The aim of this study was to develop new strategies based on virtual reality that can provide additional information to clinicians for the rehabilitation assessment. Virtual reality system Toyra has been used to record kinematic information of 15 patients with cervical spinal cord injury (SCI) while performing evaluation sessions using the mentioned system. Positive correlation, with a moderate and very strong association, has been found between clinical scales and kinematic data, considering only the subscales more closely related to the upper limb function. A set of metrics was defined combining these kinematic data to obtain parameters of reaching amplitude, joint amplitude, agility, accuracy, and repeatability during the evaluation sessions of the virtual reality system Toyra. Strong and moderate correlations have been also found between the metrics reaching and joint amplitude and the clinical scales. It has been estimated that the prevalence of spinal cord injury (SCI) is 223–755 per million inhabitants, with an incidence of 10.4–83 per million inhabitants per year [In patients with tetraplegia, the arm and hand function is affected to a different degree, depending on the level and severity of the injury .Several studies have shown that the improvement in upper extremity function is one of the greatest needs in patients with tetraplegia . In thisVirtual reality (VR) has emerged in the rehabilitation context in an effort to promote task oriented and repetitive movement training of motor skills while using a variety of stimulating environments . This apThe aim of VR is to create a feeling of immersion within the simulated environment so that the patient's behaviour during the game resembles as much as possible the one that he would have in the real world.There are different technologies of motion capture that permit the transfer of the actual movement of the patient to a virtual environment. One of them is the inertial measurement technology. There are several advantages of using inertial measurement systems (IMUs) as motion capture systems for VR applications, since they are compact, light, resistant to environmental interference, and easy to wear.VR technology increases the range of possible tasks, partly automating and quantifying therapy procedures and improving patient motivation using real-time task evaluation and reward . It alsoEvaluation of the SCI patient's functional status is usually carried out by means of clinical scales, although they have a high subjective component depending on the observer who scores the test. Therefore, a better understanding of human movement requires more objective testing and accurate analysis of motion to describe the arm movements more precisely and specifically during functional testing. Kinematic analysis is one such method .Clinical scales are not very sensitive to slight improvements in functionality and also they are not able to establish the biomechanical characteristics that explain the clinical changes in the scores obtained by the patients during their rehabilitation. Thus, it is important to find the kinematic parameters that correlate with clinical scales. In a previous study from our group, correlations were already found between kinematic data and clinical scales . These sIt is important to underline that kinematic data by themselves are not always sufficiently clear and understandable for clinicians in order to reliably evaluate a patient. However, combining them to obtain new metrics could enhance their potentiality as tools for physical assessment.The objectives of the present study are (i) to analyze the correlations between kinematic data after performing upper limb tasks included in the VR system Toyra and upper limb clinical scales; (ii) to define kinematic metrics based on data recorded by the VR system Toyra that could offer additional information to clinicians; and (iii) to analyze the correlation between the defined kinematic metrics and clinical scales by applying them to a group of 15 patients with tetraplegia.Quantification of upper extremity movements has been researched since many years ago. One of the first studies in this field was carried out by Fitts in 1954 with the aim of analyzing the speed-accuracy trade-off and, as a result, calculating the performance and an index of difficulty of a task from three parameters: the time spent on performing the movement, the distance, and the size of the object to be reached .The interest in obtaining parameters that could provide relevant information to clinicians from the quantification of the upper extremity movements is relatively recent. To this aim, there are some studies that analyzed the movements performed by patients with neurological disorders during reaching tasks and also while drawing –11. TherSome of the kinematic parameters calculated to obtain information that could be clinically relevant are the time spent on the task, velocity, and range of motion during the movement , 13, 14;Efficacy: the percentage of the task successfully completed by patient's voluntary movement.Accuracy: the spatial deviation between the path followed by the patient's hand and the theoretical trajectory (in other studies it has been named “trajectory error”).Efficiency: it is a measure of the ratio between the length of the hand's path during the movement and the length of the theoretical trajectory.Smoothness: it is computed from the speed profile of the hand during the movement as the number of peaks.These metrics are more easily applicable to reaching movements in which the theoretical hand's path is considered as the straight line between the starting point and the target location.In neurological rehabilitation, the assessment of upper limb motor recovery should include smoothness, efficacy, and efficiency of the movement . In thisMost of the metrics proposed are a measure of the error or deviation between two variables. So, for example, smoothness as the number of peaks is a measure of error, since a higher number of peaks are related to a less smooth movement. The same occurs in accuracy and efficiency metrics, in which a decrease in these metrics indicates an improvement in motor performance for a functional task. For that reason, it seems necessary to obtain parameters that could be directly proportional to the patient's functional status .There are plenty of scales in the literature which pretend to assess the patients in order to detect functional changes during the upper limb rehabilitation process . These aThe upper extremity motor function tests are classified in the following categories: (1) strength tests; (2) functional tests; and (3) ADL tests . In thisThe evaluation of key muscle groups is important to identify the motor level in patients with tetraplegia. Motor index gives a rapid overall indication of a patient's limb impairment using principal components analysis (Hotelling's method), where the large number of movements was reduced to one movement at each joint which represented the general strength of movement at the joint .Jebsen Taylor Test of Hand Function  is a scaThe Action Research Arm (ARA) test provides a rapid yet reliable and standardized performance test appropriate for use in assessing recovery of upper limb but it is solely used in stroke patients.The Fugl-Meyer Assessment (FMA) was developed to measure sensorimotor stroke recovery based on Twitchell and Brunnstrom's concept of sequential stages of motor return in patients with hemiplegic stroke .The Motor Activity Log (MAL) is a scripted, structured interview that was developed by Taub et al. to measure the effects of constraint-induced movement (CI) therapy on use of the more-impaired arm outside the laboratory in individuals with stroke .Two of the most used ADL evaluations for patients with tetraplegia are the functional independence measure (FIM) and the spinal cord independence measure II (SCIM II). These tests are validated and reliable and show strong correlation with each other .The purpose of the FIM is the measurement of the severity of the patient's disability and the outcomes of medical rehabilitation in patients. The FIM has a good clinical interrater agreement in patients undergoing inpatient medical rehabilitation (ICC = 0.97). FIM scores were significantly lower in complete C4 tetraplegics than in C6 tetraplegics, which indicated that the FIM is sensitive enough to differentiate between different levels of injury .r = 0.98). Besides, the sensitivity of the SCIM is higher than the sensitivity of the FIM, showing in patients with tetraplegia that this scale missed 22% of the functional changes detected by the SCIM [The SCIM scale was developed specifically for SCI persons in order to make the functional assessments of persons with paraplegia or tetraplegia more sensitive to changes. The SCIM has a good interrater reliability , has been used. In this application, 5 inertial sensors were located on the head, trunk, arm, forearm, and hand. The placement of the sensors can be seen in A biomechanical model was developed, previously reported, based on inertial sensor data and upper limb (UL) anthropometric data. The MTx includes triaxis accelerometers, gyroscopes, and magnetometers. As long as the inertial sensors only