-band activity (e.g., McFarland et al., 2000; Pfurtscheller et al., 2003). An inspection of individual trial data showed that the single-trial β-band signal was indeed characterized by clearly visible, burst-like events, rather than by steady modulations (Fig. 1c,e show data from one representative subject; plots of single trial data for each individual participant can be downloaded from https://osf.io/v3a78/).
β-burst properties during movement initiation on Go-trials. a, Topographical distribution of the average number of β-bursts on the scalp in consecutive time-windows following the Go-signal, for both left-hand (top) and right-hand (bottom) responses. There are bilateral peaks over electrode sites C3 and C4 until ∼200 ms following the Go-signal. Channels at which the number of bursts did not exceed significant increases from zero (when corrected using FDR) were set to 0. b, Temporal development of β-burst rates following the Go-Signal at right lateral sensorimotor electrode site C4 during both left- and right-hand responses. A significant linear trend is evident, such that the β-burst rates steadily decreased after the Go-signal. Moreover, a significant lateralization of this effect is evident starting at 325 ms following the Go-signal, such that the β-burst rates for the contralateral hand kept diminishing, while the rates showed an earlier asymptote for the ipsilateral response hand. c, β-band data at right sensorimotor electrode C4 from 36 individual Go-trials in a representative subject, showing the burst-like nature of β-activity. Each plot shows frequency on the y-axis (15–29 Hz) and time relative to the Go-signal on the x-axis, as demarcated on the bottom right trial (labels removed from the other trial plots for easier viewing). These same plots of single trial data for all individual participants can be downloaded from the OSF URL in the Methods section. d, Same as b, but for left-lateral electrode C3. e, Same as c, but for C3. f, Intersubject correlation between average Go-trial reaction time (green dots)/average Stop-signal reaction time (orange dots) and each subject's C3/C4 β-burst rates contralateral to the instructed movement, separately for each of the time windows in b. Black line, Least-squares fit; gray lines, confidence intervals. *p < 0.05 (FDR-corrected). Increased amounts of contralateral β-bursting between 75 and 125 ms following the Go-signal were related to longer Go-trial reaction times, as well as shorter Stop-signal reaction times.
With regards to movement cancellation, Figure 2a shows that after Stop-signals (which followed Go-signals on 1/3 of all trials at a variable delay and prompted the participants to attempt to cancel the movement instructed by the Go-signal), no coherent spatiotemporal organization of the rate of β-bursts could be observed until ∼200 ms after the stop-signal. At that point, a clear radial fronto-central topographical distribution emerged, centered around electrode FCz. Just like Go-signal-related activity at C3 and C4, single-trial Stop-signal-related activity at FCz clearly showed the presence of β-bursting (Fig. 2d).
β-burst properties during movement cancellation on Stop-trials. a, Topographical distribution of the number of β-bursts on the scalp in consecutive time-windows following the Stop-signal, separately for successful (top) and failed (bottom) Stop-trials. In the time-window toward the end of SSRT, a clear fronto-central organization of β-bursting centered around electrode FCz is evident. Channels at which the number of bursts did not exceed significant increases from zero (when corrected using FDR) were set to 0. b, Temporal development of β-burst rates following the Stop-Signal at fronto-central electrode FCz. c, Comparison of the number of β-bursts between successful and failed Stop-trials in the Stop-signal-to-SSRT period (as well as during a matched time period on Go-trials) for each subject. d, β-band data at electrode FCz from 18 individual Stop-trials in a representative subject, showing the burst-like nature of β-activity following the stop-signal (vertical line). Each plot shows frequency on the y-axis (15–29 Hz) and time relative to the Stop-signal on the x-axis, as demarcated on the bottom right trial (labels were removed from the remaining trials for easier viewing). The same plots of single trial data for each individual participant can be downloaded from the OSF URL in the Methods section.
Fronto-central β-bursting is increased during successful movement cancellation
Behavior in the Stop-signal task was typical for healthy young adults (mean Go-trial reaction time: 534 ms, SEM: 6.6; mean failed Stop-trial reaction time: 460 ms, SEM: 5.79; stop accuracy: 0.52, SEM: 0.002, mean Stop-signal delay: 282 ms, SEM: 7.91; mean Stop-Signal reaction time: 245 ms, SEM: 3.62). In accordance with the race model, failed Stop-trial reaction times were consistently faster than Go-trial reaction times: t(233) = 34.7, p = 4.9 × 10−94, d = 0.78. On the individual-subject level, 232 of 234 participants showed this pattern.
The observed increase of fronto-central β-bursting after Stop-signals (compared with Go-signals) starting ∼200 ms following the Stop-signal was highly significant. Figure 3 shows the topographical difference plots between the distribution of β-bursts following Stop- versus Go-trials, thresholded to a family-wise p = 0.0001 using the FDR-correction for 62 channels and 12 time windows. Moreover, the time after the Stop-signal at which this fronto-central organization of β-bursting developed overlaps with the end of SSRT (∼245 ms). This time period, before the end of SSRT, is the exact time period during which neural activity reflecting movement cancellation should be maximal according to both computational models of the Stop-signal task (Boucher et al., 2007) and neural recordings from the basal ganglia and the frontal eye fields (Hanes et al., 1998; Ogasawara et al., 2018). Moreover, a direct comparison of the pre-SSRT time period in each individual participant (i.e., the time range between the appearance of the Stop-signal and the end of each participant's individual SSRT estimate) revealed that successful Stop-trials yielded an increased rate of β-bursts at FCz compared with failed Stop-trials (t(233) = 3.46, p < 0.0007, d = 0.25; Fig. 2b,c) as well as compared with a matched time-period on Go-trials (t(233) = 12.28, p = 4.8 × 10−27, d = 0.88). This suggests that fronto-central β-bursts are related to movement cancellation, with successful stop-trials being accompanied by greater burst-rate increases. These findings parallel reports of increased trial-averaged β-band power found in intracranial recordings from neurological patients, where increased β-power can be found at sites in the medial wall of the frontal cortex; most notably, in the pre-supplementary motor area (Swann et al., 2012; Jha et al., 2015).
Statistical comparison of β-burst topographies following Stop- and Go-signals, separately for successful (top) and failed (bottom) Stop-trials. Thresholded for significance at p < 0.0001, FDR-corrected for multiple comparisons across channels and time windows. It is evident that while movement initiation (Go-trials) was accompanied by a significantly relative increase in β-burst count at bilateral sensorimotor sites until ∼175 ms after the Go-signal, movement cancellation was accompanied by significant relative increase of β-burst rates at fronto-central electrodes, starting at ∼200 ms after the Stop-signal.
Fronto-central β-bursts are followed by increased bilateral sensorimotor β-bursting
Figure 4a shows the temporal development of β-bursting at sensorimotor sites ipsilateral and contralateral to the to-be-stopped movement on successful stop-trials. Importantly, rather than being time-locked to the Stop-signal (or the Go-signal), these plots are time-locked to the latency of the first fronto-central β-burst event that occurred within the pre-SSRT time period on successful Stop-trials (i.e., between the Stop-signal and the participant's SSRT estimate). These plots show a significant increase in bilateral sensorimotor β-bursting within 25 ms of the first fronto-central β-burst. To evaluate significance, these values were compared with matched time-periods on successful stop-trials that did not show fronto-central β-bursts in the pre-SSRT period (exact values for the pairwise comparisons between trials with and without β-bursts: Z = 7.81, p = 5.65 × 10−15 and Z = 4.36, p = 1.28 × 10−05 for the two significant time windows for contralateral sites and Z = 8.06, p = 7.7 × 10−16 and Z = 4.8, p = 1.55 × 10−06 for ipsilateral sites). Additionally, these same time windows of post-burst activity were compared with the average number of sensorimotor β-bursts that occurred on trials with fronto-central bursts, but before the occurrence of that burst (i.e., the average number of bursts in the four time windows before the fronto-central β burst on the same trials). That comparison confirmed the significant increase in sensorimotor β bursting following the fronto-central β-burst, though in this analysis, this was limited to the first post-burst window (Z = 8.02, p = 1.1 × 10−15 for contralateral, Z = 6.63, p = 3.35 × 10−11 for ipsilateral). Moreover, the same analysis performed on failed instead of successful stop-trials (which was suggested by a reviewer) yielded no significant increases in sensorimotor β following fronto-central β bursts. In essence, these findings suggest that there is a low-latency increase in lateral sensorimotor β-bursts immediately following fronto-central β-bursts that occur within the Stop-signal-to-SSRT period on successful Stop-trials. Potential effects of volume-conduction are a concern in this analysis (though these data were current-source density transformed; see Materials and Methods). However, a topographical representation of the effect reveals that the increase in β-bursting following the first fronto-central β-burst showed local maxima at bilateral sensorimotor sites (in addition to the fronto-central electrodes surrounding FCz; Fig. 4b); i.e., at remote locations that do not follow a linear or exponential signal decay from FCz. Therefore, although volume conduction effects can never be ruled out in scalp-recordings, there seems to be some specificity of this effect to bilateral sensorimotor sites.
