Cortical changes in motor preparation facilitate changes in motor execution following an intervention in severe chronic hemiparetic stroke

Effective interventions have demonstrated the ability to improve motor function by reengaging ipsilesional resources, which has been shown to be critical and feasible for hand function recovery even in individuals with severe chronic stroke. However, previous studies focus on changes in brain activity related to motor execution. How changes in motor preparation may facilitate these changes at motor execution is still unclear. In order to address this question, we had 8 individuals with severe chronic hemiparetic stroke participate in a device-assisted intervention. We then quantified changes in both coupling between regions during motor preparation and changes in topographical cortical activity at motor execution for both the hand in isolation and together with the shoulder using high density EEG. We hypothesized that intervention-induced changes in cortico-cortico interactions during motor preparation would lead to changes in activity at motor execution specifically towards an increased reliance on the ipsilesional hemisphere. We found that, following the intervention, individuals displayed a reduction in coupling from ipsilesional M1 to contralesional M1 within gamma frequencies during motor preparation for hand opening. This was followed by a reduction in activity in the contralesional primary sensorimotor cortex during motor execution. Meanwhile, during lifting and opening, a shift to inhibitory coupling within ipsilesional M1 from gamma to beta frequencies was accompanied by an increase in ipsilesional primary sensorimotor cortex activity following the intervention. Together, these results show that intervention-induced changes in coupling within or between motor regions during motor preparation lead to corresponding changes in focal cortical activity at execution.


Introduction
In individuals with stroke, improvements following an intervention are at least partially mediated by neural changes. One of the more common findings for effective interventions is a return to "normal" cortical activity patterns characterized by an increased reliance on the ipsilesional hemisphere that resembles patterns observed in healthy controls 1,2 . To this point, most investigations of intervention-induced neural changes have focused on cortical activity related to motor execution [3][4][5] . However, the proper motor command during execution requires appropriate motor preparation. How intervention-induced changes in motor preparation may facilitate cortical changes related to execution is still unknown.
Meanwhile, power in higher gamma frequencies  increases (i.e., synchronization) during movement preparation. Unlike lower frequencies, this increase in gamma reflects local intracortical processing rather than descending motor commands 14,15 .
Following a stroke, spatial and frequency features during motor preparation have been shown to be altered. For example, previous studies have reported overactivation of the supplementary motor area (SMA) 9 as well as increased overlap of limb representations on the cortex 10 , which is especially pronounced in individuals with severe impairments. Post-stroke individuals also display stroke-induced changes in these neuronal oscillations during movement such as reduced beta desynchronization and changes in gamma-beta coupling between sensorimotor regions 16,17 .
Following interventions, changes occur not only at motor performance level but also in motor related brain activity. The majority of intervention studies to date have focused on cortical-changes related to motor execution. We argue that understanding how changes in motor preparation may facilitate changes in motor execution is also critical. One piece of the evidence in favor of this is from Norman and colleagues, who showed that specifically training preparation-related cortical oscillations led to subsequent improvements in motor function, further cementing the crucial role for motor preparation in proper movement 11 . Given the critical role of motor preparation in performance, investigating how these frequency characteristics during motor preparation change following an intervention would provide additional details into the nature of any observed cortical reorganization.
The goal of this study was to examine how cortical changes in motor preparation may facilitate changes in motor execution following an intervention that targeted hand/arm function recovery in individuals with severe chronic hemiparetic stroke. We hypothesized that intervention-induced changes in cortico-cortico interactions during motor preparation would lead to changes in activity at motor execution specifically towards an increased reliance on the ipsilesional hemisphere. To test this hypothesis, we examined dynamic cortical coupling between motor regions during motor preparation and cortical activity within motor regions at motor execution following an effective device-assisted intervention. Through this combined approach, we explored how the interactions between regions during motor preparation gave rise to any intervention-induced activity changes.
Specifically, we investigated regions that synchronize power at the same frequency (i.e., linear coupling) or across different frequencies (i.e., nonlinear coupling) because the well-established physiological underpinnings of specific frequency bands within the motor system would provide insight into the underlying neural mechanisms that may be shaping cortical activity at motor execution 20-23 .