provide information on the orientation of each body segment, a biomechanical model is required to calculate the angular magnitudes of clinical relevance on the basis of each orientation. The kinematic model used was based on the Euler method; thus the results depend on the sequence of rotations used. The kinematic chain proposed in this model consists of 7 DoF: three in the shoulder joint ; two in the elbow joint (flexion-extension and pronation-supination); and two in the wrist . More details about this biomechanical model applied here have been previously described .The kinematic assessment protocol consists of the execution of an Evaluation Session with the VR System Toyra. This session comprises 14 exercises whose principal objective is to assess the patient's functional capacity, based on the record of the kinematic variables during the execution of analytical movements of the UL joints in each of its degrees of freedom. The same therapist carried out the Evaluation Sessions on all patients in order to minimize the errors due to the different placements of the sensors by different therapists. In Joint ranges of motion (ROM) of shoulder, elbow, and wrist were analysed with the mathematics software tool MATLAB , thus obtaining 14 different kinematic variables: step-by-step shoulder abduction (AbdshoulderS), complete shoulder abduction (AbdshoulderC), step-by-step shoulder flexion (FlexshoulderS), complete shoulder flexion (FlexshoulderC), shoulder rotation (Rotshoulder), step-by-step elbow flexion (FlexelbowS), complete elbow flexion (FlexelbowC), elbow extension (Extelbow), elbow supination (Supelbow), elbow pronation (Proelbow), wrist extension (Extwrist), wrist flexion (Flexwrist), wrist radial deviation (Raddevwrist), and wrist ulnar deviation (Uldevwrist). The “step-by-step” variables have been measured during exercises in which the goals that the patients have to reach appear on the screen sequentially from the bottom to the top of the screen in such a way that they have to perform discrete movements and stay in the object for one second, approximately, thus requiring a certain degree of control of the muscles involved in this movement, whereas for the “complete” variables, all goals are displayed at the same time, so that the patients perform a continuous trajectory. The reason to measure separately these two kinds of variables is that “step-by-step” movements require holding the arm in a fixed position, so that the patient needs to exert the task with greater control movement. Depending on the level of SCI, some patients can be able to perform complete movements but not the step-by-step ones.Ranges of Motion (ROM) have been calculated from the 14 kinematic variables previously mentioned as the difference between the maximum and the minimum value reached by the patients during each specific exercise.Five different metrics have been defined based on the kinematic data obtained during the Toyra sessions.(i) Joint Amplitude. It has been defined as the sum of the ROMs obtained by a patient normalized by the corresponding ROM obtained by a healthy subject, defined as “ideal ROM”:i (°) = degrees covered by the joint under study  and ki = weighting coefficients of the exercises, chosen to give more importance to the ones that are more linked with the motor abilities of the patient.(ii) Reaching Amplitude. It has been defined as the range that the patient is able to reach at the three different axes . The X-axis has been established horizontally, parallel to the screen, the Y-axis horizontally, perpendicular to the screen, and the Z-axis is vertical, parallel to the screen..It is expected that, as long as a patient with SCI is able to reach further the objects that surround him, he would get more autonomy and functionalitykj = weighting coefficient to assign to each axis a different contribution to the total reach amplitude and hji = trajectory of the hand's position at each axis j for each exercise i carried out by the patient. Ideal hji = trajectory of the hand's position at each axis j for each exercise i carried out by a healthy subject.It is calculated at each axis as the difference between the maximum and the minimum value of the position of the patient's hand, getting a range of reaching for each exercise while the patient is carrying out the three-dimensional movements required by the task. Then, these ranges of reaching are summed and normalized by the sum of ranges obtained by a healthy subject. Finally, the three values obtained for each of the three axes are weighted according to their contribution:kj , it is possible to compute the reaching amplitude separately for each direction.Depending on the value assigned to (iii) Accuracy. It has been calculated considering 2 parameters: mean distance from the trajectory performed by the patient's hand to the ideal trajectory of the hand performed by a healthy subject (dmean) and the maximum distance between these 2 trajectories (dmax). Considerdmean will not be affected to a great extent by these peaks, so that dmean ≪ dmax, and thus the penalization for the accuracy would be approximately 2dmean.dmean will be affected by these values, so that dmean will increase. Consider as an example an extreme case in which there were so many peaks of deviation that dmean ≈ dmax; then the penalization for the accuracy would be 4dmean, much higher than in the previous case.However, if there are a lot of peaks of deviation, (iv) Agility. It has been considered that an agile movement should not only be fast but also precise. To this aim, this metric takes into consideration three parameters: accuracy (as defined in the previous metric), angular velocity, and time needed to execute the task. Considerdimean (m) = mean distance from the trajectory performed by the patient's hand to the ideal trajectory of the hand performed by a healthy subject, dimax (m) = maximum distance between the trajectory performed by the patient's hand and the ideal trajectory of the hand performed by a healthy subject, vimax (°/s) = maximum angular velocity of the joint under study in each exercise, vimean (°/s) = mean angular velocity of the joint under study in each exercise, ti (s) = time spent by the patient on performing the exercise i, and tideal (s) = time spent by a healthy subject on performing the exercise i.In order to obtain values in percentages, as in the previous metrics, accuracy has been normalized by the value of accuracy obtained by a healthy subject:The first term of the agility penalization is the one regarding the accuracy error and it has been already explained in the previous metric.The second term is regarding angular velocity. A very high maximum angular velocity is considered as a penalization, unless the mean velocity is also high. The reason to calculate it in this way is that patients with a badly preserved functionality will carry out the exercises quite slowly, obtaining a low mean angular velocity, but they will also carry out uncontrolled movements, for example, dropping the arm, thus getting a high maximum angular velocity. Therefore, it is important to evaluate the relationship between the maximum and the mean angular velocity, not only one of them separately.The third term takes into account the time spent by the patient on performing the exercise in relation with the time spent by a healthy subject on performing the same exercise.In order to obtain values in percentages, as in the previous metrics, agility has been normalized by the value of accuracy obtained by a healthy subject:(v) Repeatability. It computes the inverse of the area comprised between the upper and the lower envelopes of the repetitions of the same movement during a session:Ai = area comprised between the upper and the lower envelopes of the repetitions of the exercise I and k, k0 = normalizing coefficients used to adjust the scale. Here k = 1000 and k0 have been used. nrep = number of repetitions for each exercise .Ai has been computed by calculating the upper and the lower envelopes along time of all repetitions of the kinematic variable corresponding to exercise i. For example, for the first exercise, shoulder abduction curve along time has been used, as it can be seen in Figures For this metric, only exercises 1 to 8 have been used. They are step-by-step shoulder abduction, complete shoulder abduction, step-by-step shoulder flexion, complete shoulder flexion, step-by-step elbow flexion, complete elbow flexion, elbow extension, and shoulder rotation. These exercises are the ones that require the patient to perform a determined trajectory to accomplish the task, so the trajectories of different repetitions should be similar if the task has been correctly executed. Area nrep) because the area tends to increase with the number of repetitions used.Area comprised in each exercise is being weighted by the number of repetitions  participated in the study. Subject's demographic and clinical characteristics are shown in Eligible participants met the following criteria: (1) at least 18 years of age; (2) less than 12 months from the injury; (3) motor complete spinal cord injury according to the ASIA's impairment scale at the level of C5 to C8 (A-B ASIA level ); (4) noSubjects remained seated in their own