Interaction between fronto-central and bilateral sensorimotor β-bursting during successful movement cancellation. a, Rates of contralateral and ipsilateral β-bursts at sensorimotor electrodes C3/C4, time-locked to individual fronto-central β-bursts during the Stop-signal-to-SSRT period. Twenty-five milliseconds following fronto-central β-bursts (green highlighting), both sensorimotor β-bursting was significantly increased at sites both ipsilateral and contralateral to the to-be-stopped movement. b, Topographical representation of β-bursts in the highlighted time period (25 ms following the fronto-central β-burst event) showing local maxima in increased β-burst activity at bilateral sensorimotor sites (in addition to the fronto-central sites that surround FCz).
Early β-bursting at lateral sensorimotor sites may reflect a tonic inhibitory state
The fact that lateral sensorimotor β-bursting steadily declined during movement initiation (Fig. 1b,d) and restarted immediately following fronto-central β-bursts during movement cancellation (Fig. 4a) suggests that lateral sensorimotor beta-bursting may reflect an inhibited motor state, which has to be overcome during movement initiation and which can be rapidly reinstated following frontal control signals during movement cancellation. To further test this possibility, we correlated the amount of contralateral sensorimotor β-bursting on Go-trials with the two main behavioral indices for each subject (Go-trial reaction time and Stop-signal reaction time). This was done for the same time windows that were used to visualize these same β-burst data in Figure 1, b and d. As can be seen in Figure 1f, the amount of lateral sensorimotor β-bursting was significantly positively related to Go-trial reaction times in all nine of the early time windows following the Go-signal (significant time-windows in which the correlation survived FDR-correction covered a time range from the Go-signal to 425 ms after the signal). The strongest correlation was found during the earlier time windows, in particular the ones ranging from 75 to 175 ms after the Go-signal. Specifically, subjects that showed increased rates of β-bursting during these time windows showed systematically longer Go-trial reaction times. Furthermore, the rate of these early sensorimotor β-bursting showed a negative relationship with SSRT: subjects with higher initial sensorimotor β-bursting rates showed faster SSRT. The latter relationship survived FDR-corrections for multiple comparisons in the 75–125 ms post-Go-signal window. Together with the Go-reaction time correlation, this suggests that the rate of early β-bursting over lateral somatosensory areas does indeed reflect a (proactive) inhibition of the motor system, which is detrimental to fast movement execution (as reflected in slower Go-trial reaction times), but beneficial to fast movement cancellation (as reflected in faster Stop-signal reaction times).
β-burst rates are a more accurate level of description compared with mean amplitude changes
As is evident from the individual-trial plots in Figures 1, c and e, and 2d (as well as from prior literature), raw, single-trial beta-band activity is characterized by transient, burst-like events, rather than slow and steady modulations of overall power. However, it is an open question whether β-bursts actually provide a more accurate description of the relationship between human brain activity and motor behavior especially compared with more standard mean-amplitude measurements. To investigate this question, two of the three main analyses in the current paper [(1) the comparison of fronto-central β-activity between the different trial types, Fig. 2c; and (2) the correlation between sensorimotor β-activity and Go-/Stop-signal reaction time, Fig. 1f] were repeated using standard mean amplitude measurements. (The third main finding in this current study, i.e., the increase in β-bursting at sensorimotor sites following individual instances of fronto-central β-burst events, cannot be reproduced using amplitude measurements.) In other words, instead of quantifying β-burst rates for the time periods of interest, overall β-amplitude activity was averaged across the same period of time, and the analyses were repeated just as presented above.
Figure 5a shows the thresholded topographical distribution of mean β-amplitude across the time periods used in the β-burst analyses in Figures 1, 2, and 3. From the statistical trial-comparison maps (Fig. 5a, bottom two rows, i.e., the analog of Fig. 3), it is evident that β-band amplitude shows topographically similar features compared with β-burst-rates: there is bilateral sensorimotor desynchronization following Go-signals and a fronto-central power increase following Stop-signals. However, unlike for β-burst rates, where fronto-central electrode sites showed significant increases from zero following Stop-signals, the β-band power increase at fronto-central electrodes on those same trials was not significantly different from zero (Fig. 5a, compare second and third rows).
β-power amplitude analyses. a, Topographical distributions of β-power amplitude following Go- and Stop-signals. These figures are analog to Figures 1a, 2a, and 3, except they do not show changes in β-burst rates, but changes in mean β-amplitude during the same time windows. Any activity that did not exceed a significant change from 0 dB (using the same significance threshold of p < 0.0001, FDR-corrected that was used in Figs. 1a, 2a, and 3) was set to 0. b, Changes in mean β-power amplitude at fronto-central electrode FCz on the three trial types of interest in the Stop-signal task. Shaded area around each curve represents the SEM. c, Comparison of mean β-amplitude in the Stop-signal-to-SSRT period (and a matched time period on Go-trials); analog to Figure 2c. d, Changes in mean β-power amplitude at sensorimotor electrodes C3 and C4 following Go-signals on left/right-hand Go-trials. Shaded area around each curve represents the SEM. e, Correlations between mean β-power amplitude at sensorimotor sites contralateral to the response hand on Go-trials in 12 successive time windows following the Go-signal and Go/Stop-behavior (Go-trial reaction time/Stop-signal reaction time); analog to Figure 1f.
The average time course of the fronto-central amplitude-signal for each individual trial type can be seen in Figure 5b. An analysis of the β-amplitude data during the Stop-signal-to-SSRT time period (as well as a matched time period on Go-trials) revealed that, just like for the β-burst analyses, there was a significant increase of β-activity on successful compared with failed Stop-trials (t(233) = 2.09, p = 0.037, d = 0.12), as well as compared with Go-trials Go-trials (t(233) = 7.81, p = 1.93 × 10−13, d = 0.55; Fig. 5c). However, the effect sizes for both comparisons were substantially reduced compared with the β-burst analysis (indeed, the 95% confidence interval for the effect size of the successful vs failed Stop-trials contrast included an effect size of 0, which was not the case for the β-burst analysis).
The time course of the sensorimotor signal on Go-trials can be seen in Figure 5d. As to be expected based on prior work, there was a pronounced β-desynchronization following the Go-signal, which lateralized toward the contralateral side in the lead-up to the response. Although this also matches the properties of the β-burst rates reported in Figure 1, b and d, the same correlations analyses that yielded highly significant relationships between β-burst rates and both Go- and Stop-signal reaction time (Fig. 1f) did not yield any significant relationships between mean β-power amplitude and behavior. Not only did none of the 12 time periods yield any correlations that survived corrections for multiple comparisons, but none of the windows showed even an uncorrected p < 0.05 (Fig. 5e).
Together, these results show that while β-burst rates and amplitude changes are clearly related both theoretically (a β-burst represents a sudden, short-lived increase in β-power) and empirically (the overall topographies of both β-burst rates and β-power after Stop- and Go-signals are morphologically similar), β-burst rates not only provide a superior description of the signal on the trial-to-trial level, they also provide a much stronger relationship to behavior; and can in fact be used to uncover neural-behavioral associations that are absent in averaged β-power data.
Is single-trial β truly burst-like?
One issue that bears discussion regarding the current paper (as well as the existing β-burst literature) is that the judgment of whether the β-band signal is truly burst-like (or, instead, reflects a transient modulation of an ongoing oscillation) is made based on largely qualitative, subjective criteria (van Ede et al., 2018). In other words, β is deemed "burst-like" because the data look burst-like; and because there are concrete biophysical models that explain the burst-like nature of the β signal (Sherman et al., 2016), which provide a priori credence to this idea.
However, one way to actually quantify whether a neural signal is truly burst-like is lagged phase coherence (Fransen et al., 2015). Lagged phase coherence quantifies the phase consistency in successive segments of data. The assumption is that if a signal at a specific frequency is marked by an ongoing oscillation, it should be possible to predict the phase of a given data segment based on past segments. Hence, lagged phase coherence was quantified at the three main electrode sites of interest in the current study (FCz, C3, C4). Figure 6 shows the lagged coherence spectrum. It is evident that although there is some degree of lagged coherence across all frequencies surrounding (and including) β, the β band notably represents the trough of the spectrum and is flanked by higher relative degrees of coherence in both the α and γ bands. Although there is currently no way to quantify what constitutes a "significant" degree of lagged phase coherence (Fransen et al., 2016), it is save to conclude that beta is at least less "oscillatory", and hence, more burst-like, than the surrounding frequency bands.
