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Causal manipulation of functional connectivity in a specific neural pathway during behaviour and at rest

  1. Vanessa M Johnen  Is a corresponding author
  2. Franz-Xaver Neubert
  3. Ethan R Buch
  4. Lennart Verhagen
  5. Jill X O'Reilly
  6. Rogier B Mars
  7. Matthew F S Rushworth
  1. Oxford University, United Kingdom
  2. National Institutes of Health, United States
  3. Uniformed Services University of Health Sciences, United States
  4. Radboud University Nijmegen, Netherlands
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Cite this article as: eLife 2015;4:e04585 doi: 10.7554/eLife.04585

Abstract

Correlations in brain activity between two areas (functional connectivity) have been shown to relate to their underlying structural connections. We examine the possibility that functional connectivity also reflects short-term changes in synaptic efficacy. We demonstrate that paired transcranial magnetic stimulation (TMS) near ventral premotor cortex (PMv) and primary motor cortex (M1) with a short 8-ms inter-pulse interval evoking synchronous pre- and post-synaptic activity and which strengthens interregional connectivity between the two areas in a pattern consistent with Hebbian plasticity, leads to increased functional connectivity between PMv and M1 as measured with functional magnetic resonance imaging (fMRI). Moreover, we show that strengthening connectivity between these nodes has effects on a wider network of areas, such as decreasing coupling in a parallel motor programming stream. A control experiment revealed that identical TMS pulses at identical frequencies caused no change in fMRI-measured functional connectivity when the inter-pulse-interval was too long for Hebbian-like plasticity.

https://doi.org/10.7554/eLife.04585.001

eLife digest

When a person has their brain scanned, the resulting images show that regions with similar roles tend to be active at the same time. These coordinated patterns of activity are often altered in the brains of patients with neurological or psychiatric disorders. However, relatively little is known about how the patterns are generated.

The degree to which brain regions are active at the same time is thought to depend partly on how well they are connected by brain cells. However, it is also possible that the coordinated activity reflects the extent to which one brain region is able to influence the activity of another. More than 50 years ago, it was demonstrated that this is the case between individual brain cells. If one brain cell repeatedly helps to activate another, the connection between the two cells will be strengthened. This process—known as synaptic plasticity—is thought to support learning and memory.

Now, Johnen, Neubert et al. have shown that the same process can also act between different brain regions. A technique called transcranial magnetic stimulation—in which magnetic fields are applied to specific areas of the scalp to excite brain tissue—was used on human volunteers to activate two regions involved in producing grasping movements with their hands.

If the first region of the brain was repeatedly activated a few milliseconds before the second region as the volunteers reached towards objects, the ability of the first region to activate the second increased. Notably, the effect was not seen when the interval between the activation of the regions was increased to 500 milliseconds: a delay long enough to ensure that brain cells in the first region were no longer active when the second region was stimulated.

This suggests that coordinated changes in the activity of brain regions might reflect the same plasticity processes as changes in activity seen between individual brain cells. This finding raises the possibility that, by deliberately altering the degree of coordinated activity between specific brain regions, it might be possible to recover abilities that have been lost as a result of disorders such as stroke.

https://doi.org/10.7554/eLife.04585.002

Introduction

Temporal correlations in activity between brain areas can be measured with functional magnetic resonance imaging (fMRI) and are often referred to as indices of ‘functional connectivity’ (Friston, 1994). Statistical dependencies between remote cortical regions exist both in the absence of external stimuli or task demands (i.e., during the resting state) and during execution of a task (Hampson et al., 2002). In the following, the term functional connectivity therefore simply describes a statistical relationship of neural elements with each other. In addition to providing insights into the basic anatomical and physiological organization of healthy neural networks (Fox and Raichle, 2007; Bullmore and Sporns, 2009; Smith et al., 2009; O'Reilly et al., 2013), functional connectivity has been used to identify pathological changes occurring in neural circuits in conditions such as stroke (Wang et al., 2010), traumatic brain injury (Bonnelle et al., 2011; Ham and Sharp, 2012), neurodegeneration (Seeley et al., 2009), or psychiatric disorders (Bassett et al., 2008).

Although changes in fMRI-based functional connectivity can be highly specific, their underlying biological mechanisms are less clear. It is thought that functional connectivity patterns are shaped largely by the relatively stable underlying skeleton of structural connections (O'Reilly et al., 2013). However, modifications in functional connectivity might also be influenced by changes in synaptic efficacy, for example through changes in the quantity of neurotransmitter release, changes in astrocytes or dendritic spine stabilization.

Here, we aimed to elucidate the contribution of changes in short-term synaptic efficacy to fMRI-based functional connectivity. To this aim, we modulated synaptic efficacy in a specific corticocortical pathway using repetitive paired pulses of transcranial magnetic stimulation (TMS) with a brief inter-pulse interval (IPI; 8 ms) that evoked synchronous pre- and post-synaptic activity and monitored whether those changes were reflected in altered functional connectivity (Experiment 1) (Figure 1A). Several TMS protocols have been shown to induce changes in excitability in primary motor cortex (M1) using repetitive stimulation of M1 itself (Chen et al., 1997) or stimulation of premotor regions projecting to M1 (Munchau et al., 2002). These changes are often thought to reflect frequency-dependent potentiation of synaptic transmission. Furthermore, it has been shown that repetitive paired stimulation of an input into M1—such as the median nerve—and then of M1 itself can change M1 cortico-spinal excitability (Stefan et al., 2000; Wolters et al., 2003). These paired associative stimulation (PAS) protocols are based upon Hebbian principles of synaptic plasticity and appear to modify connectivity in a controlled manner. Investigations that applied paired-pulse TMS over interconnected sites—for example, homotopical M1 sites (Rizzo et al., 2009), M1 and the supplementary motor area (SMA) (Arai et al., 2011), and M1 and posterior parietal cortex (Koch et al., 2013)—demonstrated altered motor cortical excitability. Notably, the current protocol of repetitive paired-pulse TMS has been shown to induce a causal and directional change of influence of the first brain region (ventral premotor cortex: PMv) over the anatomically connected second region (M1) (Buch et al., 2011). This is important since it is such pathway-specific changes that occur in animal models of synaptic plasticity (Markram et al., 1997; Jackson et al., 2006) and these are argued to underlie the self-organization proposed to occur in mono-synaptically connected networks in response to regularly occurring input (Sussillo and Abbott, 2009).

Experimental procedure.

(A) 90 paired pulses were applied over ventral frontal cortex near PMv and M1 (mean MNI coordinates [−56 19 19] and [−40 −18 59] respectively) at 0.1 Hz for 15 min. (B) Individual stimulation locations for 8 ms IPI (red) and 500 ms IPI (blue). (C) Participants performed visually guided grasping movements towards one of two objects (small or large; see inset) while lying supine in the MR scanner. The head coil was tilted forward by 30° to allow for direct line of sight of the objects to be grasped. A response button box was positioned on the upper leg. (D) Experimental design and setup for all experiments (for both 8 ms IPI and 500 ms IPI experiment). The order of resting-state and grasping task fMRI as well as of pre-TMS (baseline) and post-TMS sessions was counterbalanced.

https://doi.org/10.7554/eLife.04585.003

PMv and M1 are a part of the so-called ‘dorsolateral circuit’ of areas composed of the anterior intraparietal (AIP) area, areas PF and PFG in the inferior parietal lobule, and PMv and M1 in the frontal lobes. During complex motor behaviour such as reaching and grasping this dorsolateral sensorimotor stream is complemented by a ‘dorsomedial circuit’ composed of dorsal premotor (PMd), medial intraparietal area (MIP), and posterior superior parietal cortex (pSPL) (Jeannerod et al., 1995; Wise et al., 1997; Tanne-Gariepy et al., 2002; Galletti et al., 2003; Brochier and Umiltà, 2007; Grafton, 2010; Turella and Lingnau, 2014).

As is the case for other inter-regional connections, the connections between premotor cortex and M1 are glutamatergic, excitatory ones, but there are synapses on both pyramidal neurons and inhibitory interneurons within M1 (Tokuno and Nambu, 2000). This means that although paired stimulation of PMv and M1 leads to strengthening of the excitatory connections between PMv and M1, such strengthening can lead to both enhanced facilitatory and enhanced inhibitory influences of PMv on M1. Enhanced facilitatory influences are more apparent when subjects are subsequently tested while performing a simple reaching and grasping task, and enhanced inhibitory influences are more apparent when subjects are subsequently tested while at rest (Buch et al., 2011). These different effects appear as a function of the subject's behavioural and cognitive state at the time of testing (Bäumer et al., 2009; Buch et al., 2010), but they have not been shown to depend on the subject's cognitive state at the time of plasticity induction (Buch et al., 2011). PMv microwire stimulation in macaques has also been shown to exert both facilitatory and inhibitory effects on corticospinal outputs as a function of the animal's state (Prabhu et al., 2009).

In the following, this causal and directional influence as quantified by motor-evoked potentials (MEPs) is referred to as ‘effective connectivity’ (Friston, 1994). Classic Hebbian synaptic learning rules such as pathway specificity, spike timing dependency, rapid evolution, persistence for several hours, and reversibility have been demonstrated for this directed pathway manipulation. Using TMS in this way entails a direct and specific inter-areal manipulation distinct from the compensatory plasticity that occurs following single-site manipulations of neural activity by means of TMS (Lee et al., 2003; O'Shea et al., 2007; Grefkes et al., 2010; Hartwigsen et al., 2012).

Recent studies have shown that lesions and disruption of brain areas as well as lesions to connections between brain areas can affect distant areas and connections (O'Shea et al., 2007; Hartwigsen et al., 2012; O'Reilly et al., 2013). These changes are thought to be partly compensatory. For example, in the study by O'Shea et al. (2007), it is suggested that ‘activity’ in contralateral ‘non-dominant’ PMd is increased after interruption of ipsilateral PMd. This enhancement of contralateral PMd is accompanied by preserved performance in a stimulus-response matching task. Similarly, Hartwigsen et al. (2012) show that action reprogramming can be preserved after PMd interference if the supramarginal gyrus is uncompromised. This study suggests a rapid redistribution of functional weights in order to compensate for interference. Moreover, it has been shown that the interruption of specific pathways has effects far beyond the regions that are directly connected by the pathway (O'Reilly et al., 2013). Here by contrast, we study the functional enhancement of a pathway, rather than the disruption of a region or pathway, and its effect on coupling within and outside the targeted network.

To ensure that the changes in functional connectivity we observed could be attributed to plasticity induction, we performed a control experiment of paired-pulse TMS over the same cortical regions (Experiment 2) stimulating with the same number of pulses at the same frequency but with an IPI which precluded spike timing-dependent plasticity (STDP) (IPI: 500 ms). We decided on a 500 ms IPI for the control condition following the exclusion of several other alternative IPIs; we decided against reversing the order of conditioning and test stimulus because we have demonstrated in a previous study that this stimulation order leads to long-term depression-like effects (as assessed by examining the impact of further PMv TMS pulses on M1 (Buch et al., 2011); against stimulating both areas at the same time because I-wave interactions may occur at such IPIs (Prabhu et al., 2009); against any time interval below 50 ms because there is evidence of plasticity induction at such intervals within the motor system of freely behaving monkeys (Jackson et al., 2006). Moreover, we noted that long-interval intracortical inhibition (LICI) within M1 has been demonstrated with TMS using IPIs of up to 200 ms (Valls-Solé et al., 1992). Admittedly, other intervals in the hundred milliseconds range might equally have been chosen. Targeting the same cortical areas controlled for the impact of stimulation on brain activity in each component node that the pathway interconnects; if changes in connectivity are simply attributable to stimulation of each area, rather than increased pathway efficacy, then changes in functional connectivity ought to be comparable in Experiment 1 and 2. Here, we show that pathway functional connectivity was not modulated in Experiment 2.

By increasing synaptic efficacy in a corticocortical connection—PMv-M1—involved in complex motor behaviour, we were able to study the relationship of induced plasticity and functional connectivity during the performance of a motor task as well as during the resting state. Further, investigation of a wider motor network provided information about functional reorganisation in response to pathway-specific plasticity induction.

In Experiment 3, we directly tested whether estimates of pathway connectivity based on fMRI data share construct validity with measures of effective connectivity indexed by MEP amplitude ratios across subjects. We demonstrated a direct correlation between the strengths of the two measures for both cognitive states (i.e., resting and grasping), however, the sign of net effective influence of one neural node over another was only determined by TMS-evoked measures and not by fMRI functional connectivity.

Results

In Experiment 1, each participant (N = 15) underwent two sets of two 5-min fMRI scans for the purpose of assessing functional connectivity in both a baseline state and then again immediately after application of repeated paired-pulse TMS to both PMv and M1 (90 pulse pairs; repeated at 0.1 Hz for 15 min; Figure 1A,B). The connections from premotor cortex to M1 are excitatory glutamatergic ones (Tokuno and Nambu, 2000), but within M1 they synapse on both excitatory pyramidal neurons and inhibitory interneurons. Such PMv TMS pulses therefore induce a combination of facilitatory and inhibitory influences on M1 activity; which influence becomes most visible depends on the TMS pulse intensity and subjects' cognitive state at the time of testing (i.e., following plasticity induction) (Bäumer et al., 2009; Buch et al., 2010, 2011). We counterbalanced the order of baseline and post-TMS scans across subjects (half of the subjects had the post-TMS scan before the baseline scan on two different days; Figure 1D).

