Category Archives: Tms

Logistics providers have one goal in mind: Ensure shipments arrive on time and in one piece. But its easier said than done, as growth can often be a double-edged sword.

When demand surpasses the capabilities of a business, growing pains make it difficult to scale operations. This is the case for many carriers as well as shippers and 3PLs with growth in mind.

The juggling act of sourcing, fulfillment and management often distracts logistics providers from the business at hand, as each department requires more attention. Solutions are available to manage each task, but this siloed approach makes cross-platform synchronization nearly impossible and ultimately creates friction between departments.

There are systems that do those individual things for you, but you still end up putting a lot of effort into moving data from one system to another making sure theyre synchronized, said Nick Darmanchev, Alvys founder and CEO. That kind of defeats the entire purpose of using technology because youre spending more time managing the technology instead of reaping its benefits.

What the industry needs is a single platform to satisfy all stakeholders, and Alvys has answered the call.

The Denver-based logistics software provider simplifies logistics and transportation workflows, carrier procurement, dispatch, and relationship management, starting at the ground level with small and midsize carriers the industrys most overlooked segment.

Alvys offers something beyond the conventional functions of a TMS. Unique among TMS offerings, it focuses on workflow. Its TMS levels the playing field through affordable add-scale technology that integrates a variety of management systems into one intuitive dashboard.

Darmanchev expounded on the platforms affordability and convenience, noting that Alvys builds its technology around customers business.

Ask an Alvys user what they think about the system and theyll tend to say that it feels like its designed for what theyre doing and to exactly support the workflow theyre engaged in, as opposed to the other way around, where theyve got to try to fit workflows into the technology, Darmanchev said.

Igor Balorda, CEO of Colorado-based motor carrier Skyline Transportation, made the switch to Alvys earlier this year and said the platform has provided a breath of fresh air.

Alvys has really checked all the boxes on our end as far as what we want from a truck management software, Balorda said.

As a midsized company, Skylines 35-truck fleet has experienced rapid growth, which had become a bit too much for its current operating systems to handle. Balorda said that its biggest challenge was data entry and dispatching challenges, conflicting with day-to-day operations. But after joining Alvys, managing truckings nitty-gritty tasks became a lot less challenging.

My team absolutely loves the drag-and-drop feature for rate confirmations, Balorda said. Alvys automatically generates an invoice for us and puts in all the pertinent information that the driver needs. This differs from before, when Skyline spent hours building out loads every day.

Smaller carriers, especially those operating on thin margins, found themselves unable to afford such technology before discovering Alvys. However, carriers are no longer expected to shell out a lot of money for a sophisticated TMS or develop software in-house.

We promise logistics service providers that well help them move from point A to point B faster, meaning from one phase of the company to another, said Alvys co-founder and CTO Leo Gorodinski. It takes a different strategy tool to manage a five-truck operation versus a 20-truck, and Alyvs has the tools in our TMS to incorporate these different phases.

Poor visibility inevitably leads to frustration. Gorodinski said that communication gaps are common for carriers operating disparate platforms. He explained that a dispatcher snagging a load from a DAT load board at 9 a.m., for example, may go ahead and book a noon pickup for a specific trailer sitting idle at the receiver. The dispatcher is under the impression that the trailer has already been unloaded and is ready for pickup due to the GPS showing inactivity, but unbeknown to the dispatcher, the trailer has yet to be unloaded and will probably miss its noon scheduling.

Gorodinski noted that this situation couldve easily been avoided if the dispatcher and driver had a direct communication channel. The Alvys mobile Driver App allows drivers to provide check-in and checkout updates at every stop. Drivers can also issue comcheck and EFS checks for lumpers and advances, as well as upload bills of lading and proof of deliveries at each load level.

Imagine if drivers are empowered with a mobile application where theyre obligated to actually register their check-in and checkout time activity? Gorodinski said. If the checkout was not yet registered from the mobile application, then the dispatcher wouldve known for sure that the truck had not been unloaded. Therefore, the dispatcher wouldnt have bothered talking with the broker.

The Alvys app helps managers keep fleets in shape by sending drivers confirmation requests and schedules instantly without hassle. Skyline has enjoyed its improved communication capabilities, as it found corresponding with drivers through email and text messages to be a tedious task. In fact, Balorda said that the driver app has inadvertently reduced the amount of messages drivers send while in transit, ultimately reducing distracted driving.

Alvys streamlines the accounting process too, through its integration with QuickBooks and other accounting software. Users can automate invoices, upload instantly to a preferred factoring company, manage customer aging reports, and even generate drivers pay stubs almost instantaneously.

For instance, a driver traveling an additional 200 miles for truck repairs would understandably want to be paid for the trouble. But if dispatch fumbles the payment request and forgets to tell accounting, all while the driver assumes the payment is being processed, things get heated pretty quickly.

If that driver was able to report the extra 200 miles on his own, then accounting would only need to check with the dispatcher, Gorodinski said. The dispatcher would then confirm, therefore posting that extra 200 miles on his pay stub; the driver is happy, retained, and so on and so forth.

Alvys end-to-end platform saves carriers from the hassle of using multiple software solutions. Gorodinski reasons that carriers benefit from incorporating appointments automatically with shippers and receivers within the TMS, where all parties can stay up to date instead of tediously drafting yet another set of dull spreadsheets.

I dont know of any carrier that uses a TMS only; they use a TMS and a bunch of spreadsheets on top of other softwares in order to do their work, Gorodinski said. The question is, why do they even have a TMS? Why not incorporate the entire end-to-end workflow, and make the lives of all logistics stakeholders easier?

Click for more FreightWaves content by Jack Glenn.

Excerpt from:

More than a TMS, Alvys spares carriers from needing multiple software solutions - FreightWaves

Significance

Oscillatory activity is prominent in the brain, and one hypothesis is that it is, in part, due to the nature of coupling or interaction patterns between brain areas. We tested this hypothesis by manipulating the strength of coupling between two brain regions (ventral premotor cortex, PMv, and motor cortex, M1) in two directions (increase or decrease) while carefully controlling for the impact each manipulation had on activity in each area. We looked at the PMvM1 connection because it is the major cortical route by which prefrontal cortex might influence, inhibit, and curtail action-related activity in M1. Manipulating PMvM1 coupling in accordance with Hebbian-like spike-timingdependent plasticity resulted in changes in beta and theta frequencies linked to action control.

The origins of oscillatory activity in the brain are currently debated, but common to many hypotheses is the notion that they reflect interactions between brain areas. Here, we examine this possibility by manipulating the strength of coupling between two human brain regions, ventral premotor cortex (PMv) and primary motor cortex (M1), and examine the impact on oscillatory activity in the motor system measurable in the electroencephalogram. We either increased or decreased the strength of coupling while holding the impact on each component area in the pathway constant. This was achieved by stimulating PMv and M1 with paired pulses of transcranial magnetic stimulation using two different patterns, only one of which increases the influence exerted by PMv over M1. While the stimulation protocols differed in their temporal patterning, they were comprised of identical numbers of pulses to M1 and PMv. We measured the impact on activity in alpha, beta, and theta bands during a motor task in which participants either made a preprepared action (Go) or withheld it (No-Go). Augmenting cortical connectivity between PMv and M1, by evoking synchronous pre- and postsynaptic activity in the PMvM1 pathway, enhanced oscillatory beta and theta rhythms in Go and No-Go trials, respectively. Little change was observed in the alpha rhythm. By contrast, diminishing the influence of PMv over M1 decreased oscillatory beta and theta rhythms in Go and No-Go trials, respectively. This suggests that corticocortical communication frequencies in the PMvM1 pathway can be manipulated following Hebbian spike-timingdependent plasticity.

The origins of oscillatory activity in the brain are currently an area of active debate, but common to many accounts is the idea that they partly reflect interaction or communication between brain areas (1, 2). Here, we directly test this possibility in the human brain by using manipulations that either increase or decrease the influence of one cortical area, the ventral premotor cortex (PMv), on another cortical area, the primary motor cortex (M1). Importantly we do this by carefully controlling for the impact on each component area when altering the strength of the pathway between them.

The PMvM1 pathway is an ideal pathway in which to examine the effects of manipulating connection strength; it is well established that PMv exerts a powerful influence over M1 and that changes in connectivity are functionally relevant and correlated with motor control (39). Moreover, the pathway can be examined in humans; by stimulating PMv shortly (6 to 8 ms) before the stimulation of M1, it is possible to influence how activity in M1 evolves (812). Even though the impact of the first pulse in PMv is spatially circumscribed (13), it alters the activity in PMv neurons that project to M1 (3, 4, 6). When this is done repeatedly, the influence that PMv exerts over M1 is strengthened (7, 14, 15). Such a procedure is referred to as paired associative stimulation (PAS) or corticocortical PAS (ccPAS) when, as in this case, the two regions stimulated are areas of cortex. The evoked effects have been described as Hebbian in nature (16, 17, 18). If exactly the same amount of stimulation is applied to the same two areas but in the opposite temporal order, then the influence of PMv over M1 is, instead, diminished (14, 15). These effects have been established by examining changes in the coupling of blood oxygen leveldependent (BOLD) signals in PMv and M1 before and after ccPAS (15). From such experiments, it is clear that the increases and decreases in coupling that result from the two types of ccPAS are prominent between the stimulated areas themselvesPMv and M1but they also extend to other motor association areas with which PMv and M1 are closely interconnected in the frontal and parietal cortex. The impact of ccPAS can also be visualized by measuring M1 excitability, which can be done by measuring motor-evoked potentials (MEPs) in hand muscles when single pulses of transcranial magnetic stimulation (TMS) are applied to M1 (14, 15). When this is done before and after ccPAS, M1 excitability increases in contexts, such as movement production, in which PMv normally exerts an excitatory influence over M1 (14, 15). Such effects are, however, context dependent, and in other settings in which PMv inhibits M1, it is this inhibitory action that is augmented by ccPAS (14).