Lagged phase coherence (3 cycles) at the three electrode sites of interest at frequencies including and surrounding the β-range. What can be seen is that the β-range (highlighted) contains the trough of the lagged coherence spectrum, suggesting that the β signal at those electrodes is more burst-like than the signal in the surrounding frequencies.
Human movement is accompanied by systematic spatiotemporal changes in β-bursting. Before movement initiation, bilateral sensorimotor sites showed tonic, local β-bursting, potentially representing a net-inhibited state of the motor system (Engel and Fries, 2010; Picazio et al., 2014; Rossiter et al., 2014). This β-bursting steadily reduced during movement initiation, suggesting net-disinhibition. Notably, this reduction lateralized just before movement execution; comparable to the movement-related lateralization of β-desynchronization in the trial-average (McFarland et al., 2000; Kaiser et al., 2001; Doyle et al., 2005). However, β-bursts contained systematic relationships to both movement execution and movement cancellation that were absent in the averaged β-power amplitudes.
In situations in which this state of inhibition had to be rapidly reinstated, i.e., to cancel movements following Stop-signals, β-burst rates were first significantly increased at fronto-central sites. This increase showed its most coherent spatiotemporal organization during the time period toward the end of SSRT (Fig. 1a). Such fronto-central β-bursts were then followed by a rapid, low-latency (<25 ms) increase of β-bursting over sensorimotor sites; both ipsilateral and contralateral to the to-be-cancelled movement. We tentatively propose that this may reflect a low-latency re-instantiation of inhibition at the level of the motor system (tonic lateral sensorimotor β-bursting), triggered by a fronto-central control signal (phasic fronto-central β-bursts).
The β-burst patterns show intriguing conceptual and empirical overlap with the proposed properties of the neural and behavioral processing-cascade underlying movement cancellation. First, β-band power increases are routinely found in trial-averaged activity during movement cancellation (Swann et al., 2009, 2011, 2012; Ray et al., 2012; Wagner et al., 2018), including in intracranial recordings from sites that could conceivably underlie the fronto-centrally distributed pattern of β-bursting observed here (e.g., the pre-supplementary motor area; Swann et al., 2012). The current results suggest that these changes may actually result from increases in β-burst rates on individual trials. Second, the race-model of the Stop-signal task proposes that action-stopping can be modeled by a race between two processes; a Go-process working toward the execution of the motor response and a Stop-process working toward the cancellation of that response (Logan and Cowan, 1984). Notably, it is still controversial whether these two processes operate independently, or whether the Stop-process directly influences the Go-process (Boucher et al., 2007; Verbruggen and Logan, 2008; Schmidt et al., 2013; Schall et al., 2017). In our data, stopping-related β-bursts at fronto-central sites were immediately followed by a re-instantiation of β-bursting over sensorimotor sites; the same signature whose initial reduction marked the start of the Go-process. This suggests that the initiation of the Stop-process is followed by a substantial alteration in the neural activity underlying the Go-process; speaking in favor of an interactive race-model (Boucher et al., 2007; Schall et al., 2017). Third, previous research has shown that humans proactively inhibit their movement initiation when they anticipate having to potentially cancel an action, resulting in longer Go-trial reaction times. The degree of this proactive inhibition is inversely related to the amount of reactive inhibition necessary to successfully cancel an action. In other words, subjects that exert higher degrees of proactive Go-trial reaction time slowing in turn exhibit shorter SSRTs (Chikazoe et al., 2009; Verbruggen and Logan, 2009). The degree of such proactive inhibitory control may be reflected in the level of initial bilateral sensorimotor β-bursting found in individual participants: Figure 1f shows that higher initial sensorimotor β-burst rates predicted both slower Go-trial reaction times and faster SSRT. This suggests that early β-bursting may reflect the tradeoff between proactive control and reactive control, with more cautious responding being reflected in increased bilateral β-bursting. Fourth, the Stop-related re-instantiation of sensorimotor β-bursting that was observed following fronto-central β-bursts was notably present at both ipsilateral and contralateral sensorimotor sites (Fig. 4). This parallels prior work using physiological measures of motor excitability in specific corticospinal tracts. Such studies found that rapid movement cancellation is nonselective and affects the entire motor system (Badry et al., 2009; Majid et al., 2012; Wessel et al., 2013; Duque et al., 2017). The simultaneous, bilateral reactivation of sensorimotor β-bursting after fronto-central Stop-related β-bursts observed here could be the neurophysiological expression of that same nonselective property.
Beyond this theoretical and conceptual coherence with prior work, the quantification of β-bursts also substantially extends our insights into the neural dynamics of movement. β-bursts not only provide a more accurate description of the actual raw β-signal (compare Figs. 1c,e, 2b), but reveals relationships to behavior that are not evident in amplitude-averages; such as the relationship between early sensorimotor activity and Go-/Stop-signal reaction time. Moreover, trial-level comparisons of β-burst rates yielded much larger effect sizes compared with amplitude analyses. For example, the effect size for the fronto-central successful versus failed Stop-trial comparison was twice as large for β-burst rates compared with mean amplitudes, suggesting that averaging partially obscures the nature of this relationship. Finally, the β-burst-locked analysis presented in Figure 4 yielded fundamentally novel insights into the interactions between neural concomitants of the Go- and Stop-processes and their interaction during movement cancellation. Notably, the current study could speak to another recent controversy in the field of inhibitory control. Some studies have suggested that SSRT overestimates the latency of the stopping process (Matzke et al., 2013), and that true indices of inhibition occur much earlier before SSRT as previously thought (Raud and Huster, 2017). This is a challenge for many established signatures of inhibitory control, which occur close to the end of the stopping period indicated by traditional, potentially inflated SSRT estimates (Hanes et al., 1998; Wessel and Aron, 2015; Ogasawara et al., 2018). In this respect, it is notable that while the spatiotemporal coherence in fronto-central β-bursting after stop-signals took ∼200 ms to develop in the current study (∼45ms before the end of average SSRT), the FCz channel data show clear separation of successful and failed stop-trials even earlier than that (compare Fig. 2). In fact, the data show successful/failed differences as early as the time window that is centered ∼50 ms after the Stop-signal. This could reflect differences in the anticipatory recruitment of reactive stopping circuitry, which has been shown to partially underlie proactive control (Swann et al., 2012; Greenhouse and Wessel, 2013; van Belle et al., 2014), though this needs further testing in future studies.
The current study has key implications for future research. First, the precise neural origin of movement-related β-bursts needs to be investigated. Biophysical modeling has suggested that these bursts may results from the integration of near-synchronous bursts of excitatory synaptic drive, targeting the dendrites of pyramidal neurons in specific cortical layers (Sherman et al., 2016). It remains to be tested whether the patches of cortex from which the scalp-recorded activity reported here are generated are subject to such layer-specific drive. Second, if β-bursting over sensorimotor sites is indeed indicative of a "tonic" inhibitory mode of the motor system, it would make it an interesting target for more systematic investigations of proactive inhibitory control that use different likelihoods of Stop-signals (Jaffard et al., 2008; Verbruggen and Logan, 2009; Greenhouse et al., 2012; Stuphorn and Emeric, 2012; Vink et al., 2015; Elchlepp et al., 2016). Third, there is great interest in pathological features of the β-frequency band in movement disorders, especially Parkinson's disease (Hammond et al., 2007; Bronte-Stewart et al., 2009; Jenkinson and Brown, 2011; Quinn et al., 2015). Recent studies have used trial-level β-burst measurements in subcortical areas (e.g., the subthalamic nucleus) to identify gait problems in PD (Anidi et al., 2018) and to develop adaptive brain stimulation approaches using closed-loop algorithms (Little et al., 2013; Tinkhauser et al., 2017). Future studies should aim to investigate whether similar pathological features can be found in scalp-recorded β-bursts (Gauggel et al., 2004; van den Wildenberg et al., 2006; Obeso et al., 2011). Fourth, β-bursts may help elucidate the interactions between the subcortical aspects of the extrapyramidal motor system and the cortical areas governing higher-order motor control (Jenkinson and Brown, 2011; Leventhal et al., 2012; Bartolo and Merchant, 2015; Feingold et al., 2015). Indeed, it is tempting to hypothesize that β-bursting may be a "universal" language of the motor system, signifying distributed processing throughout the both pyramidal and extrapyramidal motor pathways.
