Khabaz Sehla

Thi Mai

Trang Nguyen

Guy Pujolle
email: guy.pujolle@lip6.fr

Pedro Braconnot Velloso
email: pedro.velloso@lip6.fr

Nana Khabaz

Thi-Mai-Trang Nguyen

A New Clustering-based Radio Resource Allocation Scheme for C-V2X

and 4 as illustrated in Figure 1. In mode 3, the resources are scheduled and allocated by the evolved Node B (eNB) to vehicles. However, in mode 4, the vehicles autonomously select their radio resources using the sensing-based Semi-Persistent Scheduling (SPS) algorithm. Contrary to mode 4, the 3GPP has not standardized a specific resource allocation algorithm for mode 3.

Several solutions have been already proposed for the LTE-V2X modes, and especially for mode 4. LTE-V2X mode 4 has a huge volume of research works compared to mode 3. This is explained by the fact that mode 3 is less challenging thanks to the control of the eNB while mode 4 presents different issues related to collision, interference and congestion control problems caused by its decentralized nature. However, even though mode 3 has to cope with a significant reduced set of problems, it remains very important to propose new resource allocation algorithms aiming to optimize the network capacity, the data rate of vehicles or the reuse of resources between vehicles.

Therefore, the novelty of this work is to propose a new scheduling algorithm for mode 3 named Maximum Inter-Centroids Reuse Distance (MIRD) based on vehicle clustering, resource clustering and resource reuse between clusters. The main idea of the MIRD algorithm relies on three steps. The first step is the clustering of vehicles according to their geographical positions and directions. The second step consists of dividing resources into multiple resource sets. The third Abstract-The Cellular Vehicle-to-Everything (C-V2X) is an emerging technology for vehicular networks in which resource allocation mechanisms play a crucial role in its overall performance. In C-V2X, there are two resource allocation modes known as mode 3 and mode 4. This study focuses on mode 3 where the resources are scheduled and allocated by the eNB to vehicles. Contrary to mode 4, the 3GPP has not standardized a specific resource allocation algorithm for mode 3. In this context, we propose in this paper, a new clustering-based resource allocation algorithm for mode 3, named Maximum Inter-Centroids Reuse Distance (MIRD). The main goal of our scheduling scheme is to efficiently a llocate r esources t o v ehicles w ith t he application of a specific r esource r euse m echanism b etween c lusters. MIRD scheduling scheme is validated through simulations in realistic urban scenario, conducted using the LTEV2VSim simulator. The results demonstrate that our proposed algorithm outperforms another reference algorithm for mode 3 found in the literature.

Index Terms-C-V2X, mode 3 scheduling, clustering, distancebased resource reuse approach I. INTRODUCTION In the near future, the connected vehicles will revolutionize the transport sector and the automotive industry. Road safety remains the main objective of the automotive industry. For that, the integration of wireless communication technologies in this sector has given rise to a new paradigm known as Vehicleto-Everything (V2X) communications. V2X communications aim to ensure road safety and traffic m anagement services. Furthermore, they are also able to provide other types of services, such as entertainment applications.

The resource allocation in C-V2X has drawn the attention of the research community in the last few years. In fact, radio frequencies, which are scarce and precious resources, must be efficiently m anaged t o a void i nterferences a nd collisions. Besides, vehicular communications impose more challenges to the resource allocation problem due to constant changes in network topology caused by high mobility. Therefore, resource allocation in C-V2X is a key aspect to improve its performance and enable Internet-of-Vehicles (IoV) applications. C-V2X presents two modes of resource allocation: a centralized undercoverage mode and a distributed out-of-coverage mode. In LTE-V2X, these modes are respectively known as modes 3 step is the application of a specific resource reuse mechanism between clusters.

The present work is interested in the road safety applications in V2X communications. Road safety applications are based on the broadcasting of periodic basic safety messages. Here, it is relevant to highlight that the basic safety messages cannot admit the blocking of transmitters caused by the unavailability of radio resources. Therefore, we propose in this work a more practical resource reuse mechanism based on clustering. Generally, the resource reuse algorithms are based on the calculation of the distance between two vehicles in order to reuse resources between them. However, MIRD algorithm takes advantage of the clustering of vehicles and calculates the distance between the centroids of two clusters in order to enable the reuse of resources between all vehicles of both clusters. MIRD tries to maximize the distance between two clusters reusing the same resources to keep the interference under control. This solution makes the resource management task simpler since computing the inter-centroid distance between two clusters allows providing resource reuse between all vehicles of both clusters. As MIRD reduces the complexity of the resource reuse algorithm, it decreases the power consumption as well as the delay to make decisions for the resource reuse.

The rest of the paper is organized as follows.We first present an overview of the side-link resource allocation mode 3 in Section II. Section III presents the state-of-the-art of research works related to mode 3. Next, we present our contribution in Section IV. Then, we detail the MIRD algorithm in Section V. The simulation scenario and the analysis of results are presented in Section VI. Finally, we conclude our paper in Section VII.

II. SIDE-LINK RESOURCE ALLOCATION IN MODE 3 A. Resources in LTE-V2X

As in LTE, the LTE-V2X adopts Orthogonal Frequency Division Multiplexing (OFDM) in its physical layer and the Single-Carrier Frequency Division Multiple Access (SC-FDMA) at the Medium Access Control (MAC) layer. The available bandwidth, i.e. a 10 or 20 MHz channel, is subdivided in time and frequency domains into several orthogonal resources. In time domain, the signal is organized into frames of 10 ms. Thus, each frame is formed by 10 sub-frames of 1 ms and each sub-frame is formed by 2 time-slots (TS). In the frequency domain, the signal is formed by Resource Blocks Pairs (RBP), which is defined by 12 sub-carriers, spaced by 15 kHz and carrying 14 OFDM symbols. In LTE-V2X, a subchannel is defined by a group of RBPs in the same sub-frame. A sub-channel is the smallest unit of resource that can be allocated to a vehicle to transmit a Cooperative Awareness Message (CAM). The Beacon Resource (BR) is defined by one or more sub-channels required to transmit the CAM packet. In LTE-V2X, Quadrature Phase-Shift Keying (QPSK) and 16-Quadrature Amplitude Modulation (QAM) modulations are used with the Turbo coding.

In LTE-V2X, a resource pool is formed by a set of subchannels within a certain number of sub-frames. The number Fig. 2: Resource Pool with N sf = 100 sub-frames and N sc = 3 sub-channels of sub-frames in the resource pool depends on the transmission interval of the beacon messages, and it is computed as 1,000 divided by the beacon transmission rate λ. For example, if the beacon transmission rate is λ = 10 pps, the number of sub-frames in the pool is then equal to N sf = 1,000/10 = 100 sub-frames. Thus, if the number of sub-channels is set to N sch = 3 sub-channels, then the total number of available resources is N res = 300 resources as illustrated in Figure 2.

B. Mode 3

As previously pointed out, the 3GPP has not standardized a specific resource allocation algorithm for mode 3. Therefore, each operator can propose its own algorithm. However, there are two choices for resource allocation in this mode :

a) The dynamic allocation: in the dynamic allocation, each vehicle requests sub-channels from the eNB for each CAM packet transmission. The drawback of the dynamic allocation is the increase of the signaling overhead and the latency.

b) The Semi-Persistent Scheduling (SPS): in the SPS, the eNB reserves sub-channels for the periodic transmissions of vehicles. Each vehicle sends to the eNB the necessary information such as packet size, transmission frequency, and priority of packets. This information is refered to as the assistance information, which is used by the eNB to semipersistently reserve the appropriate resources for each vehicle.

The main advantage of the SPS approach is the reduction of the signaling overhead and the delay introduced by the resource allocation mechanism. It is important here to note that the semi-persistent-scheduling approach is the more suitable approach for the LTE-V2V services. This is due to the fact that the basic safety messages, on which are based the primary use cases in LTE-V2V, are of periodic nature.

III. RELATED WORK

As previously mentioned, there are few research works, in the literature, interested in mode 3 compared to the huge mode 4 research interest. In [START_REF] Luis F Abanto-Leon | Subchannel Allocation for Vehicle-to-Vehicle Broadcast Communications in Mode-3[END_REF], Abanto-Leon et al. propose a new resource allocation solution for mode 3 in order to guarantee the Quality-of-Service (QoS) for each vehicle. In this context, the QoS refers to the number of sub-channels and the data rate that each vehicle needs for its transmission. This proposal also aims to avoid the conflicts that might occur between vehicles during transmissions. These conflicts are mainly related to the intra-cluster and interclusters interference problems, assuming that vehicles are grouped into clusters based on their geographical location. The intra-cluster problem is caused by the Half-Duplex (HD) nature of the sidelink interface PC5, where two vehicles on the V2V range cannot transmit simultaneously at the same time. The inter-clusters problem is related to the positioning of vehicles at the intersection of clusters. Those vehicles can receive messages broadcasted by other vehicles belonging to different clusters using the same sub-channel simultaneously, causing interference problems. The authors use a mathematical framework in order to propose a resource allocation solution that can avoid these conflicts. Then, the same authors have represented the centralized resource allocation in mode 3 by a weighted bipartite graph in [START_REF] Luis F Abanto-Leon | Graph-based Resource Allocation with Conflict Avoidance for V2V Broadcast Communications[END_REF] and [START_REF] Luis F Abanto-Leon | Parallel and successive resource allocation for V2V communications in overlapping clusters[END_REF]. In these solutions, vehicles and spectral resources are represented by the vertices of a graph and the edges represent the achievable data rate in each resource based on the Signal-to-Interference plus Noise Ratio (SINR) that the vehicles perceive. In [START_REF] Luis F Abanto-Leon | Graph-based Resource Allocation with Conflict Avoidance for V2V Broadcast Communications[END_REF], the resource allocation solution aims to efficiently manage resources by avoiding the conflicts previously cited in [START_REF] Luis F Abanto-Leon | Subchannel Allocation for Vehicle-to-Vehicle Broadcast Communications in Mode-3[END_REF] and also by enabling the reuse of resources between vehicles with an interference avoidance mechanism. Authors show mathematically and through simulations that this approach allows improving the performance in terms of the achieved data rate requested by vehicles. Then, in [START_REF] Luis F Abanto-Leon | Parallel and successive resource allocation for V2V communications in overlapping clusters[END_REF], the proposed resource allocation algorithm is based on two approaches, namely bipartite graph matching-based successive allocation (BGM-SA) and bipartite graph matching-based parallel allocation (BGM-PA). The main objective of this algorithm is to find the optimal one-toone vertex assignment in order to maximize the capacity of the system defined as the achieved data rate. The same authors propose in [START_REF] Luis F Abanto-Leon | System level simulation of scheduling schemes for C-V2X mode-3[END_REF] two resource allocation schemes. The first scheme aims to minimize the perceived power of subchannels. In this approach, it is assumed that vehicles send the channel conditions to the eNB. Then, the eNB schedules resources based on this information with a minimization of perceived power of allocated sub-channels. The suitable allocated sub-channels are non-occupied sub-channels or subchannels experiencing a negligible interference. However, the second approach aims to maximize the distance for the reuse of sub-channels between vehicles in order to avoid the co-channel interference. The authors demonstrate through simulations that these two approaches outperform the decentralized approach, i.e mode 4, in terms of the Packet Reception Ratio (PRR), as the sub-channels are assigned in a more efficient manner with mitigated interference.