Placement of the frontal TMS coil, determined on the basis of sulcal anatomy, was just ventral to the convergence of the inferior precentral sulcus and inferior frontal sulcus and therefore at the border of the dysgranular premotor–prefrontal transition area, the inferior frontal junction (IFJ) and ventral premotor area 6v (Neubert et al., 2014). For brevity, we refer to the area as PMv.

We chose to examine this pathway because it is better understood than many and can be investigated in a number of ways even in humans; in monkeys PMv provides one of the principal inputs into M1, and it exerts a powerful influence over M1 output (Shimazu et al., 2004; Dum and Strick, 2005), and in humans it has been established that paired-pulse TMS of PMv-M1 at an 8 ms IPI, or at similar IPIs, modulates the behavioural and electromyographic (EMG) consequences that are normally observed when M1 is stimulated alone (Davare et al., 2008, 2009; Buch et al., 2010; Neubert et al., 2010; Buch et al., 2011). Moreover, the broader functional circuits with which the pathway is related have been characterized both at rest (Neubert et al., 2014) and during simple motor tasks (Grol et al., 2007).

The modulating influence of PMv over M1 changes depending on whether subjects are at rest or engaged in different types of motor tasks during application of the TMS pulses (Davare et al., 2008, 2009; Buch et al., 2010; Neubert et al., 2010; Buch et al., 2011). This suggests that the characteristic connectivity modes within the stimulated connection are related to different cognitive states. We therefore probed whether repeated paired-pulse TMS affected functional connectivity in this pathway differently during resting state or prehension performance.

During the scans, participants were instructed either to be at rest or repeatedly to make prehension movements towards one of two objects of different sizes (task; Figure 1C). Each rest and task period lasted 5 min and during each period one fMRI scan was acquired. This reaching-and-grasping task is known to produce activity in a network of motor and motor association areas including M1 and PMv and also in AIP (Grol et al., 2007). Together these areas, AIP-PMv-M1, constitute a dorsolateral sensorimotor circuit. Another parallel parieto-frontal circuit—the dorsomedial sensorimotor circuit—has also been linked to the control of reaching and grasping and consists of more dorsal areas, including PMd and parietal area pSPL. Visual input into the two parallel visuomotor circuits during the task is provided via area V3A. Grol et al. (2007) assessed connectivity between these same parietal and premotor areas during performance of the same task, with the same apparatus, as used here. Using dynamic causal modelling (DCM), they validated a parietal-to-premotor feed-forward network node model in the context of the same experimental task that we employ here. The model is furthermore supported by other anatomical evidence of direct projections between AIP-PMv-M1 nodes in the dorsolateral circuit and pSPL-PMd-M1 nodes in the dorsomedial circuit (Matelli et al., 1986; Johnson et al., 1997; Wise et al., 1997; Matelli et al., 1998; Luppino et al., 1999; Geyer et al., 2000; Tanne-Gariepy et al., 2002; Dum and Strick, 2005; Rushworth et al., 2006; Grol et al., 2007; Tomassini et al., 2007; Mars et al., 2011; Sallet et al., 2013; Neubert et al., 2014). Our analyses are therefore based on this feed-forward model with parallel dorsolateral and dorsomedial streams.

Importantly, resting state and grasping task fMRI data sets were analysed in an identical way but were never compared directly due to categorical differences in movement artefacts which are difficult to disentangle from differences in functional interactions in the cognitive states. Because fMRI affords whole brain activity measurements, we also investigated how interactions between other nodes in the dorsolateral and dorsomedial sensorimotor circuits might dynamically reorganize in response to the paired TMS of PMv and M1.

For Experiment 2, each participant (N = 15) also underwent two fMRI scans both before and after TMS intervention. Once again the order of pre- and post-TMS scans was counterbalanced by conducting the post-TMS scan on a different day and prior to the baseline pre-TMS scan in half the subjects. The IPI was the only stimulation parameter changed in Experiment 2; an identical number of pulses were applied at the identical frequency (0.1 Hz) to identical brain areas as in Experiment 1 but now the IPI was set to 500 ms.

Data from previous fMRI studies suggest that the two areas we are investigating, PMv and M1, increase their functional connectivity during performance of the task we use (Grol et al., 2007). Paired-pulse TMS affords simple, direct quantification of the causal influence of one node over another node in a given context (effective connectivity). It is known that PMv exerts an inhibitory physiological influence over M1 at rest but this turns into a facilitatory influence during grasping (Davare et al., 2008; Buch et al., 2010, 2011). To understand how such TMS-based indices of effective connectivity related to functional connectivity indices derived from fMRI, we conducted a follow-up experiment (Experiment 3). In Experiment 3, we formally tested the relationship between effective connectivity, as measured with TMS and EMG, and fMRI-derived connectivity indices in the PMv-M1 pathway during rest and during the grasping task (N = 10).

Experiment 1: paired stimulation of PMv and M1 at 8 ms IPI

Changes in functional connectivity between the two stimulated areas

A qualitative sense of topographically distinct coupling patterns in the left hemisphere can be obtained by examining whole-brain co-activation maps, seeded in PMv, before and after repeated paired TMS during task performance (Figure 2A,B) and in the resting state (Figure 2D,E). In order to quantify TMS-induced changes in the relationship between left PMv and left M1, we examined the correlation between the fMRI-measured blood oxygen level dependent (BOLD) signal in the stimulated areas using the seed–based correlation analysis (SBCA) tool in the FMRIB Software Library (FSL, 56). Repeated paired TMS led to an increase in PMv-M1 BOLD functional connectivity while participants performed the task (paired t-test: t(13) = -2.59, p = 0.023; Figure 2C), while PMv-M1 BOLD coupling at rest was not modulated by the intervention (t(14) = -0.07, p = 0.94; Figure 2F).

Figure 2 with 1 supplement see all
8 ms IPI repeated paired TMS (Experiment 1).

Group correlation maps seeded from PMv (red circle) during the grasping task (N = 14) (A, B) and in the resting state (N = 15) (D, E) in the baseline (A, D) and post-TMS sessions (B, E) with a spatial extent threshold of Z > 2.3 and a significance threshold of p < 0.05. There was an increase in PMv-M1 coupling during grasping (C), but not in the resting state (F). The blue circle covers the M1 ROI from which correlation coefficients were extracted. Error bars represent 1 s.e.m.

https://doi.org/10.7554/eLife.04585.004

Changes in connectivity (psychophysiological interactions) between the two stimulated areas

Using a psychophysiological interaction (PPI) analysis (Friston et al., 1997; O'Reilly et al., 2012), we confirmed that the responsiveness of M1 to input from PMv increases following paired TMS of the areas. Increases in connectivity between the stimulated areas (psychophysiological interactions) support the notion that PMv has a greater influence on M1 after repeated paired stimulation during performance of the prehension task (paired t-test: t(13) = −4.78, p = 0.0004; Figure 2—figure supplement 1A). PPI analysis of resting state fMRI data confirmed that the relationship between activity in PMv and M1 did not change in that condition following the plasticity-inducing TMS intervention (t(14) = 0.08, p = 0.93; Figure 2—figure supplement 1B).

Distinct patterns of reorganization in dorsolateral and dorsomedial sensorimotor circuits

In the following analysis, we examined the broader impact of repeated paired TMS of the PMv-M1 pathway on interactions within the dorsolateral (AIP-PMv-M1) and dorsomedial (pSPL-PMd-M1) sensorimotor circuits known to be active during this grasping task (Grol et al., 2007). Mean Montreal Neurological Institute (MNI) coordinates of regions of interest (ROIs) are displayed in Table 1. We used a partial correlation analysis approach; for example, when examining pairwise PMv-AIP coupling, we did so after partialling out effects in all other nodes in the circuits: M1, PMd, pSPL, and V3A. Functional coupling measured during grasping task performance confirmed that PMv-M1 coupling was increased following repeated stimulation of those areas (PMv-M1: t(13) = -3.72, p = 0.003). At rest, repeated paired TMS led to increased dorsolateral circuit coupling (AIP-PMv: t(14) = -2.50, p = 0.025; Figure 3), but decreased dorsomedial circuit coupling (PMd-pSPL: t(14) = 2.22, p = 0.04; PMd–M1: t(14) = 2.84, p = 0.013; Figure 3). In other words, functional connectivity increased in the extended sensorimotor circuit that includes both PMv and M1 as nodes. At the same time, connectivity decreased in one of the other major sensorimotor circuits that influences M1. Finally, functional connectivity involving area V3A, a relatively early visual area that links to both the dorsomedial and dorsolateral circuits, did not change following repeated paired TMS applied to the PMv-M1 pathway (V3A-AIP: t(14) = 0.51, p = 0.618; V3A-pSPL: t(14) = 1.45, p = 0.170).

Table 1

Regions of interest from which BOLD time series were extracted

https://doi.org/10.7554/eLife.04585.006
ROIs in MNI standard spacexyz
Left M1−36−2462
Left PMv−58430
Left PMd−22−458
Left AIP−44−4246
Left pSPL−22−6454
Left V3A−26−8618
  1. 6 mm diameter masks were created in Montreal Neurological Institute (MNI) space. Coordinates refer to MNI152_standard brain as provided by FSL. The ROI mask for M1 was based upon the meta-analysis of functional brain imaging data of motor control (Mayka et al., 2006). The PMv ROI location was then identified by finding the region in which BOLD activity was significantly correlated with activity in M1 at the group level, both during rest and during the grasping task. The peak resulting MNI coordinate [−58 4 30] was located in what is defined as PMv by Mayka et al. (2006) which had a centre-of-mass at [−52 4 24]; more specifically, it lies in the 6v/F5c subdivision of PMv identified by Neubert et al. (2014). The ROI masks for AIP, PMd, pSPL, and V3A were the same size, but were centred on the group peak activation average coordinates of an fMRI study that employed a similar visually guided grasping task and the same apparatus (Grol et al., 2007). Masks were registered to individual EPI space in a two-step process: the mask was transformed into individual, high-resolution structural space via non-linear registration (FSL FNIRT) and then into individual functional space via affine registration (FSL FLIRT; Jenkinson et al., 2002).

Figure 3 with 1 supplement see all
8 ms IPI repeated paired TMS (Experiment 1).

Partial correlation analysis of resting-state fMRI. There was a significant increase in coupling between other nodes (PMv-AIP) within the dorsolateral sensorimotor network that are linked to PMv and M1 over which repeated paired stimulation was applied (A). At the same time, there were significant decreases in functional connectivity within the dorsomedial sensorimotor network; between PMd and M1 (B) and PMd and pSPL (C). Error bars represent 1 s.e.m. (D and E) Schematic representation of mean group connectivity weights (grey lines) in baseline and post-TMS sessions. (D) All weights are standardised to the baseline partial connectivity of each connection. (E) Significant increments in PMv-AIP connectivity (red line) and decrements in PMd-M1 and PMd-pSPL connectivity (black lines) in the post-TMS session (N = 15).

https://doi.org/10.7554/eLife.04585.007

Distinct patterns of reorganization in dorsolateral and dorsomedial sensorimotor circuits (multiple regression analysis)

Both the increase in inter-areal connectivity within the dorsolateral circuit and the parallel decrease in inter-areal connectivity within the dorsomedial circuit were corroborated with an additional analysis based on a multiple linear regression analysis with the same six sensorimotor network nodes (Table 1).

The analysis corroborated the finding that M1 becomes more responsive to inputs from PMv during task performance following repeated stimulation of those areas in Experiment 1 (PMv-M1: t(13) = -2.53, p = 0.0064). Again, at rest responses between more distant network node pairs were shown to alter following application of the plasticity-inducing paired-pulse TMS protocol. Within the dorsolateral circuit, PMv became more responsive to inputs from AIP (AIP-PMv: t(14) = -2.55, p = 0.023; Figure 3—figure supplement 1A,D,E); this finding is in line with our results from the partial correlation analysis (Figure 3). The multiple regression analysis also confirmed the decreased interaction of PMd and M1 within the dorsomedial circuit (PMd–M1: t(14) = 2.84, p = 0.013; Figure 3—figure supplement 1B,D,E); the response of PMd to activity in pSPL showed a tendency to be decreased (PMd-pSPL: t(14) = 1.78, p = 0.097; Figure 3—figure supplement 1C,D,E).

Exploring reorganization in cortical networks

SBCA, partial correlation analysis, and PPI are hypothesis-driven analyses focussing on changes in functional connectivity within specific nodes of the reaching-and-grasping network. To assess the possibility that dynamic changes might occur in other neural networks and to guard against any bias in our selection of ROIs, we performed an additional exploratory analysis that employed dual-regression (Filippini et al., 2009). Here, voxel-wise comparisons of functional connectivity were performed across the whole brain.

First, functional networks were identified with independent components analysis (ICA) on the basis of their temporally correlated, low-frequency resting-state BOLD fluctuations. ICAs were conducted separately for fMRI data acquired during task performance and during resting state. Using the Laplace approximation for ICA dimensionality estimation, 15 and 22 large-scale spatial components—representing group-averaged neuronal networks—were extracted from the ‘baseline’ grasping task fMRI and from the ‘baseline’ resting-state fMRI of all participants, respectively. In the next stage of the analysis, two regressions were conducted: (1) the spatial regression extracted subject-specific time series for each group-averaged ICA component and (2) the temporal regression computed subject-specific weighted spatial maps for each group-averaged ICA component. During the last step, the weighted network masks were regressed back onto to the baseline and post-TMS fMRI time series in order to identify component networks in which BOLD correlations significantly changed following repeated paired PMv-M1 TMS.