Although the current study provides fundamentally novel insights into the neural underpinnings of movement in a large dataset, it also has shortcomings. First, because the neural activity in the current study was non-invasively recorded from the scalp, only limited conclusions can be drawn regarding the neural origins of the observed β-bursts. Second, although movement cancellation was clearly related to increased β-burst rates at fronto-central sites, β-bursts were neither necessary nor sufficient to cancel a movement on individual trials; at least when measured at the scalp-surface. Indeed, many failed Stop-trials also included β-bursts before SSRT. Moreover, not all successful Stop-trials included a suprathreshold β-burst before SSRT (Fig. 2c). Part of this outcome can likely be attributed to the imperfect signal-to-noise ratio of scalp-recordings. Indeed, intracranial recordings of the local field potential closer to its source will likely yield substantially higher β-burst rates. Furthermore, the search window for fronto-central β-bursts in this study was defined very conservatively. Although the time period between the Stop-signal and each participant's SSRT estimate is arguably the most theoretically motivated, straightforward window of interest, it is based on a variable, implicit measure (SSRT). Combined with the fact that frequency-resolved methods sacrifice some degree of temporal precision, behaviorally-relevant β-bursts may have fallen just outside of the detection window. Indeed, extending the window by a single β-cycle increased the number of detected fronto-central β-bursts on successful Stop-trials by 27%, while the trial-differences remained highly significant (successful Stop vs Go: t(233) = 12.07, p = 2.27 × 10−26, d = 0.94; successful vs failed Stop: t(233) = 2.97, p = 0.003, d = 0.23). Future investigations, ideally using intracranial recordings, are necessary to investigate whether fronto-central β-bursts are truly necessary for stopping, and how the scalp-recorded β-burst signal relates to the original local field potential.
In summary, the current study found systematic changes in β-burst activity during both movement initiation and cancellation in humans. The data suggest that β-bursting over lateral sensorimotor sites reflects the inhibition of the underlying areas of the motor system, which has to be overcome during movement initiation, and which can be rapidly re-instated when movement cancellation is necessary (likely signaled by phasic increases in fronto-central β-bursts). This suggests that β-bursts are a fundamental signature of human motor control.
This research was supported by Grants from the National Institute of Health (R01 NS102201), the National Science Foundation (CAREER 1752355), and the Roy J. Carver Charitable Trust (Research Program of Excellence 17-4885). I thank Daniel Thayer, Julian Scheffer, Cailey Parker, Hailey Billings, Carly Ryder, Megan Hynd, Carly Iacullo, Brynne Dochterman, Isabella Dutra, Alec Mather, Kylie Dolan, Nathan Chalkley, Darcy Waller, Tobin Dykstra, and Cheol Soh for help with data collection; Mark Blumberg, John Freeman, and Bob McMurray for helpful discussions; and Freek van Ede, Eric Maris, and Jan-Mathijs Schoffelen for help with the lagged coherence method, and Bernhard Spitzer for suggesting it.
The author declares no competing financial interests.
Correspondence should be addressed to Jan R. Wessel at Jan-wessel{at}uiowa.edu
Anidi et al., 2018.↵
Anidi C,
O'Day JJ,
Anderson RW,
Afzal MF,
Syrkin-Nikolau J,
Velisar A,
Bronte-Stewart HM
(2018) Neuromodulation targets pathological not physiological beta bursts during gait in Parkinson's disease. Neurobiol Dis 120:107–117. doi:10.1016/j.nbd.2018.09.004 pmid:30196050
Badry et al., 2009.↵
Badry R,
Mima T,
Aso T,
Nakatsuka M,
Abe M,
Fathi D,
Foly N,
Nagiub H,
Nagamine T,
Fukuyama H
(2009) Suppression of human cortico-motoneuronal excitability during the stop-signal task. Clin Neurophysiol 120:1717–1723. doi:10.1016/j.clinph.2009.06.027 pmid:19683959
Bartolo and Merchant, 2015.↵
Bartolo R,
Merchant H
(2015) beta oscillations are linked to the initiation of sensory-cued movement sequences and the internal guidance of regular tapping in the monkey. J Neurosci 35:4635–4640. doi:10.1523/JNEUROSCI.4570-14.2015 pmid:25788680
Bell and Sejnowski, 1995.↵
Bell AJ,
Sejnowski TJ
(1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159. doi:10.1162/neco.1995.7.6.1129 pmid:7584893
Benjamini et al., 2006.↵
Benjamini Y,
Krieger AM,
Yekutieli D
(2006) Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93:491–507. doi:10.1093/biomet/93.3.491
Boucher et al., 2007.↵
Boucher L,
Palmeri TJ,
Logan GD,
Schall JD
(2007) Inhibitory control in mind and brain: an interactive race model of countermanding saccades. Psychol Rev 114:376–397. doi:10.1037/0033-295X.114.2.376 pmid:17500631
Brittain and Brown, 2014.↵
Brittain JS,
Brown P
(2014) Oscillations and the basal ganglia: motor control and beyond. Neuroimage 85:637–647. doi:10.1016/j.neuroimage.2013.05.084 pmid:23711535
Bronte-Stewart et al., 2009.↵
Bronte-Stewart H,
Barberini C,
Koop MM,
Hill BC,
Henderson JM,
Wingeier B
(2009) The STN beta-band profile in Parkinson's disease is stationary and shows prolonged attenuation after deep brain stimulation. Exp Neurol 215:20–28. doi:10.1016/j.expneurol.2008.09.008 pmid:18929561
Chikazoe et al., 2009.↵
Chikazoe J,
Jimura K,
Hirose S,
Yamashita K,
Miyashita Y,
Konishi S
(2009) Preparation to inhibit a response complements response inhibition during performance of a stop-signal task. J Neurosci 29:15870–15877. doi:10.1523/JNEUROSCI.3645-09.2009 pmid:20016103
Delorme and Makeig, 2004.↵
Delorme A,
Makeig S
(2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21. doi:10.1016/j.jneumeth.2003.10.009 pmid:15102499
Delorme et al., 2007.↵
Sejnowski T,
(2007) Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34:1443–1449. doi:10.1016/j.neuroimage.2006.11.004 pmid:17188898
Palmer J,
Onton J,
Oostenveld R,
(2012) Independent EEG sources are dipolar. PLoS One 7:e30135. doi:10.1371/journal.pone.0030135 pmid:22355308
Doyle et al., 2005.↵
Doyle LM,
Yarrow K,
(2005) Lateralization of event-related beta desynchronization in the EEG during pre-cued reaction time tasks. Clin Neurophysiol 116:1879–1888. doi:10.1016/j.clinph.2005.03.017 pmid:15979401
Duque et al., 2017.↵
Duque J,
Greenhouse I,
Labruna L,
Ivry RB
(2017) Physiological markers of motor inhibition during human behavior. Trends Neurosci 40:219–236. doi:10.1016/j.tins.2017.02.006 pmid:28341235
Dutra et al., 2018.↵
Dutra IC,
Waller DA,
Wessel JR
(2018) Perceptual surprise improves action stopping by non-selectively suppressing motor activity via a neural mechanism for motor inhibition. J Neurosci 38:1482–1492. doi:10.1523/JNEUROSCI.3091-17.2017 pmid:29305533
Dykstra et al., 2019.↵
Dykstra T,
Hazeltine E,
(2019) Leveling the field for a fairer race between going and stopping: neural evidence for the race model of motor inhibition from a new version of the stop signal task. The MIT Press Journals, in press.