Authors of [START_REF] Cecchini | Localization-based resource selection schemes for network-controlled LTE-V2V[END_REF], [START_REF] Cecchini | Maximum reuse distance scheduling for cellular-V2X sidelink mode 3[END_REF] and [START_REF] Fritzsche | Location-based scheduling for cellular V2V systems in highway scenarios[END_REF] focus on the geographical position-based resource allocation schemes and the distancebased resource reuse approaches. Cecchini et al. [START_REF] Cecchini | Localization-based resource selection schemes for network-controlled LTE-V2V[END_REF] propose a resource allocation mechanism based on the knowledge of vehicle position. The position of a vehicle is known through the Up-link Time Difference of Arrival (UTDOA) or the Global Navigation Satellite System (GNSS) positioning. They compare the effect of the positioning error on the system performance. They demonstrate that the localization accuracy is more important than the frequency of GNSS update, which leads to a low error rate. Besides, authors propose that a resource can be allocated for two vehicles if the distance between those vehicles is greater than a fixed resource reuse distance. Next, the same authors propose a new algorithm that aims to maximize the resource reuse distance between vehicles in order to control the probability of interference [START_REF] Cecchini | Maximum reuse distance scheduling for cellular-V2X sidelink mode 3[END_REF]. The authors prove that this last algorithm allows improving the PRR compared to its predecessor, presented in [START_REF] Cecchini | Localization-based resource selection schemes for network-controlled LTE-V2V[END_REF].

The above-mentioned algorithms do not take into account the traffic density and channel conditions at the same time. Authors in [START_REF] Sempere-García | LTE-V2X mode 3 scheduling based on adaptive spatial reuse of radio resources[END_REF] propose to use the geographical location of the vehicles and the environmental conditions, such as the channel load and the traffic density, to allocate resources to vehicles. This proposal is called aDaptive spatIal Reuse of rAdio resourCes (DIRAC). DIRAC defines 2 distances: the reuse distance and the HD distance. The reuse distance is the distance between 2 vehicles reusing the same resource while the HD distance is the distance between two vehicles transmitting in the same sub-frame but using different subchannels. This proposal adapts the computed distances to the context conditions, such as the traffic density and the channel load. DIRAC aims to minimize the probability that two interfering vehicles are close to each other in order to minimize the QoS degradation. The simulation results show that DIRAC outperforms existing LTE-V2X Mode 3 and Mode 4 scheduling schemes in terms of the Packet Delivery Ratio (PDR) in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) conditions.

The novelty of our work is to propose a new resource allocation scheme based on the clustering. Grouping vehicles into clusters facilitates the resource allocation process by assigning a resource set to each cluster, which also facilitates the resource reuse between two clusters instead of reusing resources between only two vehicles.

IV. MOTIVATION AND CONTRIBUTION

The resource allocation solution proposed in this paper comprises three steps. The first step is the clustering of vehicles according to their geographical positions and directions. The second step consists of dividing the beacon resources into multiple resource sets. The third step relies on the application of a distance-based resource reuse approach for reusing resources between clusters. It is worth noting that the MIRD algorithm makes the resource reuse task easier. In fact, in the distance-based resource reuse algorithms, the distancebased approach is applied between vehicles which requires the computation of the distance between all pairs of vehicles to make decisions for the resource reuse. However, in our proposal, it is sufficient to compute the inter-centroid distance between two clusters in order to enable the reuse of resources between all vehicles of both clusters. For that, MIRD has the advantage of reducing the number of calculation operations Fig. 3: (Geographical positions and directions)-based clustering architecture required, the power consumption of computation resources and the time needed to make decisions for the resource reuse.

In the following, we present in detail and separately the clustering of vehicles, the clustering of resources and the distance-based resource reuse approach.

A. Clustering of vehicles

In the literature, there exist many clustering approaches for vehicular networks [START_REF] Senouci | Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues[END_REF] [10]. In our approach, we assume that the eNB can group the vehicles into clusters according to their geographical positions and directions. Here, we define a cluster by its centroid, which refers to the central position of the cluster and its radius denoted as R, as illustrated in Figure 3. The eNB retrieves the geographical positions of vehicles either by the UTDOA technique or by the GNSS. The UTDOA is a technique based on the difference between the arrival times of two consecutive uplink packets.

B. Clustering of resources

After the clustering of vehicles, the eNB executes the second task which consists of dividing resources into multiple resource sets. In this context, we mean by resources the BR as already defined in Section II-A. In our scheduling scheme, MIRD, we take into account the HD nature of the sidelink interface PC5. To this aim, we propose to allocate sub-channels within different sub-frames to vehicles of the same cluster. Figure 4 an option when resources are divided into 3 time-orthogonal resource sets, denoted by subset 1 , subset 2 and subset 3 . In addition, we apply a specific resource reuse approach between clusters. In fact, two clusters can use the same resource set if the distance between their centroids is greater than a given threshold parameter RC reuse that we will detail in the following.

C. MIRD-Resource Reuse Approach

While the previous algorithms presented in the literature [START_REF] Cecchini | Localization-based resource selection schemes for network-controlled LTE-V2V[END_REF] [6] apply the resource reuse distance approach between vehicles, MIRD applies it between clusters. We denote by Inter-Centroid Distance (ICD) the distance between the centroids of two clusters. In this approach, two clusters C i and C j can reuse the same resource set, if the ICD ij of clusters C i and C j is greater than a fixed threshold, known as RC reuse as computed in equation ( 1) and illustrated in Figure 4.

RC reuse = R reuse + 2 * R (1)
We note that the R reuse is computed following the equations presented in [START_REF] Bazzi | How many vehicles in the LTE-V2V awareness range with half or full duplex radios?[END_REF] and it depends on the choice of the Modulation and Coding Scheme (MCS). The variation of R reuse in function of MCS is presented in Figure 5.

In Figure 6, we present the variation of RC reuse in function of the cluster radius for different MCS values. When the MCS level increases, the R reuse increases also as presented in Figure 5. Thus, the RC reuse increases with the high MCS level values for the same cluster radius.

V. SYSTEM MODEL AND MIRD ALGORITHM

Let V = {v 1 , v 2 , .., v M } designate the set of M vehicles present in the cell at a given time, when the cell starts the resource allocation procedure. For the sake of simplicity, we suppose that all vehicles in the cell follow the same direction. Based on the positions of the vehicles, the eNB runs the clustering algorithm to divide M vehicles into K clusters C =

{C 1 , C 2 , C 3 , ..., C K } so that K i=1 |C i | = M and K i=1 C i = ∅.
If we denote the number of vehicles in cluster C i by M i , we also have

K i=1 M i = M .
In this contribution, we have chosen the clustering algorithm presented in [START_REF] Yann Marcon | distance-based clustering of a set of xy coordinates[END_REF]. However, any clustering algorithm based on the geographical positions of vehicles can be chosen.

Let R={r 1 , r 2 , r 3 , ..., r N } be the set of resources in the cell. Each resource corresponds to a BR which is a fixed number of sub-channels necessary to transmit a CAM message. We also suppose that each vehicle only needs one resource allocated by the eNB. Depending on the number of vehicles present in the cell, the number of resources can be greater than, equal to or smaller than the number of vehicles. When M < N , the eNB doesn't run out of resources and there are still unused resources in the cell. When M = N , each vehicle is allocated a distinct resource in the cell and there is no free resource available in the cell any more. When M > N , it is necessary to reuse resources in different clusters. The contribution of this paper is focused on the last case.

Let's consider in detail the resource reuse between clusters. We denote the set of resources allocated to cluster C i by R Ci = {r 1 Ci , r 2 Ci , r 3 Ci ..., r Mi Ci }. As each vehicle only needs one resource, we can consider r j Ci as the resource that the eNB allocates to vehicle j in cluster C i . For each cluster C i , i = 1, 2, ..., K, the eNodeB executes Algorithm 1 and allocates a set of M i resources. Algorithm 1 presents the first phase of resource allocation. In the case that M > N , the first phase of resource allocation in which each resource is allocated once ends with some cluster C j so that the number of remaining resources is smaller than the number of vehicles of this cluster. That means N -

j-1 i=1 M i < M j .
After the first phase, there are j-1 clusters having resources allocated and K -j+1 clusters waiting for resource allocation. In phase two, we adopt a cluster-based resource allocation approach to reduce the complexity of the resource allocation algorithm. The whole number of resources allocated to a cluster in the first phase will be given to another cluster in the second phase. Algorithm 2 presents the second phase of resource allocation in which different clusters can use the same resources when they are sufficiently far from each other.