During performance of the grasping task, a significant increase in activation of the left intraparietal sulcus area AIP [−44 −40 46] and the adjacent supramarginal gyrus (SMG) [−62 −34 34] was observed following TMS (Figure 4A; blue regions, p = 0.012; t-statistic images were subjected to cluster-based thresholding and corrected for multiple comparisons for a one-sided t-test at t > 1.76 with alpha-level p ≤ 0.05). This means that AIP and SMG, after paired TMS and during grasping, showed significantly more co-activation with what has previously been called the ‘sensorimotor network’ (Smith et al., 2009; Power et al., 2011) (red-to-yellow region, Figure 4—figure supplement 1A), which includes much of motor and premotor cortex and interconnected regions of parietal cortex (Tomassini et al., 2007; Mars et al., 2011).

Figure 4 with 1 supplement see all
Short-term potentiation of PMv-M1 connectivity led to increased activation of left AIP [−44 −40 46] (blue indicates areas of increased coupling) during grasping.

(A) AIP activity became significantly more coupled with the sensorimotor network (see Figure 4—figure supplement 1A). (B) At rest, pathway potentiation evoked coactivation of PMv [−56 −4 30] and a prefrontal region close to the site of stimulation (inferior frontal junction (IFJ) [−56 20 22]). These areas (blue) became specifically more coupled with a left-lateralised frontoparietal network (see Figure 4—figure supplement 1B). All effects (p < 0.05; N = 15).

https://doi.org/10.7554/eLife.04585.009

During rest, an analogous exploratory analysis revealed increased activity in IFJ, and adjacent areas 44d and PMv [−56 4 30] (Figure 4B; blue regions, p = 0.007; cluster-based thresholding and multiple comparison-correction for one-sided t-test at t > 1.76 with alpha-level p ≤ 0.05). The activity overlapped with the stimulation site of the anterior TMS coil which had been placed over the border between 6v and IFJ (Neubert et al., 2014) (Figure 1). The IFJ is a key prefrontal transitional region involved in high-level control of the motor system and interacts prominently with PMv, dorsolateral prefrontal cortex, and visual association areas in the occipitotemporal cortex (Neubert et al., 2014). Regions IFJ, area 44d, and PMv are part of a left-lateralised ‘frontoparietal network’ (red-to-yellow region, Figure 4—figure supplement 1B) and the results mean that they become more coupled with the rest of this network following repeated paired-pulse TMS.

Experiment 2: paired stimulation of PMv and M1 at 500 ms IPI

Since increased functional connectivity between two areas is difficult to distinguish from increased mean firing in the two areas, it is possible that the measured connectivity changes observed following repeated paired PMv-M1 TMS at 8 ms IPI in Experiment 1 could have been the result of an increase in activity in each of the stimulated areas instead of a induced change in functional connectivity (Chawla et al., 2000). In Experiment 2, we therefore applied identical numbers of pulses at identical frequencies over the identical brain regions, but we did so at 500 ms IPI. This interval is many times longer than the longest one at which PMv-M1 interactions have been observed (Davare et al., 2008; Neubert et al., 2010). While such a protocol ought to induce similar changes in each stimulated region, it should not result in their co-activation or in STDP. Using a higher-level analysis (mixed-model ANOVA) with between-subjects factor ‘PROTOCOL’, we directly contrasted the effects from Experiment 1 and Experiment 2 for each of the analyses conducted. We present the results in Table 2 and will go through the findings in the following order: (1) functional connectivity analysis between PMv-M1; (2) PPI analysis between PMv-M1; (3) partial correlation analysis between pairwise network nodes; (4) multiple regression PPI analysis between pairwise network nodes; and (5) dual-regression analysis.

Table 2

Summary of results from hypothesis-driven analyses conducted on 8 ms-IPI Experiment 1 and control Experiment 2 (IPI of 500 ms)

https://doi.org/10.7554/eLife.04585.011
Expt 1 (IPI 8 ms)Expt 2 (IPI 500 ms)Expt 1 vs Expt 2
PMv-M1AIP-PMvpSPL-PMdPMd-M1PMv-M1AIP-PMvpSPL-PMdPMd-M1PMv-M1AIP-PMvpSPL-PMdPMd-M1
Functional connectivity (fc)graspt(13) = −2.59; p = 0.023*t(13) = 0.94; p = 0.36F(1,26) = 4.64; p = 0.041*
restt(14) = −0.07; p = 0.94t(14) = 0.07; p = 0.95n.a.
partial correlation fcgraspt(13) = −3.72; p = 0.003*n.s.n.s.n.s.t(13) = 1.00; p = 0.34n.s.n.s.n.s.F(1,26) = 7.76; p = 0.011*n.s.n.s.n.s.
restt(14) = −0.07; p = 0.95t(14) = −2.50; p = 0.025*t(14) = 2.22; p = 0.04*t(14) = 2.84; p = 0.013*t(14) = −0.39; p = 0.70t(14) = 1.08; p = 0.30t(14) = −1.24; p = 0.24t(14) = 0.47; p = 0.65n.a.F(1,28) = 7.15; p = 0.012*F(1,28) = 5.29; p = 0.029*F(1,28) = 5.92; p = 0.08
Psycho-physiological interaction (PPI)graspt(13) = −4.78; p = 0.0004*t(13) = 0.98; p = 0.35F(1,26) = 6.92; p = 0.014*
restt(14) = 0.08; p = 0.93t(14) = 0.20; p = 0.85n.a.
multiple regression PPIgraspt(13) = −2.53; p = 0.0064*n.s.n.s.n.s.t(13) = 1.18; p = 0.26n.s.n.s.n.s.F(1,26) = 7.47; p = 0.011*n.s.n.s.n.s.
restn.a.t(14) = −2.55; p = 0.023*t(14) = 1.78; p = 0.097t(14) = 2.84; p = 0.013*n.a.t(14) = 0.41; p = 0.96t(14) = −1.18; p = 0.26t(14) = 0.01; p = 0.99n.a.F(1,28) = 5.74; p = 0.024*F(1,28) = 3.66; p = 0.066F(1,28) = 4.44; p = 0.044*
  1. Analyses were conducted on rest and task data. Moreover in order to show that specific effects relate to plasticity induction (8 ms IPI) several higher-level analyses contrasting Experiment 1 and 2 are presented. T-tests were conducted as two-tailed paired t-tests (within subjects). Mixed-model ANOVAs were conducted between experiments (across subjects). Detailed information on all analyses is provided in the ‘Materials and methods’ section. Asterisks indicate significant results, p < 0.05. Abbreviations: n.s. = non-significant.

A higher-level analysis of functional connectivity between PMv-M1 confirmed that PMv-M1 coupling was not changed during task performance following paired TMS with a 500 ms IPI; this is in contrast to significantly greater connectivity following paired TMS with an 8 ms IPI (mixed-model ANOVA: TIME by PROTOCOL interaction: F(1,26) = 4.64, p = 0.041; Experiment 2 during task: paired t-test: t(13) = 0.94, p = 0.36). At rest, functional connectivity was not changed in the PMv-M1 connection following either protocol (Experiment 2 at rest: paired t-test: t(14) = 0.07, p = 0.95). A higher-level PPI analysis of PMv-M1 connectivity supports the finding from the functional connectivity analysis (mixed-model ANOVA: TIME by PROTOCOL interaction during task: F(1,26) = 6.92, p = 0.014; Experiment 2 during task: paired t-test: t(13) = 0.98, p = 0.35). At rest, no changes in PMv-M1 connectivity were found either (Experiment 2 at rest: paired t-test: t(14) = 0.20, p = 0.85).

In parallel to the analyses for Experiment 1, we then examined the wider dorsolateral and dorsomedial sensorimotor circuits. A partial correlation analysis contrasting Experiment 1 with Experiment 2 confirmed that during task, PMv-M1 coupling was only changed in the grasping condition following plasticity induction with an 8 ms IPI (mixed-model ANOVA: TIME by PROTOCOL interaction: F(1,26) = 7.47, p = 0.011; Experiment 2 during task: paired t-test: t(13) = 1.18, p = 0.26).

At rest, no changes in coupling in the wider dorsolateral and dorsomedial sensorimotor circuits were found after 500 ms IPI TMS (Experiment 2 at rest: paired t-tests: AIP-PMv: t(14) = 1.08, p = 0.30; pSPL-PMd: t(14) = -1.24, p = 0.24; PMd-M1: t(14) = 0.47, p = 0.65). Further statistical testing showed that the strengthening of functional connectivity in the AIP-PMv pathway was significantly stronger after 8 ms IPI TMS (Experiment 1) than was after 500 ms IPI TMS (Experiment 2) (mixed-model ANOVA: TIME by PROTOCOL interaction: F(1,28) = 7.15, p = 0.012) as were the decreases in the PMd-pSPL pathway strength (mixed-model ANOVA: TIME by PROTOCOL interaction: F(1,28) = 5.29, p = 0.029). The PMd-M1 connection showed a similar trend (mixed-model ANOVA: TIME by PROTOCOL interaction: F(1,28) = 5.92, p = 0.08).

The lack of reorganisation within the dorsolateral and dorsomedial circuits in Experiment 2 was confirmed by employing the same multiple regression PPI analysis used for Experiment 1. Table 2.

Finally, employing a dual-regression analysis, we confirmed that it was only after 8 ms IPI TMS in Experiment 1 that the left frontoparietal network was found to be more coherently coupled with itself and co-active with PMv in the resting state, but not after 500 ms IPI TMS in Experiment 2 (mixed-model ANOVA: TIME by PROTOCOL interaction: p = 0.022). The fronto-parietal network did not significantly alter its coupling pattern in Experiment 2 (p = 0.446).

Experiment 3: comparison of connectivity measures: paired-pulse TMS-derived effective connectivity contrasted with fMRI-derived functional connectivity

In Experiment 3, we investigated how both TMS-based effective connectivity and fMRI-based functional connectivity indices relate to each other within the same subjects, focussing on the PMv-M1 pathway in ten of the subjects tested in Experiment 1. We measured the size of MEPs evoked by TMS of M1 alone and evoked by M1 TMS applied 8 ms after a PMv pulse. Such PMv TMS pulses are known to either augment or diminish the size of the MEP induced by M1 TMS depending on whether or not subjects are making grasping movements or are at rest, respectively (Davare et al., 2008; Buch et al., 2010, 2011). To quantify the influence of PMv over M1, we compared the MEPs induced by M1 stimulation alone with MEPs induced by M1 stimulation that was preceded by PMv-stimulation. A TMS-based index of effective connectivity between PMv and M1 was calculated as the ratio of the difference in MEP amplitudes evoked by paired-pulse TMS and single-pulse TMS divided by single-pulse-evoked MEP amplitudes. The PMv-M1 TMS ratio is positive when PMv TMS augments the size of M1 TMS-induced MEPs, but negative when PMv TMS diminishes M1 TMS MEP size. Moreover, the ratio was measured both while subjects were at rest and during the reaching task. In this way, the influence of PMv over M1 could be quantified for both cognitive states, and the direction (facilitatory or inhibitory) and magnitude of effective connectivity could then be compared to functional connectivity as measured with fMRI in Experiment 1.

During task performance, the two connectivity measures (a TMS-based effective connectivity index and the fMRI-based functional connectivity measure) for the PMv-M1 pathway were positively correlated across subjects at baseline (Pearson's correlation coefficient: R = 0.74, p = 0.01) (Figure 5A). A positive correlation indicates that the greater the facilitatory influence of PMv on M1 as measured with TMS, the greater the fMRI-derived functional connectivity. Furthermore, during task performance, paired-pulse TMS-derived effective connectivity was still significantly correlated with fMRI-derived connectivity after repeated paired 8 ms IPI TMS (R = 0.87, p = 0.0008) (Figure 5B).

Experiment 3: correlation of PMv-M1 connectivity measures before and after 8 ms IPI paired TMS (N = 10).

When subjects were making grasping movements, there was a significant correlation between functional connectivity (derived from partial-correlation analysis of fMRI) in the baseline (A) and post-TMS session (B) and the baseline effective connectivity measure derived from the paired pulse TMS MEP ratio at baseline. There was a significant negative correlation between functional connectivity in the post-TMS session and the baseline effective connectivity measure derived from the paired-pulse TMS MEP ratio at baseline (D). The correlation did not reach significance when the functional connectivity measure as well as the effective connectivity measure was taken from the baseline session (C).

https://doi.org/10.7554/eLife.04585.012

At rest, the correlation between the two measures was not significant at baseline (R = −0.21, p = 0.57) (Figure 5C). However, a significant correlation was observed between the baseline TMS-derived effective connectivity measure and the fMRI-derived functional connectivity measure following repeated paired 8 ms IPI TMS (R = −0.68, p = 0.03) (Figure 5D). Intriguingly, the correlation was negative which implies that when PMv had a stronger net inhibitory influence on M1 (as indexed by neurophysiological measurements), fMRI indicated stronger net positive functional connectivity between the two areas across participants. Finally, we note that baseline effective connectivity strength at rest (i.e., inhibitory) and during grasping (i.e., excitatory) were also negatively correlated across individuals (R = −0.63, p = 0.049).