Elchlepp et al., 2016.↵
Elchlepp H,
Lavric A,
Chambers CD,
Verbruggen F
(2016) Proactive inhibitory control: a general biasing account. Cogn Psychol 86:27–61. doi:10.1016/j.cogpsych.2016.01.004 pmid:26859519
Engel and Fries, 2010.↵
Engel AK,
Fries P
(2010) Beta-band oscillations: signalling the status quo? Curr Opin Neurobiol 20:156–165. doi:10.1016/j.conb.2010.02.015 pmid:20359884
Feingold et al., 2015.↵
Feingold J,
Gibson DJ,
DePasquale B,
Graybiel AM
(2015) Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks. Proc Natl Acad Sci U S A 112:13687–13692. doi:10.1073/pnas.1517629112 pmid:26460033
Fransen et al., 2015.↵
Fransen AM,
van Ede F,
Maris E
(2015) Identifying neuronal oscillations using rhythmicity. Neuroimage 118:256–267. doi:10.1016/j.neuroimage.2015.06.003 pmid:26054877
Dimitriadis G,
(2016) Distinct alpha- and beta-band rhythms over rat somatosensory cortex with similar properties as in humans. J Neurophysiol 115:3030–3044. doi:10.1152/jn.00507.2015 pmid:27009160
Gauggel et al., 2004.↵
Gauggel S,
Rieger M,
Feghoff TA
(2004) Inhibition of ongoing responses in patients with Parkinson's disease. J Neurol Neurosurg Psychiatry 75:539–544. doi:10.1136/jnnp.2003.016469 pmid:15026491
Greenhouse and Wessel, 2013.↵
(2013) EEG signatures associated with stopping are sensitive to preparation. Psychophysiology 50:900–908. doi:10.1111/psyp.12070 pmid:23763667
Greenhouse et al., 2012.↵
Oldenkamp CL,
Aron AR
(2012) Stopping a response has global or nonglobal effects on the motor system depending on preparation. J Neurophysiol 107:384–392. doi:10.1152/jn.00704.2011 pmid:22013239
Hammond et al., 2007.↵
Hammond C,
Bergman H,
(2007) Pathological synchronization in Parkinson's disease: networks, models and treatments. Trends Neurosci 30:357–364. doi:10.1016/j.tins.2007.05.004 pmid:17532060
Hanes et al., 1998.↵
Hanes DP,
Patterson WF 2nd.,
(1998) Role of frontal eye fields in countermanding saccades: visual, movement, and fixation activity. J Neurophysiol 79:817–834. doi:10.1152/jn.1998.79.2.817 pmid:9463444
Jaffard et al., 2008.↵
Jaffard M,
Longcamp M,
Velay JL,
Anton JL,
Roth M,
Nazarian B,
Boulinguez P
(2008) Proactive inhibitory control of movement assessed by event-related fMRI. Neuroimage 42:1196–1206. doi:10.1016/j.neuroimage.2008.05.041 pmid:18588986
Jenkinson and Brown, 2011.↵
Jenkinson N,
(2011) New insights into the relationship between dopamine, beta oscillations and motor function. Trends Neurosci 34:611–618. doi:10.1016/j.tins.2011.09.003 pmid:22018805
Jha et al., 2015.↵
Jha A,
Nachev P,
Barnes G,
Husain M,
Brown P,
Litvak V
(2015) The frontal control of stopping. Cereb Cortex 25:4392–4406. doi:10.1093/cercor/bhv027 pmid:25754518
Kaiser et al., 2001.↵
Kaiser J,
Birbaumer N,
Lutzenberger W
(2001) Event-related beta desynchronization indicates timing of response selection in a delayed-response paradigm in humans. Neurosci Lett 312:149–152. doi:10.1016/S0304-3940(01)02217-0 pmid:11602332
Kühn et al., 2004.↵
Kühn AA,
Williams D,
Kupsch A,
Limousin P,
Hariz M,
Schneider GH,
(2004) Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. Brain 127:735–746. doi:10.1093/brain/awh106 pmid:14960502
Lee et al., 1999.↵
Lee TW,
Girolami M,
(1999) Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput 11:417–441. doi:10.1162/089976699300016719 pmid:9950738
Leventhal et al., 2012.↵
Leventhal DK,
Gage GJ,
Schmidt R,
Pettibone JR,
Case AC,
Berke JD
(2012) Basal ganglia beta oscillations accompany cue utilization. Neuron 73:523–536. doi:10.1016/j.neuron.2011.11.032 pmid:22325204
Little and Brown, 2014.↵
Little S,
(2014) The functional role of beta oscillations in Parkinson's disease. Parkinsonism Relat Disord 20:S44–S48. doi:10.1016/S1353-8020(13)70013-0 pmid:24262186
Little et al., 2013.↵
Pogosyan A,
Neal S,
Zavala B,
Zrinzo L,
Foltynie T,
Ashkan K,
FitzGerald J,
Green AL,
Aziz TZ,
(2013) Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol 74:449–457. doi:10.1002/ana.23951 pmid:23852650
Logan and Cowan, 1984.↵
Cowan WB
(1984) On the ability to inhibit thought and action: a theory of an act of control. Psychol Rev 91:295–327. doi:10.1037/0033-295X.91.3.295
Majid et al., 2012.↵
Majid DS,
Cai W,
George JS,
Verbruggen F,
(2012) Transcranial magnetic stimulation reveals dissociable mechanisms for global versus selective corticomotor suppression underlying the stopping of action. Cereb Cortex 22:363–371. doi:10.1093/cercor/bhr112 pmid:21666129
Matzke et al., 2013.↵
Matzke D,
Love J,
Wiecki TV,
Brown SD,
Wagenmakers EJ
(2013) Release the BEESTS: Bayesian estimation of ex-Gaussian stop-signal reaction time distributions. Front Psychol 4:918. doi:10.3389/fpsyg.2013.00918 pmid:24339819
McFarland et al., 2000.↵
McFarland DJ,
Miner LA,
Vaughan TM,
Wolpaw JR
(2000) Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 12:177–186. doi:10.1023/A:1023437823106 pmid:10791681
Murthy and Fetz, 1992.↵
Murthy VN,
Fetz EE
(1992) Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys. Proc Natl Acad Sci U S A 89:5670–5674. doi:10.1073/pnas.89.12.5670 pmid:1608977
Obeso et al., 2011.↵
Obeso I,
Wilkinson L,
Casabona E,
Bringas ML,
Álvarez M,
Álvarez L,
Pavón N,
Rodríguez-Oroz MC,
Macías R,
Obeso JA,
Jahanshahi M
(2011) Deficits in inhibitory control and conflict resolution on cognitive and motor tasks in Parkinson's disease. Exp Brain Res 212:371–384. doi:10.1007/s00221-011-2736-6 pmid:21643718
Ogasawara et al., 2018.↵
Ogasawara T,
Nejime M,
Takada M,
Matsumoto M
(2018) Primate nigrostriatal dopamine system regulates saccadic response inhibition. Neuron 100:1513–1526.e4. doi:10.1016/j.neuron.2018.10.025 pmid:30415993
Oostenveld et al., 2011.↵
Fries P,
Maris E,
Schoffelen JM
(2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011:156869. doi:10.1155/2011/156869 pmid:21253357
Perrin et al., 1989.↵
Perrin F,
Pernier J,
Bertrand O,
Echallier JF
(1989) Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol 72:184–187. doi:10.1016/0013-4694(89)90180-6 pmid:2464490
Pfurtscheller et al., 2003.↵
Pfurtscheller G,
Graimann B,
Huggins JE,
Levine SP,
Schuh LA
(2003) Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin Neurophysiol 114:1226–1236. doi:10.1016/S1388-2457(03)00067-1 pmid:12842719
Picazio et al., 2014.↵
Picazio S,
Veniero D,
Ponzo V,
Caltagirone C,
Gross J,
Thut G,
Koch G
(2014) Prefrontal control over motor cortex cycles at beta frequency during movement inhibition. Curr Biol 24:2940–2945. doi:10.1016/j.cub.2014.10.043 pmid:25484293
Quinn et al., 2015.↵
Quinn EJ,
Blumenfeld Z,
Shreve LA,
Trager MH,
Kilbane C,
Brontë-Stewart H
(2015) Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation. Mov Disord 30:1750–1758. doi:10.1002/mds.26376 pmid:26360123
Raud and Huster, 2017.↵
Raud L,
Huster RJ
(2017) The temporal dynamics of response inhibition and their modulation by cognitive control. Brain Topogr 30:486–501. doi:10.1007/s10548-017-0566-y pmid:28456867
Ray et al., 2012.↵
Ray NJ,
Holland P,
Joundi RA,
Stein JF,
Jenkinson N
(2012) The role of the subthalamic nucleus in response inhibition: evidence from local field potential recordings in the human subthalamic nucleus. Neuroimage 60:271–278. doi:10.1016/j.neuroimage.2011.12.035 pmid:22209815
Rossiter et al., 2014.↵
Rossiter HE,
Davis EM,
Clark EV,
Boudrias MH,
Ward NS
(2014) Beta oscillations reflect changes in motor cortex inhibition in healthy ageing. Neuroimage 91:360–365. doi:10.1016/j.neuroimage.2014.01.012 pmid:24440529
Sanes and Donoghue, 1993.↵
Sanes JN,
Donoghue JP
(1993) Oscillations in local field potentials of the primate motor cortex during voluntary movement. Proc Natl Acad Sci U S A 90:4470–4474. doi:10.1073/pnas.90.10.4470 pmid:8506287
Schall et al., 2017.↵
Schall JD,
Logan GD