Let's call a cluster which has not received allocated resources and must reuse resources of another cluster a requesting cluster. The cluster which has already resource allocated in phase one and its resources can be given to a requesting cluster is called an offering cluster. To minimize the interference between two clusters using the same resources, the distance between them should be maximized. In order to allocate resources to a requesting cluster C j , the eNB establishes a list of candidate clusters. The candidate clusters are the clusters which have been already allocated with resources in phase 1 and have the inter-centroid distance with cluster C j greater than RC reuse . This list is sorted by the inter-centroid distance with C j in a decreasing order. The eNB traverses the list from one cluster to another and selects one or more clusters so that the number of total resources is greater than or equal to M j . The selected resources may be already reused by another requesting cluster C x but the inter-centroids distance ICD jx between clusters C j and C x must be greater than the RC reuse threshold.

Algorithm 1: Resource allocation in phase 1

Result: RC i , ∀Ci ∈ P1 1 P1 = Null;
/ * P1 is the set of clusters having resources allocated in phase 1 * / 2 S = N; / * S is the remaining resources * / 3 i = 1; 4 while (S >= Mi) && (i <= K) do 5 Add cluster into P1 (Ci) ; We present the main parameters of MIRD algorithm in Table I.

In this contribution, as we are interested in the basic safety messages, which are of periodic nature, the Semi-Persistent Scheduling (SPS) approach is the best choice. In this approach, a reselection time (T resel ), which is a multiple of the beacon period, is used in order to transmit with the same resource for a successive number of transmissions. The work in [START_REF] Cecchini | Maximum reuse distance scheduling for cellular-V2X sidelink mode 3[END_REF] has already demonstrated that a reselection time (T resel ) of 2 seconds is the best compromise between control signaling and performance. For this reason, the MIRD algorithm is executed every 2 seconds.

VI. SIMULATION SCENARIO AND RESULT ANALYSIS

In this section, we present the scenario of our simulation. We present also the reference algorithm which is used to compare the performance with the MIRD algorithm. Finally, Algorithm 2: Resource allocation for a requesting cluster C j in phase 2

Result: RC j 1 L= Distances from Cj greater than RCreuse(P1); 2 Sort by Decreasing Distance from Cj (L); 3 RC j = Null; 4 X = Null ; / * X is the set of resources already reused by requesting clusters * / 5 resource found = false; 6 p = 0; 7 while (!resource found) && (L != ∅)

do 8 C k = L[0]; 9 p = p + M k ; 10 RC j = RC j RC k ; 11 if p >= Mj then 12 if (RC k / ∈ X) OR (RC k ∈ X AND ICDjx ≥ RCreuse) then 13 resource found = true; 14 X = X RC k ; / *

Rreuse

Minimum distance between two vehicles reusing the same resource as computed in [START_REF] Bazzi | How many vehicles in the LTE-V2V awareness range with half or full duplex radios?[END_REF].

RCreuse

Minimum distance between two clusters reusing the same resource set as computed in equation [START_REF] Luis F Abanto-Leon | Subchannel Allocation for Vehicle-to-Vehicle Broadcast Communications in Mode-3[END_REF] we analyse the simulation results and present the performance of our proposal.

A. Simulation Scenario

The simulation is conducted through the LTEV2VSim simulator [START_REF] Cecchini | LTEV2Vsim: An LTE-V2V simulator for the investigation of resource allocation for cooperative awareness[END_REF] implemented by the simulator is the WINNER + B1 channel model as presented in [START_REF] Meinilä | WINNER+ Final Channel Models v1[END_REF].

In our simulations, we have chosen a scenario with M = 60 vehicles grouped into K= 9 clusters. The radius of the cluster is 200 m. The total number of available resources is N = 40 resources.

In order to demonstrate the performance of our algorithm, we compare it with the reference algorithm, denoted as Algo ref , in which the eNB sorts the resources and assigns to each vehicle the first free BR.

B. Result Analysis

In our simulations, we evaluate the performance of the proposed algorithm with the following Key Performance Indicators (KPI):

• The Packet Reception Ratio (PRR) is defined as the ratio between the number of successfully received beacons and the total number of neighbors. • The Average Error Rate (AER) is defined as the ratio between the number of not correctly decoded beacons and the total number of beacons expected to be received. • The Average Blocking Rate (ABR) is defined as the ratio between the number of blocked vehicles due to the unavailability of resources and the total number of vehicles.

• The resource reuse rate (λ) is defined as the ratio between the number of clusters reusing the resource sets and the total number of clusters in the scenario. • The complexity gain (G Nco ) is computed as the difference between the number of needed operations to make resource reuse decision in the standard Resource Reuse Distance (RRD)-based approach and the number of needed operations to make resource reuse decision in the MIRD algorithm. The comparison between the performance of MIRD algorithm and the reference algorithm is made by analyzing the PRR, AER and ABR as shown in Figures 7, 8 and9 respectively. These parameters indicate the reliability level which is one of the most important requirements of the V2X applications in order to ensure the basic safety service. Figures The PRR decreases when the distance between transmitter and receiver increases. On the contrary, the AER increases when the distance between transmitter and receiver increases. The difference between MIRD and Algo ref is very clear in the figures for the distances higher than 100 m. For example, for the same distance of 150 m, the PRR of MIRD is about 92%, while the PRRs of Algo ref is about 81%. Also, for the same distance, the AER of MIRD is about 0.09 while the AER of Algo ref is about 0.19. We conclude that MIRD algorithm achieves better performance than the reference algorithm in terms of PRR and AER. Figure 9 illustrates the ABR of MIRD compared to the reference algorithm. As shown in the figure, MIRD presents an ABR equal to 0% thanks to the resource reuse approach, while the ABR of Algo ref is not null. This is explained by the blocking of transmitters when there are no resource available. As already explained, the blocking of transmitters is very dangerous in the context of the basic safety messages because the blocked vehicle is not detected by its neighbors.

Figure 10 shows the values of the resource reuse rate for three traffic scenarios with a fixed number of resources N = 40 resources, as presented in Table III. As shown in the figure, the resource reuse rate increases with the increase of the number of vehicles. For M = 80 vehicles, the resource reuse rate is T = 50% which increases also the resource allocation capacity. Here, we define the resource allocation capacity as the total number of resources that can be allocated to vehicles without collision. Assuming for example that we have 100 resources, so 100 vehicles can be allocated without collision. However, when we apply the resource reuse approach with a resource reuse rate of 50%, the resource allocation capacity will be of 150 resources which is more beneficial in case of high vehicle density areas.

We can also highlight another advantage of the MIRD algorithm which is related to the algorithm complexity. For that, we define another parameter, referred to the gain in the number of computation operations needed, denoted by G Nco as already explained in VI-B. We remind that in the standard RRD-based approach, the resource reuse for a given vehicle v i is applied after the computation of the distance between this vehicle and all other vehicles in the scenario. Let us suppose that we have 60 vehicles grouped into 9 clusters and 40 resources. Among those clusters, there are 6 offering clusters and 3 requesting clusters. The total number of vehicles in the offering clusters is 40 vehicles and the total number of vehicles in the requesting clusters is 20 vehicles. Here, we compare the resource reuse mechanism for both approaches. In the standard approach, for each requesting vehicle v i , we need the computation of the distance between v i and all other offering vehicles. In our scenario, each vehicle needs 40 operations and thus we need 40 × 20 = 800 operations for all requesting vehicles. However, when we apply the MIRD approach, we need for each requesting cluster 6 operations and thus we only need 6 × 3 = 18 operations for all requesting clusters. So, the gain in the number of computation operations is very high and it is of G Nco = 800 -18 = 782 operations, which reduces the complexity of the MIRD algorithm. We illustrate the complexity gain in Figure 11 for three traffic scenarios as presented in Table III. As shown in the figure, the number of computation operations increases with the increase of the total number of vehicles in the scenario. So, the gain achieved by the MIRD algorithm is very important in the congested traffic scenarios.

VII. CONCLUSION AND PERSPECTIVES

In this paper, we propose a new clustering-based resource allocation algorithm for mode 3, named Maximum Inter-Centroids Reuse Distance (MIRD). The main idea of MIRD algorithm is firstly the clustering of vehicles according to their geographical positions and directions. Secondly, the resources are divided into orthogonal resource sets and each resource set is assigned to a cluster. Finally, MIRD applies a specific resource reuse approach between clusters by computing the inter-centroids distance between clusters. The MIRD is validated through simulations conducted using the LTEV2V simulator. The obtained results show that MIRD algorithm outperforms the reference algorithm for mode 3 found in the literature in terms of PRR, AER and ABR. In addition, the MIRD reaches a very high complexity gain compared to the standard RRD-based approach. In our future work, we will apply this proposal in the 5G-V2X context taking into account different propagation conditions and mixed traffic circumstances.

Fig. 1 :

 1 Fig. 1: Resource Allocation Modes in LTE-V2X

Fig. 4 :Fig. 5 :

 45 Fig. 4: Clustering-based resource allocation scheme undercellular coverage

Fig. 6 :

 6 Fig. 6: RC reuse in function of the cluster radius for different MCS values

  [START_REF]LTEV2VSim Simulator[END_REF]. LTEV2VSim is a Matlab-based open-source simulator dedicated for resource allocation in LTEV2V communications. The vehicle mobility is based on realistic traffic traces generated with PTVVISim simulator [15] of an urban scenario of Bologna. The propagation and NLOS modeling

Fig. 7 :

 7 Fig. 7: The Packet Reception Ratio (PRR) for MIRD and Algo ref in an urban scenario of Bologna.

Fig. 8 :Fig. 9 :Fig. 10 :Fig. 11 :

 891011 Fig. 8: The Average Error Rate (AER) for MIRD and Algo ref in an urban scenario of Bologna

TABLE I :

 I Main parameters of MIRD algorithm

	Parameter	Description
	ICD	Inter-centroids distance: distance between
		the centroids of two clusters
	R	Cluster radius

TABLE II :

 II Simulation Parameters

	Parameter	Value
	Beacon Period	0.1 s
	Beacon size	300 bytes
	Channel Bandwidth	10 MHz
	Frequency	5.9 GHz
	Modulation and Coding Scheme (MCS)	5
	Duplexing	HD
	Equivalent Radiated Power (dBm)	20 dBm
	Tx/Rx antenna Gain	3 dB
	Antenna height	1.5 m
	Path Loss model	WINNER +(B1)
	Reselection time (T resel )	2s

TABLE III

 III

				: traffic scenarios
	Number	Number	Total num-	Number	Number of
	of	of	re-	ber of clus-	of offering	requesting
	vehicles	sources	ters	clusters	clusters
	Scenario	40		9	6	3
	(a): 60
	Scenario	40		11	6	5
	(b): 70
	Scenario	40		12	6	6
	(c): 80


Deanna Anderlini

Helena Hybbinette

Ellika Schalling

Jeanette Plantin

Catharina Nygren-Deboussard

Marika Schütz

G Lindberg

Recovery of Apraxia of Speech and Aphasia in Patients With Hand Motor Impairment After Stroke

Keywords: stroke, recovery, apraxia of speech, aphasia (language), hand motor impairment, prevalence

Objective: Aphasia and apraxia of speech (AOS) after stroke frequently co-occur with a hand motor impairment but few studies have investigated stroke recovery across motor and speech-language domains. In this study, we set out to test the shared recovery hypothesis. We aimed to (1) describe the prevalence of AOS and aphasia in subacute stroke patients with a hand motor impairment and (2) to compare recovery across speech-language and hand motor domains. In addition, we also explored factors predicting recovery from AOS.