Discussion

In this study, we describe the functional connectivity signature in fMRI data of short-term synaptic potentiation within a specific anatomical pathway. Using two different paired-pulse TMS manipulations, we demonstrated that application of a TMS protocol known to change synaptic efficacy within a motor pathway (PMv-M1) results in increases in functional connectivity along the same pathway that can be measured with fMRI. Furthermore, we established a significant correlation between the size of the causal influence of PMv on M1 (effective connectivity as assessed by paired-pulse TMS) and the fMRI-based index of functional connectivity across individuals.

PMv provides one of the principal inputs into M1, and it exerts a powerful influence over M1 output (Shimazu et al., 2004; Dum and Strick, 2005) but the degree of interaction between PMv and M1 can be modulated by repeated paired pulse TMS with an 8 ms IPI over PMv and M1 (Buch et al., 2011). By means of two correlation analyses and two PPI analyses on fMRI data acquired during the performance of a grasping task, we confirmed our a priori hypothesis that augmented pathway efficacy is mirrored in increases in inter-regional functional connectivity. We specifically show that the activity in PMv was more tightly related to activity in M1 following intervention (Figure 2C) and that the influence of PMv on M1 (or the responsiveness of M1 to input from PMv) was increased in response to short-term pathway manipulation (Figure 2C and Figure 2—figure supplement 1A). In the current study, we did not track the duration of these changes in functional coupling after the intervention. However, we note that in a previous study changes in effective connectivity were shown to last more than 1 hr (Buch et al., 2011).

An alternative interpretation of the increased correlation in PMv and M1 activity might be attempted not by referring to synaptic change involving the pathway between them but by simply referring to the changes that pulses over each area induce even when applied in isolation. Such an explanation, however, is unlikely to be correct. First, there is no empirical reason to think that TMS stimulation of any one area at a frequency of 0.1 Hz would lead to a protracted change in that area's activity which is detectable by fMRI many minutes later. Moreover, Experiment 2 employed a control procedure, a repeated paired-pulse TMS protocol which precludes the temporal contiguity required for pathway plasticity induction by using a longer IPI (500 ms vs 8 ms in Experiment 1) even though it involved stimulation of the same areas at the same frequency and intensity. Functional connectivity between PMv and M1 was not altered in response to the control procedure in Experiment 2 suggesting that the influence of PMv on M1 in Experiment 1 was indeed attributable to short-term changes in synaptic efficacy.

These observations extend previous studies that described acute compensatory plasticity of the motor system following single-site TMS manipulations albeit in the context of task performance (Lee et al., 2003; O'Shea et al., 2007; Grefkes et al., 2010; Hartwigsen et al., 2012). The results also extend the understanding of the effect of the repeated paired pulse TMS plasticity induction procedure that we previously examined in the absence of fMRI data (Buch et al., 2011). For example, greater M1 output was previously observed by measuring MEP sizes with M1 TMS during grasping after the paired pulse plasticity induction procedure but the origin of the effect was unclear. The new results make it clear that it is driven by M1 being more responsive to activity in PMv.

Moreover, the current results reveal that the increase in PMv-M1 connectivity was very specific. Although several analyses demonstrated that functional connectivity also increased between PMv and its principal parietal input in the dorsolateral sensorimotor pathway, AIP and adjacent parts of the parietal cortex (Godschalk et al., 1984; Davare et al., 2010) (Figures 3A,D,E,4B, Figure 4—figure supplement 1B), increased functional connectivity was not seen between M1 and other premotor areas. In fact the reverse was true; functional connectivity between PMd and M1 significantly decreased. In addition, functional connectivity in other parts of the dorsomedial sensorimotor circuit, between PMd and pSPL, also declined (Figure 3D,E).

The exact functional role of these accessory decreases in functional coupling in distant connections requires further investigation. It is unclear whether they should be thought of as ‘compensatory’ as they were more prominent at rest than during the grasping task. Inhibitory plasticity might accompany excitatory plasticity in order to stabilise neural networks involved in learning (Vogels et al., 2011). They suggest that inhibitory spike timing-dependent plasticity could balance excitatory inputs. Learning or the formation of associative excitatory connections in such networks would require the co-adaptation of excitatory and inhibitory synapses. Although Vogels' et al. ideas largely make predictions about structural and functional properties of local neural circuits, the results of this experiment could be taken to suggest that similar principles apply to the network and systems level.

Additionally enhancement of one pathway might be accompanied by diminution of a parallel pathway if both of them compete to influence a particular target structure such as M1. It has been argued that two pathways for movement preparation—the dorsomedial visuomotor stream (pSPL–PMd) and the dorsolateral stream (AIP–PMv)—complement each other by driving movement selection proportional to the amount of information available in each stream (Verhagen et al., 2008). It remains to be determined how exactly movement selection is biased towards dorsolateral or dorsomedial streams and whether there are categorical or graded differences. The study of multi-sensory integration has generated proposals concerning how integration of information from two different streams might be achieved (Ernst and Banks, 2002).

Future research needs to understand the relation of these different pathways and how they interact and potentially compete to guide movement selection. With more detailed knowledge about the structural skeleton and the functional relationship of these streams, we might be able to predict the complex effects of learning and plasticity not only on the particular network primarily involved in learning and plasticity but also on other parallel streams and networks. More generally this might eventually contribute to a better understanding of network effects relating to learning, development and degeneration (Fair et al., 2008; Seeley et al., 2009; Dayan and Cohen, 2011). For this line of research whole-brain approaches such as fMRI or magnetoencephalography (MEG) might have some advantages in some contexts in comparison to examining more local effects of plasticity, such as changes in MEPs (Buch et al., 2011).

The absence of reduced dorsomedial pathway coupling in Experiment 2 suggests that it cannot simply be due to the repeated asynchrony of activity in PMd and M1 that is induced by the paired TMS protocol (repeated TMS-induced activation of M1 without corresponding activation of PMd). If this were the case, then one would expect the protocol in Experiment 2, in which PMd and M1 activity was also stimulated asynchronously, to induce similar decrements in dorsomedial pathway coupling, but this was not observed in the current study.

Some of the changes in functional connectivity seen in Experiment 1 were more apparent when subjects were engaged in the motor task while others were more apparent when subjects were at rest. Broadly speaking, changes in the interactions between the stimulated areas themselves, PMv and M1, areas known to be intimately involved in the making of grasping movements, were most apparent when subjects were actively engaged in just such motor activity (Figure 2C, Figure 4A, Figure 2—figure supplement 1A, Figure 4—figure supplement 1A). By contrast, changes in interactions between PMv and adjacent ventral frontal areas such as IFJ with other prefrontal and parietal areas concerned with high-level cognitive control and attention were more apparent when subjects were at rest (Figure 4B). Changes in interactions between PMv and the parietal areas surrounding AIP were apparent both when subjects were engaged in grasping and when they were at rest (Figure 4A,B). The differential sensitivity of the two conditions to different aspects of functional connectivity change may be related to the varying roles of these pathways in the control, planning, and coordination of movement and the actual implementation and execution of movements.

Finally, Experiment 3 demonstrated that similar patterns of fMRI-measured functional connectivity are associated with either net facilitatory or net inhibitory influences being exerted by PMv over M1 when subjects are engaged in a grasping task or at rest (Figure 5). Despite these differences in the sign of the relationship between separate indices of connectivity, the sizes of functional connectivity indices were correlated across subjects, that is, subjects with stronger functional connectivity between PMv and M1 also showed higher degrees of TMS-measured effective connectivity between the two areas. The paired pulse TMS approach provides a particularly direct and simple assay of the effective connectivity that exists between two brain areas but it complements other techniques, such as DCM, that attempt to recover effective connectivity estimates from fMRI data in particular behavioural contexts (Friston, 1994; Friston et al., 2003).

From our experiments, we infer that functional connectivity is not only shaped by structural connections but also by short-term plastic changes in synaptic efficacy. It still, however, remains a challenge to link the changes seen with neuroimaging measures to specific cellular and molecular level changes at the synapse. Paired stimulation of two brain regions led to increased functional connectivity between the two regions but also to a limited set of other functional connectivity changes, both positive and negative, in other parts of the cortical sensorimotor circuits. In addition, we showed that positive functional connectivity between two areas may reflect either facilitatory or inhibitory effective connectivity. Such changes in functional connectivity are not only interesting in their own right but also because different patterns of premotor–M1 interaction are seen in patients who do and who do not recover motor skills after stroke (Gerloff et al., 2006; Lotze et al., 2006). An interesting possible future avenue for research is to employ pathway-specific non-invasive stimulation protocols in patients to induce directed changes in connectivity and thereby potentially drive neural network reorganisation so as to assist in recovery of motor function.

Materials and methods

Volunteers

15 subjects (eight males) participated in Experiment 1; fifteen subjects (nine males) participated in Experiment 2. For Experiment 3, paired-pulse TMS data were obtained for 10 participants from Experiment 1 (five males). The overall mean age of all participants was 24 ± 4 years (mean ± SD). The study was approved by the local ethics committee and informed consent was obtained from all subjects.

Transcranial magnetic stimulation (TMS)

TMS was applied using two Magstim 200 stimulators each of which was connected to a 50 mm figure-8 coil. On a day prior to the day of the combined TMS-fMRI experiment, resting motor threshold (RMT) was determined for each participant for the left M1 ‘hot spot’, which is the scalp location where TMS evoked the largest MEP amplitude in right first dorsal interosseous (FDI) (Rossini et al., 1994) (mean ± SD: 40 ± 7% stimulator output). Electromyographic (EMG) activity in right FDI was recorded with bipolar surface Ag-AgCl electrode montages. Responses were bandpass filtered between 10 and 1000 Hz, with additional 50 Hz notch filtering, sampled at 5000 Hz, and recorded using a CED 1902 amplifier, a CEDmicro1401 Mk.II A/D converter, and PC running Spike2 (Cambridge Electronic Design).

To stimulate left M1, one coil was placed over the scalp location of the left FDI ‘hot spot’ at average MNI coordinates [−40 −18 59]. The location was projected onto the high-resolution, T1-weighted MRI brain scan of each participant using frameless stereotactic neuronavigation (Brainsight; Rogue Research). The second coil, over left PMv, was positioned so as to be ventral to the convergence of the inferior frontal sulcus and inferior precentral sulcus on each individual's MRI scan. The mean MNI location [−56 19 19] was within the region defined previously as human PMv (Mayka et al., 2006) but which more precisely corresponds to the border between IFJ and 6v (Neubert et al., 2014) (Figure 1B). As in previous studies PMv was stimulated with 110% of RMT and M1 with a stimulation intensity sufficient to elicit a 1-mV MEP following a single TMS pulse (Neubert et al., 2010; Buch et al., 2010 and 2011). For the duration of the experiment, TMS coils were fixed in place tangentially to the skull by means of adjustable metal arms and monitored throughout the experiment. In all three experiments, an attempt was made to induce plasticity between PMv and M1 by repeated paired stimulation of the two areas. Paired TMS lasted for 15 min and was applied at a frequency of 0.1 Hz (i.e., 90 pairs of pulses), with an IPI of 8 ms (Experiments 1 and 3) and an IPI of 500 ms (Experiment 2).

In Experiment 3, 8 ms IPI paired pulses were also used in a second way in order to provide a neurophysiological index of effective connectivity between PMv and M1. Ten participants drawn from Experiment 1 took part on a day separate from the two MRI image acquisition days. MEPs were recorded from the right FDI muscle in response to either M1 TMS (20 trials) or paired-pulse TMS delivered over both PMv and, 8 ms later, over M1 (20 trials). Trials were administered in pseudorandom order. The ratio of MEP sizes in the paired pulse trials compared to the single M1 pulse trials provided an index of the modulatory influence of PMv over M1.

Experiment 3 was conducted under two conditions. In the grasping condition, volunteers sat in a darkened room and made right-hand reaching and grasping movements cued by illumination of one of two concentrically arranged cylinders (15 and 65 mm diameter) located 30 cm in front of the starting hand position (Buch et al., 2010 and 2011). Each trial was initiated by pressing a touch bar with the right hand. Intertrial intervals were therefore variable (mean ± SD, 6.90 ± 0.79 s) but did not differ significantly across phases of each experiment. Following a variable delay of 5–7 s (uniformly distributed), one cylinder was illuminated. Volunteers responded by grasping it with their thumb and index finger before lifting it from its pedestal. Reaction and movement times were recorded. All trials were accompanied by either M1 TMS or paired PMv-M1 TMS, with the pulse applied to M1 always occurring 100 ms after cylinder illumination, which was before movement onset.

For TMS when at rest, volunteers still attended to cylinder illumination, as they had done during the motor task, but now they simply maintained a static hand posture. To control for the overall temporal distribution of the TMS pulses, ITIs for rest blocks were defined as the sum of the ITIs used in task blocks plus a reaction and movement time sample drawn from probability density functions for these variables (Buch et al., 2010 and 2011). ITIs were therefore variable (mean ± SD, 6.23 ± 0.07 s) but did not differ significantly across phases of each experiment.