Methods
The EEG-data during the motor execution phase related to pure hand opening have been reported before 5 . Here, we analyzed these data, however, during motor preparation phase, to investigate the cortico-cortico interactions during motor preparation. Other data related to hand opening while lifting the arm are original.

Stroke participants
Eight individuals with chronic hemiparetic stroke (mean age: 63.5+4) with subcortical lesions and severe impairment (UEFMA: [11][12][13][14][15][16][17][18][19][20][21][22][23][24] participated in this study. Participant information and the methods of the intervention have been reported before 5,24 . We re-state this information here to make this paper self-contained. All individuals were screened for inclusion by a licensed physical therapist. Inclusion criteria included a UEFMA between 10 and 30 out of 66, no botulinum toxin within the last 6 months, MRI compatibility, the ability to elicit enough EMG activity at wrist/finger extensors, the ability for the FES to generate a hand opening of at least 4 cm between thumb and index finger, the ability to lift the paretic arm, and the ability to give informed consent.
See Table 1 for full demographics. This study was approved by the Northwestern University institutional review board. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants gave informed written consent. The stimulation electrodes were applied to the wrist/finger extensors with the following settings: biphasic symmetrical waveform, frequency = 50 Hz + 20%, pulse width = 300 µs, amplitude = sufficient for maximum hand opening without discomfort, and duration = 3 s. The ReIn-Hand device allows for intuitive control since the same muscles the participant is attempting to drive (i.e. finger/wrist extensors) are being used to control the stimulation. All participants could successfully use the device to complete the described task.

EEG Experiment and Behavioral Task
Participants took part in an EEG experiment within one week prior to and following the Scalp recordings were made with a 160-channel High Density EEG system using active electrodes (Biosemi, Inc., Active II, Amsterdam, The Netherlands) mounted on a stretchable fabric cap based on a 10/20 system. The impedance was kept below 50 kW for the duration of the experiment.
Simultaneously, EMGs were recorded from the extensor carpi radialis, flexor carpi radialis, and deltoid of the paretic arm, which were used to detect movement onset for post-processing purposes.
All data were sampled at 2048 Hz. Additionally, the positions of EEG electrodes on the participant's scalp were recorded with respect to a coordinate system defined by the nasion and pre-auricular notches using a Polaris Krios handheld scanner and reflective markers (NDI, Ontario, Canada producing an isotropic voxel resolution of 1x1x1 mm 3 . Visual inspection of acquired images was performed immediately following the data acquisition to guarantee no artifacts and stable head position.