wheelchair in front of the screen. A total of five MTx IMUs were used to capture movements of the dominant UL, wirelessly connected (Bluetooth) to a computer via a digital data bus (Master Xbus), which was responsible for the synchronization, data collection, and transmission. The IMUs were strategically placed on the trunk, the back of the head, the arm, the forearm, and the hand . Each suAs it has been described in the “new kinematic metrics” section, some of the metrics require some data recorded from healthy subjects in order to compare the results of the metrics with a reference value, thus yielding a final value expressed in percentage with respect to the healthy reference. In order to obtain these reference values, a group of five healthy subjects  was previously registered. The following parameters were extracted from the healthy subjects and then averaged to obtain the reference values: ROMs, trajectories, time spent on each exercise, and absolute value for the metrics.Neurological examinations of all the patients were performed according to the ASIA standards . The funThe second assessment scale was the UL part of motor index scale (UL MI), which assesses power and range of the following movements: shoulder abduction, elbow flexion, and pinch between the thumb and index finger. The total score is rated between 0 (no movement) and 100 , which consists of 18 items organized in six categories, four corresponding to motor functions  and two corresponding to cognitive functions . The lowest and highest scores of the total ranged from 18 to 126 . As in tBoth the kinematic assessment with Toyra and the clinical evaluation were carried out for each patient with a maximum difference of 2 days.P less than 0.05 has been used. All statistical analysis was performed with Matlab .Descriptive analysis including means and standard deviations (SD) for continuous variables was initially performed to characterize each subject and also each group of subjects considering the neurological level of injury (C5–C8). The Pearson correlation coefficient was used to correlate kinematic ROMs with clinical and functional variables. A significance level of Kinematics recorded by Toyra  were obtained for each patient and averaged by levels of neurological injury. These averages can be seen in Tables Values obtained by all patients in the clinical scales SCIM, UL MI, and FIM have also been obtained and averaged by level of injury, showing the results in r = 0.776, P = 0.00067), self-care SCIM, and complete shoulder flexion , UL MI and shoulder flexion step-by-step , and UL MI and complete shoulder flexion .Positive strong correlations between kinematic variables and clinical scales have been found in the following parameters: self-care SCIM and shoulder flexion step-by-step , self-care SCIM and complete shoulder abduction , self-care SCIM and ulnar deviation , UL MI and shoulder abduction step-by-step , self-care FIM and shoulder abduction step-by-step , and self-care FIM and complete shoulder flexion . Results are shown in Positive moderate correlations between kinematic variables and clinical scales have been found in the following parameters: self-care SCIM and shoulder abduction step-by-step  and between this metric and the subscale UL MI AbdShoulder .The metrics developed in this study have been applied to 15 patients; then the obtained values with the clinical scales' scores were compared. As it can be seen in  joint amplitude and self-care FIM , reaching amplitude (Y-axis) and self-care FIM , reaching amplitude (Z-Axis) and UL MI , reaching amplitude (Z-Axis) and UL MI AbdShoulder , reaching amplitude (Z-Axis) and UL MI Flexelbow , and reaching amplitude (Z-Axis) and self-care FIM .There were moderate positive correlations between the following parameters: agility and UL MI AbdShoulder .There was also a moderate negative correlation betweenThe present study shows that the kinematic data recorded by VR system Toyra correlate with clinical scales specific for the upper limb function, which is in line with preliminary results of our group . Some meThis study supports the use of such VR systems not only as rehabilitation tools but also as an objective assessment tool of the user's performance, providing data with potential clinical relevance. The different degree of correlation found between the clinical scales and the kinematic variables yields interesting information that can be used in two directions. One is to analyse in minute resolution the patients' physical state, trying to use this information to complement the clinical scales scores and to design treatments that encourage the training of the joints more linked with a functional improvement. The second one would be to develop predictive models that could offer the clinician an estimation of the clinical scale score expected for a patient, thus adding objective data that could facilitate the and to follow the progression of a patient. Some previous studies go in this direction , 29.The highest positive correlation between clinical scales and kinematic variables was found in the step-by-step shoulder flexion. As it was previously mentioned, the step-by-step kinematic variables require higher muscle control, and this could be the reason of the high correlation of this variable with the functionality. Together with the moderate correlations found in the shoulder abduction, these results suggest the importance of the shoulder range or movement in patients with SCI, which is consistent with previous studies that established that shoulder muscle strength, in patients with tetraplegia, is an important determinant of functional ability level .In a previous study in which correlations between kinematics and clinical scales were also studied , no corrThe only kinematic variable not related with the shoulder that showed positive correlation with clinical scales was the ulnar wrist deviation. This result could be due to the tenodesis effect, an anatomical consequence of the SCI very common in patients with level of injury C6 or C7 that entails a high wrist range of motion during the execution of the activities of daily living (ADL) . joint amplitude and the clinical scales, in comparison with any of the correlations obtained between the same scales and the isolated kinematic variables, suggests that the combination of kinematic variables could offer more clinically relevant information than when individually presented.Regarding the kinematic metrics developed in this study, the higher correlation obtained between the joint amplitude and the SCIM scale and also the upper limb abduction shoulder subscore shows that this metric could be a good indicator of functionality. A similar result was obtained in [The strong positive correlation betweenained in , where tReaching amplitude along the Z-axis shows moderate correlations with four of the clinical scores or subscores . As it has been defined, the Z-axis goes vertically upwards, so that the movements in this direction require a higher force, and thus this ability could be closely related to the clinical measurements. Also reaching amplitude along the Y-axis shows a positive correlation with self-care FIM scale. The Y-axis was defined horizontally, perpendicular to the screen, and it is thereby the direction in which some of the ADL considered in the FIM scale take place, like eating or grooming. This could be the rationale of this correlation. agility with the UL MI AbdShoulder was unexpected, and it could indicate that the normalization by the mean velocity used to calculate this metric could not have been enough to counteract the presence of involuntary movements, very common in patients with SCI, that usually lead to the appearance of high velocity peaks. Further filtering strategies and an optimization of the metric's parameters will be necessary to improve this metric.The negative correlation that showed the accuracy, no correlation with clinical scores was found, in contrast with a previous report, where there were strong correlations between a metric called “trajectory error,” with similar foundations to the one presented here [With respect to the metricted here . We beliThese metrics present some limitations, such as the different number of patients in each group of injury. Therefore, it will be necessary in future researches to increase the number of patients in order to have a sufficient number to compare the averages of each level of injury. It could be also interesting to apply this metrics and kinematic analysis when the patients are performing more functional tasks such as ADLs in VR environments, not only analytical movements as in the evaluation session presented here.It has been shown that some of the kinematic data extracted by a VR system based on IMUs motion capture systems have a clinical meaning. It has also been shown that the combination of these variables could provide more information than when separately used. For this purpose, a set of kinematic metrics has been defined, showing promising results in some of them. It seems very important to give clinicians the chance to obtain clinical relevant information from technological applications of rehabilitation. This could facilitate the use of such devices in clinical settings.