FMRI acquisition

MRI data were acquired on a Siemens 3T Trio MRI scanner at the Oxford Centre for Clinical Magnetic Resonance Imaging (OCMR). For purposes of neuronavigation-guided TMS, all volunteers underwent high-resolution, T1-weighted structural MRI scans that included nose and ears. For each condition—resting state and grasping task—5 min of whole-brain T2*-weighted gradient echo planar images (EPIs) sensitive to BOLD were acquired (repetition time = 3.000 ms, echo time = 30 ms, flip angle = 87°, isotropic voxels of 3.0 mm, no slice gap, 45 slices in axial direction).

Participants were instructed to keep their eyes closed during resting-state fMRI. During the grasping task, which was based on a previous study (Grol et al., 2007; Majdandzić et al., 2007), participants performed 66 reaching-and-grasping trials towards either a small or a large cube positioned in front of them. A new trial sequence was generated for every participant and for each session, with an inter-trial interval of 4295.5 ms–4795.5 ms (mean ± SD: 4545.5 ms ± 145.5 ms) which allowed every participant to complete the movement. Participants lay supine in the MR scanner with the eight-channel head coil tilted forward by 30° enabling them to perform a naturalistic visually guided reaching-and-grasping task in front of their bodies (Figure 1C). Participants were allowed to move their eyes in order to guide their movements. An optical response button box was placed on their right upper leg and served as a start-and-finish position. Reaction times and total movement times were recorded. With the aim of avoiding movement artefacts, the participant's upper arm lay on a wedge-shaped polyfoam cushion and was firmly, but comfortably strapped to the side of the participant's chest. This setup constrained rotation movements in the plane between the button box and the target objects. The head was supported with foam wedges. The participants had received extensive training in the reaching-and-grasping task at least one day prior to the first MRI acquisition outside the MRI scanner. The target object, which consisted of a large red cube and a small green cube (Figure 1C, inset), was held in place through an arc-shaped device positioned over each participant's hips. Participants had been instructed to grasp one of the two cubes, to slide it out of its supporting rail on a rectangular box, and to return it into the same supporting rail. On a given trial, either the large red or the small green cube was to be grasped. A red or green light-emitting diode (LED) in the middle of the rectangular box instructed the participant which cube to grasp. MRI-compatible switches on the device recorded the time at which the object was removed from the supporting rail and the time at which the object was returned into the supporting rail. Control of LEDs and recording of movement-related responses was performed with a computer running Presentation 15.0 (Neurobehavioral Systems, San Francisco, CA). TMS was applied outside the MRI scanner room. Participants walked to the MRI scanner and scanning commenced within 3 to 4 min. Note, previous neurophysiological experiments (Buch et al., 2011) suggest plasticity induction should last at least 1 hr with this protocol and that there were no differences in efficacy immediately after intervention in comparison to +30 min or +60 min post-intervention.

Image pre-processing

FMRI data were pre-processed using tools from the FMRIB Software Library (FSL; www.fmrib.ox.ac.uk/fsl; Smith et al., 2004). Imaging volumes were registered to the individuals' structural scan using boundary-based registration (BBR) (Greve and Fischl, 2009) and to standard space using FMRIBs Linear Image Registration Tool (FLIRT) with 12° of freedom. Pre-processing involved: motion correction (McFLIRT), brain extraction (BET), spatial smoothing with a Gaussian 5 mm full-width at half-maximum (FWHM) kernel, and high-pass temporal filtering at 100 s.

Image pre-processing for dual-regression

Individual subject independent-component analysis (ICA) fMRI analysis was carried out on baseline data of twelve Experiment 1 and eleven Experiment 2 data sets using Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) (Beckmann and Smith, 2005). Individual pre-statistical processing consisted of motion correction (McFLIRT), brain extraction (BET), spatial smoothing using a Gaussian kernel of full-width at half maximum (FWHM) of 5 mm, and high-pass temporal filtering. Imaging volumes were registered to the individuals' structural scan using boundary-based registration (BBR) (Greve and Fischl, 2009) and to standard space using FMRIBs Linear Image Registration Tool (FLIRT) with 12° of freedom. Pre-processed functional data were temporally concatenated across subjects.

Seed-based correlation analysis (SBCA)

SBCA maps the functional connectivity of one ‘seed’ ROI across the entire brain in a voxel-wise manner on the basis of the correlation between the seed ROI's BOLD time series and the BOLD time series at each voxel in the rest of the brain (O'Reilly et al., 2010). We employed SBCA to assess if paTMS-based modulation of the PMv-M1 pathway dynamically altered the functional interactions of either of these two nodes with each other and/or with other nodes within the reaching and grasping network. We assessed the functional connectivity of a 6 mm diameter seed mask in left PMv with the whole brain (target mask) before and immediately after paTMS and contrasted PMv-M1 connectivity at baseline vs connectivity during post-TMS (for details about statistical analyses see below). The analyses of resting state and grasping task fMRI data were conducted independently. All analyses conducted for Experiment 1 and Experiment 2 were identical, which allowed us to directly contrast the effects in a higher-level analysis. For the first step of SBCA, statistical connectivity maps for every individual and for each of the four conditions (resting-state baseline/resting-state plasticity expression and task baseline/task plasticity expression) were created using the SBCA tool implemented in FSL (fsl sbca).

The time series for the left PMv seed mask was calculated. The SBCA model also accounted for the time series resulting from structured noise in the average BOLD signal in white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) and head movement (six regressors resulting from McFLIRT motion correction). WM, GM, and CSF masks were derived from individual T1-weighted structural images using the FSL segmentation tool FAST and registered to EPI space using FLIRT (Jenkinson and Smith, 2001; Zhang et al., 2001). The resulting connectivity map described the correlation between the average BOLD time series of the PMv mask and the time series for each voxel within the whole brain. The individual correlation maps were transformed into MNI space, using FLIRT affine registration of EPI to structural space and subsequently FNIRT non-linear registration to MNI space. Standard space group correlation maps (z-score maps) were generated by entering SBCA-derived individual correlation maps into a group general linear model (GLM) and thresholding at Z > 2.3 with a significance threshold of p < 0.05. These thresholded group z-score maps were projected onto the Midthickness.32k CaretBrain as provided by the Human Connectome Project Workbench using the ‘surf proj’ algorithm as implemented in FSL and then visualized using the Human Connectome Project Workbench (http://www.humanconnectome.org/connectome/get-connectome-workbench.html).

As the next step, we computed the average time series resulting from these statistical connectivity maps for the M1 ROI. We then compared time series correlations of PMv with M1 at baseline and during post-TMS. For statistical comparisons, we conducted a paired t-test, contrasting PMv-M1 interactions at baseline vs during post-TMS. Prior to statistical analysis, correlation coefficients were Fisher z-transformed. We also conducted a higher-level analysis, contrasting Experiment 1 with Experiment 2, using a mixed-model ANOVA with within-subject factors TIME (baseline / post-TMS) and between-subjects factor PROTOCOL (Experiment 1/Experiment 2). We used a significance level of p < 0.05. 15 participants contributed to the resting-state group z-score map (for both Experiment 1 and Experiment 2); 14 participants contributed to the grasping task group z-score map, since the data of one participant had to be removed due to excessive head movement during data acquisition (for both Experiment 1 and Experiment 2).

Partial correlation analysis

To investigate changes in functional connectivity between pairs of grasping network nodes, we conducted a partial correlation analysis between the BOLD time series of directly connected nodes of the left hemisphere using Matlab R2013b (MathWorks). Partial correlation analysis generated correlations represent only correlations specific to the pair of cortical regions in question by regressing out the time series of all other network nodes under investigation. We focussed on pairs of regions thought to be monosynaptically connected (Matelli et al., 1986; Johnson et al., 1997; Wise et al., 1997; Matelli et al., 1998; Luppino et al., 1999; Geyer et al., 2000; Tanne-Gariepy et al., 2002; Dum and Strick, 2005; Rushworth et al., 2006; Grol et al., 2007; Tomassini et al., 2007; Mars et al., 2011; Sallet et al., 2013; Neubert et al., 2014): M1-PMv, PMv-AIP, AIP-V3A, M1-PMd, PMd-pSPL, and pSPL-V3A. Individual BOLD time series for each network node mask (6 mm diameter) were generated using a GLM-based design that incorporated regressors denoting potentially confounding factors such as variation in WM, GM, and CSF, and whole brain BOLD signal as implemented in FSL (fsl glm). Individual partial correlations were normalised using Fisher's z-transform.

Analogous to statistical tests used in SBCA, we conducted paired t-tests contrasting pairwise interactions at baseline vs during post-TMS on Fisher z-transformed partial correlation coefficients (independently for Experiment 1 and Experiment 2). At a later stage, we also subjected Experiment 1 and Experiment 2 to a direct comparison by means of a mixed-model ANOVA with factors PROTOCOL (Experiment 1/Experiment 2; between-subjects factor) and TIME (baseline/post-TMS; within-subjects factor). We used a significance level of p < 0.05. Resting state and grasping task MRI data sets were analysed in an identical way, but were not compared directly due to a categorical difference in movement artefacts (movement artefacts were larger in the grasp task than in the resting-state MRI). The severity of movement artefacts required the removal of one grasping task data set for both Experiment 1 and Experiment 2.

Psychophysiological interaction (PPI) analysis

Psychophysiological interaction (PPI) analysis refers to the interaction between physiological activity and experimental context and thereby identifies brain areas (specifically, voxels) in which activity is more related to activity in a seed region of interest in a given experimental context. To test whether there is a change in the influence PMv (seed region) has on M1, the analysis tested for differences in the regression slope of activity in M1 on the activity in the seed region (PMv) under the experimental contexts of ‘baseline’ and ‘post-TMS’. The change in influence of PMv on M1 can also be understood as a change in responsiveness of M1 to input from PMv. PPI analysis requires an a priori hypothesis about directionality; from physiological models it is well established that PMv provides a major input into M1 (Dum and Strick, 2005). Directionality of the predominant information flow from PMv to M1 was also supported by a feed-forward model validated on fMRI data acquired during performance of a grasping task (Grol et al., 2007) and paired-pulse TMS studies (Davare et al., 2008; Buch et al., 2010).

To test the hypothesis that repeated paired-pulse TMS stimulation of PMv and M1 altered the responsiveness of M1 to activity in PMv, we conducted a regression analysis between BOLD time series of the network nodes using Matlab R2013b (MathWorks). Individual BOLD time series for each of the two network node masks (6 mm diameter) were generated using a GLM-based design that incorporated regressors denoting potentially confounding factors such as variation in white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF), and whole brain BOLD signal as implemented in FSL (fsl glm). Time series were demeaned and variance-normalised.

Analogous to statistical tests used in SBCA, we conducted paired t-tests contrasting pairwise interactions at baseline vs during post-TMS on regression coefficients (independently for Experiment 1 and Experiment 2). At a later stage, we also subjected Experiment 1 and Experiment 2 to a direct comparison by means of a mixed-model ANOVA with factors PROTOCOL (Experiment 1/Experiment 2; between-subjects factor) and TIME (baseline/post-TMS; within-subjects factor). We used a significance level of p < 0.05. Resting-state and grasping task MRI data sets were analysed in an identical way but were not compared directly due to a categorical difference in movement artefacts (movement artefacts were larger in the grasp task than in the resting-state MRI). The severity of movement artefacts required the removal of one grasping task data set for both the Experiment 1 and Experiment 2 condition.

Multiple linear regression psychophysiological interaction (PPI) analysis

In analogy to a partial correlation analysis, we conducted a multiple linear regression analysis on the reaching-and-grasping network nodes (V3A, pSPL, PMd, AIP, PMv, and M1; for MNI coordinates see above in ‘Regions of interest [ROI]’) to understand the influence of one network node upon a specific other network node in terms of the interaction of activity in the remaining network nodes and the experimental context. Time series from the six seed masks (6 mm diameter) were generated as described above in ‘Psychophysiological interaction (PPI) analysis’. To analyse the influence of a given brain area upon another, the time series of all other brain areas of interest are entered as a regressor into the multiple linear regression analysis. Statistical tests on regression coefficients were conducted as described in ‘Psychophysiological interaction (PPI) analysis’.

Dual-regression analysis (spatial regression followed by temporal regression)

To understand if co-activation patterns in large-scale networks of functional connectivity change dynamically in response to plasticity induction, we investigated networks defined by their shared spontaneous low-frequency fluctuations (<0.1 Hz). Coherence within resting-state networks (RSNs) (Friston, 1994) and networks during task performance (Hampson et al., 2002) were analysed before and after paired pulse TMS intervention using a whole-brain corrected approach. Whereas SBCA and partial correlation analyses focused on nodes of the fronto-parietal grasping-network, this approach has the potential to identify any networks (defined as areas sharing BOLD signal temporal correlations) in which connectivity is changing as a result of the TMS intervention. This procedure was carried out completely separately for resting-state fMRI and fMRI during task performance. The approach proceeds in three stages.