CcPAS may, therefore, be an ideal tool for looking at the impact of manipulating coupling between two brain areas; if the effects of two different ccPAS protocols are compared, then it should be possible to establish the effect of increasing or decreasing coupling between the two areas while holding constant the total amount of stimulation to each component area. We therefore examined the impact of either increasing or decreasing PMvM1 coupling on electroencephalogram (EEG) oscillatory activity while human participants performed a simple Go/No-Go motor task in two blocks (referred to as Baseline and Expression blocks; Fig. 1). In participant group A, we applied 15 min of ccPAS over PMv followed by M1 (PMvM1-ccPAS; each PMv pulse was followed by an M1 pulse at either 6- or 8-ms interpulse interval [IPI]). Before and after ccPAS, participants performed a Go/No-Go task in which participants responded to Go stimuli (blue square) and withheld responses to No-Go stimuli (red square). Furthermore, we investigated whether changes in oscillatory activity were dependent on ccPAS stimulation order by reversing the order of ccPAS stimulation (participant group B), that is, applying the first paired pulse over M1 and the second pulse over PMv (Fig. 1). Exactly the same number of pulses were applied to PMv and M1 in both participant groups A and B.

Representation of the set up for groups A and B and individual subject scalp hotspot for rM1 and rPMv. (Top) Experimental design and setup for both experimental groups. The ccPAS period was preceded (Baseline) and followed (Expression) by Go/No-Go task blocks. EEG activity was recorded during the task blocks. (Bottom) Individual subject scalp hotspot (filled circles) and 95% group confidence ellipses for rM1 (red) and rPMv (blue) locations for the main and preliminary experiments in standardized MNI space.

The use of a Go/No-Go task enabled us to look at a range of oscillatory effects in the EEG. Power increases in the beta range, called post-movement beta rebound, are related to activity in M1, and closely interconnected areas as movements are completed and should be observable on Go trials (19, 20). By contrast, activity in the theta range should be prominent on No-Go trials as in other situations that require the reorienting of behavior such as stopping an action from being made (2125). Beta and theta band activity occurs in medial and lateral frontal and centroparietal areas that interact with PMv and adjacent inferior frontal cortex during action inhibition (10, 2628). It is also possible to record activity in the alpha band in the EEG, although task-related modulations of alpha were less anticipated in a Go/No-Go task of this type. Given the difficulty of recording reliable gamma-band activity using EEG, we did not attempt to examine activity at this frequency.

In experimental groups A (n = 16) and B (n = 17), we investigated, respectively, whether increasing or decreasing coupling across motor and motor association areas led to modulation of either fast (transient) or slow (sustained) EEG oscillatory dynamics associated with action control. We contrasted the effects of the two types of ccPAS, repeated paired stimulation of PMv followed by M1 (group A) or, vice versa, M1 followed by PMv (group B) on time-frequency oscillatory responses (computed as ExpressionBaseline block separately for Go and No-Go trials), recorded in a simple motor task.

Prior to starting the main experiment, in a preliminary investigation probing M1 excitability, we carried out two initial checks to ensure the effectiveness of the TMS protocol in the context of the current behavioral task (SI Appendix; Fig. S5A). First, we compared MEPs when we applied either single-pulse TMS (spTMS) over right M1 (16, 29) or paired-pulse TMS (ppTMS) over right PMv (conditioning pulse) followed by right M1 (8, 9, 14, 15). We recorded MEPs from the left first dorsal interosseus (FDI) muscle while participants performed Go trials in the Go/No-Go task. We demonstrated that PMv TMS did indeed alter the impact of M1 pulses on Go trials, confirming that the paired pulse procedure allowed us to probe the PMvM1 pathway (SI Appendix; Fig. S5B). Second, we examined the impact of repeatedly inducing PMv activity either just before or just after inducing M1 activity during ccPAS. Again, we did this by measuring MEPs recorded in response to single pulses of M1 TMS on Go trials, but we did so before and after a 15-min period of ccPAS. Here, we demonstrated that we could manipulate the pathways connectivity; the two ccPAS protocols used in groups A and B did indeed exert distinct effects on Go trials. While PMvM1-ccPAS significantly enhanced the cortical excitability of M1 in Go trials, this M1 excitability remained the same after M1PMv-ccPAS (SI Appendix; Fig. S5C).

Next, we examined the impact of the ccPAS in the EEG in groups A and B. We first compared the two groups of participants in the two groups before examining the changes occurring in each group in more detail. We focused on motor-relevant frequency bands theta, alpha, and beta (4 to 30 Hz) in frontocentral and centroparietal electrodes (EEG Recording and Analysis) known to reflect top-down control of motor processes likely to be relevant for performance of the Go/No-Go task (1922, 30, 31). Because ccPAS can affect the motor system both ipsilaterally (14) and contralaterally (10), we examined a bilateral group of electrodes spanning both hemispheres.

We used cluster-based nonparametric permutation analysis procedures for identifying statistically significant clusters in the time, frequency, and spatial domain (EEG Recording and Analysis) (3234). This revealed that ccPAS had a significant impact on motor-related beta and theta bands but little impact on the alpha band. Moreover, ccPAS effects significantly differed for Go and No-Go trials, and they diverged between the two participant groups (group A versus Bsee Materials and Methods for a detailed explanation of analysis procedure). The significant effects of ccPAS were identified by the cluster-based permutation test as occurring in frequency bands typically regarded as being within the beta band range (19 to 24 Hz; Monte Carlo P value = 0.018) and within the theta band range (4 to 10 Hz; Monte Carlo P value = 0.008) between 0.15 and 1.2 s after the Go/No-Go stimulus onset.

Following these results, we contrasted the ccPAS effect, testing the difference across the two participant groups, for Go and No-Go trials separately, by subtracting EEG responses recorded at Baseline from Expression and contrasting this difference across groups (group A versus B) in the two types of trials. In the beta band, post hoc between-subject Students t tests showed that the PMvM1-ccPAS in group A led to an increase in beta synchronization only for Go trials (0.7 to 1.2 s after Go stimulus onset, consistent with the time of the post-movement beta rebound, PMBR) in the Expression versus the Baseline block. However, the opposite effects were found in Go trials when the ccPAS order was reversed in group B (Monte Carlo P value = 0.002, Fig. 2 A, Left). Note that, as we describe below, these differences could not be an indirect consequence of changes in reaction time because no changes in reaction time were apparent (SI Appendix, Table S2 and Behavioral Results). No significant differences in the beta band were observed for No-Go trials in the between-subject Students t test analyses (Monte Carlo P value > 0.05) (Fig. 2 A, Right). In addition, we contrasted the ccPAS effects on beta activity recorded in the Baseline versus the Expression block for Go and No-Go trials separately for group A and B. The results of this within-subject Students t test analysis revealed a late increase in beta synchronization after (versus before) PMvM1-ccPAS for Go trials only (0.9 to 1.2 s after Go stimulus onset; Monte Carlo P value = 0.002, SI Appendix, Fig. S1, Left). By contrast, when the ccPAS order was reversed, changes in beta power were only observed in No-Go trials; beta responses first decreased before increasing in a later time window (0.3 to 1.1 s after No-Go stimulus onset; Monte Carlo P value = 0.0009, SI Appendix, Fig. S1, Right). No significant differences were seen in the beta band when comparing Baseline and Expression blocks for No-Go trials in group A, PMvM1-ccPAS, nor for Go trials in group B, M1PMv-ccPAS (Monte Carlo P value > 0.05). Furthermore, control analysis confirmed that the beta changes after the ccPAS manipulation in Go trials were not driven by group differences at baseline. Additional details of the results (mainly the data for each condition as opposed to the contrasts between conditions) and control analysis are shown in SI Appendix, Fig. S1 and SI Appendix.