Methods: Seventy participants with mild to severe paresis in the upper extremity were assessed; 50% of these (n = 35) had left hemisphere (LH) lesions. Aphasia, AOS and hand motor assessments and magnetic resonance imaging were conducted at 4 weeks (A1) and at 6 months (A2) after stroke onset. Recovery was characterized in 15 participants showing initial aphasia that also had complete follow-up data at 6 months.

Results: All participants with AOS and/or aphasia had LH lesions. In LH lesioned, the prevalence of aphasia was 71% and of AOS 57%. All participants with AOS had aphasia; 80% of the participants with aphasia also had AOS. Recovery in aphasia (n = 15) and AOS (n = 12) followed a parallel pattern to that observed in hand motor impairment and recovery correlated positively across speech-language and motor domains. The majority of participants with severe initial aphasia and AOS showed a limited but similar amount of recovery across domains. Lesion volume did not correlate with results from behavioral assessments, nor with recovery. The initial aphasia score was the strongest predictor of AOS recovery.

Conclusion:

Our findings confirm the common occurrence of AOS and aphasia in left hemisphere stroke patients with a hand motor impairment. Recovery was similar across speech-language and motor domains, even in patients with severe impairment, supporting the shared recovery hypothesis and that similar brain recovery mechanisms are involved in speech-language and motor recovery post stroke. These observations contribute to the knowledge of AOS and its relation to motor and language functions Hybbinette et al.

Stroke Recovery, AOS, Aphasia, Handmotor and add information that may serve as a basis for future studies of post stroke recovery. Studies including neuroimaging and/or biological assays are required to gain further knowledge on shared brain recovery mechanisms.

INTRODUCTION

Stroke patients often suffer from multiple impairments. A left middle cerebral artery (MCA) stroke with damage to the left inferior frontal cortex and precentral cortex often causes a rightsided hemiparesis, non-fluent (Broca's) aphasia and may also lead to apraxia of speech (AOS) [START_REF] Ramsey | Behavioural clusters and predictors of performance during recovery from stroke[END_REF][START_REF] Duffy | Motor Speech Disorders: Substrates, Differential Diagnosis, and Management[END_REF]. AOS is traditionally described as an impairment that occurs at an intermediate level of speech production; it is neither regarded as a linguistic impairment (aphasia), nor as a problem with speech motor execution (dysarthria). Instead, it is defined as a deficit in planning and programming speech motor movements [START_REF] Darley | Motor Speech Disorders[END_REF]. It has been argued that the impairments in AOS reflect a disconnection between linguistic and motor processes (4), and suggested that the association between AOS and right-sided motor impairments is stronger than that between aphasia and such motor impairments [START_REF] Duffy | Motor Speech Disorders: Substrates, Differential Diagnosis, and Management[END_REF]. The core symptoms associated with AOS include a slow rate of speech, disturbed prosody and impaired articulation with sound errors that are predominately distortions [START_REF] Mcneil | Clinical Management of Sensorimotor Speech Disorders[END_REF]. Non-verbal oral apraxia (NVOA), an impairment of non-speech volitional movements, is frequent especially in severe AOS [START_REF] Duffy | Motor Speech Disorders: Substrates, Differential Diagnosis, and Management[END_REF]. AOS after stroke often co-occurs with aphasia (6, 7) and patients with nonfluent aphasia often also have AOS [START_REF] Duffy | Motor Speech Disorders: Substrates, Differential Diagnosis, and Management[END_REF]. However, quantitative information on the prevalence of AOS is limited. At the Mayo Clinic Speech Pathology practice, AOS was documented as the primary communication disorder in 4.7% of the patients with motor speech disorders. The prevalence would presumably be much higher if co-occurring AOS was included in the data [START_REF] Duffy | Motor Speech Disorders: Substrates, Differential Diagnosis, and Management[END_REF]. Although there is a consensus that speech-language functions are supported by predominantly left lateralized brain regions, there is also an ongoing debate about the contribution of the right hemisphere (RH) in speech production. Several neuroimaging studies have examined the RH's involvement and proposed a bilateral organization of the speech sensory-motor system [START_REF] Cogan | Sensorymotor transformations for speech occur bilaterally[END_REF][START_REF] Zaca | Whole-brain network connectivity underlying the human speech articulation as emerged integrating direct electric stimulation, resting state fMRI and tractography[END_REF].

Numerous studies have described a close connection between hand motor behavior and speech-language functions. Corballis [START_REF] Corballis | From mouth to hand: gesture, speech, and the evolution of right-handedness[END_REF] claimed that language evolved from manual gestures, and that this transition can be traced by studying changes in the function of Broca's area. Several studies have reported a significant activation of Brodmann area 44 in the posterior portion of Broca's area during manual action, e.g., Binkofski et al. [START_REF] Binkofski | A frontoparietal circuit for object manipulation in man: evidence from an fMRI-study[END_REF] and Gerardin et al. [START_REF] Gerardin | Partially overlapping neural networks for real and imagined hand movements[END_REF]. In post stroke patients, Meister et al. [START_REF] Meister | Functional connectivity between cortical hand motor and language areas during recovery from aphasia[END_REF] reported enhanced excitability in the nondominant hand motor area during reading. This effect had earlier been found only in the language-dominant hemisphere in healthy individuals [START_REF] Meister | Motor cortex hand area and speech: implications for the development of language[END_REF]. Harnish et al. [START_REF] Harnish | Language changes coincide with motor and fMRI changes following upper extremity motor therapy for hemiparesis: a brief report[END_REF] studied five chronic post-stroke patients with upper extremity hemiparesis and aphasia during the course of 6 weeks of high intensive motor therapy. All exhibited both hand motor and language improvements, in the three patients with the largest motor improvements also significant language improvements were observed. Meinzer et al. [START_REF] Meinzer | Electrical stimulation of the motor cortex enhances treatment outcome in post-stroke aphasia[END_REF] reported that transcranial direct current stimulation over the left primary motor cortex induced long lasting language improvements. Both naming ability and functional communication were ameliorated in individuals with chronic post-stroke aphasia, adding support for a close connection between speech-language and motor systems.

There is a shortage of research involving patients with AOS in an acute/subacute phase after stroke and of studies that investigate how AOS symptoms evolves over time [START_REF] Haley | Recovering with acquired apraxia of speech: the first 2 years[END_REF][START_REF] Basilakos | Contemporary Approaches to the management of post-stroke apraxia of speech[END_REF]. Studies of speech-language recovery after stroke have mainly focused on aphasia [e.g., see [START_REF] Pedersen | Aphasia after stroke: type, severity and prognosis. The Copenhagen aphasia study[END_REF][START_REF] Seghier | The PLORAS database: a data repository for predicting language outcome and recovery after stroke[END_REF]] and very limited information exists about the dynamics of recovery from AOS relative to concomitant aphasia. In a single case study, Mauszycki et al. [START_REF] Mauszycki | A sub-acute case of resolving acquired apraxia of speech and aphasia[END_REF] found a parallel recovery pattern of AOS and aphasia over the first 8 months after stroke. Baumgaertner et al. [START_REF] Baumgaertner | Diffrential recovery of aphasia and apraxia of speech in an adolescent after infarction of the left frontal lobe: longitudinal behavioral and fMRI data[END_REF] described a disparate recovery process during the first 4 weeks in a single post stroke patient that showed improvement from aphasia but not from AOS. Primaßin et al. [START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF] examined possible interactions of motor and speech-language processes after 7 weeks of intensive language and motor therapy in four patients in a subacute or chronic phase after stroke. In this study sample, dissociation in the recovery of AOS vs. aphasia was noticed with a tendency of more persistent AOS symptoms compared to those related to aphasia.