Data Analysis for Cortico-cortical Connectivity During Motor Preparation
To investigate the cortico-cortico connectivity, we used dynamic causal modeling for induced responses (DCM-IR) 29 to model the task-related time-varying changes in power both within and across a range of frequencies by estimating the coupling parameters within and between sources in a network. This approach has been used in previous hand movement tasks to elucidate the dynamic interactions within a motor network 6,30,31 .  Table   2. Bilateral primary sensory cortices were not included to reduce the computational demand and complexity of the model. Bilateral SMAs were treated as a single source due to their mesial position on the cortices. SMA also served as the input to the modelled network. It was chosen due to its critical role in motor preparation during self-initiated motor tasks 33,34 , and has previously been demonstrated to be an appropriate input for self-initiated motor tasks using DCM-IR 6,30,31 .
Different within-and cross-frequency connections between these 5 sources were used to create 12 models as shown in Supplementary Figure 1 which have successfully been used before in a similar motor task in healthy controls 30 . These 12 models were separated into 2 groups. Group 1 (models 1 to 6) allowed nonlinear and linear extrinsic (between region), but only linear intrinsic (within region) connections. Group 2 (models 7 to 12) allowed both nonlinear and linear connections for both extrinsic and intrinsic connections. Within each group, the 6 models consisted of 1 fully connected model, and the other 5 models missing 1 or 2 connections that were from one premotor area (PM) to either the other PM or to M1. Using this model, we tested the importance of nonlinear frequency interactions within regions as well as the importance of various connections to premotor regions. trials (-2200 to +500 ms with 0 ms indicating EMG onset), and baseline-corrected. Trials exhibiting artifacts were removed. Artifact free trials were projected from channel space to the sources using the generalized inverse of the lead-field matrix using an equivalent current dipole for our chosen sources (see below) 29 . The spectrogram from 4 to 48 Hz at each source was computed using a Morlet wavelet transform (wavelet number: 7). This range includes theta (4-7 Hz), alpha (8-12 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31-48 Hz) frequencies. The spectrogram was then averaged over all trials, cropped between -1000 to 0 ms, and then baseline-corrected by subtracting the frequency-specific power of the first 1/6 samples of the time window (-1000 to -833 ms). The input from SMA to the whole network was modelled as a gamma function with a peak at 400 ms prior to EMG onset with a dispersion of 400 ms. These values were chosen in order to capture the peak of the bereitschaftspotential during a self-initiated movement 35 . The model simulation was restricted to the time leading up to EMG onset (-1000 to 0 ms) to capture purely the motor preparation and command, rather than any potential sensory feedback related to the task.
The dimensionality of the spectrogram was then reduced to four modes using singular value decomposition (SVD). The four modes preserved > 96% of the data variance on average. This dimensionality reduction both reduced the computational demand of the model inversion and denoised the data.

Bayesian model selection
We performed Bayesian model selection (BMS) with random effects 36 on the 12 models described above for both the hand opening and the simultaneous lifting and opening conditions using the data from all of the participants to assess which model best explained the observed data. BMS with random effects was chosen since it is better equipped to handle potential heterogeneity associated with the study of a diseased population such as stroke 36 , and it contains a complexity term that penalizes a model based on the number of parameters it uses. The winning model, which was then used for further analysis of intervention-induced changes, was chosen based on the highest posterior exceedance probability (i.e. the probability that a given model is more likely than any of the other models tested).

Inference on coupling parameters
Predicted spectra and A matrices from the four modes were projected back to frequency domain allowing for characterization of the coupling parameters as a function of frequency for the winning model. The coupling matrices for each intrinsic and extrinsic connections in the winning model for each participant were further smoothed with a Gaussian kernel (full-width half-maximum of 8 Hz) for each condition. These matrices include the frequency-to-frequency (both within-and crossfrequency) coupling values for each connection.

Data Analysis for Cortical Topography During Motor Execution
EEG data were aligned to the earliest EMG onset of the 3 muscles and segmented from -2200 to +200 ms (with EMG onset at 0 ms) using Brain Vision Analyzer 2 software (Brain Products, Gilching, Germany) and baseline-corrected (from -2180 to -2050 ms). Data were then visually inspected for the presence of artifacts. Trials exhibiting artifacts (e.g., eye blinks) were eliminated from further analysis. The remaining EEG trials were low pass filtered at 70 Hz and ensembleaveraged. The averaged EEG signals were down-sampled to 256 Hz and imported into CURRY 6 (Compumedics Neuroscan Ltd., El Paso, TX). The cortical current density strength (μA/mm 2 ) from 150 ms to 100 ms prior to EMG onset was computed using the standardized low-resolution electromagnetic brain tomography (sLORETA) method (Lp = 1) based on a subject-specific boundary element method model with the regulation parameter automatically adjusted to achieve more than 99% variance accounted for. Possible sources were located on a cortical layer with 3 mm distance between each node. Although the inverse calculation was performed over the whole cortex, only the activity in the specific regions of interest (ROIs) was further analyzed. These ROIs included bilateral primary sensorimotor cortices (primary motor cortex (M1) + primary sensory cortex (S1)) and secondary motor cortices (supplementary motor area (SMA) + premotor area (PM)).
We quantified a cortical activation ratio = ∑ 9 : ; < ∑ 9 = > < for each of the 4 ROIs, where sn represents the current density strength of the n th node, and n and m are the indices of nodes in a specific ROI and the whole sensorimotor cortices, respectively. The cortical activity ratio reflects the relative strength from one ROI as normalized by the total combined strength of the 4 ROIs.