EEG time-frequency responses in the beta band in frontocentral sites for Go and No-Go trials (n = 33). (A and B) EEG time-frequency responses (TFR) in the beta band (15 30 Hz) in frontocentral sites (C4, CZ, FC2, CP2, FCZ, C1, C2, FC4, CP4, and CPz; electrodes highlighted in white in Top Right topoplot) time locked to the onset of the Go/No-Go stimuli, computed as (A) the difference between Expression and Baseline blocks, (B) the mean of Baseline and Expression blocks collapsing across groups A + B. While B shows the PMBR effect was especially prominent in the Go trials, A illustrates how this changed as a function of the two types of ccPAS used in groups A and B. The dashed red square in A indicates the time window (0.7 to 1.2 s) in which significant modulation in beta responses after ccPAS were found. Dashed red line in B indicates the mean RT across Baseline and Expression for Go trials in both participant groups (mean = 352.36 s). (C) Mean beta frequency increase (PMv M1 ccPAS) and decrease (M1 PMv ccPAS) computed as the difference between Expression and Baseline in Go trials in the 0.7- to 1.2-s time window. Error bars represent SEM, single dots represent individual data points. In A, EEG TFR represent percentage change in power computed by subtracting the Baseline from the Expression block (0 = no percentage change). In C, EEG TFR represent relative percentage change in power with respect to the prestimulus interval (1 = no percentage change).

The PMBR may reflect a short-lasting state of deactivation or resetting of premotormotor networks after movement completion (35). The increased PMBR observed in Go trials during Expression may reflect an augmentation of active inhibition from PMv over M1 following strengthening of PMvs influence over M1 through ccPAS. The projections from PMv to M1 are excitatory, but many of these projections are onto inhibitory interneurons in M1 (36). Thus, PMv exerts both inhibitory and facilitatory influences over M1, and both of these influences can be augmented by PMvM1-ccPAS (14). Moreover, the observation of the opposite effects on beta synchronization on Go trials, when reversing the order of the ccPAS stimulation in group B, are in line with previous evidence showing contrasting effects of reversed versus forward order ccPAS on M1 cortical excitability as well as on functional connectivity in motor networks (14, 15).

While the PMvM1-ccPAS effects in the beta frequency occurred on Go trials, the theta effects occurred in No-Go trials in both groups. In No-Go trials, post hoc Students t test analyses revealed that PMv-M1-ccPAS in group A led to a significant increase in theta power, whereas theta power decreased after reversed-order M1PMv-ccPAS in group B (Monte Carlo P value = 0.002; 0.15 to 1.2 s after stimuli onset) (Fig. 3). In the same vein, the results of the post hoc within-subjects Students t test analysis contrasting the ccPAS effects on theta activity between Baseline and Expression blocks revealed that the PMv-M1-ccPAS in group A led to a late increase of theta activation in No-Go trials (0.8 to 1.2 s after No-Go stimulus onset; Monte Carlo P value = 0.0009, SI Appendix, Fig. S2, Top Right), whereas the opposite effects in early theta activation were observed for No-Go trials after reversing ccPAS in group B (0.15 to 0.65 s after No-Go stimulus onset; Monte Carlo P value = 0.001, SI Appendix, Fig. S2, Bottom Right). Several findings have linked increased theta power in midfrontal regions to top-down executive control and action reprogramming during response conflict and motor inhibition, for example, after a No-Go command (21, 22). Notably, theta oscillatory changes increase with the level of response conflict, reflecting a larger top-down influence over motor circuits (31). It is clear that the inhibition of a specific action is associated with a series of interactions between medial frontal cortex areas such as the presupplementary motor area and PMv and possibly immediately adjacent tissue in the posterior inferior frontal cortex (10, 26, 27). Therefore, the increased theta power in No-Go trials after PMvM1-ccPAS observed in experimental group A suggests augmentation of oscillatory activity associated with top-down motor control in response conflict, whereas the reversed-order M1PMv-ccPAS suggests diminution of the same oscillatory activity in the same No-Go trials in experimental group B. No ccPAS effects on theta power were found in Go trials (Monte Carlo P value > 0.05) (Fig. 3). Moreover, control analysis confirmed that the theta changes after the ccPAS manipulation in No-Go trials were not driven by group differences at baseline. Further details of the results (mainly the data for each condition) and control analysis are shown in SI Appendix, Fig. S2 and SI Appendix.

EEG time-frequency responses in the theta band in frontocentral sites for Go and No-Go trials (n = 33). (A and B) EEG time-frequency responses in the theta band (4 to 15 Hz) in frontocentral sites (C3, C4, CZ, FC1, FC2, FCZ, C1, C2, FC3, FC4, CP4, and CPZ; electrodes highlighted in white in Top Left topoplot) time locked to the onset of the Go/No-Go stimuli, computed as (A) the difference between Expression and Baseline blocks, (B) the mean of Baseline and Expression blocks collapsing across groups A + B. While B shows the theta effect that was especially prominent in the No-Go trials, A illustrates how this changed as a function of the two types of ccPAS used in groups A and B. The dashed red square in A indicates the time window (0.15 to 1.2 s) in which a significant modulation in theta responses after ccPAS was found. The dashed red line in B indicates the mean RT across Baseline and Expression for Go trials in both participant groups (mean = 352.36 s). (C) Mean theta frequency increase (PMv M1 ccPAS) and decrease (M1 PMv ccPAS) computed as the difference between Expression and Baseline in No-Go trials in the 0.15- to 1.2-s time window. Error bars represent SEM, single dots represent individual data points. In A, EEG time-frequency responses represent percentage change in power computed by subtracting the Baseline from the Expression block (0 = no percentage change). In C, EEG time-frequency responses represent relative percentage change in power with respect to the prestimulus interval (1 = no percentage change).

We performed additional analyses to investigate the effects of ccPAS on nonstate-dependent oscillatory responses irrespective of motor state (i.e., collapsing across Go and No-Go trials). When contrasting the effects of PMvM1-ccPAS in group A versus reversed M1PMv-ccPAS in group B on cortical entrained motor activity (computed as the Expression-minus-Baseline difference), we found a lack of significant differences between the ccPAS manipulations (Monte Carlo P value > 0.05). This lack of difference between group A and B suggest that the direction of the stimulation, that is, PMv to M1 versus M1 to PMv, is ultimately driving the state-dependent effects observed in Go and No-Go trials. Furthermore, we investigated the absolute effect of PMvM1- and reversed M1PMv-ccPAS on activity recorded in Baseline versus Expression blocks. The analyses revealed that the ccPAS manipulation had a significant impact on motor-related theta, alpha, and beta (PMvM1-ccPAS: 0.25 to 1.2 s after stimulus onset; 4 to 15 Hz; Monte Carlo P value = 0.004; M1PMv-ccPAS: 0.25 to 1.1 s after stimulus onset; 9.9 to 14 Hz; Monte Carlo P value = 0.008; channels: C3, C4, CZ, FC1, FC2, CP1, CP2, FCZ, C1, C2, FC3, FC4, CP3, CP4, and CPZ). These results corroborate the absolute effect of the ccPAS manipulation on nonstate-dependent activations.

Oscillatory signals can reflect both transient, evoked activity and sustained, induced neural oscillations. Evoked responses are phase locked to external stimuli, whereas induced oscillations are not. PMvM1-ccPAS manipulation led to long-latency oscillatory changes, whereas the reverse order led to frequency changes with an early onset. Thus, it is possible that these beta and theta modulations occurring after ccPAS reflect changes in either one or other neurophysiological mechanism or even a mixture of both mechanisms. In order to understand the nature of the ccPAS modulations, we carried out an analysis to identify any evoked oscillatory effects by computing the phase coherence across trials (i.e., intertrial linear coherenceITLC) for each condition. First, we determined which parts of the Go/No-Go cue-related activity were evoked or sustained regardless of ccPAS. We observed phase coherence across all frequencies tested (4 to 30 Hz; Monte Carlo P value = 0.001) from 0.15 to 1.2 s after stimulus onset, but this was particularly obvious in the theta range during an early short-lived period around 0.3 s after stimulus presentation (SI Appendix, Fig. S3yellow area in Right). In comparison to Go trials, No-Go trials were associated with stronger, transient, evoked activity in the theta band accompanied by milder sustained changes in alpha and beta activity (SI Appendix, Fig. S3ITLC for all conditions tested). This analysis shows that some EEG changes are likely to be evoked responses that are phase locked to external stimuli even if later effects were likely to reflect induced oscillatory activity. We, therefore, next examined the impact of ccPAS to determine whether it affected only one type of activity or the other. We found that it modulated the amplitude of both early-evoked components as well as sustained changes of the theta oscillations in No-Go trials (Fig. 3 A, Right, dashed red line) and sustained changes in beta oscillations in Go trials (Fig. 2 A, Left, dashed red area). However, it did not modulate the phase consistency either in the theta or the beta band (SI Appendix, Fig. S3, comparable phase coherence between Baseline and Expression, before and after ccPAS, for Go/No-Go trials; Monte Carlo P value > 0.05). In summary, it is clear that the effects of ccPAS are not limited to an impact on evoked neural activity but include a clear effect on induced neural oscillations in both beta and theta bands. In the same vein, there were no significant differences in ccPAS effects on event-related potential (ERP) data between group A and B (EEG Recording and Analysis and SI Appendix, Fig. S4).