The ability of road vehicles to efficiently execute different sensing tasks varies because of the heterogeneity in their sensing ability and trajectories. Therefore, the data collection sensing task, which requires tempo-spatial sensing data, becomes a serious problem in vehicular sensing systems, particularly those with limited sensing capabilities. A utility-based sensing task decomposition and offloading algorithm is proposed in this paper. The utility function for a task executed by a certain vehicle is built according to the mobility traces and sensing interfaces of the vehicle, as well as the sensing data type and tempo-spatial coverage requirements of the sensing task. Then, the sensing tasks are decomposed and offloaded to neighboring vehicles according to the utilities of the neighboring vehicles to the decomposed sensing tasks. Real trace-driven simulation shows that the proposed task offloading is able to collect much more comprehensive and uniformly distributed sensing data than other algorithms. In mobile crowd sensing systems, ordinary citizens are recruited to collect and share data from their surrounding environments through mobile devices ,2. On thIn a vehicular mobile sensing system, sensing data are collected by vehicles and then transmitted to a sensing data center via wireless networks. Vehicular mobile crowd sensing can collect a large amount of comprehensive data, which are collaboratively collected by multiple sensing vehicles. To meet the sensing data requirements of vehicular mobile crowd sensing applications, a large amount of sensing tasks should be generated by the sensing system to collect user-required sensing data. Therefore, vehicular mobile crowd sensing systems mainly collect various kinds of sensing data that encompass all the tempo-spatial dimensions required by sensing tasks. However, sensing vehicles are heterogeneous in terms of sensing interfaces and mobility trajectories. Thus, collecting all required data for sensing tasks by one sensing vehicle is impossible.Sensing data are temporal, and collected sensing data may expire after certain time. For example, atmospheric temperature changes with time in any given day; as a result, collected temperature data will be expired after a few hours. Moreover, different types of sensing data may have different life cycles. For example, the life cycle of noise data is much shorter than that of temperature data. Thus, vehicular crowd sensing systems should continuously generate sensing tasks and collect sensing data in target sensing areas to ensure relevance of the user-required data.In vehicular mobile crowd sensing systems, the quality of collected sensing data increases with the increase in number of sensing vehicles used to collect data, which, consequently, equates to high cost . TherefoThis work proposed a utility-based sensing task decomposition and offloading algorithm. A mobility model based on a continuous-time Markov chain is established to forecast the spatial distribution of sensing vehicles. A utility function is designed to estimate the sensing task execution capacity of sensing vehicles on the basis of the spatial distribution and interfaces of sensing vehicles, tempo-spatial coverage of collected data, requirements of the tempo-spatial coverage, and types of sensing data. Then, the sensing tasks are decomposed and offloaded to neighboring vehicles according to the utilities of the neighboring vehicles to the decomposed sensing tasks.The remainder of this paper is organized as follows. Mobile crowd sensing  is a proZhang et al.  proposedSong et al.  satisfieFrequent encounters between mobile vehicles provides sensing vehicles opportunity to collaborate with each other. Xiang et al.  reduced Collaboration between vehicles in the task execution stage can further improve the coverage of collected sensing data. For example, in the task execution stage, offloading sensing tasks from mobile devices to the environmental embedded sensors can improve quality of collected sensing data and reduce energy consumption of mobile devices ,31. In mIn early vehicle-based environmental sensing applications, vehicles were used to monitor locations , air quaA vehicular mobile crowd sensing system is composed of sensing data servers, wireless networks, and vehicles , as shown in The sensing data center generates sensing tasks according to the data requirements from the system users. Then, the sensing tasks are delivered to the sensing vehicles to collect matching sensing data. Sensing task-based data collection can collect data with limited sensing resources. In practical scenarios with a significantly large number of sensing tasks and available heterogeneous sensing vehicles, the execution of a sensing task by an appropriate sensing vehicle becomes an arduous problem. This work presents a sensing task decomposition and offloading strategy  that canN subareas in the form of grids based on geographical coordinates . The sensing area is divided into lattice cells based on the geographical location , and eacrdinates . Let I=1M types of sensing interfaces embedded in the vehicle sensing nodes. Let lth type of sensing data. Let α,α can collect the sensing data of type l, and α cannot collect the data of type l.Given that sensing data are usually sensitive to time , sensing data should be sampled periodically in each unit sensing area. We suppose that there are α travels into subarea i, and α stay in subarea i. Then, the valid function of the sensing data of type l collected by α when it travels in subarea i could be denoted as follows:Vehicles collect sensing data of a specific subarea when it is travelling in the subarea. Let t.l in subarea i at time t.t . Normally, the state of the vehicle is only related to its state at the last moment. Thus, for any In the vehicular crowd sensing system, vehicles move in and transfer between the subareas. Let i to subarea j after time duration t. Let Equation  indicateQ matrix. For any We define the transfer rate matrix of the mobility procedure i to subarea j. Since the number of the subareas in the sensing area is limited, for For any According to the Kolmogorov backward differential equationX could be derived as follows:Let In this section, a utility function is designed to estimate the sensing task execution capacity of sensing vehicles on the basis of the spatial distribution and interfaces of sensing vehicles, tempo-spatial coverage of collected data, requirements of the tempo-spatial coverage, and types of sensing data. Then, the sensing tasks are decomposed and offloaded to neighboring vehicles according to the utilities of the neighboring vehicles to the decomposed sensing tasks. By decomposing and offloading the sensing tasks, each part of the sensing tasks can be executed more efficiently. Therefore, the sensing efficiency of the whole sensing system will be improved.α to task α to the sensing data of type l in subarea i at time t.l collected by α in subarea i. The calculation of the utility of a node will consume resources of the sensing node, and the node should predict its location in finite time. Thus, a system duty cycle α arrives the subarea i at time t, and then stay in the subarea i for time τ, (15(15Tk isEquations  and 16)16) are ti at time 0, then i, the residence time If the sensing node arrives in subarea B indicates that the sensing node stays in subarea i in time duration First, the uncountable events are translated into countable events, as shown in Equation .(20)B=Υi at time A also indicates the event where the sensing node stays in subarea i from time 0 to time t. Obviously, i is subject to the exponential distribution with parameter Thus, the residence time of a vehicle in subarea i.Consequently, j from subarea i  is denoted as The arrival time of a vehicle in subarea If If Equation  is true.Utility of a sensing vehicle to a sensing task can be derived based on the historical traces of the vehicles, sensing interfaces of the participant vehicles, and task requirements in terms of sensing data types and tempo-spatial coverage. We divide time into duty for convenient calculation. Therefore, each task consists of unit tasks, and each unit sensing task covers a unit subarea, sensing data type, and unit duty cycle.