Most spontaneous language and motor recovery is assumed to occur within the first 3 months after stroke [START_REF] Wade | Recovery after stroke-the first 3 months[END_REF], supporting the hypothesis of common spontaneous recovery coherent with general plasticity mechanisms [START_REF] Lazar | Improvement in aphasia scores after stroke is well predicted by initial severity[END_REF]. However, studies have shown that the extent and timing of recovery at the inter-individual level vary considerably, both regarding hand motor [START_REF] Prabhakaran | Inter-individual variability in the capacity for motor recovery after ischemic stroke[END_REF] and speech-language recovery [START_REF] Lazar | Variability in language recovery after first-time stroke[END_REF][START_REF] Cahana-Amitay | Redefining Recovery From Aphasia[END_REF]. Traditionally, motor recovery is regarded to start earlier than language recovery in post stroke patients [START_REF] Ramsey | Behavioural clusters and predictors of performance during recovery from stroke[END_REF], and studies have reported that cognitive functions continue to recover after 3 months, for example language [START_REF] Kertesz | Recovery patterns and prognosis in aphasia[END_REF] and visual attention [START_REF] Levine | Left spatial neglect: effects of lesion size and premorbid brain atrophy on severity and recovery following right cerebral infarction[END_REF]. There are also alternative hypotheses, referred to in e.g., Primaßin et al. [START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF], about competitive vs. additive interactions in the recovery process. Competitive interactions would entail that during the course of a general recovery, one domain would draw a proportionately greater amount of neural resources at the disadvantage of a "competing" domain. The alternative assumption is that there is a positive interaction, and that recovery in one domain would benefit also to the recovery in other domains. Recovery has yet most often been studied in a single domain within selected patient groups and few studies address both motor and speech-language abilities [START_REF] Anderlini | Language as a predictor of motor recovery: the case for a more global approach to stroke rehabilitation[END_REF]. Knowledge about determinants of concurrent recovery is therefore missing [START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF]. This is particularly the case for patients with severe speech-language impairments, which often are excluded from studies [START_REF] Dalemans | Facilitating the participation of people with aphasia in research: a description of strategies[END_REF]. As a consequence, the generalization of research findings relating to aphasia and AOS patients is limited [START_REF] Jayes | Initial evaluation of the consent support tool: a structured procedure to facilitate the inclusion and engagement of people with aphasia in the informed consent process[END_REF]. The exclusion of patients with a severe speech-language impairment also create an imbalance in the recruitment of patients with a hemiparesis in the dominant hand [START_REF] Van Der Vliet | Predicting upper limb motor impairment recovery after stroke: a mixture model[END_REF].

Among the predictors for post stroke recovery, initial severity, lesion site and lesion size are often proposed as important factors [START_REF] Watila | Factors predicting post-stroke aphasia recovery[END_REF]. The relation between lesion volume and recovery has been addressed in several studies. Generally, large left hemisphere lesions are associated with poor language recovery whereas smaller lesions are assumed to have a better prognosis [START_REF] Plowman | Post-stroke aphasia prognosis: a review of patient-related and stroke-related factors[END_REF][START_REF] Kiran | Neuroplasticity of language networks in aphasia: updates, and future challenges[END_REF]. Trupe et al. [START_REF] Trupe | Chronic apraxia of speech and Broca's area[END_REF] found that patients with persistent AOS had large infarcts involving most of the territory supplied by the superior division of the left MCA. However, research has questioned the role of global lesion size as a reliable correlate for motor and language recovery and its importance as a prognostic factor has been debated [START_REF] Payabvash | Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans[END_REF][START_REF] Gerstenecker | Language recovery following stroke[END_REF].

Since basic knowledge on recovery from AOS and its relation to motor and language functions is missing, we set out to describe recovery occurring across domains. A prospective longitudinal observational study design was applied. Given previous findings supporting similar recovery across domains (1) we set out to test the hypothesis that motor and speech and language recovery would be similar in stroke patients presenting with initial arm paresis.

The aims of the study were threefold:

1) To investigate the prevalence of AOS and aphasia in patients with arm and hand motor impairment in an early phase after first ever stroke. Since studies point to shared neurophysiological mechanisms (2), we hypothesized that the prevalence of both AOS and aphasia would be high in the patient group with LH lesions. 2) To describe recovery patterns of AOS, aphasia and hand motor impairment spanning all severity levels, from a subacute phase at 4 weeks to a follow-up at 6 months. Given initial accounts of similar recovery of aphasia and motor impairment [START_REF] Harnish | Language changes coincide with motor and fMRI changes following upper extremity motor therapy for hemiparesis: a brief report[END_REF], we hypothesized that AOS and aphasia recovery would be closely related to hand motor recovery, even in patients with severe initial impairment, reflecting shared plasticity mechanisms. 3) To explore factors predicting recovery from AOS. In line with current findings [START_REF] Lazar | Variability in language recovery after first-time stroke[END_REF][START_REF] Gerstenecker | Language recovery following stroke[END_REF], we also predicted that AOS and aphasia recovery would not be related to lesion volume.

METHODS

Participants

The study was performed as a part of the ProHand study, a prospective longitudinal study designed to identify key determinants for recovery of hand motor function after a first stroke (ClinicalTrials.gov Identifier: NCT02878304). It was approved by the Regional Ethical Review Board in Stockholm and all participants provided informed consent prior to participation. Since the study included patients with severe aphasia, both oral and written information were modified and presented in an aphasia friendly manner to make it accessible also to individuals with compromised language skills. Inclusion criteria: (1) Patients aged ≥18 years admitted to inpatient care after first ever-stroke (2) clinical evidence of hand motor deficits based on neurological examination and medical records (3) time of enrolment: between 2 and 6 weeks after stroke onset (4) Swedish as first language, (5) awake, alert and capable of participating in assessment procedures. 1). Complete follow-up assessments at A2 were performed in 15 participants with speech-language impairment at A1. This subgroup all had LH lesions. As can be seen in Table 2 and Figure 1, all in this group had middle cerebral artery lesions. Most were caused by ischemic strokes, but three participants had haemorrhagic strokes. In one case, the lesion was subcortical only; all other participants had cortical lesions extending into the subcortical white matter. The mean lesion volume was 133.8 cm 3 .

Behavioral Assessments

Presence and degree of AOS was examined using the Apraxia of Speech Rating Scale 2.0 (ASRS 2.0) [START_REF] Strand | The apraxia of speech rating scale: a tool for diagnosis and description of apraxia of speech[END_REF][START_REF] Clark | Revisions to the apraxia of speech rating scale[END_REF]. The ASRS has been applied in several studies and was recently found as a reliable indicator of AOS after stroke [START_REF] Wambaugh | Interrater reliability and concurrent validity for the apraxia of speech rating scale 3.0: application with persons with acquired apraxia of speech and aphasia[END_REF]. Total maximum score is 52, higher scores reflect greater severity, cut-off value for an AOS diagnosis ≥8 points. To assess presence, degree and type of aphasia, the Neurolinguistic Aphasia Examination (A-NING) was used [START_REF] Lindström | A-ning: neurolingvistisk afasiundersökning[END_REF]. A-NING is a standardized test and includes evaluation of seven linguistic modalities: "oral expression abilities, " "repetition, " "auditory comprehension, " "reading comprehension, " "reading aloud, " "dictation, " and :informative writing." The maximum result is 220 points, with a corresponding aphasia index of 5.0. The cut-off value for an aphasia diagnosis is 208 < (index 4.8) (45). For visual confrontation naming ability, the Boston Naming Test (BNT) was applied [START_REF] Kaplan | Boston Naming Test[END_REF]. The BNT is a frequently used assessment instrument internationally, both in research and clinical settings [START_REF] Harry | Is the Boston naming test still fit for purpose?[END_REF]. Maximum result is 60 and the cut-off value ≤47-55 based on Swedish-language norms [START_REF] Tallberg | The Boston naming test in Swedish: normative data[END_REF]. For assessment of non-verbal oral apraxia (NVOA), a screening instrument developed by Josephs et al. [START_REF] Josephs | Characterizing a neurodegenerative syndrome: primary progressive apraxia of speech[END_REF] and Botha et al. [START_REF] Botha | Nonverbal oral apraxia in primary progressive aphasia and apraxia of speech[END_REF] was applied. This 8-item protocol consists of four gestures repeated twice. Total maximum score is 32, whereas the recommended cut-off for a NVOA diagnosis is <29. To assess hand motor function, the motor domain of the Fugl-Meyer assessment for the upper extremity (FM-UE) was applied [START_REF] Fugl-Meyer | The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance[END_REF]. The FM-UE is a standard outcome measure in clinical stroke research and has shown excellent interand intrajudge reliability and construct validity [START_REF] Duncan | Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident[END_REF][START_REF] Gladstone | The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties[END_REF]. With the three reflex items excluded, maximum total score is 60 [START_REF] Woodbury | Dimensionality and construct validity of the Fugl-Meyer assessment of the upper extremity[END_REF].

Upper extremity motor impairment level was classified as severe impairment (FM-UE <19), moderate (FM-UE 19-47), and mild (FM-UE >47) [START_REF] Woodbury | Rasch analysis staging methodology to classify upper extremity movement impairment after stroke[END_REF].

Magnetic Resonance Imaging

For description of lesion size and location, brain imaging was performed with an Ingenia 3.0T MR system (www.usa.philips.com) with an 8HR head coil. High-resolution T1-weighted anatomical images were acquired using TFE 3D

Statistical Methods

The amount of recovery was defined as the percentage that a participant improved over time on a test in relation to the possible maximum improvement on that specific measure. Recovery ratio was calculated as the absolute amount of recovery (change from the subacute score, A2-A1) divided by the amount corresponding to full recovery (difference between subacute score and maximum score, max-A1). To minimize known ceiling effects, patients with results at ceiling at the initial assessment were excluded from the analysis [START_REF] Hope | Recovery after stroke: not so proportional after all?[END_REF]. To test the strength of association between recovery ratios in different domains and between behavioral score result at A1 and A2, Spearman's correlation coefficients were calculated. The non-parametric option was chosen to avoid a strong influence of possible outliers in the relatively small dataset. Between-group differences were tested using Kruskal-Wallis Test. For within-group differences, Related-Samples Wilcoxon Signed Rank Test was applied. To investigate predictors of AOS recovery, univariate regression analyses were performed. Since this data was not severely skewed, the parametric option could be allowed in order to explore the explained variance among the predictors. Statistical significance level was set at p < 0.05. A separate subgroup analysis was undertaken in participants with severe speech-language impairments at A1, since descriptions of recovery in these patients are scarce.

RESULTS

Prevalence of AOS and Aphasia at 4 Weeks After Stroke Onset

All participants with a speech-language impairment had LH lesions (n = 35). In this group, 20 participants (57%) had AOS with concomitant aphasia. Five participants (14%) had aphasia without concomitant AOS, while 10 participants (28.6%) showed no signs of AOS or aphasia. Unilateral upper motor neuron dysarthria was present in approximately half of the group with AOS and/or aphasia, but all to a mild degree that did not interfere the speech-language measurements. As seen in Table 1, a Kruskal-Wallis test showed a statistically significant difference in FM-UE score across the three participant groups (H = 7.8, p = 0.02), with a much lower median FM-UE score in the group with AOS and aphasia than in the groups with aphasia only or with no speech-language impairment. Pairwise post-hoc comparisons indicated that the only significant difference was between the group with AOS and concomitant aphasia and the group without speech-language impairment (p = 0.04).