The application of TMS pulses to PMv prior to TMS pulses to M1 evoke synchronous pre- and postsynaptic activity in the PMv-to-M1 pathway and alters the manner in which activity in M1 evolves (812, 3739). Moreover, repeated paired stimulation of PMv followed by M1, PMvM1-ccPAS leads to a subsequent state-dependent augmentation of PMvs influence over M1 expressed during action control (7, 14, 15). However, the same effects are not observed when M1 is stimulated prior to PMv in M1PMv-ccPAS, and instead, such a protocol may even lead to a reduced influence of PMv over M1. These observations were replicated in the context of the current task (SI Appendix, Fig. S5). This means that ccPAS can be used to increase the interactions between two brain areas in order to examine the impact of connectivity change on oscillatory activity associated with the motor system. Importantly, the control ccPAS procedure, M1PMv-ccPAS, comprises the same amount and intensity of both PMv and M1 stimulation as PMvM1-ccPAS, and thus, it has the same impact on the component elements of the PMvM1 circuit, but because of its different temporal patterning, it is associated with no augmentation of the influence of PMv over M1. This means that any change in oscillatory activity that is induced by PMvM1-ccPAS that is not present with, or reversed with, M1PMv-ccPAS cannot be attributed to the activation of either PMv or M1 but only to the manipulation of the connectivity between them.

Our results demonstrate that ccPAS delivered at rest leads to task-related changes in beta and theta oscillatory activity during action control. PMvM1-ccPAS led to increased beta power in the PMBR in Go trials. Decreases and increases in beta frequency oscillations have, respectively, been linked to action initiation and cessation (40, 41), and the route between right PMv and adjacent inferior frontal cortex and M1 has been linked to both action initiation and inhibition (10, 11, 14, 26). In addition, PMvM1-ccPAS led to increased theta power when there was greater demand for motor control in No-Go trials. While the changes occurred principally in the theta band, the fact that they occurred between 4 to 10 Hz meant that they extended into the low alpha band. Theta band activity occurs in medial and lateral frontal areas that interact with PMv and the adjacent inferior frontal cortex during action inhibition (10, 21, 22, 26, 27). These areas include the presupplementary motor area in the dorsal frontomedial cortex, PMv, the immediately adjacent cortex in the inferior frontal cortex, and M1 (10, 26, 27). It is increasingly clear that neurons concerned with the control of hand movements are present not just in PMv itself but in the inferior frontal cortex anterior to PMv (42) and that PMv receives a strong monosynaptic projection from many parts of prefrontal cortex including inferior frontal regions (43, 44).

By contrast, the opposite beta and theta patterns were seen after reversed-order M1PMv stimulation in group B. The reversed-order M1PMv stimulation protocol is unlikely to lead to simultaneous pre- and postsynaptic activity in the PMvM1 pathway; as a result, connectivity in the pathway should either remain constant or, more likely, decrease (14, 15). More generally, according to the principles of Hebbian-like spike timingdependent plasticity (16), the firing of presynaptic cells before postsynaptic cells leads to long-term potentiation, whereas the firing of postsynaptic activity before presynaptic activity usually induces long-term depression. In tandem, results from group A and B demonstrate that it is possible to entrain the cortical oscillatory dynamics of action control by repeated stimulation of a directed projection in a specific motor circuit. They also suggest that transmission of causal influences between PMv and M1 is linked to state-dependent channels of communication tuned to specific frequencies, specifically, the beta rhythm for action initiation and cessation on Go trials and the theta rhythm for action inhibition on No-Go trials. Different cortical rhythms in the beta and theta range are associated with distinct functional roles in motor control and inhibition (23, 25).

PMvM1-ccPAS selectively modulated induced beta oscillatory activity at the time of movement completion (there was no evidence for stimulus-locked evoked beta responses). This suggests that PMv exerts an influence over M1 that is associated with resonant activity in the beta range (19, 20). In contrast, reversed-order M1PMv-ccPAS led to moderate PMBR reductions. Although there are strong projections from PMv to M1, projections from M1 to PMv also exist (43). The moderate decrease of PMBR after M1PMv-ccPAS may, therefore, reflect not just a reduction in influence exerted by PMv over M1 but a change in the projections in the opposite direction. Interestingly, the beta band effects of ccPAS were most apparent at the time of increased synchronization when movements were completed rather than at the time of desynchronization when movements were being initiated. Similar to neurons in M1, neurons in PMv also project directly to the spinal cord (45). Therefore, the increased synchronization at the time of movement completion may reflect not only plasticity changes in the motor cortex but also changes on the descending projections to the spinal cord. Future studies should investigate the potential premotor origin of these PMBR after the ccPAS manipulation. In addition to induced neural oscillations in the beta range, it is possible that ccPAS also affects short-lasting beta-burst activity only visible on single trials during movement initiation (46). Further research in the future might investigate the effects of ccPAS on the trial-to-trial dynamics of action control.

Theta band power increases have been suggested as spectral fingerprints of top-down executive control (2125, 30, 31, 47). Here, we observed increased theta oscillations in No-Go trials after PMvM1-ccPAS, suggesting greater top-down motor control during response conflict as a result of entrainment of PMvM1 connections. Opposite effects on theta oscillations are observed after reversed-order M1PMv-ccPAS, suggesting decreased executive control over motor output. Notably, while the ccPAS may cause some changes in early-evoked and later-induced theta activity (Fig. 3), these modulations cannot be explained by changes in phase-locked responses (SI Appendix, Fig. S3) or in ERP components (SI Appendix, Fig. S4). Instead, the ccPAS appears to affect the amplitude of oscillatory activity linked to response inhibition. The results are also consistent with previous investigations emphasizing theta oscillatory activity in integrative mechanisms and as mediators of information transfer between prefrontal and motor areas in decision-making and action control (2325).

Given the clear influence of ccPAS on beta and theta oscillations during action performance and inhibition, changes in task performance might, therefore, also have been expected. Changes in task performance after ccPAS have been reported in both the visual and motor system (7, 48). Despite conducting a number of analyses (Behavioral Analysis), we were unable to find robust evidence for such changes in the current study (SI Appendix, Behavioral Results). The task was chosen for its simplicity, and it is possible that ccPAS-induced changes in performance might only have been seen in more demanding tasks as has been previously reported (7). Another possibility is that the effects of the ccPAS manipulation on behavior might not be most apparent immediately after the stimulation. Further future studies should investigate the possibility of longer-term influence of ccPAS on either speed or accuracy rates. As it stands, however, the oscillatory changes induced by ccPAS in the current setting can be interpreted as a direct result of the ccPAS rather than a secondary consequence of ccPAS-induced changes in task performance. The current findings complement previous evidence of oscillatory changes at rest after ccPAS (49) and of selective enhancement of functional specific pathways outside the PMvM1 network (50)

It is notable that the ccPAS procedure induced a suite of changes that were apparent at several different points in time after Go and No-Go cues. The modulatory effect of ccPAS on a beta oscillatory activity and theta oscillatory were apparent 700 and 150 ms after Go and No-Go stimuli, respectively, approximately during the same period when beta and theta oscillations appeared most robustly in the baseline state in our study (Figs. 2 and 3). The ccPAS also produced changes in MEPs following application of spTMS to M1 125 ms after Go cues (SI Appendix, Fig. S5C). The 125-ms time point was examined because it is close to times at which PMv has been shown to influence M1 in previous studies (10, 11, 37), but it is possible that additional effects might have been observed had we tested other time points after the Go cue.

In summary, corticocortical communication frequencies in the human PMvM1 pathway can be manipulated, leading to state-dependent changes during action control. The frequency-specific patterns of oscillatory activity change found after different types of ccPAS on Go versus No-Go trials reflects spectral fingerprints of augmentation versus reduction of top-down PMv influence over M1. The patterns are consistent with Hebbian-like (16) spike timingdependent long-term potentiation and depression and with hierarchical models of action control in which top-down motor control occurs in tandem with oscillations with specific resonant properties in the beta and theta frequency ranges (23, 25).

A total of 36 healthy, right-handed adults participated across the two experimental groups. Three participants were excluded due to excessive noise in the EEG signal, resulting in 33 participants16 in group A (23.75 4.59; 10; 0.81 0.17) and 17 in group B (22.64 2.31; 5; 0.93 0.13) (where numbers correspond to mean age SD; number of female participants, handiness mean SD; as measured by the Edinburgh handedness inventory, adapted from ref. 51). All participants had no personal or familial history of neurological or psychiatric disease, were right handed (except for one participanthandiness score 0.045), were screened for adverse reactions to TMS and risk factors by means of a safety questionnaire, and received monetary compensation for their participation. Participants underwent high-resolution, T1-weighted structural MRI scans. Sample sizes were determined based on previous studies that have used the same ccPAS protocol to measure the influence of PMv over M1 cortical excitability (14, 15) and studies that have used the Go/No-Go paradigm to investigate oscillatory responses during action control in humans. All participants gave written informed consent, and all the experimental procedures were approved by the Medical Science Interdivisional Research Ethics Committee (Oxford, No. R29477/RE004).