(2017) Models of inhibitory control. Philos Trans R Soc Lond B Biol Sci 372:20160193. doi:10.1098/rstb.2016.0193 pmid:28242727
Schmidt et al., 2013.↵
Mallet N,
Chen F,
(2013) Canceling actions involves a race between basal ganglia pathways. Nat Neurosci 16:1118–1124. doi:10.1038/nn.3456 pmid:23852117
Sherman et al., 2016.↵
Sherman MA,
Lee S,
Law R,
Haegens S,
Thorn CA,
Hämäläinen MS,
Moore CI,
Jones SR
(2016) Neural mechanisms of transient neocortical beta rhythms: converging evidence from humans, computational modeling, monkeys, and mice. Proc Natl Acad Sci U S A 113:E4885–E4894. doi:10.1073/pnas.1604135113 pmid:27469163
Shin et al., 2017.↵
Shin H,
Tsutsui S,
(2017) The rate of transient beta frequency events predicts behavior across tasks and species. eLife 6:e29086. doi:10.7554/eLife.29086 pmid:29106374
Stuphorn and Emeric, 2012.↵
Stuphorn V,
Emeric EE
(2012) Proactive and reactive control by the medial frontal cortex. Front Neuroeng 5:9. doi:10.3389/fneng.2012.00009 pmid:22723779
Swann et al., 2009.↵
Swann N,
Tandon N,
Canolty R,
Ellmore TM,
McEvoy LK,
Dreyer S,
DiSano M,
(2009) Intracranial EEG reveals a time- and frequency-specific role for the right inferior frontal gyrus and primary motor cortex in stopping initiated responses. J Neurosci 29:12675–12685. doi:10.1523/JNEUROSCI.3359-09.2009 pmid:19812342
Poizner H,
Houser M,
Gould S,
Strunk J,
George J,
(2011) Deep brain stimulation of the subthalamic nucleus alters the cortical profile of response inhibition in the beta frequency band: a scalp EEG study in Parkinson's disease. J Neurosci 31:5721–5729. doi:10.1523/JNEUROSCI.6135-10.2011 pmid:21490213
Conner CR,
Pieters TA,
Claffey MP,
Aron AR,
Tandon N
(2012) Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: electrophysiological responses and functional and structural connectivity. Neuroimage 59:2860–2870. doi:10.1016/j.neuroimage.2011.09.049 pmid:21979383
Tenke and Kayser, 2005.↵
Tenke CE,
Kayser J
(2005) Reference-free quantification of EEG spectra: combining current source density (CSD) and frequency principal components analysis (fPCA). Clin Neurophysiol 116:2826–2846. doi:10.1016/j.clinph.2005.08.007 pmid:16257577
Tinkhauser et al., 2017.↵
Tinkhauser G,
Beudel M,
Herz DM,
Tan H,
(2017) The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson's disease. Brain 140:1053–1067. doi:10.1093/brain/awx010 pmid:28334851
van Belle et al., 2014.↵
van Belle J,
Vink M,
Durston S,
Zandbelt BB
(2014) Common and unique neural networks for proactive and reactive response inhibition revealed by independent component analysis of functional MRI data. Neuroimage 103:65–74. doi:10.1016/j.neuroimage.2014.09.014 pmid:25224995
van den Wildenberg et al., 2006.↵
van den Wildenberg WP,
van Boxtel GJ,
van der Molen MW,
Bosch DA,
Speelman JD,
Brunia CH
(2006) Stimulation of the subthalamic region facilitates the selection and inhibition of motor responses in Parkinson's disease. J Cogn Neurosci 18:626–636. doi:10.1162/jocn.2006.18.4.626 pmid:16768365
van Ede et al., 2018.↵
Quinn AJ,
Woolrich MW,
Nobre AC
(2018) Neural oscillations: sustained rhythms or transient burst-events? Trends Neurosci 41:415–417. doi:10.1016/j.tins.2018.04.004 pmid:29739627
Verbruggen et al., 2019.↵
Band GP,
Beste C,
Bissett PG,
Brockett AT,
Brown JW,
Chamberlain SR,
Colonius H,
Colzato LS,
Corneil BD,
Coxon JP,
Dupuis A,
Eagle DM,
Garavan H,
Heathcote A,
Huster RJ,
Jahfari S, et al
. (2019) A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife 8:e46323. doi:10.7554/eLife.46323 pmid:31033438
Verbruggen and Logan, 2008.↵
(2008) Response inhibition in the stop-signal paradigm. Trends Cogn Sci 12:418–424. doi:10.1016/j.tics.2008.07.005 pmid:18799345
(2009) Proactive adjustments of response strategies in the stop-signal paradigm. J Exp Psychol Hum Percept Perform 35:835–854. doi:10.1037/a0012726 pmid:19485695
Vink et al., 2015.↵
Kaldewaij R,
Zandbelt BB,
Pas P,
du Plessis S
(2015) The role of stop-signal probability and expectation in proactive inhibition. Eur J Neurosci 41:1086–1094. doi:10.1111/ejn.12879 pmid:25832122
Wagner et al., 2018.↵
Wessel JR,
Ghahremani A,
(2018) Establishing a right frontal beta signature for stopping action in scalp EEG: implications for testing inhibitory control in other task contexts. J Cogn Neurosci 30:107–118. doi:10.1162/jocn_a_01183 pmid:28880766
Waller et al., 2019.↵
(2019) Common neural processes during action-stopping and infrequent stimulus detection: the frontocentral P3 as an index of generic motor inhibition. Int J Psychophysiol. Advance online publication. Retrieved November 27, 2019. doi:10.1016/j.ijpsycho.2019.01.004. pmid:30659867
Wessel, 2018a.↵
(2018a) A neural mechanism for surprise-related interruptions of visuospatial working memory. Cereb Cortex 28:199–212. doi:10.1093/cercor/bhw367 pmid:27909006
Wessel, 2018b.↵
(2018b) Prepotent motor activity and inhibitory control demands in different variants of the go/no-go paradigm. Psychophysiology 55:e12871. doi:10.1111/psyp.12871 pmid:28390090
Wessel and Aron, 2015.↵
(2015) It's not too late: the onset of the frontocentral P3 indexes successful response inhibition in the stop-signal paradigm. Psychophysiology 52:472–480. doi:10.1111/psyp.12374 pmid:25348645
Wessel et al., 2013.↵
Reynoso HS,
(2013) Saccade suppression exerts global effects on the motor system. J Neurophysiol 110:883–890. doi:10.1152/jn.00229.2013 pmid:23699058
Udupa K,
Saha U,
Kalia SK,
Hodaie M,
Lozano AM,
Chen R
(2016) Stop-related subthalamic beta activity indexes global motor suppression in Parkinson's disease. Mov Disord 31:1846–1853. doi:10.1002/mds.26732 pmid:27474845
Greenlee JD
(2019) Non-selective inhibition of inappropriate motor-tendencies during response-conflict by a fronto-subthalamic mechanism. Elife 8:e42959.
You are going to email the following β-Bursts Reveal the Trial-to-Trial Dynamics of Movement Initiation and Cancellation
Journal of Neuroscience 8 January 2020, 40 (2) 411-423; DOI: 10.1523/JNEUROSCI.1887-19.2019
Manipulations of Central Amygdala Neurotensin Neurons Alter the Consumption of Ethanol and Sweet Fluids in Mice
Long-term Monocular Deprivation during Juvenile Critical Period Disrupts Binocular Integration in Mouse Visual Thalamus
Top-Down Feedback Controls the Cortical Representation of Illusory Contours in Mouse Primary Visual Cortex
Behavioral/Cognitive
Dopamine-Dependent Loss Aversion during Effort-Based Decision-Making
Unimodal and Bimodal Access to Sensory Working Memories by Auditory and Visual Impulses
Temporal invariance in SCA6 is related to smaller cerebellar lobule VI and greater disease severity
Show more Behavioral/Cognitive


제이리움의 시즌별 컨셉을 제시하고, 니트웨어를 직접 경험해 봄으로써, 제이리움이 추구하는 라이프스타일을 제시할 수 있는 공간입니다.
니트웨어,니트케어용품 등 니트웨어의 모든 정보를 담는 공간이며, 니트에 광한 다양한 정보를 얻어 가실 수 있도록 최선을 다하겠습니다.
제이리움의 제품 뿐만이 아닌, 제이리움이 지향하는 남성상의 라이프스타일을 전체적으로 제시할 수 있는 다양한 파트너쉽 브랜드들의 제품도 만나보실 수 있습니다.
This space is the place to present J'RIUM lifestyle to pursue through seasonal concept proposal as well as direct knit wear experience.
This space contains all information on knitwear such as knit wear and knitted goods, etc.
You may encounter J'RIUM goods as well as various partnership brand items that wholly suggest J'RIUM's pursuing men's life style.
We look forward to your frequent visits.


text:
From Engineer to International Businessman
Fascinated by all things mechanical as a child, cars and motorcycles have featured heavily in Gerald Davison's life and work. His regular visits to the Science Museum near his home in London when a small boy inspired his life-long love of all things mechanical (as well as a passion for Asian art from the Victoria and Albert museum opposite).