Statistical Analysis for Cortical Activity
Statistics were performed using SPM and SPSS (IBM, V24). For the coupling parameters from the DCM analysis, T-statistics were used to calculate statistical parametric maps separately for each connection and condition pre/post intervention. Significance for intervention-induced changes in specific coupling parameters was set at p < 0.05 with family wise error (FWE) correction. For the cortical activity ratio during motor execution, a 2 (time) x 2 (task) x 4 (region) repeated measures ANOVA was performed after checking the data did not violate Mauchly's sphericity test. We performed post-hoc paired t-tests when a main ANOVA effect or interaction was found.
Significance was set a p < .05. Individual data are depicted for significant findings.

Results
ReIn-Hand assisted reaching-grasping training improved the score of Box and Block test significantly, as well as scores in other clinical assessments as reported previously 5,24 . The results included here will focus exclusively on the observed cortical neural changes pre-versus postintervention.

Bayesian model selection and model fit
In order to determine any intervention-induced changes in connectivity, we first had to evaluate which DCM model tested best explained the observed data. BMS with random effects clearly preferred model 12 for each condition (see Supplementary Table 1

Intervention-induced changes in coupling parameters
Once we determined the model that best explained the observed data, we examined whether any intervention-induced changes in connectivity for any of the region-region connections during motor preparation occurred. We found that the intervention induced two significant changes in the coupling parameters, one for each motor task. After the intervention, participants demonstrated significantly less excitatory coupling from ipsilesional M1 to contralesional M1 in gamma frequencies (47 Hz à 36 Hz) during hand opening (see Figure 1A). When looking at the individual coupling values for this particular regional coupling, we found that prior to the intervention, 6 out of 8 participants demonstrated positive coupling values, indicating that increases in gamma in ipsilesional M1 led to increases in gamma in contralesional M1 (see Figure 1B). However, following the intervention, 5 out of 8 participants showed zero or negative coupling, indicating that increases in gamma in ipsilesional M1 no longer led to increases in gamma in contralesional M1 (see Figure 1B).
For the task of simultaneous lifting and opening, the intervention induced significantly more negative or inhibitory coupling within ipsilesional M1 from gamma to beta (44 Hz à 25 Hz) (see Figure 2A). When looking at the individual coupling values for this particular regional coupling, we found that prior to the intervention, 6 out of 8 participants showed positive coupling values, indicating that increases in gamma power within ipsilesional M1 led to subsequent increases in beta power also within ipsilesional M1 (see Figure 2B). However, following the intervention, 7 out of 8 participants showed negative or inhibitory coupling, indicating that increases in gamma  power within ipsilesional M1 led to subsequent decreases in beta power within ipsilesional M1 (see Figure 2B).

Changes in Cortical Activity Ratio (CAR)
We then tested for any intervention-induced changes in cortical activity for the 2 tasks related to motor execution. A 2 (time) x 2 (task) x 4 (region) repeated measures ANOVA found a significant

Discussion
The current study quantified changes in both cortico-cortico coupling during motor preparation and cortical topography at motor execution related to paretic hand opening with and without coordination of shoulder abduction following a device-assisted intervention. Overall, we observed that intervention-induced changes in coupling during motor preparation accompanied similar changes in cortical activity at motor execution. For hand opening, this was characterized by a reduction in coupling with contralesional M1 during motor preparation and similarly decreased activity in contralesional M1 at motor execution. For hand opening while lifting, this was characterized by a change in coupling within ipsilesional motor cortex during motor preparation followed by an increase in activity in ipsilesional motor cortex at execution. As we reported previously, participants demonstrated decreased contralesional primary sensorimotor activity related to paretic hand opening following the intervention. In this study, we further demonstrated that following the intervention, these individuals demonstrated a reduction in coupling from ipsilesional M1 to contralesional M1 within gamma frequencies.