α is α. α identify its neighbor set β who has the highest efficiency to execute unit task β after all tasks In Algorithm 1, each utility of the neighbor nodes to each unit sensing task is firstly calculated. Then, the unit tasks are assigned to different neighbors according to the utilities of the neighbors to the unit tasks. We suppose the task set of sensing vehicle Algorithm 1: Distributed Sensing Task Offloading AlgorithmT-Drive trajectory ,47 is utBased on the vehicular traces, we constructed our simulation program using Python language and Numpy package. In the program, we designed a trace based mobility module, where the traces of the 1000 vehicle candidates are preloaded into the memory. Before collecting sensing data and sensing task offloading between sensing vehicles, the vehicles train their time continuous Markov process based mobility model for 24 h. After mobility model training, sensing vehicles periodically collect sensing data and offload sensing tasks according to Algorithm 1.In our simulations, a rectangular region around the Fourth Ring Road of Beijing is adopted as the target sensing area, between The default parameters of the experiments are set as follows. The default number of participant sensing vehicles is 60, which are selected randomly from the 1000 sensing vehicle candidates. The total time of the experiment is We configured number of sensing vehicles, mean of task durations and task generation rate in our simulation, where number of sensing vehicles ranges from 5 to 100; mean of task duration ranges from 1 system duty cycle to 19 system duty cycle; task generation rate ranges from 10 per system duty cycle to 100 per system duty cycle. We did not configure the mobility model parameters during the simulation procedure because of the mobility of the vehicles is determined by the real traces, which are not configurable.We compared the Task Offloading algorithm with two other algorithms, namely, Random and Dynamic Participant Selection (DPS) . In the N is the number of subareas, M is the number of sensing data types, and A sensing record is generated when sampling data, which include sensing time, area, data type, and value, of a sensing node are recorded. Furthermore, the length of the validity period of sensing data is determined by the type of sensing data. Therefore, the validity period is also determined after the sensing time is recorded.S to count the effective coverage of the collected sensing data. j in subarea i. S. When inserting one sensing record into S, its effective sensing period pair is inserted into S according to the type of the sensing data and the subarea where it was collected. The cumulative coverage of collected sensing data should get rid of the impact of overlapping coverage to the same time, subarea and data type. The algorithm for inserting an effective sensing data is shown as Algorithm 2. The input L in Algorithm 2 is a list of ordered time pairs and the input p is the ordered time pair to be inserted into L.We define a 2-D array L, time pairs are sequential and non-overlapping as shown in p into time pair list L, as shown in Figure Algorithm 2. In line 20 of Algorithm 2, a target temp time pair L. The start time of p is between the end time of L. If the end time of p is smaller than the end time of p will not be processed. If the start time of p is earlier than the start time of p, else the new start time will be set to the start time of p (line 32–42). The new end time is set to the relatively late one in the end time of p and L, and the covered temp time pairs are deleted from L (line 50–57).In time pair list S.Then, the cumulative sensing data coverage In Algorithm 2: Time Pair Insertion AlgorithmFurthermore, the slope of the Task Offloading curve is also steeper than the other two algorithms. In other words, the sensing data coverage ratio in the Task Offloading algorithm increases faster than that of other two algorithms. If more vehicles are recruited into the system, then more opportunities for collaboration between sensing vehicles are generated, resulting in more chances of offloading one sensing task to a sensing vehicle with higher sensing utility. As a result, the sensing data coverage ratio increases much faster than the other two algorithms.We also compared the sensing data coverage ratio of the Task Offloading algorithm and DPS with period selection of sensing participants (P-DPS). The duty cycle of the both algorithms is set to 1000 s. For convenience, we take temporal coverage of the sensing data into consideration in P-DPS, and the coverage ratios are shown in The sensing server can get the utilities of all the sensing vehicles given that a centralized participant selection method is used in the P-DPS algorithm. Then, the P-DPS algorithm is able to select the best participants out of all the candidates to collect sensing data in the following duty cycle. However, P-DPS cannot maintain high performance in a distributed sensing scenario, due to its requirements to global node information. Task Offloading exchanges sensing task between neighboring vehicles in a distributed way to improve quality of collected sensing data. In the proposed Task Offloading algorithm, the location prediction and task decomposition and offloading are utilized to improve the quality of the collected sensing data. Although the sensing data coverage ratio of Task Offloading is lower than that of P-DPS, the performance of Task Offloading is better than that of DPS in the distributed scenarios.Equation ,(30)rt=The task coverage ratios of the three algorithms exhibit the same variation trend with the sensing coverage ratio under the impact of the number of vehicles. However, the task coverage ratios are higher than that of the sensing data coverage ratios with the same number of sensing vehicles. In our task offloading algorithm, since sensing tasks are decomposed and offloaded, each part of the sensing tasks is offloaded to the most efficient sensing vehicle. As a result, the task offloading algorithm outperforms DPS and Random algorithm, where the sensing tasks are not decomposed and offloaded.The task coverage ratio is higher than 1 when there are more than 25 sensing vehicles in the Task Offloading algorithm. This phenomenon is unlikely because the task coverage ratio is the ratio between the coverage of collected sensing data and the cumulative coverage of the sensing task requirements. In our simulation, the vehicles continuously collect sensing data if there are sensing task requirements, and the temporal coverage of the collected sensing data may exceed the required range of the sensing task. Therefore, the coverage of the collected sensing data may be larger than the cumulative requirements of the sensing tasks.By offloading the tasks between the sensing vehicles, the Task Offloading algorithm executes the sensing tasks by the vehicles with high utilities. DPS selects the vehicles with higher utilities at the beginning of the simulation. Thus, both Task Offloading and DPS are able to improve the spatial distribution of the collected sensing data. As shown in Temporal entropy reveals the stability of the sensing system on collecting the sensing data continuously. As shown in The temporal entropy of the Random algorithm fluctuates around a small value. The Random algorithm selects sensing vehicles randomly from the candidates; as a result, the Random algorithm experiences difficulties in collecting sensing data continuously. For the same reason, the fluctuation degree of the Random algorithm is also higher than that of the other two algorithms. When the number of sensing vehicles is small, the temporal entropy of Task Offloading is lower than DPS, given that DPS selects vehicles according to their utilities, whereas Task Offloading distributes sensing task randomly to the sensing vehicles at the beginning of the simulation. When the number of sensing vehicles is small, there are less chances of offloading sensing tasks among sensing vehicles. Therefore, the temporal entropy