Gerald on his Norton, 1959
Growing up in an age when young people could only expect to have a motorcycle for a few years while car ownership was out of reach, the excitement and freedom that personal transport offered appealed long before he was old enough to have a licence. From the age of 14 he was studying every feature and detail of all motorcycles then available and started riding at 16 with all of his early machines being Nortons.
He had set out to train as an assessing engineer for a specialist motor insurance company based in London which involved travelling all over the home counties visiting dealers to inspect damaged cars and motorcycles. This brought him into contact with a major car and motorcycle distributor in South London who represented Norton among other brands. Regular contact resulted in him starting the Norton Owners Club with their help and, in turn, through the club struck up a friendship with actor Richard Wyler who was then regularly appearing on TV screens as the 'Man from Interpol' and a very enthusiastic motorcyclist. Gerald was eager to race motorcycles and through Wyler he was offered help to go racing and to eventually follow in the footsteps of other British motorcycle racers and move to the USA. Needing an employment sponsor he was offered work helping to assemble the Scarab sports-racing car then being planned in California by Wyler's friend Lance Reventlow. To gain more practical experience he left his training with a plan to work for the Norton distributors until he could take up the opportunity in the USA. He also joined the Westminster Racing Club and went racing with his Honda 125 and a Greeves 'works' prototype 250.
Racing a 250 Greeves prototype, Brands Hatch 1961
Very wet race at Crystal Palace on a Honda 125, 1961
Sadly, new rules by the motorsport governing body, the FIA, prevented the Scarab racing in Europe and the project was curtailed just before Gerald was due sail to the USA. Following this disappointment he was fortunate to be able to rejoin his old employer and complete his training as an engineer.
His mind was still drawn to the USA and at just 23 he saw the opportunity to join the international division of the Chrysler Motor Corporation, headquartered in Geneva. They had advertised for an engineer aged 30-35 but, not short of confidence, he rather cheekily applied and 3 interviews later he got the job and the salary to go with it! This was his first experience of dealing with global markets and he was a Technical Administrator covering Europe and the ME. Within a few weeks he found himself in Sweden dealing with their DOT legislators. The early 60s were a good time for Chrysler in the ME because they were the US major that was not on the Arab boycott list. The role was quite a challenging one which among other things gave Gerald first-hand experience of working with main frame computer systems. Chrysler's Europe based operations began to struggle after the purchase of car makers Rootes in the UK and Simca in France. In this environment his work horizons widened enormously and he discovered the new 'buzz-word' from the other side of the Atlantic – marketing! After 3 years a posting to Beirut where most US companies had their ME headquarters loomed and he sought something new instead that would keep him in the UK.
With company founder Soichiro Honda and his wife Sachi, 1982
So, 1968 saw Gerald join the embryonic UK arm of the Honda Motor Company as a young Manager at a time when the company was just becoming commercially established in Europe. He had already had some contact with Honda's engineering excellence while racing motorcycles a few years earlier and was deeply impressed by their creative flair and obsession with detail and quality. This led to a 17 year relationship with the company and a business friendship with the founder, Soichiro Honda.
As Import and Distribution Manager for their newly developed range of small cars Gerald established preparation, rectification and distribution centres in London and the North of England. Earlier systems had not properly accessed the benefits of shipping insurance in Japan and he was also able to recover a very substantial sum (£8m in todays values) that had not until then been accounted for. This success in his first year setting up this operation led to him being asked to also take on management of the parts division. This had just been unsuccessfully relocated from Nottingham to London with availability of parts to customers dropping to less than 60% – he responded to this crisis by developing a unique 2-tier distribution network which in just a few months raised the availability level to 90%. He also discovered that large numbers of 'fake' parts were being distributed in the UK and established a 'ghost' company to identify the sources, track these and eventually choke off their import though legal action with HM Customs. These further successes resulted in the service division also coming under his management. Gerald also spent periods working in other European affiliate Honda companies introducing similar systems. By this time Honda was producing more and more sophisticated motorcycles, many with 4-cylinder engines, which the dealer network was ill equipped to easily service and maintain. His response to this was to create a training school for technicians and the 'Honda Five-Star' service scheme which redeveloped dealer's workshops with the latest in diagnostic and workshop equipment.
Honda had withdrawn completely from all racing by 1968 but with Honda UK's growing strength Gerald then planned to take the company back into racing. With the ambition of having a presence in all areas of motorcycle sport, this was still a time when 'win on a Sunday would sell on a Monday'. In 1976 he created 'Honda Britain Racing', initially to compete first in Endurance Racing across Europe and for 1977 the newly introduced World Championships which entailed taking the company back to the Isle of Man TT for an inaugural win in F1, F2 and F3 classes. He worked closely with the ACU and FIM, where he was Honda's representative, to create the opportunity for Honda to race mainly 4-stroke engines in an arena that had become dominated by 2-strokes technology. Ironically, in motocross taking the 500 cc World Championship also followed and the creation of a new Honda MT125 championship was the first ever one make series – needed 2-stroke engines to succeed.
Director of the Honda International Racing Team, 1978
Gerald was creative with other solutions introducing a range of clothing and accessories under the 'Hondastyle' brand and 'Honda Lubricants' to provide customers with a specification of oil not then available from the leading oil companies. In the face of major insurance problems for learner riders, he then set up one of the first ever insurance comparison systems under the 'Hondacare Insurance' brand using computer technology to broker rates from many insurance companies. By now Gerald was the company's Senior Manager and also had the role of coordinating Europe for product development of cars and motorcycles, working closely with Honda's R&D companies. His innovative strategies and marketing policies for the rapid growth of the company were used in Honda operations in many parts of the developed world. He was also Honda's representative to the FIM and responsible for its racing policy.
Testing the prototype of Honda's CBX 6-cylinder bike, Suzuka Japan 1977
At the end of 1977 Honda's second president, Kiyoshi Kawashima, who had succeeded the company's founder Soichiro Honda, asked Gerald to set up and manage a new Grand Prix operation, which entailed him setting up a new company the 'Honda International Racing Company' (HIRCO) with him as its Director at a new base he chose to locate in Slough, UK. This was another dimension in addition to motocross and initially focused on developing the legendary 'NR' 4-stroke oval piston engine project. These revolutionary machines made their debut unsuccessfully in 1979, so for 1980 Gerald moved the complete chassis' design and construction to Slough UK where they were built to a more conventional design.
Industry magazine retrospective, 2003
With Frank Williams announcing Honda's partnership with Williams F1 Racing, 1983
Industry magazine cover, 1985
At the end of 1981 Gerald returned full time to Honda UK and eventually became Director of UK Operations for cars, motorcycles and power products as well as remaining an adviser to the newly created Honda Racing Corporation (HRC) which had grown out of HIRCO, and remained Honda Motor Company's representative to the FIM. During 1980-81 he also provided liaison between Honda and British Leyland on the successful project to produce the Honda Ballade in the UK as the Triumph Acclaim. He restructured Honda UK's management structure mainly to emphasis the growing divisional nature of Honda's product line up and for the first time set up dealer councils for each product division. Through these he was able to ensure more orderly marketing of Honda products, stop sales by unauthorized channels and distressed selling. He then embarked on a very ambitious programme to redevelop the UK subsidiary's car dealer network to keep in step with the advancing range and sophistication of the cars being produced in Japan and the prospect of a UK factory. He had positioned the Honda car brand ahead of its Japanese rivals in the UK. By 1985 Honda share of the UK motorcycle market was 50% and the fast emerging car franchise the third most profitable behind only Mercedes and Porsche. At the same time he reorganised the Motorcycle Industry Association, removing its 'Council' structure and introducing a modern board structure to administer the industries business. During 1985 he had a series of major disagreements with Japan about the future direction of Honda's car operations in Europe following the decision to create a car factory at Swindon in the UK – Gerald chose to resign on a point of principle. Attempts were made to persuade him to stay but at the time the Honda Motor Company was very factional and following his resignation in Europe he was offered a vice-presidency of American Honda. He chose instead to go in another direction altogether – see Businessman. As it happens, the very poor performance of Honda's car sales in Europe and the forthcoming closure of the Swindon factory in 2021 confirm his judgement of the plans being laid back in 1985.
Organising and presenting charity auction for Dorset Air Ambulance
Presenting the annual 'Gerald Davison' silver trophy for best newcomer, Classic Motorcycle Racing Club
Still watching MotoGP racing with multi world-champion Valentino Rossi, Mugello Italy
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text:
Ian Somerhalder Dating Molly Swenson: New Girlfriend After Nina Dobrev
BY CDL Intern on April 21, 2014 | Comments: 5 comments
Related : Celebrity Couple, Ian Somerhalder, Nina Dobrev
It's about damn time, I say. According to [legitimate] eyewitness accounts, Ian Somerhalder is hooking up with Molly Swenson, the CEO of RYOT News – a company that Ian is incidentally on the advisory board for.