Intervention-Induced Changes Related to Paretic Hand Opening
In healthy controls, gamma band power increases in contralateral primary sensorimotor cortex just before the onset and during movement [37][38][39][40][41][42] . This increase in gamma power has been shown to facilitate movement in studies using transcranial alternation current stimulation (tACS) to artificially increase gamma power over the contralateral primary sensorimotor cortex [43][44][45] .
Typically, gamma is associated with local intracortical processing, particularly within GABAergic interneuronal circuits 14,15 , with increases in gamma power related to movement associated with decreases in GABAA 44 . However, gamma synchronization is notable usually confined to a small area in the contralateral primary sensorimotor cortex in healthy controls 37,40,46 , rather than bilateral increases in gamma during movement as observed here prior to the intervention.
Before the intervention, individuals with severe chronic stroke showed positive gamma coupling (as demonstrated by the positive coupling shown in Figure 2B) from ipsilesional M1 to contralesional M1. This abnormal initial positive gamma coupling between ipsilesional and contralesional M1 may indicate abnormal intercortical communication between the two hemispheres (via callosal or subcortical means 47 ) and may give rise to the initial increased contralesional activity in these individuals. This is supported by previous findings in stroke showing weaker GABAergic inhibition from the ipsilesional to the contralesional hemisphere following stroke [48][49][50][51] , and that this imbalance is associated with greater abnormal contralesional activity during paretic hand movements 52 .
Following the intervention, we found that the abnormal interhemispheric gamma coupling decreased in 6 out of 8 participants (see Figure 1B), which supports the intervention-induced reduction in contralesional activity in the primary sensorimotor cortex (see Figure 3A). The intervention-induced decrease in positive gamma coupling between ipsilesional and contralesional M1 may underlie the subsequent decrease in CAR in contralesional primary sensorimotor cortex and reflect a return to a more normal state as observed in healthy controls.

Intervention-Induced Changes Related to Paretic Hand Opening While Lifting
For the simultaneous lifting and opening task, we observed altered coupling within ipsilesional M1 in which the intervention induced a shift from positive to negative coupling between gamma and beta within ipsilesional M1 during motor preparation (see Figure 2B). This change was accompanied by a subsequent increase in ipsilesional primary sensorimotor cortex activity at motor execution following the intervention. The observed intervention-induced change in crossfrequency coupling is particularly relevant since gamma and beta power are typically inversely related in healthy controls during movement 23 . Whereas gamma tends to increase in power prior to and during the onset of movement, beta decreases in power 38,53 . This decrease in beta power during motor preparation in healthy controls has been linked with the reduction of inhibition in M1 just prior to movement to release the motor command 12,54 . Consequently, movements executed during elevated beta synchrony are slower 55 , and increasing beta power using tACS has been shown to impair movement in healthy controls 43,56 . Importantly, stroke individuals show less decreases in beta during movement compared to controls 16 , and persistence of inhibition in ipsilesional M1 during motor preparation 57 . Therefore, the shift from positive to negative coupling between gamma and beta in ipsilesional M1 following the intervention may reflect a return to a more typical pattern seen in healthy controls.
Prior to the intervention, individuals with severe chronic stroke demonstrated increased gamma power associated with increased beta power in ipsilesional M1 (as demonstrated by positive coupling between gamma and beta power in 6/8 participants, see Figure 2B). This positive coupling shifted to negative after the intervention, where increases in gamma power were associated with decreases in beta power. Importantly, decreases in beta power are inversely related to BOLD activity in the sensorimotor cortex, suggesting some interplay or association between these two physiological processes 58,59 . Given beta's role in descending layer V pyramidal neurons 60,61 , the observed intervention-induced increase in CAR may reflect increased drive and use of remaining descending ipsilesional motor resources during the lifting and opening condition following the intervention, rather than purely an intracortical change. This is significant since reliance on descending tracts from the contralesional hemisphere has been linked with synergyinduced impairments 62,63 , particularly during tasks involving lifting at the shoulder 26,64 , as examined here. Meanwhile, increased use of descending corticospinal tract from the ipsilesional hemisphere has been shown to be crucial for improved function following stroke [65][66][67] , especially for independent control of multijoint movements of the upper extreminty 68 . Although contralesional corticobulbar pathways can support more proximal paretic arm movements such as reaching 69 , they do not offer sufficient control of independent joints during multijoint movements 70,71 . Thus, the ability to maintain ipsilesional recruitment during combined shoulder-hand tasks is critical for potential functional improvement since ipsilesional corticospinal tract, unlike contralesional corticobulbar tracts, has more specific branching in the spinal cord that allows for independent control of the different parts of the arm 71-74 .