of the Task Offloading algorithm is smaller than that of DPS and higher than that of Random. More opportunities to offload sensing tasks among sensing vehicles are generated with the increase in number of sensing vehicles. Thus, the temporal entropy of Task Offloading exceeds that of DPS at high number of sensing vehicles.The performance of algorithms under different temporal coverage expectations of the sensing tasks is evaluated. The task coverage ratio is the ratio between the cumulative tempo-spatial coverage of the collected sensing data and the cumulative tempo-spatial coverage of the sensing tasks.In our simulations, each vehicle collects sensing data according to the tempo-spatial coverage requirements of the sensing tasks. Therefore, more sensing task tempo-spatial coverage requirements lead to more sensing data collected by the vehicles. However, the increase rate of the amount of collected sensing data is slower than that of the required tempo-spatial coverage of a task. As a result, the task coverage ratio exponentially decreases with the increase in sensing task duration for the required temporal coverage. The task coverage ratio of Task Offloading is higher than that of the other two algorithms, because sensing tasks are executed by the sensing vehicles with higher sensing utilities.The sensing data coverage ratio of Task Offloading is much higher than that of DPS and Random, because Task Offloading considers the lifetime of the collected sensing data, whereas the collected sensing data in Random and DPS cover only one system duty cycle. Furthermore, Task Offloading is able to offload sensing tasks to sensing vehicles with the highest utilities for a specific sensing task, and the sensing data collected by the task performer offer the highest coverage ratio.The impact of task generation rate on the sensing task coverage ratio and tempo-spatial entropy of the collected sensing data is evaluated. The task generation rate denotes the amount of generated sensing task in a system duty cycle. The task coverage ratio of Task Offloading is higher than that of DPS and Random because the sensing tasks are offloaded to the sensing vehicles with higher utilities. The impact of task generation rate on the task coverage ratio is similar to the impact of required temporal coverage duration of the sensing data for a sensing task on the task coverage ratio, because sensing task generation rate is increased to increase the tempo-spatial coverage requirements of the sensing tasks.For the same reason, spatial and temporal entropies have similar variation trends under different task generation rates as that under different temporal coverage of sensing tasks, as shown in Task Offloading algorithm is proposed for vehicular sensing systems to collect comprehensive covered sensing data. In the proposed Task Offloading algorithm, the mobility of the vehicles is modeled with a time-continuous Markov chain, and the utility function of a vehicle to a sensing task is established according to the required tempo-spatial coverage of the sensing data, required sensing data types, sensing interfaces of the vehicle, and mobility trace of the sensing vehicle. The sensing tasks are decomposed and offloaded to other participants according to the utilities of the sensing vehicles. Real trace-driven simulations show that the proposed Task Offloading algorithm is able to obtain considerably more uniformly-distributed tempo-spatial sensing data and higher task coverage ratio than the other algorithms, namely, Random and DPS.


It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. To which extent a single neuron can influence brain circuits/networks dynamics? Why only a few neurons display such a strong power? These open questions are inspired by recent experimental observations in developing and adult neuronal circuits, as well as by classical debates within the framework of the single neuron doctrine. In this work we identify and present a mechanism which can explain in neuronal circuits, at some early stage of their development, how and why only a few specific neurons can exhibit such power. For this purpose, we consider a standard neuronal network model whose population activity is characterized by bursting behavior. The introduction of a distribution of correlated neuronal excitabilities and degrees, inspired by the simultaneous presence of younger and older neurons in the network, leads to the emergence of functional hub neurons. These critical cells, whenever perturbed, are capable of suppressing network synchronization. Notably, we show that their strong influence on the population dynamics is not related to their structural properties, but to their operational and structural integration into a clique. These results highlight how network-wide effects can be induced by single neurons without any need for a specific topological architecture. There is increasing experimental evidence that single neuron firing can impact brain circuits dynamics A recent computational study on the synchronization properties of a specific neural circuit clique regulating the neuronal bursting. We show that the level of available synaptic resources influences the reciprocal firing times of the synaptically connected neurons of the clique. However, the fundamental mechanism responsible for the burst triggering relies on an unsupervised process leading to a precise firing sequence between the neurons which are not structurally connected. Furthermore, frequency locking of the same neurons led, counter-intuitively, to anti-resonances In order to analyze the population dynamics in a neural circuit at the initial stage of its development, when both mature and young cells are simultaneously present, we consider a random diluted network presenting developmentally inspired correlations between neuronal excitability and connectivity. The presence of these correlations can render the network sensitive to single neuron perturbation of a few peculiar neurons. The coherent activity of the network can be even arrested by removing or stimulating any of these neurons, which are functional hubs arranged in a frozen at a certain stage of its initial development, similar to the one examined in the experimental work on developmental hippocampal circuits In this paper we intend to mimic an immature neuronal network population bursts, PBs) and long time intervals of asynchronous firing. Notably, the presence of short-term-synaptic-plasticity is the crucial ingredient to observe PBs, even without an inhibitory population In particular, we considered a directed random network made of Furthermore, in developing networks, both mature and young neurons are present at the same time, and this feature is reflected in the variability of the structural connectivities and of the intrinsic excitabilities. Experimental observations indicate that younger cells have a more pronounced excitability The presence at the same time of younger and older neurons can be modeled by considering correlations among the in-degree and out-degree of each cell as well as among their intrinsic excitability and connectivity. In particular, in the attempt to find the network organization which is more sensitive to single neuron perturbations, we compare the dynamics of networks where none, one or more of the following correlations have been embedded ;setup T3: positive correlation between the intrinsic neuronal excitability and the total connectivity (in-degree plus out-degree).Correlated networks with all possible combinations of the setups T1-T3 have been examined. However, the paper is mainly devoted to the comparison of the properties of the network with correlations of type T1 and T2  on the occurrence of PBs on our network model. SNS was obtained by adding abruptly a DC current term to the considered neuron. For illustrative purpose, we report in In the developing hippocampus it has been shown how the stimulation of specific single neurons can drastically reduce the frequency of the PBs The stimulation process was completely reversible and after the end of the SNS both the firing rate of the cell and the PBs frequency returned