To begin, concatenated multiple fMRI data sets are decomposed using ICA to identify large-scale spatial patterns of functional connectivity. We used the baseline fMRI data sets of all 23 participants who participated in this study and obtained group-averaged ICA-network maps. For seven participants who contributed to both experimental conditions, only one baseline data set was randomly selected to generate the ‘group-averaged baseline’ network masks; specifically, 12 ‘baseline’ data sets were drawn from Experiment 1 and 11 ‘baseline’ data sets were drawn from Experiment 2. By identifying ICA components based on data from both experiments, we avoided biasing our analysis as a result of any possible differences in the two groups of subjects. At the second stage, two regressions are carried out in which the ICA-derived components are regressed back against the BOLD time series from the baseline and post-plasticity induction periods in the two experiments (8 ms IPI and 500 ms IPI): firstly, to identify subject-specific temporal dynamics for each group-averaged ICA spatial component via a linear model fit (spatial regression) and, secondly, to compute subject-specific associated spatial maps, by using the generated time series as a regressor against the associated fMRI data (temporal regression). At the final stage, the resulting individual spatial component maps are collected across subjects into single 4D files (1 per original ICA network map, with the fourth dimension being subject to identification). The resulting maps—baseline and post-TMS—with one for each of the two experimental conditions, that is, Experiment 1 and Experiment 2—were then tested for voxel-wise statistical significance against the ICA maps generated from all 23 participants (‘group-averaged baseline’ fMRI data sets) using nonparametric permutation testing (5000 permutations) (Nichols and Holmes, 2002) and cluster-based thresholding and normalisation of the design matrix columns to unit standard deviation. Voxel-wise testing excluded the cerebellum. The only difference in how resting-state fMRI and grasping task fMRI were treated lay in the dimensionality estimation of ICA. The number of components was estimated automatically for both resting-state fMRI and task-positive fMRI using the Laplace approximation to the Bayesian evidence for a probabilistic principal component model (Beckmann and Smith, 2005), which resulted in 22 and 15 independent components for resting-state and task-positive fMRI, respectively.

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Decision letter

  1. Jody C Culham
    Reviewing Editor; University of Western Ontario, Canada

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Causal manipulation of functional connectivity in a specific neural pathway during behaviour and at rest” for consideration at eLife. Your article has been favorably evaluated by Eve Marder (Senior editor), Jody Culham (Reviewing editor), and 3 external reviewers, one of whom, Alexander Sack, has agreed to share his identity.

The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

All three reviewers and the Reviewing editor were very positive about the manuscript. As one reviewer stated: “This is a very clever, elegant, and informative study on how repetitive paired-pulse-induced short term synaptic efficiency changes are reflected in fMRI-based measures of functional connectivity”. Another mentioned the importance of the results for elucidating functional interactions in sensorimotor control circuits. In sum, the paper makes important methodological contributions in using fMRI to better understand and corroborate the putative effects of dual-site TMS as well as important theoretical contributions in revealing how regions within sensorimotor networks interact.

Although all involved agreed that the manuscript is well done and warrants publication in eLife, a number of suggestions were provided to improve the clarity of the manuscript and interpretation of the data. These points should be addressed in a revision. As per eLife's policy to provide clear guidelines about the changes expected in the revision, suggestions have been grouped by the main points that must be addressed in a revision and other points that should be considered. Although the points themselves are concise, where applicable, specific reviewers' comments are appended for further context.

Main points:

1) It should be made clearer (in the first paragraph of the Results section) that the paired-pulse TMS was administered at rest and the implications (vs. TMS administration during a grasping task) should be discussed.

The specific comments from Reviewer #1 provide further detail about the concerns raised:

As said by the authors, the net effect of paired-pulse TMS between PMv-M1 changes based on the task context. Using the same conditioning TMS intensity over PMv there is a net inhibition of MEPs when evoked at rest, but net facilitation when preparing a movement. The concept of 'net' effect is important because it is highly likely that both types of interactions are occurring concurrently, with only one being dominant, and visible, based on the task context. To explain these effects, it is possible that different neuronal populations are being recruited by the conditioning TMS pulse (PMv) during rest or during movement preparation because of their different excitability states in these two contexts. Therefore, assuming they have a different connectivity profile with M1, inhibition or facilitation can be seen depending on which population is recruited. This may be corroborated by the Baumer et al. study (2009). Here, different conditioning TMS (PMv) intensities were used while subjects were at rest with resulting changes in effective connectivity between PMv and M1. Again, it is likely that sub- or suprathreshold conditioning pulses recruit different neuronal populations thus leading to reveal different connectivity. To sum up, paired-TMS can show inhibition or facilitation and this may depend on which underlying neuronal population is being active.

This is where I found difficult to bring the TMS literature together with fMRI. In the present study, paired-TMS was applied always at rest, hence potentially enhancing efficacy of a single 'at rest' sub-circuit. Yet, the authors find: (Friston, 1994) changes in PMv-M1 fMRI connectivity during grasp preparation but not at rest; (Hampson et al., 2002) changes in remote dorsolateral/dorsomedial circuits mainly at rest, but not during action, and (O'Reilly et al, 2013) more importantly, this is even more confusing in Experiment 3. There, the authors correlate paired-TMS effect at rest or during action preparation with corresponding values of fMRI connectivity (i.e. at rest or during action preparation) but following a TMS intervention at rest, likely to potentiate synaptic efficacy of only one subtype of connections between PMv and M1.

I think it is critical the authors address these issues in the paper. In particular, how can potentiation of one sub-circuit enhance functional connectivity of a completely different one for controlling actions (see (Friston, 1994) above); the lack of effects in changes in the extended circuits controlling reach-to-grasp actions can be explained as only the resting-state sub-circuit has been potentiated (Hampson et al., 2002); further explaining mechanisms by which one can compare effective and functional connectivity (O'Reilly et al, 2013). This is important because the authors use one method to affect the other.

2) Further discussion should be provided regarding the use of fMRI functional connectivity to study a manipulation that causes directed effective connectivity.

Specifically, Reviewer #2 states:

The authors use paired-pulse TMS over PMv and M1 with IPI of 8ms to induce directed effective connectivity (directed influence PMv exerts over M1). This implies a temporal sequence from PMv to M1. In contrast, measures of functional connectivity during rest or task intend to capture instantaneous correlations across remote voxels. Spatial ICA is a good example of this (also used in the current paper). Isn't this than contradictory to have an increase in effective connectivity (directed influence in time from region A to B) be reflected in an increase of functional connectivity (instantaneous correlation between A and B at time point t1)? Would it not have been more straightforward to use an effective connectivity measure also in the fMRI part?

3) Relatedly, perhaps a better control condition in Experiment 2 would've been synchronous stimulation of PMv and M1 (IPI = 0)? One also wonders if the same effects would have been found if the order of stimulation had been reversed (i.e., M1 8 ms before PMv). More justification for the choice of a 500-ms IPI should be presented.

4) Because fMRI does not measure such things, the conclusions regarding “changes in synaptic efficacy” (e.g., in the first paragraph of the Discussion section) should be qualified by language that makes it clearer this is an inference.

5) As Reviewer #3 suggests (and another supports): “One of the most interesting aspects of the paper showing the competitive interaction between the dorsolateral (increased coupling) and dorsomedial (decreased coupling) circuits should be highlighted in the Abstract and Introduction sections. This finding provides new insight on how this PMv-M1 plasticity protocol induces different pathway-specific changes at a system-wide level and might further our understanding of possible interactions in the neural pathways involved in goal-directed prehension. This finding warrants further consideration in the Discussion, even if it might be hypothetical at present.”

6) The authors need to address why resting-state and grasping fMRI were conducted ∼5-15 minutes after the associative plasticity induction, given that previous PAS protocols seem to suggest maximal efficiency at about 15-30 min after the intervention and not immediately after (please see Stefan et al., 2000, Brain).

Recommended for consideration:

Some additional points were raised that may be considered at the authors' discretion:

7) Reviewer #2 suggests: “It would have been most interesting to chart the persistency of these proposed short term synaptic efficiency changes by including several post TMS fMRI session (considering they only lasted 5 minutes) and/or MEP assessments. Is there data available to still do this analysis and provide such information (e.g. in terms of duration of MEP modulation?). It would be moist revealing to show and chart that after certain duration the increase in FC decreases again to baseline.”

8) Reviewer #2 also notes: “Resting state and task fMRI data are not directly statistically compared which limits conclusions across conditions. I understand the problem of different movement artefacts. Have the authors considered normalizing the data?”

9) Reviewer #3 states: “It seems somewhat surprising that effects of PMv-M1 PAS on M1 excitability and PMv-M1 connectivity were not measured using TMS before and immediately after the associative plasticity induction during Experiment 1, rather than in a separate third experiment (especially given that this group has shown previously that this protocol lasts for at least an hour). This would provide a direct comparison between the neurophysiological index of connectivity and neuroimaging-derived index.”

10) There were mixed reviews in terms of the writing of the paper. While some thought it was well written, one reviewer thought there was room to make the manuscript clearer and less redundant. The Reviewing editor recommends the authors provide a more in-depth review of past paired associate stimulation studies and consider whether some revision would improve the flow of the manuscript.

Specifically, Reviewer #3 suggests:

A) The Introduction section would benefit from revisions to provide a more in-depth review of the background and clarify what is unique and novel in the present study. In its existing form, required background information is provided too late throughout the Results section. This makes the study motivation/rationale difficult to understand, particularly for a broader audience. Some specific topics that could better motivate and justify the current study include: a) a detailed survey of previous paired associate stimulation (PAS) studies (i.e., Stefan, et al., 2000, 2002; Wolters et al., 2003; Rizzo et al., 2009; Arai et al., 2011; Chao et al., 2013; Koch et al., 2013; etc.), specifically for a non-expert reader; b) a discussion of the differing impacts of PMv stimulation in different cognitive states, particularly for at rest versus grasp states used here; c) review of the different neural correlates of dorsolateral and dorsomedial circuits in motor control (see recent reviews on this topic by Turella and Lingnau, 2014; Grafton, 2010); and d) a clear distinction and comparison to the group's previous work (Buch et al., 2011).

B) The Results could be reduced greatly (by ∼25%), and substantial re-writing of the text is necessary not only to improve the narrative, but also to remove redundancy (i.e., the first eight paragraphs of the Results section could easily be combined with text from Introduction section and Results section for Experiment 1). In addition, findings from the control experiment (Experiment 2; in the subsection “Experiment 2: paired stimulation of PMv and M1 at 500ms IPI” of the Results) could easily be combined with the text in the Results section of Experiment 1 to provide a direct contrast between the main experiment and control. This would further bolster the narrative.

11) The Reviewing editor wondered why functional connectivity between the dorsomedial and dorsolateral subnetworks (esp. PMv-PMd) was not investigated, especially considering that PMd may play a more important role in grasp programming than the standard two-substreams connections suggest (e.g., Raos, 2004, J Neurophysiol).

https://doi.org/10.7554/eLife.04585.013

Author response

1) It should be made clearer (in the first paragraph of the Results section) that the paired-pulse TMS was administered at rest and the implications (vs. TMS administration during a grasping task) should be discussed.

The specific comments from Reviewer #1 provide further detail about the concerns raised:

As said by the authors, the net effect of paired-pulse TMS between PMv-M1 changes based on the task context. Using the same conditioning TMS intensity over PMv there is a net inhibition of MEPs when evoked at rest, but net facilitation when preparing a movement. The concept of 'net' effect is important because it is highly likely that both types of interactions are occurring concurrently, with only one being dominant, and visible, based on the task context. To explain these effects, it is possible that different neuronal populations are being recruited by the conditioning TMS pulse (PMv) during rest or during movement preparation because of their different excitability states in these two contexts. Therefore, assuming they have a different connectivity profile with M1, inhibition or facilitation can be seen depending on which population is recruited. This may be corroborated by the Baumer et al. study (2009). Here, different conditioning TMS (PMv) intensities were used while subjects were at rest with resulting changes in effective connectivity between PMv and M1. Again, it is likely that sub- or suprathreshold conditioning pulses recruit different neuronal populations thus leading to reveal different connectivity. To sum up, paired-TMS can show inhibition or facilitation and this may depend on which underlying neuronal population is being active.

We agree with the reviewer that different neuronal subpopulations will drive excitatory or inhibitory patterns of PMv-M1 interactions. It is important to remember, however, that the projections from premotor cortex to M1, like many inter-regional connections, are mainly of a glutamatergic (excitatory) nature but within M1 they can either synapse directly onto pyramidal cells (facilitatory influence; less common) or onto inhibitory interneurons (majority) (Tokuno & Nambu, 2000, Cereb Cortex). The cognitive state may determine the weight of either connection or else it may determine activity levels in the inhibitory neurons via connections from areas other than premotor cortex. (Baumer et al., 2009, Clin Neurophysiol) did indeed show different patterns of connectivity between PMv and M1 in response to varying conditioning stimulus intensities and an inhibitory effect of PMv on M1 was demonstrated for a PMv conditioning pulse with an intensity of 110% of resting motor threshold at rest (see also: Buch et al., 2011, J Neurosci). Importantly, we also chose a PMv conditioning pulse of 110% RMT and we concur with Baumer and colleagues in finding that it predominantly causes inhibition of M1-induced MEPs at rest. However, just like the reviewer, we agree that influences other than inhibitory ones are likely to be at play. In line with the reviewer’s suspicion we have reported that facilitation of MEPs during the grasping task is also seen (Buch et al., 2011, J Neurosci). In line with the editorial guidance we received we have noted this fact at the beginning of the Results section where we have cited the work of Baumer and colleagues. We have written the following:

“In Experiment 1 each participant (N=15) underwent two sets of two 5-minute fMRI scans for the purpose of assessing functional connectivity in both a baseline state […] We counterbalanced the order of baseline and post-TMS scans across subjects (half of the subjects had the post-TMS scan before the baseline scan on two different days; Figure1D).”