Limitations
One of the major limitations of the current study is the small sample size (N=8). However, these participants are fairly homogeneous in that they are each severely impaired (FMA: 10-24; Chedoke McMaster: 3-4) with subcortical lesions impacting the posterior limb of the internal capsule and are in the chronic phase. The robotic setup during the EEG experiment also allowed for a wellcontrolled environment to investigate cortical activity related to the paretic arm and hand, thus minimizing potential session to session variability.
There are numerous methods to analyze connectivity besides DCM-IR when measuring EEG.
Methods such as correlation or coherence between two time-series are the most commonly used metrics. However, unlike DCM-IR, these are limited to within-frequency relationships. Methods evaluating both within and cross-frequency relationships are rarer, including bicoherence, n:m phase synchrony, and multi-spectral phase coherence methods [75][76][77][78] . However, these methods either do not evaluate directionality, like correlations and coherence, or do not directly link to cortical regions. DCM-IR allowed us to evaluate both linear and nonlinear relationships between regions within a network, while also looking at directionality. However, DCM-IR only examines the temporal dynamics of power, but does not contain information of phase, which has been shown to play a potential role in cortical communication 18 . Lastly, DCM is limited by the models tested, and it is possible that there is a better model not considered here.
EEG is inherently limited in its spatial resolution, particularly in comparison to fMRI. However, using our high-density EEG setup, along with the sLORETA inverse calculation based on subjectspecific boundary element models created with individual's anatomical MRI, we have demonstrated a resolution of roughly 5 mm 79,80 . This is suitable for distinguishing primary sensorimotor cortex and secondary motor areas as investigated here in our topographical analysis and allowed us to investigate multi-joint movements which would have been impractical inside an MRI scanner. Meanwhile, the source reconstruction in DCM is based on an equivalent current dipole (ECD) method using the coordinates chosen based on our previous cortical activity findings in combination with standardized locations from the literature. Although these dipoles capture a smoothed portion of the cortex, it is possible that additional activity is not captured within the 5 regions investigated. It is worth noting that these 2 independent inverse approaches (sLORETA and ECD) yielded complementary results in the regions investigated.

Conclusion
The current study demonstrates that intervention-induced changes in coupling both within and between motor regions during motor preparation in movement-related frequencies complement changes in cortical activity at motor execution related to both the hand and arm. Together, these findings suggest that changes in cortico-cortico interactions during motor preparation may lead to corresponding changes in focal cortical activity.

Conflict of Interest
None of the authors have potential conflicts of interest to be disclosed.

Competing Interests
The authors declare no competing interests.