Recovery in Speech-Language and Hand Motor Domains

Recovery was studied in 15 of the 25 LH lesioned participants presented in Table 1 with a speech-language impairment. The missing data was due to the initial design of the ProHand study, where a follow-up assessment of speech-language functions initially was not included. The second assessment occasion was instead added to the protocol 18 months after the data collection had started.

In this group (Table 3), 12 also had AOS at A1. The mean ASRS was 22.8, indicating moderate/severe AOS. Aphasia severity ranged from mild to very severe, the mean A-NING value was 93.6 (A-NING aphasia index 2.0) which corresponds to severe aphasia. The BNT scores were generally low and correlated significantly with A-NING scores. All participants with AOS had concomitant NVOA with significantly correlated severity. The initial ASRS results were also significantly correlated with results from A-NING and BNT. The mean FM-UE score was 14.9 with a median value of 2 points reflecting an almost total arm and hand paresis. FM-UE score correlated significantly with measures of NVOA but did not reach significance in relation to scores of ASRS, A-NING and BNT. No correlation was found between lesion volume and behavioral assessments (Table 4).

At A2, total results of ASRS, A-NING and FM-UE, respectively demonstrated statistically significant improvements (Related-Samples Wilcoxon Signed Rank Test; ASRS p < 0.002, A-NING and FM-UE p < 0.001). Two of the former 15 participants with aphasia showed a complete recovery while 3 still had severe to very severe aphasia. Out of the 12 participants with AOS at A1, 9 still had AOS of varying severity. The ASRS scores correlated significantly with A-NING, BNT, and NVOA results. Three participants showed a complete hand motor recovery with maximum results on the FM-UE, whereas several participants still had a severe hand motor impairment. At this timepoint, the FM-UE total score also correlated significantly with all speechlanguage results (Table 4).

Comparison of Recovery Across Domains

At a group level, the mean recovery ratios varied between 26% for FM-UE up to 48% for A-NING; for ASRS the mean recovery was 38% (Table 5). At an inter-individual level, the magnitude of the recovery varied widely in all of the applied behavioral measurements. In analyses of the relation between recovery ratios, a parallel pattern with significant correlations between hand motor and speech-language domains was found (Figure 2 and Table 6). In all domains, no significant correlations were found between lesion volume and recovery ratio (Table 6).

Prediction of AOS Recovery

Univariate linear regression analysis revealed initial A-NING total score as the strongest predictor for the magnitude of AOS recovery ratio; 84% of the variance could be explained by this factor. The initial ASRS score accounted for 52% of the variance, initial BNT score for 45%, initial NVOA for 35%, while the initial FM-UE score had no explanatory power (0.6%). The regression coefficients for lesion volume and age were also low and not statistically significant (Table 7).

Recovery in Speech-Language and Hand Motor Domains in the Group With Severe Aphasia

Separate analyses were performed in a subgroup of 7 participants (ID 1, 2, 3, 7, 8, 14, 15 in Table 3) with very severe aphasia at the initial assessment (A-NING severity index ≤0.9). At A1, all in this group had AOS, the mean ASRS total score was 28.4 indicating severe AOS. The majority also had severe NVOA, only one participant (ID 14) showed a moderate impairment. Almost no naming ability was demonstrated; one participant scored 1/60 points on BNT, the others 0/60. Except for two participants (ID 8 and 14), all in this subgroup had an almost total arm and hand paresis (FM-UE 0 or 1 point). Participant ID 8 had a FM-UE of 16, indicating severe motor impairment, ID 14 had a very discrete hand motor impairment (FM-UE = 58). The lesion volume varied from 30.7 cm 3 to maximum 317.8 cm 3 , with a mean value of 127.9 cm 3 . At A2, four participants showed aphasia recovery with A-NING scores in the moderate impairment range (A-NING severity index ≤1.9-3.8). One participant progressed from very severe to severe aphasia (A-NING severity index ≤1.0) while two participants still had very severe (global) aphasia. The same pattern was found in naming ability and the results of BNT and A-NING showed a strong correlation (rho = 0.96). All 4 participants that showed a clinically significant improvement in aphasia (i.e., moved ≥1 index in A-NING) also improved a minimum of 5 points on the ASRS, the remaining 3 participants showed very limited/no recovery. In hand motor function, the overall FM-UE result remained low (mean 16.4, median 4.0). The participant with very discrete impairment at A1 showed a complete recovery (from 58 to 60 points). Only one participant, ID 8, showed a clinically significant hand motor improvement (i.e., recovery ≥9 points in FM-UE) (57) (Figure 3).

Comparison of Recovery Across Domains in the Group With Severe Aphasia

Low recovery ratios were found in all three domains; the mean value was 30% for A-NING, 23% for ASRS and 12% for FM-UE. Higher recovery magnitudes were found in only two participants, ID 8 and ID 14, with recovery ratios around 70% in the language domain (Figure 4). A parallel pattern across language and motor domains was noticed also in this subgroup. There were significant correlations between AOS and aphasia recovery (ASRS vs. A-NING rho = 0.79 * ) and between hand motor and aphasia recovery (FM-UE vs. A-NING rho = 88 * ), while the correlation between AOS recovery and hand motor recovery did not reach significance (ASRS vs. FM-UE rho= 0.74). The lesion volume did not correlate significantly with the recovery ratio in any of the domains (Table 8).

DISCUSSION

The findings of this study provide original data on the prevalence of AOS and aphasia in post stroke patients with arm and hand motor impairment. In addition, a parallel recovery pattern for AOS, aphasia and hand motor domains across all severity levels is demonstrated. These observations add to the knowledge of AOS and its relation to motor and language functions, and provide information that may serve as a basis for future studies of post stroke recovery mechanisms.

Prevalence of AOS and Aphasia in Patients With Arm and Hand Motor Impairment

We examined 70 patients with a hand motor impairment after first ever stroke with a comprehensive speech-language assessment battery. Since the RH involvement in the speechmotor network has been discussed, most often considered to be involved in feed-back control and/or in lower levels of speech production [START_REF] Basilakos | Functional characterization of the human speech articulation network[END_REF][START_REF] Tourville | Functional parcellation of the speech production cortex[END_REF], the initial study sample also included 35 patients with RH lesions. However, no participant with AOS or aphasia after a RH lesion was found. Instead, the majority (71%) of the participants with a left hemisphere lesion had aphasia. Over half of the group (57%) also suffered from AOS, several with moderate to severe impairments. The co-occurrence of AOS and aphasia was high; all participants with AOS had aphasia and 80% of all participants with aphasia also had AOS. These findings support the current view regarding the common occurrence of AOS with concomitant aphasia after a LH stroke ( 2) and add new quantitative information to the limited data regarding the prevalence of AOS in early post stroke patients.

Similar Speech-Language and Motor Recovery Post-stroke

In accordance with our hypothesis, a parallel recovery pattern was found between speech-language and hand motor domains. This pattern was also apparent in severely impaired patients. Neither recovery ratio nor severity of impairment co-varied with lesion volume, coherent with recent reports on hand motor recovery [START_REF] Pennati | Recovery and prediction of dynamic precision grip force control after stroke[END_REF].

In speech-language domains, strong correlations were found both between the level of impairment in all measurements of behavioral functions and of the magnitude of recovery during the first 6 months. Consistent with earlier studies, e.g., Dronkers et al. [START_REF] Dronkers | A new brain region for coordinating speech articulation[END_REF] and New et al. [START_REF] New | Altered resting-state network connectivity in stroke patients with and without apraxia of speech[END_REF], a frequent co-occurrence of NVOA and AOS was found. The severity of AOS and NVOA was significantly correlated, which also is in accordance with findings by Botha et al. [START_REF] Botha | Nonverbal oral apraxia in primary progressive aphasia and apraxia of speech[END_REF] in studies of patients with primary progressive aphasia and/or primary progressive AOS. In contrast to earlier findings of a disparate AOS and aphasia recovery [START_REF] Baumgaertner | Diffrential recovery of aphasia and apraxia of speech in an adolescent after infarction of the left frontal lobe: longitudinal behavioral and fMRI data[END_REF][START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF], a parallel pattern was noticed between recovery from AOS and aphasia. The covariance was more apparent between the recovery ratio of AOS and the recovery ratios from the broad aphasia score of A-NING, covering all language modalities, compared to recovery in naming ability measured by BNT that did not reach statistical significance. A correlated parallel pattern was also apparent for NVOA recovery ratio in relation to recovery from both AOS and aphasia.