Both experimental groups started with a Baseline block, followed by a ccPAS period, and an Expression block (Fig. 1). During Baseline and Expression blocks, participants performed a visual Go/No-Go task. Trials started with the presentation of either a blue (Go trials70% of trials) or a red (No-Go trials) square (1.8 1.8 cm) displayed for 500 ms. These were followed by a yellow fixation cross (1.3 1.3 cm) presented centrally on the screen for a time interval between 2 and 3 s. There was a total of 304 trials per block (equal number of trials in the Baseline and the Expression blocks) with a short break halfway through the block. Blocks always started with four consecutive Go trials. Participants were instructed to press a button with their left index finger as soon as the blue square was presented and to withhold the response when the red square appeared on the screen. Reaction times and accuracy were recorded. During the task, participants were seated at 50 cm from the screen in a sound and electrically shielded booth.

In the two experimental groups, the ccPAS period that intervened between Baseline and Expression blocks consisted of 15 min of ccPAS over PMv and M1 applied at 0.1 Hz (90 total stimulus pairings) with an IPI of either 6 or 8 ms. Both resting-state and task-state interactions between M1 and PMv, and adjacent areas, emerge at 6- to 8-ms intervals (8, 9, 14, 15). Precise interpulse timing is critical if both PMv and M1 pulses are to produce coincident influences on corticospinal activity. Therefore, we employed an IPI of 8 ms when testing half of the participants in group A and in group B and an IPI of 6 ms in the other half of participants in each group. The impact of this difference in the experimental manipulation was tested by a repeated-measures ANOVA with within-subject factors block (Baseline, Expression) and trial type (Go, No-Go), between-subject factor ccPAS order (PMvM1-ccPAS, M1PMv-ccPAS), and the IPI (8 ms, 6 ms) as a covariate. No effects of the 6-ms IPI versus 8-ms IPI was seen even when the analysis focused on the time window and frequency bands in which the key effects of ccPAS on neural oscillations had been found (Monte Carlo P values > 0.05). Because these analyses found no effect of the 2-ms difference, we do not consider this difference in IPI further. In the experimental group A, the pulse applied to PMv always preceded the pulse over M1, while the opposite was true in experimental group B, which served as an active control.

ccPAS was applied using two Magstim 200 stimulators, each connected to 50-mm figure eightshaped coils. The M1 scalp hotspot was the scalp location where the TMS stimulation evoked the largest left FDI MEP amplitude. This scalp location was projected onto high-resolution, T1-weighted MRIs of each volunteers brain using frameless stereotactic neuronavigation (Brainsight; Rogue Research). In contrast to the scalp hotspot, the right M1 cortical hotspot was the mean location in the cortex where the stimulation reached the brain for all participants in Montreal Neurological Institute (MNI) coordinates (X = 41.03 6.59, Y = 16.74 9.35, Z = 63.69 8.20; Fig. 1cortical coordinates computed using Brainsight stereotactic neuronavigation for each participant; mean cortical coordinates computed by averaging all individuals cortical coordinates). These coordinates were similar to that reported previously (9, 11, 14, 15). The PMv coil location was determined anatomically as follows. A marker was placed on each individuals MRI and adjusted with respect to individual sulcal landmarks to a location immediately anterior to the inferior precentral sulcus. The mean MNI cerebral location of the PMv stimulation was at X = 59.66 3.41, Y = 17.07 6.28, Z = 14.85 8.50 (Fig. 1) and lies within the region defined previously as human PMv (rostral part) and the adjacent inferior frontal gyrus (posterior/mid part) (52), more precisely over areas 44d and 44v of the pars opercularis within the inferior frontal gyrus (53), which resembles parts of macaque PMv in cytoarchitecture and connections (54, 55).

Resting motor threshold (RMT) of the right M1 (mean SD, 43.13 7.22% stimulator output) was determined as described previously (56). As in previous ccPAS studies (14, 15), PMv TMS was proportional to RMT110% (47.76 7.35). M1 stimulation intensity during the experiment was set to elicit single-pulse MEPs of 1 mV (47.23 7.58% stimulator output). TMS coils were positioned tangential to the skull, with the M1 coil angled at 45 (handle pointing posteriorly) and the PMv coil at 0 relative to the midline (handle pointing anteriorly). The PMv coil was fixed in place with an adjustable metal arm and monitored throughout the experiment. The M1 coil was held by the experimenter. Left FDI electromyography activity was recorded with bipolar surface Ag-AgCl electrode montages. Responses were band-pass filtered between 10 and 1.000 Hz, with additional hardwired 50-Hz notch filtering (CED Humbug), sampled at 5,000 Hz, and recorded using a CED D440-4 amplifier, a CED micro1401 Mk.II A/D converter, and PC running Spike2 (Cambridge Electronic Design). All trials with muscle preactivation between Go/No-Go onset and TMS pulse were offline discarded.

EEG was recorded with sintered Ag/AgCl electrodes from 64 scalp electrodes mounted equidistantly on an elastic electrode cap (64Ch-Standard-BrainCap for TMS with Multitrodes; EasyCap). All electrodes were referenced to the right mastoid and re-referenced to the average reference offline. Continuous EEG was recorded using NuAmps digital amplifiers (Neuroscan, 1000-Hz sampling rate).

Offline EEG analysis was performed using FieldTrip (33). The data were down sampled to 500 Hz and digitally band-pass filtered between 1 to 40 Hz. Bad/missing channels were restored using a FieldTrip-based spline interpolation. Next, the data were segmented into 3.5-s intervals starting from 1.4 s before stimulus onset. This was done for Go and No-Go trials separately, and incorrect trials and trials in which reaction times (RTs) were too slow or too fast ( 2SD) were excluded from the analysis. Automatic artifact rejection was performed excluding trials and channels whose variance (z-scores) across the experimental session exceeded a threshold of 10. This was combined with visual inspection for all participants eliminating large technical and movement-related artifacts. Physiological artifacts such as eye blinks and saccades were corrected by means of independent component analysis (RUNICA, logistic Infomax algorithm) as implemented in the FieldTrip toolbox. Those independent components (7.22 on average across participants; 4.8 SD) whose timing and topography resembled the characteristics of the physiological artifacts were removed. For the ERP analysis, the signal was re-referenced to the arithmetic average of all electrodes, and segments were baseline corrected using an interval from 500 to 100 ms before the stimulus onset.

For the time-frequency analysis, single-subject activations for each block (Baseline, Expression) and trial type (Go, No-Go) were averaged and submitted to a complex multitaper time-frequency transformation from 4 to 30 Hz in steps of 1 Hz, with a fixed Hanning window of 0.75 s. A relative Baseline normalization was performed using a time window from 1.1 to 0 s in respect to stimulus onset. To estimate the effects of the ccPAS protocol on neural responses of action control in the Go/No-Go task, time-frequency activations time locked to stimulus onset were computed at the group level using a nonparametric randomization test controlling for multiple comparisons (32). Investigations of the neural dynamics of cognitive and motor control processes highlight the functional significance of both low- and high-frequency oscillations in action performance and inhibition. Theta (4 to 8 Hz), alpha (9 to 12 Hz), and beta (13 to 30 Hz) spectrums have all been linked to aspects of action control. Therefore, in the statistical analyses, no frequency bands were selected a priori. Instead, the statistical analyses were performed on all motor-relevant frequency bands (4 to 30 Hz) and across the entire time window in which oscillatory changes associated with motor control have been observed0.2 to 1.2 s after stimulus onset. Statistical analyses were restricted to 15 electrodes distributed over frontocentral and centroparietal areas, that is, FC3, FC1, FCZ, FC2, FC4, C3, C1, CZ, C2, C4, CP3, CP1, CPZ, CP2, and CP4, where the neural phenomena linked to motor control are typically distributed (5759).

To test if the ccPAS protocol influenced cortical correlates of action control and if this influence happened in a state-dependent manner (Go versus No Go), we used a cluster-based permutation approach as implemented in FieldTrip (see below). Since this method allows the comparison of only two conditions, we first computed the cortical entrained effect (calculated by the subtraction of each frequency at each time point of activity recorded in Baseline from the Expression block) for Go and No-Go trials separately. We then calculated the difference of the cortical entrained effect between No-Go trials versus Go trials. Thereafter, we contrasted the No-Go-minus-Go cortical entrained effect recorded from the participants that received PMvM1-ccPAS (group A; n = 16) versus the participants that received reverse-order M1PMv-ccPAS (group B; n = 17) by means of between-subject nonparametric cluster-based permutation analysis. A nonparametric cluster-based permutation approach is an efficient way of dealing with the multiple comparison problem that prevents biases in preselecting time windows or frequency bands avoiding inflation of type I error rate (32, 60). Time-frequency responses in all conditions are represented in SI Appendix, Fig. S1 (beta band) and SI Appendix, Fig. S2 (theta band). In addition, we used the same cluster-based permutation approach to investigate the effect of ccPAS on all trial types, irrespectively of the motor state (i.e., across Go and No-Go trials), by contrasting activity recorded in the Baseline and Expression period for experimental group A and B.