This is where I found difficult to bring the TMS literature together with fMRI. In the present study, paired-TMS was applied always at rest, hence potentially enhancing efficacy of a single 'at rest' sub-circuit. Yet, the authors find: (Friston, 1994) changes in PMv-M1 fMRI connectivity during grasp preparation but not at rest; (Hampson et al., 2002) changes in remote dorsolateral/dorsomedial circuits mainly at rest, but not during action, and (O'Reilly et al, 2013) more importantly, this is even more confusing in Experiment 3. There, the authors correlate paired-TMS effect at rest or during action preparation with corresponding values of fMRI connectivity (i.e. at rest or during action preparation) but following a TMS intervention at rest, likely to potentiate synaptic efficacy of only one subtype of connections between PMv and M1.

We would like to emphasise that we are very grateful to the reviewer for the time that he/she has spent in reviewing our manuscript and for the very wise comments they have made. However we think that the reviewer’s comments in this paragraph are not quite correct but instead the comments in the reviewer’s previous paragraph were more important. In the present paragraph the reviewer almost appears to argue that we should expect that different connections are stimulated when subjects are at rest or in a task state, but as we have already explained in the previous paragraph we know that there is only one main type of connection between premotor cortex and M1, a glutamatergic one that synapses on both pyramidal neurons and inhibitory interneurons. This means that there is no “'at rest' sub-circuit” within PMv and M1 or no “subtypes of connection” from PMv to M1; there rather are excitatory connections between PMv and M1 that have a variety of targets within M1. Strengthening this single type of connection while subjects are at rest is already known to lead to subsequent increments in facilitatory influences of PMv over M1 when subjects are subsequently engaged in a task and to subsequent increments in inhibitory influences of PMv over M1 when subjects are subsequently at rest (Buch et al., 2011, J Neurosci). Again, the reviewer was surely correct in their previous paragraph when they suggested that it is just that different effects of stimulation are more visible or detectable, using the very indirect indices of neural activity furnished by fMRI and TMS, depending on subjects’ cognitive state. Our present fMRI results can be interpreted from the same perspective; some changes in connectivity are simply more visible when subjects are at rest.

It addition, it is important to remember that the induction of increased excitatory connection strength between some parts of a circuit is expected to be associated with decreased connection strength in other parts of the circuits. This can be seen, for example, in the work of Vogels and colleagues on inhibitory anti-memories (Vogels et al., 2011, Science), but again the sensitivity that fMRI will have for detecting such changes may depend on the task context. While the occurrence of reduction in connectivity in other parts of the motor circuit is a novel result (that is why we are submitting the results to eLife), it is a result that has been predicted on theoretical grounds.

I think it is critical the authors address these issues in the paper. In particular, how can potentiation of one sub-circuit enhance functional connectivity of a completely different one for controlling actions (see (Friston, 1994) above); the lack of effects in changes in the extended circuits controlling reach-to-grasp actions can be explained as only the resting-state sub-circuit has been potentiated (Hampson et al., 2002); further explaining mechanisms by which one can compare effective and functional connectivity (O'Reilly et al, 2013). This is important because the authors use one method to affect the other.

It is unlikely that the PMv-M1 neural circuits which are recruited at rest in contrast to those recruited during grasping are completely independent. In fact, as explained in the previous paragraph, the evidence to date suggests precisely the opposite to be the case. Strengthening the single type of excitatory connection that exists between PMv and M1 (which synapses on both pyramidal neurons and inhibitory interneurons) while subjects are at rest is already known to lead to subsequent increments in facilitatory influences of PMv over M1 when subjects are subsequently engaged in a task and to subsequent increments in inhibitory influences of PMv over M1 when subjects are subsequently at rest (Buch et al., 2011, J Neurosci). Moreover, excitation of a smaller volume of neural tissue by means of paired microwire stimulation (PMv-M1) in macaques has also been shown to exert both facilitatory and inhibitory effects on corticospinal outputs that change in weight with different cognitive states, anaesthetised and behaving (Prabhu, 2009, J Physiol). Our previous studies and other studies investigating PMv-M1 connectivity have shown that a change in behavioural context is sufficient to cause a complete reversal of the influence exerted by PMv over M1 (Buch et al., 2010, J Neurosci; Buch et al., 2011, J Neurosci; Davare et al., 2009, J Physiol), even in the absence of a change in stimulation parameters. The change from facilitation to inhibition is driven by a shift in subpopulation weights and unlikely to be driven by independent PMv-M1 circuits. In order to pre-empt such misunderstanding we have, in the revised manuscript, explained this point in greater detail in the Introduction of our manuscript.

“As is the case for other inter-regional connections, the connections between premotor cortex and M1 are glutamatergic, excitatory ones, but there are synapses on both pyramidal neurons and inhibitory interneurons within M1. […] PMv microwire stimulation in macaques has also been shown to exert both facilitatory and inhibitory effects on corticospinal outputs as a function of the animal’s state.”

2) Further discussion should be provided regarding the use of fMRI functional connectivity to study a manipulation that causes directed effective connectivity.

Specifically, Reviewer #2 states:

The authors use paired-pulse TMS over PMv and M1 with IPI of 8ms to induce directed effective connectivity (directed influence PMv exerts over M1). This implies a temporal sequence from PMv to M1. In contrast, measures of functional connectivity during rest or task intend to capture instantaneous correlations across remote voxels. Spatial ICA is a good example of this (also used in the current paper). Isn't this than contradictory to have an increase in effective connectivity (directed influence in time from region A to B) be reflected in an increase of functional connectivity (instantaneous correlation between A and B at time point t1)? Would it not have been more straightforward to use an effective connectivity measure also in the fMRI part?

First, we think it is important to study functional connectivity with functional magnetic resonance imaging (fMRI) because this is the measure most neuroscientists including clinicians use to study the interactions between brain regions. Moreover, we note that functional connectivity is the more general process, a change in effective connectivity will always be reflected in a change in functional connectivity, but the opposite is not always the case. Second, we note that in the present investigation we can only analyze block-wise changes in connectivity; we used a block-based design in our experiment (before versus after plasticity induction) because it was the first time anybody had ever conducted an experiment of this sort (that is why we have sent the manuscript to eLife) and we wanted to have the high power that a block-based design confers. Unfortunately, this means we have to disregard temporal information as a factor of interest in the fMRI analysis. It is unlikely that the temporal resolution of fMRI, particularly in the block-based design we used, would enable determination of whether or not coupling between brain areas is occurring exactly instantaneously or is occurring with an 8 ms lag. Instead such questions of directional influences of one brain area over another are perhaps best determined by probing connectivity with a very high temporal resolution technique such as further pulses of TMS (Buch et al., 2011, J Neurosci). Such an approach, however, while providing precise temporal information, would not provide the more detailed spatial and anatomical information about coupling changes we were able to obtain in the present investigation.

3) Relatedly, perhaps a better control condition in Experiment 2 would've been synchronous stimulation of PMv and M1 (IPI = 0)? One also wonders if the same effects would have been found if the order of stimulation had been reversed (i.e., M1 8 ms before PMv). More justification for the choice of a 500-ms IPI should be presented.

For the control condition we chose an interpulse interval (IPI) which is unlikely to evoke changes in pathway connectivity but which still meant stimulation involved the same number of pulses at the exact same frequency and in the same order. We decided: 1) against reversing the order of conditioning and test stimulus as we have already demonstrated that this stimulation order leads to LTD (as assessed—albeit not with fMRI but—by examining the impact of further PMv TMS pulses on M1 (Buch et al., 2011, J Neurosci); 2) against stimulating both areas at the same time because I-wave interactions are likely to occur at such an IPI (Prahbu et al., 2009, J Physiol); 3) against any time interval below 50 ms as there is evidence of plasticity induction at such intervals within the motor system of freely behaving monkeys (Jackson et al., 2006, Nature). Moreover we noted that long-interval intracortical inhibition (LICI) within M1 has been demonstrated with TMS using IPIs of up to 200 ms (Valls-Solé et al., 1992, Electroencephalogr Clin Neurophysiol). Therefore, we chose an IPI which is unlikely to induce spike timing dependent plasticity; other intervals in the hundred milliseconds range might equally have been chosen.

We added the following to the Introduction section:

“We decided on a 500 ms IPI for the control condition following the exclusion of several other alternative IPIs; […] Admittedly, other intervals in the hundred milliseconds range might equally have been chosen.”

4) Because fMRI does not measure such things, the conclusions regarding “changes in synaptic efficacy” (e.g., in the first paragraph of the Discussion section) should be qualified by language that makes it clearer this is an inference.

We agree with the reviewer that we do not measure synaptic efficacy directly, but we have shown that using a protocol known to change synaptic efficacy does result in the predicted changes (Buch et al., 2011, J Neurosci). Therefore, we will clarify our statement in the Discussion that makes this train of inference explicit:

“In this study, we describe the functional connectivity signature in fMRI data of short-term synaptic potentiation within a specific anatomical pathway. […] From our experiments we infer that functional connectivity is not only shaped by structural connections but also by short-term plastic changes in synaptic efficacy.”

5) As Reviewer #3 suggests (and another supports): “One of the most interesting aspects of the paper showing the competitive interaction between the dorsolateral (increased coupling) and dorsomedial (decreased coupling) circuits should be highlighted in the Abstract and Introduction sections. This finding provides new insight on how this PMv-M1 plasticity protocol induces different pathway-specific changes at a system-wide level and might further our understanding of possible interactions in the neural pathways involved in goal-directed prehension. This finding warrants further consideration in the Discussion, even if it might be hypothetical at present.

We thank the reviewer for highlighting this result. We agree that this is one of the most exciting results. It demonstrates that strengthening interregional connectivity between two areas in a pattern consistent with Hebbian plasticity does not only lead to increased functional connectivity between these two regions; most notably it also affects coupling between areas that have not been targeted by stimulation.

Several recent studies have shown that lesions or temporary disruption of brain areas as well as lesions to connections between brain areas can affect distant areas and connections (O’Shea et al, Neuron, 2007; Hartwigsen et al, J Neurosci, 2012; O’Reilly et al, PNAS, 2013). These changes are thought to be partly compensatory. For example, in the study by O’Shea and colleagues (2007) it is suggested that “activity” in contralateral “non-dominant” PMd is increased after disruption of ipsilateral PMd. This enhancement of contralateral PMd is accompanied by preserved performance in a conditional action selection task. Similarly, Hartwigsen et al. (2012) show that action reprogramming can be preserved after PMd interference if the supramarginal gyrus is uncompromised. This study suggests a rapid redistribution of functional weights in order to compensate for interference. Moreover it has been shown that the interruption of specific pathways has effects far beyond the regions that are directly connected by the pathway (O’Reilly et al., PNAS, 2013).

Here by contrast, we study functional enhancement of a pathway rather than interruption of a region or pathway. We show that plasticity-induction in a given connection also affects distant pathways. The increase in functional coupling between PMv and AIP following the TMS-protocol is accompanied by a decrease in functional connectivity between PMd and M1, as well as between PMd and V6A. It is possible that these accessory decreases in distant connections fulfil similar “compensatory” roles. Inhibitory plasticity might accompany excitatory plasticity in order to stabilise neural networks involved in learning (Vogels et al., Science, 2011). Vogels et al offer a solution to the question of how the careful balance of excitatory and inhibitory inputs into a cortical neuron could self-organize within the activity dynamics of neuronal networks. They suggest that inhibitory spike timing-dependent plasticity could balance excitatory inputs. Learning or the formation of associative excitatory-excitatory connections in such networks would require the co-adaptation of excitatory and inhibitory synapses. Although Vogels’ and colleagues’ ideas largely make predictions about structural and functional properties of local neural circuits, the results of this experiment could be taken to suggest that similar principles apply to the network and systems level.

Additionally enhancement of one pathway might be accompanied by diminution of a parallel pathway if both of them compete for their influence on a particular target structure such as M1. It has been argued that two pathways for movement preparation—the dorsomedial visuomotor stream (V6A – PMd) and the dorsolateral visuomotor stream (AIP – PMv)—complement each other by driving movement selection proportional to the amount of information available in each stream (Verhagen et al., J Neurosci, 2008). It remains to be determined how exactly movement selection is biased towards dorsolateral or dorsomedial streams and whether this is a categorical or a gradient process. The study of multi-sensory integration has generated some theoretical constructs for how integration of information from two different streams might be achieved (Ernst and Banks, Nature, 2002). In this framework different inputs are combined according to maximum likelihood estimation. Hence a channel providing more reliable information for example because of higher signal-to-noise ratio might then be guiding movement selection more strongly with the weights being inversely proportional to the variance of the signal from the respective channels. If the dorsomedial and dorsolateral visuomotor pathways compete for guiding motor control then enhancement of information processing in one of the pathways might suppress the other stream.

Future research needs to understand the relation of these different pathways and how they interact and potentially compete for guiding movement selection. With more detailed knowledge about the structural skeleton and the functional relationship of these streams we might be able to predict the complex effects of learning and plasticity not only on the particular network primarily involved in learning and plasticity but also on other parallel streams and networks. More generally this might eventually contribute to a better understanding of network effects relating to learning, development and degeneration (Dayan and Cohen, Neuron, 2011; Fair et al, PNAS, 2007; Seeley et al, Neuron., 2009). For this line of research whole-brain approaches such as fMRI or MEG might prove advantageous over only looking at local effects of plasticity, such as changes in the MEP (Buch, Johnen et al., 2011).