to the pre-stimulation control level. In order to evaluate the impact of single neuron perturbation on the collective dynamics, we considered the variation of PB frequency relative to control conditions (i.e. in absence of any stimulation). In vity see , althougsingle neuron deletion, SND, on the neuronal response of the network  whenever the PBs frequency was altered more than 90% with respect to the corresponding PB activity in control conditions. Therefore, we considered as significative only the modifications of the activity which were well beyond the statistical fluctuations in the population bursting, shown by the shaded gray area in panels C and D in In all the examined cases, despite the fact that the firing frequencies distributions were quite similar to the ones measured in the correlated network embedding setups T1 and T2, we did not observe significant modifications of the bursting activity by performing SNS and SND experiments on any neuron of the network , as shown in Time Orchestration.As shown in panels D, E, F of functional clique responsible for the onset of the PBs.The results reported above suggest that the four critical neurons As clarified in ron  see . The autIn this Section, we want to address the question whether the variation of the effective strength of the synapses could be also responsible for the silencing of the network (with correlations of type T1 plus T2) during SNS experiments. So far we have clarified that the removal of any of the four neurons in the functional clique blocked the bursting activity, however it is not clear why a small stimulation of As already mentioned, the roles of the four neurons in the functional clique of the network with type T1 and T2 correlations were quite well established, and just a precise firing time sequence could induce the population avalanche. To better understand the role of each critical neuron, it is necessary to point out that, under control conditions, the neurons  to  see , the reaiods see . FurtherAs clearly shown in Similar behaviors had been observed also for the synapse connecting and  see . The entTo get a deeper insight on this issue, let us consider the anti-resonances (corresponding to minima in the PB activity) observed during the SNS experiments performed on  see . To inteIn order to confirm this hypothesis, we developed a simple model to reproduce the results of the SNS experiment on The aim of the present work was to identify neuronal network arrangements sensitive to single neuron perturbations, such as those induced by single neuron stimulation or deletion (or forced silencing). We choose as a benchmark model a random network of excitatory LIF neurons, connected via depressive synapses regulated by the TUM mechanism The introduction of a positive correlation between in- and out-degree (T1) and a negative correlation between intrinsic neuronal excitability and total degree (T2), besides being justified from a developmental point of view, favors also the stabilization of the network activity. This because, as pointed out in anti-resonances associated to a drastic reduction of the PBs. For random networks, i.e. with no correlations, or embedding just one of the correlations of type T1, T2, T3 or the combination of type T1 and T3, we did not find any evidence of functional cliques and the mechanisms of network synchronization were much more robust and immune from single neuron perturbations (see Supplementary Information).Each population burst caused the depletion of the synaptic resources, therefore another PB could occur only when the synaptic resources would be recovered, thus inducing an effective refractory time between two successive PBs. However, this is only a necessary, but not sufficient condition for PB triggering. The key element responsible for generating PBs was the unsupervised occurrence of a precise sequence of firing times of the two supra-threshold critical neurons, i.e. not mediated by any structural synaptic connection. On the other hand, the mode locking of the firing frequencies of these two neurons was instead responsible for The activity of random uncorrelated networks has been previously examined in operational hubs accordingly to the definition recently introduced in Furthermore, our results show that the integration into a clique is the key element that can enable single neurons to impact the population dynamics, without any further topological requirements for the network architecture. The functional hubs forming and operating within the clique, are actively involved in generating network synchronizations and, as a consequence, capable to impact the network dynamics when stimulated. Therefore, without necessarily being structural or effective hubs, i.e. capable to cause a direct influence on the activity of many other nodes rich gets richer rule which can generate scale-free networks At variance with hippocampal hubs, critical neurons do not have a high structural degree. This is a consequence of the correlation imposed on the network where the excitability of the neurons is anti-correlated to the total structural degree of the cells. Indeed, in the correlated network studied in this work, the orchestration of the neuronal activity relies on the coordinated firing of a few critical “young” neurons  mediated by their inter-connections. However, in real biological developing networks, it is possible that a further developmental connectivity regulation is fulfilled, with the chance of finding synaptic connections in a pair of young cells much lower compared to a pair composed of a young and a mature cells. This would be also in line to the We have verified that the main ingredients required to observe strong sensitivity to single neuron stimulation and deletion are, besides the presence of the correlations of type T1 and T2, a small number of neurons supra-threshold as well as a strongly diluted network. This can find an explanation in the fact that by increasing the degree of the neurons as well as the number of neurons supra-threshold the network dynamics becomes more cooperative. Furthermore, the synaptic time scales seem not to be crucial for the emergence of single neuron sensitivity (for more details see the subsection Dependence on the Model Parameters in Methods).Although presented within a developmental framework, our results can also have elements of interest in the wider context of brain processing. In fact, in this work we show that the introduction of an excitability gradient in the network can lead to the emergence of functional cliques capable to shape the neuronal population activity. Indeed, different brain states could dynamically modulate the level of excitability or the gain function of the neurons within a circuit  model In this paper we consider a network of Whenever the membrane potential Only the active transmitters react to the incoming spikes Finally, the synaptic current is expressed as the sum of all the active transmitters (post-synaptic currents) delivered to neuron diluted network made of In this paper we study the case of excitatory coupling between neurons, i.e. The propensity of neuron The intrinsic excitabilities of the single neurons For the other parameters, we use the following set of values: Furthermore, we have considered networks where correlations of type T1, T2 or T3 are embedded. Correlation T1 is obtained by generating randomly two pools of Correlation of type T2 (T3) imposes a negative (positive) correlation between excitability In order to have an accurate and fast integration scheme, we transformed the set of ordinary differential In order to identify a population burst we have binned the spiking activity of the network in time windows of 10 ms. A population burst is identified whenever the spike count involves more than 25% of the neural population. In order to study the PB build up, a higher temporal resolution was needed and the spiking activity was binned in time windows of 1 ms. The peak of the activation was used as time origin (or center of the PB) and it was characterized by more than 5% of the neurons firing within a 1 ms bin. The time window of 25 ms preceding the peak