Subject-wise time-frequency courses were extracted at the selected electrodes and were passed to the statistical analysis procedure in FieldTrip, the details of which are described by Maris and Oostenveld (32). Subject-wise time-frequency courses were compared to identify statistically significant clusters in the time, frequency, and spatial domain using a FieldTrip-based analysis across all time points and frequency bands focusing on frontocentral and centroparietal sites described above (33). FieldTrip uses a nonparametric method (34) to address the multiple comparison problem. T-values of adjacent temporal and frequency points whose P values were less than 0.05 were clustered by adding their t-values, and this cumulative statistic is used for inferential statistics at the cluster level. This procedure, that is, the calculation of t-values at each temporal point followed by clustering of adjacent t-values, was repeated 5,000 times, with randomized swapping and resampling of the subject-wise time-frequency activity before each repetition. This Monte Carlo method results in a nonparametric estimate of the P value representing the statistical significance of the identified cluster.

In addition, to rule out the possibility that changes in oscillatory activity after ccPAS were linked to phase-locked responses to stimulus presentation, we computed the phase coherence across trials (ITLC) for each condition (SI Appendix, Fig. S3). We tested the effects of ccPAS on ITLC, mimicking the cluster-based permutation analysis performed on time-frequency oscillatory responses across all time points and frequency bands focusing on the 15 electrodes distributed over frontocentral and centroparietal areas, that is, FC3, FC1, FCZ, FC2, FC4, C3, C1, CZ, C2, C4, CP3, CP1, CPZ, CP2, and CP4.

For the ERP analysis, single-subject ERPs for each block (Baseline, Expression) and trial type (Go, No-Go) were calculated and used to compute ERP grand averages across subjects (SI Appendix, Fig. S4). The analysis on the ERP data mimicked the time-frequency analysis. In brief, ERP activations time locked to stimulus onset were computed at the group level using a nonparametric randomization test controlling for multiple comparisons (32). To test the effects of ccPAS on ERPs related to action control, we first computed the ccPAS effect on ERPs (by the subtraction of each time point of the trials in the Baseline block from the Expression block) for Go and No-Go trials. We then computed the difference of the ccPAS effect between No-Go and Go trials. Finally, we contrasted the No-Go-minus-Go ccPAS effect between the two participant groups (PMvM1-ccPAS group versus reversed-order PMvM1-ccPAS group) by means of between-subject nonparametric cluster-based permutation analysis. Statistical analyses were done across the entire time window in which the N2-P3 component typically takes place, this is, 0.2 to 0.6 s (28), and it was restricted to 15 electrodes distributed over frontocentral and centroparietal areas (see above). Subject-wise activation time courses were extracted at the selected electrodes and were passed to the analysis procedure of FieldTrip (32). The cluster-based permutation analysis on the ERP data did not find any significant differences in the cortical entrained effect between the participant groups A and B at any electrode cluster when contrasting either Go or No-Go trials (Monte Carlo P values > 0.05). These results demonstrated that 1) the effects of ccPAS on the PMvM1 circuit are frequency specific and only affect particular oscillatory bands linked to action control, that is, beta and theta bands, and 2) the changes observed in the slow-frequency band theta cannot be explained by changes in the ERP components. There was, however, a significant difference between Go versus No-Go trials across both groups, confirming that the action control manipulation was effective (Monte Carlo P value = 0.001; electrode sitesC4, C3, CZ, FC1, FC2, CP1, CP2, FCZ, C1, C2, FC3, CP3, CP4, and CPZ; between 0.20 and 0.50 s after stimulus onset; SI Appendix, Fig. S4).

Behavioral performance measures comprised median RTs (excluding trials with RT 2SD from the mean, 3.9%) and accuracy (excluding omission errors in Go trials, 5%, and commission errors to No-Go trials, 12%). We tested the effect of the ccPAS protocol on RTs and accuracy measures. A repeated-measures ANOVA using the within-subject factors of block (Baseline, Expression) and trial type (Go, No-Go) and the between-subject factor of ccPAS order (PMvM1-ccPAS, M1PMv-ccPAS) was used to analyze the behavioral data of groups A and B. No main effects or interactions in accuracy or reaction time were found (all Ps > 0.05). We also examined if the difference in IPI (6 ms IPI versus 8 ms) influenced RTs and accuracy measures. We used the same ANOVA with the same variables and added the IPI (6 ms IPI versus 8 ms) as a covariate. We did not find an influence of IPI difference on RTs or accuracy (all Ps > 0.05). Moreover, we tested the effects of ccPAS on overall accuracy across all Go and No-Go trials in two Students t tests (Baseline versus Expression) separately for group A and B. Again, no effects of ccPAS on overall accuracy was found (all Ps > 0.05)

In addition, we explored the possibility that EEG modulations (computed as the difference between Baseline and Expression blocks for Go and No-Go trials separately) could be linked to participants performances (median RT in Go trials and accuracy rates in Go and No-Go trials) at Baseline. No relationship was found between participants median RT/accuracy and EEG changes between the Baseline versus Expression blocks neither in group A nor group B (Monte Carlo P value > 0.05). We also tested if undergoing ccPAS influenced the aftereffects of No-Go trials on subsequent Go trials. We found that there were aftereffects of No-Go trials on subsequent Go trials represented by slower median RTs in the Expression versus Baseline period for both experimental groups A and B (F(1,31) = 7.746, P = 0.009, p2 = 0.2), possibly due to fatigue.

Anonymized human brain, physiological, and behavioral data have been deposited in Open Science Framework (DOI: 10.17605/OSF.IO/6VTFB) (61).

This study was funded by the Bial Foundation to A.S. (Grant 44/16), John Templeton Foundation Prime Award (15464/ Subaward Ref. SC14), and Wellcome Trust: WT100973AIA to M.F.S.R. We would like to thank Nadescha Trudel for her help in data collection.

Author contributions: A.S. and M.F.S.R. designed research; A.S., K.A., and R.D. performed research; A.S., L.V., M.C.K.-F., and M.F.S.R. contributed new reagents/analytic tools; A.S., L.V., K.A., R.D., and M.C.K.-F. analyzed data; L.V., M.C.K.-F., and M.F.S.R. made comments on the paper; and A.S. and M.F.S.R. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2100652118/-/DCSupplemental.

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Increasing and decreasing interregional brain coupling increases and decreases oscillatory activity in the human brain - pnas.org

DALLAS, Sept. 07, 2021 (GLOBE NEWSWIRE) -- Axele, LLC, a Transportation Management System (TMS) company, will sponsor a free webinar, "Best Fleets to Drive for Trends from the C-Suite to the Driver Suite." Chris Henry, Vice-President of Customer Experience & Recognition Programs at CarriersEdge, a leading provider of online driver training for the trucking industry, will participate in the webinar, which will be held on Thursday, Sept. 23, at 2:00 PM EDT.

The industry is challenged with a lack of truck drivers, making driver recruitment and retention more important than ever before, said Ravi Ahuja, Founder & CEO of Axele. "In this webinar, attendees will learn what traits their companies need to develop to attract and keep the best drivers. This webinar will be a real game-changer for those that struggle with a lack of qualified drivers."

Chris Henry will share the top trends observed from the 2021 edition of the Best Fleets to Drive For Program. This program identifies trends, shares best practices, and publicly recognizes the for-hire carriers providing the best workplaces for their drivers. Chris also oversees a dedicated team responsible for producing the Best Fleets to Drive For recognition program, an annual evaluation of the best workplaces in the North American trucking industry, produced in partnership with the Truckload Carriers Association.

The webinar will review the key characteristics of the Best Fleets, including:

Chris's entire career has been in the transportation industry, including leadership roles at NAL Insurance, the TCA Profitability Program (Truckload Carriers Association), and FreightWaves.Chris was the co-founder and President of StakUp, a provider of online motor carrier benchmarking services, which FreightWaves acquired in 2019. He graduated from the University of Western Ontario with a degree in Economics & Sociology. He received an MBA from the University of Massachusetts at Amherst.

To take driver recruitment and retention to the next level, register for this free webinar at https://optym.zoom.us/webinar/register/WN_wRTkaA9jS0qO9C7PAvGm9w.

About Axele

Axele offers transportation management system (TMS) cloud software for truckload carriers leveraging decades of experience and insights into optimization and automation technology. Launched by Optym in 2020, Axele is the industry's first intelligent, connected solution, built specifically for small to mid-sized truckload carriers. Axele serves for-hire truckload operators and private fleets who haul general freight, dry van, flatbed, and refrigerated loads. The Axele TMS integrates with load boards, ELDs, market rates, maps, and accounting systems, to enable an owner-operator or carrier to find better loads, increase profits, and grow their business. For more information about Axele, go to http://www.axele.com.

Media Contact for Axele:Becky BoydMediaFirst PR(770) 642-2080 x 214Cell (404) 421-8497Becky@MediaFirst.Net

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Axele Free Webinar: Best Fleets to Drive for - Trends from the C-Suite to the Driver Suite - Marketscreener.com

HILLSBOROUGH COUNTY, FL. Obsessive-compulsive disorder, or OCD, is a serious anxiety disorder that affects an estimated 1.3% of the American population.

For many, symptoms of OCD develop during childhood through early adulthood. A combination of obsessions and compulsions characterize the condition.

Obsessions are persistent thoughts, images, or impulses that cause you to become anxious, upset, or disgusted.