Significantly correlated recovery ratios were also found between hand motor function compared to recovery of speech and language functions. This parallel pattern was also observed and confirmed in the pairwise correlation analyses between behavioral total score results, with a tendency of becoming even more strongly correlated at A2 (Table 4). Even though several earlier studies have suggested a link between motor and language abilities [e.g., see [START_REF] Harnish | Language changes coincide with motor and fMRI changes following upper extremity motor therapy for hemiparesis: a brief report[END_REF][START_REF] Meinzer | Electrical stimulation of the motor cortex enhances treatment outcome in post-stroke aphasia[END_REF][START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF][START_REF] Wortman-Jutt | Poststroke aphasia rehabilitation: why all talk and no action? Neurorehabil[END_REF]] studies focusing on simultaneous motor and language impairments after stroke and on concurrent recovery are scarce. To our knowledge, the only study so far that investigated longitudinal recovery after stroke in multiple domains is Ramsey et al. [START_REF] Ramsey | Behavioural clusters and predictors of performance during recovery from stroke[END_REF], who reported similar patterns of recovery across motor and cognitive domains indicative of common underlying mechanisms. Beside the impact of anatomical proximity and changes in important white matter tracts to the recovery process, the authors also reported correlated recovery patterns in specific clusters of behavioral deficits with the underlying lesion areas distributed in distant cortical regions, often in different vascular territories [START_REF] Ramsey | Behavioural clusters and predictors of performance during recovery from stroke[END_REF]. The parallel recovery pattern that was found in our behavioral hand motor and speechlanguage data extends these findings, indicating that shared plasticity mechanisms driving recovery across language and motor domains also apply to AOS recovery and to recovery in patients with severe speech-language impairments. Our observations do not clarify whether the parallel recovery pattern is specific in regard to these functions; if these findings are due to shared neural correlates for hand motor and speech-language functions or merely reflects anatomical proximity and vascular factors, or may depend upon other general brain-wide plasticity mechanisms in distributed networks. These questions need to be investigated in future studies designed for that purpose. Brain plasticity is operating at different levels, ranging from molecular, cellular and systemic to behavioral/treatment-induced aspects [START_REF] Zhao | Enhancing endogenous capacity to repair a strokedamaged brain: an evolving field for stroke research[END_REF] and has been studied both in speech-language domains [for a review, see Kiran and Thompson (37)] and in motor domains [for a review, see Cramer and Riley [START_REF] Cramer | Neuroplasticity and brain repair after stroke[END_REF] and Raffin and Hummel

]. In speech and language research, several functional and structural neuroimaging studies have shown that both residual and new neural mechanisms can be recruited in the recovery process after stroke [e.g., see [START_REF] Crinion | Neuroimaging in aphasia treatment research: quantifying brain lesions after stroke[END_REF][START_REF] Hartwigsen | Neuroimaging of stroke recovery from aphasiainsights into plasticity of the human language network[END_REF]]. Genetic [START_REF] Stewart | Genetic variation and neuroplasticity[END_REF] and biological [START_REF] Nguyen | Acute routine leukocyte and neutrophil counts are predictive of poststroke recovery at 3 and 12 months poststroke: an exploratory study[END_REF] factors also likely contribute to recovery across domains. The role of neuroplasticity in speech and language recovery is however not fully understood yet [START_REF] Crosson | Neuroplasticity and aphasia treatments: new approaches for an old problem[END_REF].

In our data, there were no signs of a lower aphasia recovery during the first 6 months after stroke in comparison to recovery of hand motor function. Instead, the opposite tendency was seen with several participants showing more improvement in language domains than in hand motor domains. Primaßin et al. [START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF] stated that there is no prospect of language recovery for stroke patients with no motor improvements. Our results do not unequivocally support this statement. In line with this postulate, the two participants in the very severe aphasia subgroup who reached recovery ratios around 70% in speech-language domains were the only ones in this subgroup without an almost total hand paresis at A1 (ID 8 and 14 in Figure 3). These two divergent participants also improved in hand motor function (ID 14 with initially a very mild impairment showed a complete recover; for ID 8 the recovery ratio for FM-UE was 41%), while the others in this subgroup showed a very discrete hand motor improvement (mean recovery ratio 6%). However, other participants in this study, several with an almost total hand paresis at the initial stage, displayed a limited hand motor recovery but showed a stronger recovery in speech and language domains. The lack of correlation between initial FM-UE score and ASRS recovery also indicates that speech-language recovery is not well-predicted by degree of initial motor impairment (Table 6). These observations do not support the straightforward proposition by Primaßin et al. [START_REF] Primaßin | Determinants of Concurrent motor and language recovery during intensive therapy in chronic stroke patients: four single-case studies[END_REF] about the role of the motor cortex in speech and language processes. Instead, our findings show that the role of the motor cortex in speech-language networks still remains unclear and that its specific involvement in speech and language processes needs further examination [START_REF] Meinzer | Electrical stimulation of the motor cortex enhances treatment outcome in post-stroke aphasia[END_REF][START_REF] Price | Predicting language outcome and recovery after stroke: the PLORAS system[END_REF].

It could be argued that although a correlated recovery pattern was found, the differences in the relative amount of change between domains could imply that speech-language recovery exploited resources at the expense of hand motor functions. Admittedly, such an interpretation cannot be totally ruled out. However, the overall pattern with correlated recovery ratios at the individual level, supported by the even more significantly correlated behavioral score result at the follow-up compared to the initial assessment (in Table 4), suggests no signs that there was a competition for resources. It should be acknowledged that beside the shared plasticity mechanisms suggested to be present, other factors undoubtedly also have an impact on the recovery process. As seen in Table 2 and Figure 1, several of the participants suffered from deep subcortical lesions, affecting important white matter tracts. It is known that lesions to specific white fiber tract often results in poorer recovery in different domains, such as the integrity of the arcuate fasciculus for speechlanguage function [START_REF] Fridriksson | Damage to the anterior arcuate fasciculus predicts non-fluent speech production in aphasia[END_REF][START_REF] Basilakos | Regional white matter damage predicts speech fluency in chronic post-stroke aphasia[END_REF] and the corticospinal tract for hand motor function [START_REF] Birchenall | Individual recovery profiles of manual dexterity, and relation to corticospinal lesion load and excitability after stroke -a longitudinal pilot study[END_REF][START_REF] Feng | Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes[END_REF]. The role of white matter damage in relation to shared plasticity mechanisms is beyond the scope of this study and needs to be addressed in studies designed for that purpose with a larger sample of patients.

Prediction of AOS Recovery

Recovery of AOS was not predictable based on lesion volume, age or the initial FM-UE score result. While the initial impairment level measured by the ASRS could explain about 50% of the variation, the strongest predictor for AOS recovery was the total score at the initial A-NING assessment. This score summarizes language production and comprehension in both spoken and written modalities. Why would such a global measure of language performance predict the recovery of AOS, defined as a motor speech disorder, better than the initial ASRS score? As one possible explanation, our finding could be interpreted by considering contemporary neurocomputational speech production models, e.g., the Gradient Order DIVA (GODIVA) model [START_REF] Bohland | Neural representations and mechanisms for the performance of simple speech sequences[END_REF][START_REF] Miller | Modelling speech motor programming and apraxia of speech in the DIVA/GODIVA neurocomputational framework[END_REF], which includes both feed-forward and feedback processes. Although AOS traditionally refers to motor planning and programming processes, a clear boundary between linguistic and motor speech processes is not admitted [START_REF] Glize | Motor evoked potential of upper-limbs is predictive of aphasia recovery[END_REF]. Since speech production is a sensorimotor behavior that depends upon monitoring of input from one or more sensory systems [START_REF] Blumstein | Neurobiology of Speech Production: Perspective from Neuropsychology and Neurolinguistics[END_REF], improvement of planning and programming speech motor movements requires that the underlying linguistic and phonological representation of the target word/phrase is intact. This theoretical argument is supported by (clinical) findings that the severity of an accompanying aphasia may hinder successful AOS treatment [START_REF] Duffy | Motor Speech Disorders: Substrates, Differential Diagnosis, and Management[END_REF]. Early arm and hand motor impairment was not predictive of ASRS recovery (Table 7). As can be seen in Table 3, more than half (60%) of the participants in this study sample had a very severe hand motor impairment with zero or near zero points on the FM-UE at the first assessment. This group showed a substantial variability in ASRS recovery and outcome. Still, when comparing recovery of ASRS and FM-UE at an inter-individual level (Figure 2B), these follow the same recovery pattern; i.e., participants with a limited amount of change in FM-UE also showed a just as limited amount of change in ASRS, and participants with a larger amount of change in FM-UE also recovered accordingly in ASRS.

LIMITATIONS

This study has several limitations. First, it should be noted that the results on prevalence of AOS and aphasia stem from a group of patients with initial upper limb motor impairment. Although the presence of a right sided hemiparesis is considered as very common in post stroke patients with non-fluent aphasia and AOS, our results do not hold for the general stroke population. Secondly, all participants included in the recovery analyses received team-based rehabilitation, with three to five sessions a week of physiotherapy and speech-language therapy during the period. The exact dose and frequency of therapy sessions extent were however not controlled for in this study. However, no participant received any form of specific high intensive therapy, e.g., participated in a period of Intensive Language Action Therapy [START_REF] Difrancesco | Intensive languageaction therapy (ILAT): the methods[END_REF] or in Constraint-Induced Movement Therapy for the upper extremity [START_REF] Wolf | Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial[END_REF]. Thirdly, the recovery in AOS and aphasia was described in a small sub-sample of patients which inevitably limits the statistical strength in the performed analyses. It should also be acknowledged that the sensitivity to change may differ among the applied behavioral assessment instruments. The results should therefore be interpreted with some caution and recovery across speech-language and hand motor domains needs to be further investigated in a larger patient sample. Finally, we acknowledge that our findings do not offer any explanatory answers. Instead, we hope that our observations may serve as a basis for future studies in this area; to identify important prognostic factors and for improvement of targeted treatment interventions for patients with AOS.

CONCLUSIONS

Despite the limited sample size, original longitudinal data was provided including descriptions of both motor and speech/language impairments. Recovery was similar across speech-language and motor domains, even in patients with severe impairment, supporting the shared recovery hypothesis. Future studies including neuroimaging and/or biological assays will permit to gain further knowledge on the shared neural substrates and mechanisms involved in recovery across motor and speechlanguage domains.

FIGURE 1 |

 1 FIGURE 1 | Lesion overlap map (n = 14) (1 missing data). All lesions were in LH MCA territory. Lesion overlap was greatest in subcortical white matter in striatocapsular region.

FIGURE 2 |

 2 FIGURE 2 | Relation recovery ratios. (A) AOS recovery ratio (ASRS) correlated with recovery ratio of aphasia (A-NING) (Rho = 0.80, p < 0.01). (B) AOS recovery ratio (ASRS) correlated with recovery ratio of hand motor impairment (FM-UE) (Rho = 0.78, p < 0.01). (C) Aphasia recovery (A-NING) correlated with recovery of hand motor impairment (FM-UE) (Rho = 0.80, p < 0.01).

FIGURE 3 |

 3 FIGURE 3 | Total score changes between A1 and A2 in ASRS, FM-UE, and A-NING. In ASRS, higher scores reflect greater severity.