Ian and Molly reportedly stopped by at the Details Magazine party that was being held in Santa Monica, and although they weren't being obviously intimate, Ian introduced her to everyone that they mingled with. The reports also add that the duo stayed at the party for the entire duration, and made sure not to leave each other's side.
Then, they proceeded to head to the Treats! Magazine party honoring Dylan Penn's latest cover at a private Beverly Hills home. That's where sources go into more detail, with E! Online adding, "Ian got off the golf cart, and was wearing a leather jacket and his hair was tousled underneath a cap. Two of Ian's friends were in front and Ian and his date were following closely behind casually holding hands. They seemed happy, casual, and enjoying the evening."
Up until then, I'd say there was a high chance that Ian and Molly were just friends – especially since they technically work together. However, there's no such thing as 'casually' holding hands. If you're holding hands with someone – especially at a party – you're either dating or hooking up with that person.
Really though, it's about time Ian was linked to someone that wasn't Nina Dobrev. It's been a year since the breakup, and either Ian's truly taken this long to move on – or he's just been much more undercover with his hookups and relationships.
What do you guys think about Ian and Molly dating? I'm so glad Ian is finally over Nina Dobrev! Do you think it's smart that Ian is choosing to date a non-celebrity, perhaps burned with the intense press speculation that surrounded his relationship with Nina? Let us know your thoughts in the comments below.
Ian Somerhalder's New Girlfriend Ocean Ramsey – Gives Up On Nina Dobrev?
Nina Dobrev Jealous Of Ian Somerhalder's New Relationship With Molly Swensons
Ian Somerhalder and Nina Dobrev Engaged Soon! – Are They Hollywood's Next Power Couple?
Nina Dobrev & Ian Somerhalder Plan Hot Loving Hookup After SXSW
Ian Somerhalder To Take Nina Dobrev Back After Losing Christian Grey Fifty Shades of Grey Movie Role?
Ian Somerhalder And Nina Dobrev Continue Twitter Flirting – Reunion Rumors On The Vampire Diaries Set
Julianne Hough Buys Nina Dobrev Sexy Valentine Lingerie – For Ian Somerhalder? (PHOTOS)
Miley Cyrus Hooks Up With Ian Somerhalder To Get Back At Liam Hemsworth and Nina Dobrev


text:
Ashes of Hope is a 1917 silent film western directed by Walter Edwards and starring Belle Bennett. It was produced and released by the Triangle Film Corporation.

Cast
 Belle Bennett as Gonda
 Jack Livingston as Jim Gordon
 Jack Richardson as 'Ace High' Lawton
 Percy Challenger as Flat Foot
 Josie Sedgwick as Belle

Preservation status
 Incomplete print at the Library of Congress.

References

External links

1917 films
1917 Western (genre) films
Triangle Film Corporation films
Films directed by Walter Edwards
American black-and-white films
Silent American Western (genre) films
1910s American films
1910s English-language films

redpajama_set_name: RedPajamaWikipedia


We are company specializing in timber decks, balustrades, stairs, pergola's, carports, solid and laminate floors, bamboo floors, fencing, ceilings.
Business Products / Services: Timber decks, ballustrades, stairs, pergola's.
Staircases, balustrades, pergolas, jetties, hardwood decks, composite decks, pool decks and boardwalks.


text:
Improving land tenure security for Madagascar's...
Improving land tenure security for Madagascar's producers and farmers
Back to the map
AFD is supporting a land tenure security project in Madagascar with the aim of increasing the incomes of rural producers, securing their plots, and reducing conflicts related to land ownership. This will lead to more equitable and sustainable land management.
The political crisis, which hit Madagascar from 2009 to 2013, has significantly worsened the level of poverty of the population, particularly in the Analamanga and Itasy regions, which have the largest number of poor rural households among the country's 22 regions. There is a major shortage in the availability of food products compared to urban demand, particularly for certain fruit and vegetable products, poultry, fresh milk, fish and fuelwood, which leads to inflationary pressure on retail prices.
In the rural communities which are directly called on to supply food to the capital, land tenure security is one of the recurrent problems which undermine agricultural development. The micro-fragmentation of peri-urban agricultural areas, difficulties and slowness in obtaining titles and lack of access to land certificates hold back investments and hinder agricultural intensification initiatives.
The project to support land tenure reform and security (ARSF) around Antananarivo aims to improve land tenure security for rural actors in Madagascar in general, and in the communities around Antananarivo in particular. This project has the three following objectives:
Provide 75 rural communities with mechanisms to secure land rights and for territorial management and property taxation;
Provide farmers in these communities with access to farmland with secure conditions, allowing them to scale up their production;
Support the implementation of the National Land Tenure Program, and in particular the institutions in charge of the land tenure reform and civil society.
At the economic level: income secured for 20,000 producers through the issuance of proof of land ownership; At the social level: reduction in conflicts related to land and the use of natural resources;
At the environmental level: promotion of agroecological practices and reforestation (fuelwood);
At the institutional level: strengthening of community institutions in charge of the land tenure reform launched in 2005 and of civil society. Over 2,000 land certificates and 2,000 land titles have already been issued, 11 community land tenure offices have opened, and 22 mayors have been trained in decentralized land management.
Download the project sheet
This project is carried out with the support of the European Union
Itasy
Financing tool
Financing amount
Ministry in charge of Presidential Projects Spatial Planning and Equipment
Local Development Fund National
Institute for Decentralization and Local Development Land
observatory Solidarity of the Stakeholders on Land
Co-financiers
The content of this project information sheet falls under the sole responsibility of the AFD and does not necessarily reflect the opinions of the European Union.
Madagascar: The Extraordinary Feat of Access to Land for All
Protecting Madagascar's forests
Easing congestion in Antananarivo and facilitating trade
Supporting agricultural development in Madagascar
Health and Social Protection
Improving food security in Haiti
Supporting the development of agriculture and income-generating activities
Rehabilitation of Aberdares Forest with Green Belt Movement
on the same financial tool
Assisting local authorities in integrated and sustainable planning
Protecting Elephants Against Poaching
Conserving island ecosystems and reducing their vulnerability to climate change


Originally posted on 5 August 2016. Scroll down for news of the release of the Maya plugin.
AMD has rebranded FireRender, its open-source GPU-based renderer, as Radeon ProRender.
The change of name brings the renderer into line with the company's workstation-class graphics cards, which have also just been renamed from the FirePro series to the Radeon Pro series.
An OpenCL-based renderer capable of making use of any manufacturer's GPUs, Radeon ProRender works across Windows, Linux and Mac OS X, and comes with plugins for 3ds Max, Maya, Rhino and SolidWorks.
It's an unbiased path tracer, with the basic range of features you would expect in a physically based renderer, including support for volumetrics in OpenVDB format.
Radeon ProRender is currently in beta, and is available via AMD's GitHub repository at the link below.
At the time of posting, compiled binaries and source code are available for the 3ds Max plugin, which is compatible with Max 2016 and 2017. The Maya plugin is being updated to Maya 2017 and will be out "soon".
Updated 23 December 2016: Radeon ProRender for Maya is now officially available for Maya 2016 and above, running on Windows 7+, Mac OS X or CentOS 6.5 Linux.
According to AMD, it's possible to share materials between the 3ds Max and Maya editions of the renderer.
You can read the feature list for the Maya plugin here and download the plugin itself here.


After touring on Easy Easy East throughout 2015, the band came together to start writing their follow up record Big Day. The songs on Big Day developed a more cohesive feeling as the four became more comfortable playing together. Big Day is set to be released in April 2017 on Father/Daughter Records and Lame-O Records.


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Earth and the Environment >
Minerals, Mining, and Metallurgy >
Mineralogy and Crystallography >
emerald, the green variety of beryl, of which aquamarine is the blue variety. Chemically, it is a beryllium-aluminum silicate whose color is due to small quantities of chromium compounds. The emerald was highly esteemed in antiquity; the stones were used for ornaments in early Egypt where some of the first emeralds were mined. The some of the finest emeralds are found in South America in Colombia, where they have been mined for over 400 years. The gem was a favorite in pre-Columbian Mexico and Peru, where it was cut in intricate designs. The treasure taken back to Spain by early explorers included emeralds. Good emeralds are rare and the most highly valued of gem stones. Zambia and Brazil are also significant sources of the natural stones. Synthetic emeralds are also manufactured in Germany, France, and the United States. The Oriental emerald, a different gem, is the transparent green variety of corundum.
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