As suggested by the reviewer we have made the following changes:

In the Abstract:

“Moreover, we show that strengthening connectivity between these nodes has effects on a wider network of areas, such as decreasing coupling in a parallel motor programming stream.”

In the Introduction:

“Recent studies have shown that lesions and disruption of brain areas as well as lesions to connections between brain areas can affect distant areas and connections. […] Here by contrast, we study the functional enhancement of a pathway rather than the disruption of a region or pathway and its effect on coupling within and outside the targeted network.”

In the Discussion:

“The exact functional role of these accessory decreases in functional coupling in distant connections requires further investigation. […] For this line of research whole-brain approaches such as fMRI or MEG might have some advantages in some contexts in comparison to examining more local effects of plasticity, such as changes in MEPs.”

6) The authors need to address why resting-state and grasping fMRI were conducted ∼5-15 minutes after the associative plasticity induction, given that previous PAS protocols seem to suggest maximal efficiency at about 15-30 min after the intervention and not immediately after (please see Stefan et al., 2000, Brain).

Relating to the study of Stefan et al. (2000) it is true that visual inspection of PAS induced changes in corticospinal output might suggest that the maximal efficacy was not achieved before 15min after plasticity induction; however, the effects were not statistically different between early and late points following intervention.

As for our study, the results from our previous study (2011) did not show any differences between efficacy immediately after intervention in comparison to +30 min or +60 min post-intervention. We therefore have no evidence for our ppTMS protocol that the effects varied in efficiency within the first 60 min following interventions.

We now refer to this issue in the Methods section:

“TMS was applied outside the MRI scanner room. Participants walked to the MRI scanner and scanning commenced within three to four minutes. Note, previous neurophysiological experiments suggest plasticity induction should last at least one hour with this protocol and that there were no differences in efficacy immediately after intervention in comparison to +30 min or +60 min post-intervention.”

Recommended for consideration:

Some additional points were raised that may be considered at the authors' discretion:

7) Reviewer #2 suggests: “It would have been most interesting to chart the persistency of these proposed short term synaptic efficiency changes by including several post TMS fMRI session (considering they only lasted 5 minutes) and/or MEP assessments. Is there data available to still do this analysis and provide such information (e.g. in terms of duration of MEP modulation?). It would be moist revealing to show and chart that after certain duration the increase in FC decreases again to baseline.

It is true that the fMRI sessions were relatively short (although not quite as short as the reviewer envisages because data from both task and resting state conditions were collected). Nevertheless collecting the data required the rapidly successive use of several pieces of equipment and procedures including electromyography, neuronavigation, transcranial magnetic stimulation, use of bespoke grasping apparatus in the magnetic resonance imaging environment, and functional magnetic resonance imaging. All of this was extremely challenging in a busy research hospital environment. We would, however, like to draw attention to the results published as Buch, Johnen et al. (2011) where we demonstrated prolonged changes in synaptic efficacy for more than one hour. The measurements of synaptic efficacy in that study were, however, obtained in a very different way. Instead of measuring whole brain activity coupling with fMRI we recorded the impact of the plasticity induction protocol on subsequent PMv TMS pulses on M1. We showed that their effect was enhanced for more than one hour after plasticity induction. We have noted this in the revised manuscript (see below). For the rest, we agree that it would have been very interesting to track the effects over multiple post-fMRI sessions. For the current study, we focussed our design and analysis on the first time points following intervention to maximise power for the pre-post analysis. We are planning to address the point of longevity of changes in functional connectivity in future studies.

The following changes were made to the Discussion and Methods sections:

“In the current study we did not track the duration of these changes in functional coupling after the intervention. However we note that in a previous study changes in effective connectivity were shown to last more than one hour.”

“Note, previous neurophysiological experiments suggest plasticity induction should last at least one hour with this protocol and that there were no differences in efficacy immediately after intervention in comparison to +30 min or +60 min post-intervention.”

8) Reviewer #2 also notes: “Resting state and task fMRI data are not directly statistically compared which limits conclusions across conditions. I understand the problem of different movement artefacts. Have the authors considered normalizing the data?”

We have tried different kinds normalization of the resting state data, but felt this comparison was not appropriate. Also, we note that the experiment was explicitly designed with the goal of separately analysing the two task periods and maximizing power for the pre-post analyses, hence the block-wise manipulation.

9) Reviewer #3 states: “It seems somewhat surprising that effects of PMv-M1 PAS on M1 excitability and PMv-M1 connectivity were not measured using TMS before and immediately after the associative plasticity induction during Experiment 1, rather than in a separate third experiment (especially given that this group has shown previously that this protocol lasts for at least an hour). This would provide a direct comparison between the neurophysiological index of connectivity and neuroimaging-derived index.

We appreciate the reviewers’ exciting suggestions for follow-ups and ways to further improve our design. It is true that our analysis is based on a between-session analysis, although we have demonstrated extremely high test-retest reliability in paired-pulse paradigms, which makes us confident our results are reliable. Given the logistical difficulties of this type of study (electromyography, neuronavigation, transcranial magnetic stimulation, use of bespoke grasping apparatus in the magnetic resonance imaging environment, and functional magnetic resonance imaging) we opted for this setup to maximize the chance of getting good and reliable data.

10) There were mixed reviews in terms of the writing of the paper. While some thought it was well written, one reviewer thought there was room to make the manuscript clearer and less redundant. The Reviewing editor recommends the authors provide a more in-depth review of past paired associate stimulation studies and consider whether some revision would improve the flow of the manuscript.

Specifically, Reviewer #3 suggests:

A) The Introduction section would benefit from revisions to provide a more in-depth review of the background and clarify what is unique and novel in the present study. In its existing form, required background information is provided too late throughout the Results section. This makes the study motivation/rationale difficult to understand, particularly for a broader audience. Some specific topics that could better motivate and justify the current study include: a) a detailed survey of previous paired associate stimulation (PAS) studies (i.e., Stefan, et al., 2000, 2002; Wolters et al., 2003; Rizzo et al., 2009; Arai et al., 2011; Chao et al., 2013; Koch et al., 2013; etc.), specifically for a non-expert reader; b) a discussion of the differing impacts of PMv stimulation in different cognitive states, particularly for at rest versus grasp states used here; c) review of the different neural correlates of dorsolateral and dorsomedial circuits in motor control (see recent reviews on this topic by Turella and Lingnau, 2014; Grafton, 2010); and d) a clear distinction and comparison to the group's previous work (Buch et al., 2011).

We thank the reviewer for this comment and have improved the introduction to make is easier to understand for a broader audience and also emphasised our motivation for the current study. We added a more detailed survey of PAS studies in the Introduction section:

“Several TMS protocols have been shown to induce changes in excitability in primary motor cortex (M1) using repetitive stimulation of M1 itself or stimulation of premotor regions projecting to M1. […] Investigations that applied paired-pulse TMS over interconnected sites—for example, homotopical M1 sites, M1 and the supplementary motor area (SMA), and M1 and posterior parietal cortex—demonstrated altered motor cortical excitability.”

Furthermore, we explained the different impacts of PMv stimulation in relation to different cognitive states, in the Introduction section:

“As is the case for other inter-regional connections, the connections between premotor cortex and M1 are glutamatergic, excitatory ones, but there are synapses on both pyramidal neurons and inhibitory interneurons within M1. […] PMv microwire stimulation in macaques has also been shown to exert both facilitatory and inhibitory effects on corticospinal outputs as a function of the animal’s state.”

We also shortly reviewed the different neural correlates of the dorsolateral and dorsomedial circuits in motor control:

“PMv and M1 are a part of the so-called “dorsolateral circuit“ of areas composed of the anterior intraparietal (AIP) area, areas PF and PFG in the inferior parietal lobule, and PMv and M1 in the frontal lobes. During complex motor behaviour such as reaching and grasping this dorsolateral sensorimotor stream is complemented by a “dorsomedial circuit“ composed of dorsal premotor (PMd), medial intraparietal area (MIP) and posterior superior parietal cortex (pSPL).”

In line with the reviewer’s suggestion, we emphasised the novel aspect of our work, with a particular focus on how our study goes beyond previous TMS manipulations inducing compensatory plasticity, also in the Introduction:

“Recent studies have shown that lesions and disruption of brain areas as well as lesions to connections between brain areas can affect distant areas and connections. […] Here by contrast, we study the functional enhancement of a pathway rather than the disruption of a region or pathway and its effect on coupling within and outside the targeted network.”

B) The Results could be reduced greatly (by ∼25%), and substantial re-writing of the text is necessary not only to improve the narrative, but also to remove redundancy (i.e., the first eight paragraphs of the Results section could easily be combined with text from Introduction section and Results section for Experiment 1). In addition, findings from the control experiment (Experiment 2; in the subsection “Experiment 2: paired stimulation of PMv and M1 at 500ms IPI” of the Results) could easily be combined with the text in the Results section of Experiment 1 to provide a direct contrast between the main experiment and control. This would further bolster the narrative.

We apologize for the redundancy. We noticed that the reviewer is correct that some results regarding the comparison of experiments 1 and 2 were indeed present twice. We have removed repetitions and we have retained comparison of the two experiments in the section of the manuscript that details the results of experiment 2. We were able to identify the relevant sections. We have followed the suggestions and substantially re-written the results section mentioned by the reviewer; for a better understanding, we also added a table to the manuscript which presents the statistical results of the different analyses (Table 2). If the addition of the table is not in agreement with the reviewers, we are happy to remove the table.

The following is an excerpt of the changes made to different parts of the manuscript:

“Using a higher-level analysis (mixed-model ANOVA) with between-subjects factor “PROTOCOL” we directly contrasted the effects from Experiment 1 and Experiment 2 for each of the analyses conducted. […] A partial correlation analysis contrasting Experiment 1 with Experiment 2 confirmed that during task, PMv-M1 coupling was only changed in the grasping condition following plasticity induction with an 8ms IPI (mixed-model ANOVA: TIME by PROTOCOL interaction: F(1,26)=7.47, P=0.011; Experiment 2 during task: paired t-test: t(13)=1.18, P=0.26).”

“We namely found no changes in pairwise coupling in Experiment 2 (Experiment 2 at rest: paired t-tests: AIP-PMv: t(14)=0.41, P=0.96; pSPL-PMd: t(14)=-1.18, P=0.26; PMd-M1: t(14)=0.01, P=0.99). When contrasting pairwise interactions from both experiments, we confirmed that increases in AIP-PMv connectivity only occurred following STDP (mixed-model ANOVA: TIME by PROTOCOL interaction: AIP-PMv: F(1,28)=5.74, P=0.024) with a further decrease in PMd-M1 connectivity (mixed-model ANOVA: TIME by PROTOCOL interaction: PMd-M1: F(1,28)=4.44, P=0.044) and a tendency for the decrease in pSPL-PMd connectivity (mixed-model ANOVA: TIME by PROTOCOL interaction: pSPL-PMd: F(1,28)=3.66, P=0.066).”

11) The Reviewing editor wondered why functional connectivity between the dorsomedial and dorsolateral subnetworks (esp. PMv-PMd) was not investigated, especially considering that PMd may play a more important role in grasp programming than the standard two-substreams connections suggest (e.g., Raos, 2004, J Neurophysiol).

In retrospect, the suggestion of examining PMv-PMd coupling seems like a sensible one. However, if there were some sort of effect it ought to have been apparent in the ICA. Even when we relax the statistical criterion we cannot see evidence of a PMd-PMv coupling change. We are not sure of the reasons and are cautious about over-emphasizing a negative result. We have, therefore, left this point undiscussed in the revised manuscript but we are happy to make further changes upon request.

https://doi.org/10.7554/eLife.04585.014

Article and author information

Author details

  1. Vanessa M Johnen

    Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
    Contribution
    VMJ, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Contributed equally with
    Franz-Xaver Neubert
    For correspondence
    vanessa.johnen@psy.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  2. Franz-Xaver Neubert

    Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
    Contribution
    F-XN, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Contributed equally with
    Vanessa M Johnen
    Competing interests
    The authors declare that no competing interests exist.
  3. Ethan R Buch

    1. Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
    2. Center for Neuroscience and Regenerative Medicine, Uniformed Services University of Health Sciences, Bethesda, United States
    Contribution
    ERB, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Lennart Verhagen

    1. Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
    2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
    Contribution
    LV, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  5. Jill X O'Reilly

    Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
    Contribution
    JXO'R, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  6. Rogier B Mars

    1. Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
    2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
    3. Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
    Contribution
    RBM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  7. Matthew F S Rushworth

    1. Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
    2. Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
    Contribution
    MFSR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.

Funding

Wellcome Trust

  • Matthew F S Rushworth

Medical Research Council (MRC)

  • Matthew F S Rushworth

University Of Oxford (Christopher Welch Scholarship)

  • Franz-Xaver Neubert

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

MFSR received funding from the Wellcome Trust and the Medical Research Council (MRC). F-XN received funding from the Christopher Welch Scholarship.

Ethics

Human subjects: Informed consent, including consent to publish was obtained from all subjects. The study was performed in accordance with local ethics committee approval (MKREC REF 07/Q1603/11 and Berkshire REC 11/SC/0537).

Reviewing Editor

  1. Jody C Culham, University of Western Ontario, Canada

Publication history

  1. Received: September 3, 2014
  2. Accepted: February 8, 2015
  3. Accepted Manuscript published: February 9, 2015 (version 1)
  4. Version of Record published: March 10, 2015 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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