You may recognize that these thoughts are not exactly in line with reality, but you cannot always ignore or control them. Some of the more common obsessions involve worries about danger, contamination, sexual behaviors, or a need for symmetry or order.

Compulsions are thoughts or behaviors that are repetitive in nature and performed in response to an obsession.

They are done to try and mitigate the perceived harm from the obsession. For example, a person with an obsession about contamination may wash their hands dozens of times a day, well beyond whats needed to reduce the risk of infection.

There are numerous behaviors linked to OCD. Some of the more common include:

Transcranial magnetic stimulation (TMS) is an FDA-approved treatment fordepression, but many psychiatrists, like those at Brandon TMS and Psychiatry, use the treatment off-label to help men and women with obsessive-compulsive disorder.

Because OCD has ties to overactive function in certain areas of the brain, TMS therapy can help regulate brain activity and improve function.

We have the MagVenture cool DB-80 coil FDA approved for OCD, said Dr. Boris Kawliche of Brandon TMS. Which is FDA approved but few TMS practices have the right equipment to reach deep enough into to brain to do this treatment properly but we do.

Its important to understand that no type of therapy has been shown to reliably cure OCD. However, men and women who undergo a TMS therapy regimen often find their symptoms are easier to manage.

That can improve your daily quality of life and help you regain control over your thought patterns and behaviors.

If youd like to learn more about TMS and other treatments for obsessive-compulsive disorder, visitBrandon TMS and Psychiatryfor a personalized consultation at your earliest convenience.

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Transcranial Magnetic Stimulation, TMS, Treatment For OCD - The Free Press

Paul Comstock| ThisWeek USA TODAY NETWORK

A zoning hearing on a proposed Sheetz location on the southwest corner of U.S. Route 23 and Orange Road in Orange Township has been continued again.

Trustee Ben Grumbles said after the Sept. 7 trustees meeting that the hearing was postponed to 6 p.m. Sept. 20 because of ongoing traffic concerns, including the proximity of curb cuts to traffic signals and Orange Road.

Sheetz and its representatives apparently are working to address the concerns, Grumbles said.

Questions about how the proposed Sheetz would affect traffic have been a feature of discussion at the trustee meetings since April, when the plan first was discussed.

Additionally, the zoning hearing has been continued several times, starting in April.

In July, a consultant working for the township TMS Engineers Inc. of Twinsburg expressed concerns about data from a traffic study provided by Sheetz with the rezoning application.

TMS called the figures out of date and questioned some of the assumptions used to assess the impact of the gas station, convenience store and car wash on already-busy traffic in the area.

Also during the Sept. 7 meeting, Grumbles recused himself on a vote to continue the Sheetz hearing.

He said after the meeting that he was not comfortable voting on any overlay-related zoning matter until he gets word from legal counsel about a possible conflict of interest, to which he is not a party.

"It does not involve me but (would affect) the township and board" of trustees, he said.

In early August, Sheetz opened its newest central Ohio store in Hilliard.

Central Ohio's first Sheetz opened April 13 at 710 Sunbury Road in Delaware.

Sheetz, established in 1952 and based in Altoona, Pennsylvania, currently operates more than 600 store locations across Maryland, North Carolina, Ohio, Pennsylvania, Virginia and West Virginia.

To learn more, go to sheetziscoming.com.

Video of the Sept. 7 meeting can be seen at youtube.com/watch?v=_qDQfXW_Xqs.

editorial@thisweeknews.com

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Orange Township trustees again continue Sheetz plans, citing ongoing traffic concerns - The Columbus Dispatch

BURLINGTON, Mass. and JERUSALEM, Sept. 09, 2021 (GLOBE NEWSWIRE) -- BrainsWay Ltd. (NASDAQ & TASE: BWAY) (BrainsWay or the Company), a global leader in advanced noninvasive neurostimulation treatments for mental health disorders, today announced that Christopher von Jako, Ph.D., President and Chief Executive Officer, will present and participate in one-on-one investor meetings at two investment conferences during the month of September, as follows:

Event: Oppenheimer & Co. Fall Healthcare Life Sciences & Medtech Virtual SummitDate: September 20-23, 2021Presentation: September 22, 2021 at 3:45pm ET

Event: Cantor Fitzgerald Global Healthcare ConferenceDate: September 27-30, 2021Presentation: September 27, 2021 at 3:20 pm ET

Management will also meet with investors throughout these events. Investors interested in meeting with the BrainsWay management team during these events should contact their respective representatives.

About BrainsWayBrainsWay is a global leader in advanced noninvasive neurostimulation treatments for mental health disorders. The Company is boldly advancing neuroscience with its proprietary Deep Transcranial Magnetic Stimulation (Deep TMS) platform technology to improve health and transform lives. BrainsWay is the first and only TMS company to obtain three FDA-cleared indications backed by pivotal studies demonstrating clinically proven efficacy. Current indications include major depressive disorder, obsessive-compulsive disorder, and smoking addiction. The Company is dedicated to leading through superior science and building on its unparalleled body of clinical evidence. Additional clinical trials of Deep TMS in various psychiatric, neurological, and addiction disorders are underway. Founded in 2003, with offices in Burlington, MA and Jerusalem, Israel, BrainsWay is committed to increasing global awareness and broad access to Deep TMS. For the latest news and information about BrainsWay, please visit http://www.brainsway.com.

Contacts:Scott Areglado SVP and Chief Financial Officer617-771-2287[emailprotected]

Investors:Bob YedidLifeSci Advisors646-597-6989[emailprotected]

Excerpt from:

BrainsWay to Participate in Two Upcoming Investor Conferences - GuruFocus.com

Dallas, TXSeptember 7, 2021Axele, LLC, a Transportation Management System (TMS) company, will sponsor a free webinar, "Best Fleets to Drive for Trends from the C-Suite to the Driver Suite." Chris Henry, Vice-President of Customer Experience & Recognition Programs at CarriersEdge, a leading provider of online driver training for the trucking industry, will participate in the webinar, which will be held on Thursday, Sept. 23, at 2:00 PM EDT.

The industry is challenged with a lack of truck drivers, making driver recruitment and retention more important than ever before, said Ravi Ahuja, Founder & CEO of Axele. "In this webinar, attendees will learn what traits their companies need to develop to attract and keep the best drivers. This webinar will be a real game-changer for those that struggle with a lack of qualified drivers."

Chris Henry will share the top trends observed from the 2021 edition of the Best Fleets to Drive For Program. This program identifies trends, shares best practices, and publicly recognizes the for-hire carriers providing the best workplaces for their drivers. Chris also oversees a dedicated team responsible for producing the Best Fleets to Drive For recognition program, an annual evaluation of the best workplaces in the North American trucking industry, produced in partnership with the Truckload Carriers Association.

The webinar will review the key characteristics of the Best Fleets, including:

CompensationBenefitsHR StrategyOperating Strategy Performance & RecognitionDevelopment & CareerWork / Life Balance

Chris's entire career has been in the transportation industry, including leadership roles at NAL Insurance, the TCA Profitability Program (Truckload Carriers Association), and FreightWaves. Chris was the co-founder and President of StakUp, a provider of online motor carrier benchmarking services, which FreightWaves acquired in 2019. He graduated from the University of Western Ontario with a degree in Economics & Sociology. He received an MBA from the University of Massachusetts at Amherst.

To take driver recruitment and retention to the next level, register for this free webinar at https://optym.zoom.us/webinar/register/WN_wRTkaA9jS0qO9C7PAvGm9w.

About AxeleAxele offers transportation management system (TMS) cloud software for truckload carriers leveraging decades of experience and insights into optimization and automation technology. Launched by Optym in 2020, Axele is the industry's first intelligent, connected solution, built specifically for small to mid-sized truckload carriers. Axele serves for-hire truckload operators and private fleets who haul general freight, dry van, flatbed, and refrigerated loads. The Axele TMS integrates with load boards, ELDs, market rates, maps, and accounting systems, to enable an owner-operator or carrier to find better loads, increase profits, and grow their business. For more information about Axele, go to http://www.axele.com.

Continued here:

Axele Free Webinar: Best Fleets to Drive for - Trends from the C-Suite to the Driver Suite - CSCMP's Supply Chain Quarterly

Data Bridge Market Research added a new research study on Europe Terminal Management System (TMS) Market in its repository, aims to offers a detailed overview of the factors influencing the worldwide business orientation and overall outlook. Study highlights recent market insights with disrupted trends and breakdown of Europe Terminal Management System (TMS) Market products and offering along with impact due to macro-economic headwinds and matured western countries slowdown. Quantitative statistics with qualitative reasoning are evaluated on Europe Terminal Management System (TMS) Market size, share, growth and trending influencing factors with Pre and Post 2021 Impact on Market leaders and emerging players.

Terminal management system (TMS) is use to manage the products distribution including gas, chemicals, oil, alcohols and renewable fuels. The operational terminal management system includes some activities such as terminal automation for process controls and business administration which helps to smoothen the enterprise activities. This system offers detection, control and management of the whole product handling process including from receiving material to storage and distribution. The data integration solution consists of auto control and business management activities which help to decrease overall procedure cost and ultimately enhance efficiency, productivity and safety. Now-a-days, operators are looking for centralized and integrated terminal management solutions which can able to control various sites. The mobility, cloud, industrial internet of things (IoT), cyber security, safety and analytics becomes necessary for customers.