TABLE 1 |

 1 Participant characteristics at A1, LH lesioned (n = 35).SLI, speech-language impairment, Aphasia diagnoses according to A-NING test score, AOS diagnoses according to ASRS, Percent is calculated as proportion within the respective group. a Kruskal Wallis Test, *Significant at p < 0.05, Bonferroni corrected pairwise comparison.AOS + Aphasia compared no SLI p = 0.04, Aphasia only compared to AOS + Aphasia p = 0.17, Aphasia only compared to no SLI p = 1.0.

	Variables	Measures	All	No SLI	Aphasia	Aphasia and AOS	Group difference
				(n = 35)	(n = 10)	(n = 5)	(n = 20)	(sig.)
	Age	Mean (SD)		53.3 (8.4)	57.7 (4.7)	51.0 (4.9)	51.6 (9.8)	0.134 a
		Median		54	58	53	53
	Sex	N (%)	Females	6 (17)	2 (20)	0 (0)	4 (20)
			Males	29 (83)	8 (80)	5 (100)	16 (80)
	Lesion Volume cm 3	Mean (SD)		94.9 (102.8)	49.4 (104.3)	88.6 (111.6)	120.9 (96.8)	0.051 a
		Median		45.5	4.7	25.6	117.1
	Stroke type	N (%)	Ischemic	24 (69)	7 (70)	4 (80)	13 (65)
			Hemorrhage	11 (31)	3 (30)	1 (20)	7 (35)
	FM-UE total (60p)	Mean (SD)		25.1 (24.9)	39.7 (22.3)	37.6 (27.0)	14.7 (21.5)	0.020 a *
		Median		16	51.5	57.0	2.0

TABLE 2 |

 2 Participant characteristics and lesion descriptives (n = 15).

	ID	Sex	Age	Stroke Les. vol.cm 3 Lesion location
						Vascular territory,
						involved region
	1	F	52	I	30.6	MCA, C and SC
	2	M	65	I	121.9	MCA, C and SC
	3	M	57	I	32.9	MCA, C and SC
	4	F	39	I	117.1	MCA, C and SC
	5	M	31	H	167.7	MCA, C and SC
	6	M	50	I	115.1	MCA, C and SC
	7	M	45	H	143.2	MCA, C and SC
	8	M	39	I	175.6	MCA, C and SC
	9	F	62	I	*	MCA, SC
	10	M	53	I	270.2	MCA, C and SC
	11	M	61	I	160.2	MCA, C and SC
	12	M	44	I	122.7	MCA, C and SC
	13	M	54	H	25.6	MCA, C and SC
	14	M	54	I	73.1	MCA, C and SC
	15	M	48	I	317.8	MCA, C and SC
	Mean(SD)		50.3 (9.4)		133.8 (84.7)
	Median		52		122.3
	F, female; M, male; I, ischemic stroke; H, hemorrhagic stroke; MCA, middle cerebral artery;
	C, cortical; SC, subcortical.

*Data missing.

TABLE 3 |

 3 Behavioral measurements, total score results, assessment 1 (A1) and 2 (A2) (n = 15).

	ID	ASRS		A-NING		FM-UE		BNT		NVOA
		A1	A2	A1	A2	A1	A2	A1	A2	A1	A2
	1	44	35	35	95	0	4	0	21	16	17
	2	18	15	36	46	0	2	1	2	6	7
	3	23	15	26	86	1	9	0	5	2	9
	4	22	14	70	120	2	10	12	38	17	20
	5	17	9	134	202	2	29	49	51	18	30
	6	17	5	160	200	1	7	15	48	21	32
	7	29	28	22	25	0	2	0	0	0	2
	8	28	18	38	173	16	34	0	44	5	21
	9	10	2	207	216	2	23	45	50	27	32
	10	6	2	199	218	57	60	57	57	29	32
	11	9	2	150	190	21	51	17	43	12	20
	12	7	7	118	192	7	40	32	42	26	27
	13	7	3	181	206	57	60	52	52	23	32
	14	32	23	18	133	58	60	0	49	26	30
	15	25	24	10	25	0	4	0	0	0	0
	Mean	19.6	13.5	93.6	141.8	14.9	26.3	18.7	33.5	15.2	20.7
	(SD)	10.9	10.5	72.8	71.0	22.8	23.0	22.1	21.4	10.4	11.5
	Median	18	14	70	173	2	23	12	43	17	21

ASRS, Apraxia of speech rating scale, max 52, cut-off value for AOS ≥ 8 points; A-NING, A-ning neurolinguistic aphasia examination, total score results and severity index classification: very severe/Global ≤ 42 (INDEX ≤ 0.9), Severe ≤ 82 (INDEX ≤ 1.8), Moderate ≤ 170 (INDEX ≤ 3,8), Moderate/mild ≤ 192 (INDEX ≤ 4.4), Mild ≤ 208 (INDEX ≤ 4.7), No Aphasia > 208 (INDEX > 4.8), maximum 220 points; FM-UE, upper extremity motor portion of the Fugl-Meyer scale, max 60, cut-off level severe-moderate imp. 19 ± 2 points, moderate-mild 47 ± 2 points; BNT, Boston naming test, max 60, cut-off value ≤ 47-55 based on normative data (education); NVOA, non verbal oral apraxia protocol, max 32, cut-off value for NVOA diagnosis < 29 points.

TABLE 4 |

 4 Correlation between assessments at A1 and A2 (n = 15).

	Measurements	Spearman's rho	Sig.
	Assessment 1
	ASRS vs.	A-NING	-0.83**	<0.001
		FM-UE	-0.46	0.08
		NVOA	-0.58*	0.02
		BNT	-0.92**	<0.001
		Lesion volume	-0.18	0.55
	FM-UE vs.	A-NING	0.45	0.10
		NVOA	0.64*	0.01
		BNT	0.48	0.07
		Lesion volume	-0.01	0.96
	A-NING vs.	BNT	0.89**	<0.001
		NVOA	0.68**	0.005
		Lesion volume	0.02	0.95
	NVOA vs.	BNT	0.72**	0.003
		Lesion volume	-0.21	0.48
	BNT vs.	Lesion volume	0.11	0.71
	Assessment 2
	ASRS vs.	A-NING	-0.83**	<0.001
		FM-UE	-0.57*	0.03
		NVOA	-0.71**	0.003
		BNT	-0.68**	0.005
		Lesion volume	-0.09	0.76
	FM-UE vs.	A-NING	0.71**	0.003
		NVOA	0.69**	0.004
		BNT	0.80**	<0.001
		Lesion volume	-0.01	0.97
	A-NING vs.	BNT	0.94**	<0.001
		NVOA	0.94**	<0.001
		Lesion volume	0.01	0.98
	NVOA vs.	BNT	0.94**	<0.001
		Lesion volume	-0.16	0.59
	BNT vs.	Lesion volume	-0.04	0.90

*Significant at p < 0.05, **Significant at p < 0.01.

TABLE 5 |

 5 Descriptive statistics, recovery ratios.

	Mean (SD)	48 (29.4)	38 (26.3)	38 (30.5)	45 (39.3)	26 (25.2)
	Median	57	35	35	31	14
	Min.	1	4	0	0	3
	Max.	90	80	82	100	77

A-NING REC. ASRS REC. BNT REC. NVOA REC. FM-UE REC.

(%) (n = 14) (%) (n = 12) (%) (n = 12) (%) (n = 14) (%) (n = 12)

Total score results at ceiling at A1 excluded in the analyses; A-NING > 200; ASRS < 9; BNT 2 points below the normative value, NVOA ≥ 28; FM-UE ≥ 57.

TABLE 6 |

 6 Relation between recovery ratios.

		Measurements	Spearman's rho	Sig.
	ASRS vs.	A-NING	0.80**	0.003
		FM-UE	0.78**	0.004
		NVOA	0.84**	0.001
		BNT	0.55	0.055
		Lesion volume	-0.005	0.989
	FM-UE vs.	A-NING	0.80**	0.003
		NVOA	0.59*	0.045
		BNT	0.65*	0.029
		Lesion volume	0.25	0.464
	A-NING vs.	BNT	0.84**	0.001
		NVOA	0.74**	0.004
		Lesion volume	0.25	0.391
	NVOA vs.	BNT	0.72**	0.008
		Lesion volume	-0.28	0.357
	BNT vs.	Lesion volume	-0.20	0.563
	*Significant at p < 0.05, **Significant at p < 0.01.

TABLE 7 |

 7 Univariate linear regression analyses (n = 15).

	Outcome	Predictors	Explained	p-value
			variance
			/R square
	ASRS recovery ratio	A-NING total at A1	0.836**	<0.0001
		ASRS total at A1	0.525**	0.008
		BNT total at A1	0.448*	0.017
		NVOA total score at A1	0.353*	0.04
		Age	0.047	0.497
		FM-UE at A1	0.006	0.818
		Lesion volume cm 3	0.001	0.942

*Significant at p < 0.05, **Significant at p < 0.01.

TABLE 8 |

 8 Relation recovery ratios in the group with severe aphasia (n = 7).

	Measurements	Spearman's rho	Sig.
	ASRS vs.	A-NING	0.79*	0.04
		FM-UE	0.74	0.09
		NVOA	0.83*	0.02
		BNT	0.81*	0.03
		Lesion vol. cm 3	-0.36	0.43
	FM-UE vs.	A-NING	0.88*	0.02
		NVOA	0.69	0.13
		BNT	0.72	0.11
		Lesion vol. cm 3	0.09	0.87
	A-NING vs.	BNT	0.90**	0.006
		NVOA	0.72	0.07
		Lesion vol. cm 3	-0.18	0.70
	NVOA vs.	BNT	0.84*	0.02
		Lesion vol. cm 3	-0.32	0.48
	BNT vs.	Lesion vol. cm 3	-0.43	0.33
	*Significant at p < 0.05, ** Significant at p < 0.01.

Frontiers in Neurology | www.frontiersin.org

March 2021 | Volume 12 | Article 634065

ACKNOWLEDGMENTS

The authors wish to express their sincere gratitude to all the participating patients.

FUNDING

This work was supported by the Promobilia Foundation and by funding from Karolinska Institutet and Karolinska University Hospital.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Regional Ethical Review Board in Stockholm. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.