Download Sample Copy of the Report to understand the structure of the complete report (Including Full TOC, Table & Figures) @https://www.databridgemarketresearch.com/request-a-sample/?dbmr=europe-terminal-management-system-tms-market

Europe terminal management system (TMS) market is projected to register a substantial CAGR in the forecast period of 2019 to 2026.

Segmentation:Europe Terminal Management System (TMS) Market

Europe terminal management system (TMS) market is segmented into four notable segments which are offering, project, application and vertical.

View Full Report@https://www.databridgemarketresearch.com/reports/europe-terminal-management-system-tms-market

Leading Key Players Operating in the Europe Terminal Management System (TMS) Market Includes:

Some of the major players operating in the Europe terminal management system (TMS) market are ABB, Honeywell International, Inc, Rockwell Automation, Inc., Yokogawa Electric Corporation, Siemens, ION, Agidens International NV, akquinet AG, Dearman Systems, Inc., EDS Systems O, Emerson Electric Co., Endress+Hauser Management AG, General Atomics, Implico, Larsen & Toubro Infotech Limited, Oceaneering International, Inc., Offspring International Limited, PumpingSol, Ramboll Group A/S, Schneider Electric, SGS SA, i.Dohmann GmbH among others.

Recent Developments

New Business Strategies, Challenges & Policies are mentioned in Table of Content, Request TOC @https://www.databridgemarketresearch.com/toc/?dbmr=europe-terminal-management-system-tms-market

The report provides insights on the following pointers:

The report answers questions such as:

Inquire Before Buying This Research Report@https://www.databridgemarketresearch.com/inquire-before-buying/?dbmr=europe-terminal-management-system-tms-market

About Us:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with an unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market.

Data Bridge Market Research provides appropriate solutions to complex business challenges and initiates an effortless decision-making process.

Contact:

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UK: +44 208 089 1725

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Europe Terminal Management System (TMS) Market Trends, Key Players, Overview, Competitive Breakdown and Regional Forecast by 2028 Eudaemonia -...

Data Bridge Market Research added a new research study on Asia-Pacific Terminal Management System (TMS) Market in its repository, aims to offers a detailed overview of the factors influencing the worldwide business orientation and overall outlook. Study highlights recent market insights with disrupted trends and breakdown of Asia-Pacific Terminal Management System (TMS) Market products and offering along with impact due to macro-economic headwinds and matured western countries slowdown. Quantitative statistics with qualitative reasoning are evaluated on Asia-Pacific Terminal Management System (TMS) Market size, share, growth and trending influencing factors with Pre and Post 2021 Impact on Market leaders and emerging players.

Terminal management system (TMS) is use to manage the products distribution including gas, chemicals, oil, alcohols and renewable fuels. The operational terminal management system includes some activities such as terminal automation for process controls and business administration which helps to smoothen the enterprise activities. This system offers detection, control and management of the whole product handling process including from receiving material to storage and distribution. The data integration solution consists of auto control and business management activities which help to decrease overall procedure cost and ultimately enhance efficiency, productivity and safety. Now-a-days, operators are looking for centralized and integrated terminal management solutions which can able to control various sites. The mobility, cloud, industrial internet of things (IoT), cyber security, safety and analytics becomes necessary for customers.

Asia-Pacific terminal management system (TMS) market is projected to register a substantial CAGR in the forecast period of 2019 to 2026.

Download Sample Copy of the Report to understand the structure of the complete report (Including Full TOC, Table & Figures) @https://www.databridgemarketresearch.com/request-a-sample/?dbmr=asia-pacific-terminal-management-system-tms-market

Segmentation:Asia-Pacific Terminal Management System (TMS) Market

Asia-Pacific terminal management system (TMS) market is segmented into four notable segments which are offering, project, application and vertical.

Leading Key Players Operating in the Asia-Pacific Terminal Management System (TMS) Market Includes:

Some of the major players operating in the Asia-Pacific terminal management system (TMS) market are ABB, Honeywell International, Inc, Rockwell Automation, Inc., Yokogawa Electric Corporation, Siemens, ION, Agidens International NV, akquinet AG, Dearman Systems, Inc., EDS Systems O, Emerson Electric Co., Endress+Hauser Management AG, General Atomics, Implico, Larsen & Toubro Infotech Limited, Oceaneering International, Inc., Offspring International Limited, PumpingSol, Ramboll Group A/S, Schneider Electric, SGS SA, i.Dohmann GmbH among others.

View Full Report@https://www.databridgemarketresearch.com/reports/asia-pacific-terminal-management-system-tms-market

Recent Developments

New Business Strategies, Challenges & Policies are mentioned in Table of Content, Request TOC @https://www.databridgemarketresearch.com/toc/?dbmr=asia-pacific-terminal-management-system-tms-market

The report provides insights on the following pointers:

The report answers questions such as:

Inquire Before Buying This Research Report@https://www.databridgemarketresearch.com/inquire-before-buying/?dbmr=asia-pacific-terminal-management-system-tms-market

About Us:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with an unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market.

Data Bridge Market Research provides appropriate solutions to complex business challenges and initiates an effortless decision-making process.

Contact:

US: +1 888 387 2818

UK: +44 208 089 1725

Hong Kong: +852 8192 7475

Email corporatesales@databridgemarketresearch.com

View original post here:

Asia-Pacific Terminal Management System (TMS) Market 2021 Industry Outline, Global Executive Players and Benefit Growth to 2028 Eudaemonia -...

Over the last two years, pressure has increased for fleet managers, their teams of drivers and maintenance staff, the vehicles themselves, and, of course, for the businesses that employ them. To see where some of the stress points happen, we examined Verizon Connects Fleet Manager Report and look to technology as a highly effective way that operations can be improved right across the business: lowering costs, meeting customer expectations better, reducing CO2 emissions, and making overall operations more profitable.

What the report shows is that over and above the political uncertainty that continues to dominate the thoughts of fleet managers post-Brexit, its a combination of staffing issues, fuel efficiency and customer expectations that are keeping professionals awake at night.

During COVID, furlough schemes at least partially alleviated some companies immediate cost concerns, but outside of that area, respondents in the haulage and delivery sectors continue to cite general administration as their biggest time drain, with 30% stating that is their most common activity.

Its interesting to note that joined-up technology deployed in delivery operations can reduce the repetitive tasks that dominate many working lives: driver and vehicle allocation, for example, is high on the list of daily activities (19% of time) alongside scheduling vehicle maintenance (15%). Backend systems that work in conjunction with vehicle tracking technologies address the amounts of time both these activities take up.

Data-based integrated systems give business owners and operations directors in the transport sector the ability to see patterns that develop over time and therefore create the context for their future. A simple example might be a rise in the occurrences of vehicle damage on specific routes regardless of the driver. These can be caused by taxing road conditions, and therefore scheduled maintenance for the vehicles on the route can be made to a slightly faster cadence.

Recognising patterns that are thrown up by data is just one advantage to integrated technology systems in operations. Company decision-makers use the same information extrapolated from joined-up systems to see where and how SLAs might not be being met and identify why. In daily routines dominated by admin, its all too easy to lose sight of the bigger picture and simply feel that SLAs are set too high. With connected tech, overall performance can be improved across the board, giving companies significant differentiators over the competition in the market.

Note that in the preceding paragraphs, weve been at pains to state joined-up technology or integrated systems. Vehicle-tracking is the technology that comes to mind most readily in this industry at the first mention of tech, but to fully utilise its power, supporting smart platforms need much more than a real-time, map-based dashboard.

Decision-makers concerns from the survey with regards drivers health and safety included that one-third of respondents most worried about mobile phone use by drivers when travelling, with another third citing driver fatigue. Data systems capable of recognising both problems can be used not just as the starting point for disciplinary actions. Driver education and support percolates down into lower costs (less maintenance, lower fuel bills), a reduction in carbon emissions and a more engaged workforce.

Nearly as high a figure (28%) stated that collision and injury or damage to a third party was their top concern. The survey results said 77% of UK fleets were affected by theft alone, with recovery costs reaching the low five figures in some cases. Obviously, the nature of accidents is such that these concerns are valid the issue is that some problems can be avoided: tired drivers or staff anywhere in the business make mistakes. Administrative staff in the company engaged in repetitive tasks make mistakes through boredom. Both on the road and in the office, joined-up technology helps alleviate both problems.

Resistance to fleet management technology often comes from ambiguities and misconceptions about the purpose of the tech. The deployment of systems needs to be done in consultation with end-users (drivers, maintenance technicians and back-office alike) therefore. While TMSs are proven to lower theft and fraud, the integrated platforms of today can help tell the story in ways that transform every part of the business in positive ways.

To learn more about Verizon Connects platforms and solutions for businesses of all sizes, you can request a free free demo of the technology today.

More:

The best TMS goes further than vehicle tracking for companies in Holland, the UK & Eire: We Review - TechHQ