of the PB was considered as the build up period for the burst. In particular, the threshold crossing times have been defined via a simple linear interpolation based on the spike counts measured in successive time bins.These PB definitions gave consistent results for all the studied properties of the network. The employed burst detection procedure did not depend significantly on the precise choice of the threshold, since during the inter-burst periods (lasting hundreds of milliseconds) only 10–15% of neurons were typically firing, while more than 80% of the neuronal population contributed to the bursting event  correlations under control conditions was In this subsection, we summarize the crucial ingredients needed to observe strong sensitivity to SNS/SND. In particular, we have checked, for different model parameters, when SNS/SND experiments were still able to noticeably modify the PB activity (i.e. more than 90% with respect to control conditions). Firstly, we considered the sparseness of the network, the reported results refer to a diluted network with a probability of 10% to have a link among two neurons. We have observed that the results of the SNS/SND experiments strongly depend on the level of dilution, however they can still be effective up to a connection probability of 50%. Another crucial aspect was the small number of neurons supra-threshold, in the studied case this number corresponded to the 10% of the neurons. By varying this percentage up to 20% we still observed that the network can be silenced by single neuron stimulation/removal. Furthermore, the dependence on the system size seemed not be crucial, since as described in We also tested the influence of the synaptic time constants on the population sensitivity to SND/SNS. As a matter of fact, the time scale ruling the depletion of the neurotransmitter In order to highlight statistically significant time-lagged activations of neurons, for every possible neuronal pair we measured the cross-correlation between their spike time series. On the basis of this cross-correlation we eventually assign a directed functional connection among the two considered neurons, similarly to what reported in i corresponded to the number of neurons which were reliably activated after (before) its firing.Let us explain how we proceeded in more details. For every neuron, the action potentials timestamps were first converted into a binary time series with one millisecond time resolution, where ones (zeros) marked the occurrence (absence) of the action potentials. Given the binary time series of two neurons In order to treat as an unique event multiple spike emissions occurring within a PB, different time series surrogates were defined for different kind of analysis according to the following procedures:for the definition of the functional in-degree for the description of the PBs build up only the timestamps of the first action potential emitted within a window of 25 ms preceding the PB peak was taken into account;for the analysis of the network activity during inter-burst periods, all action potentials emitted out of the PBs were considered.The model here reported has been developed to reproduce the network response during the SNS experiments on In summary, for each stimulation current a random number Furthermore, each time a PB was registered, for the subsequent Figure S1Network of  neurons with negative correlation between  and  and positive correlation between  and  (Setup T1 plus T2). (A) Negative correlation between intrinsic excitability (EPS)Click here for additional data file.Figure S2Network of  neurons with negative correlation between  and  and positive correlation between  and  (Setup T1 plus T2). (A), (B) Number of PBs emitted in a time window (EPS)Click here for additional data file.Figure S3Network of  without any correlation. (A) Distribution of the intrinsic excitabilities (EPS)Click here for additional data file.Figure S4Network with positive correlation between in-degree and out-degree (Setup T1). (A) Distribution of the intrinsic excitabilities (EPS)Click here for additional data file.Figure S5Network with anticorrelation between intrinsic excitability and total degree (Setup T2). (A) Distribution of the intrinsic excitabilities (EPS)Click here for additional data file.Figure S6Network with positive correlation between excitability and total degree (Setup T3). (A) Distribution of the intrinsic excitabilities (EPS)Click here for additional data file.Figure S7Network with positive correlation between intrinsic excitability and total degree and positive correlation between in-degree and out-degree (Setup T1 plus T3). (A) Distribution of the intrinsic excitabilities (EPS)Click here for additional data file.Text S1Event driven map.(PDF)Click here for additional data file.Text S2Dependence on different network realizations.(PDF)Click here for additional data file.Text S3Dependence on the network size.(PDF)Click here for additional data file.


Social communication is heavily affected in patients with neuropsychiatric disorders. Accordingly, mouse models designed to study the mechanisms leading to these disorders are tested for this phenotypic trait. Test conditions vary between different models, and the effect of these test conditions on the quantity and quality of social interactions and ultrasonic communication is unknown. The present study examines to which extent the habituation time to the test cage as well as the shape / size of the cage influence social communication in freely interacting mice. We tested 8 pairs of male mice in free dyadic social interactions, with two habituation times (20 min and 30 min) and three cage formats . We tested the effect of these conditions on the different types of social contacts, approach-escape sequences, follow behavior, and the time each animal spent in the vision field of the other one, as well as on the emission of ultrasonic vocalizations and their contexts of emission. We provide for the first time an integrated analysis of the social interaction behavior and ultrasonic vocalizations. Surprisingly, we did not highlight any significant effect of habituation time and cage shape / size on the behavioral events examined. There was only a slight increase of social interactions with the longer habituation time in the round cage. Remarkably, we also showed that vocalizations were emitted during specific behavioral sequences especially during close contact or approach behaviors. The present study provides a protocol reliably eliciting social contacts and ultrasonic vocalizations in adult male mice. This protocol is therefore well adapted for standardized investigation of social interactions in mouse models of neuropsychiatric disorders. Neuropsychiatric diseases affect heavily the social life of patients. In many cases, social communication is affected and becomes atypical. The patients get isolated in their social environment, due to a lack of interest for social interactions or atypical ways of interacting. Genetic studies of neuropsychiatric diseases led to the identification of several susceptibility genes for autism spectrum disorders (ASD) ,2 or schMice are social animals using olfactory, visual, tactile and acoustic signals to communicate –7. AcousOne issue in different tests used to evaluate social interactions and communication is to stimulate free social interactions without forcing them, i.e., favoring voluntary approaches instead of incidental contacts. The aim is to get a maximum of spontaneous affiliative social interactions. For this purpose, the shape of the test cage as well as the time the tested animal is habituated to it are supposed to be determinant . A prelioccupant, usually the tested mouse of a specific genotype) can be habituated to the test cage, while the second one is introduced later . The last situation allows to distinguish between occupant and new-comer, i.e., the emitter and the receiver of most interactions, respectively. This test does not encompass as many standardized elements as the three-chambered test since animals are freely interacting and cannot be controlled. The analysis of this type of interactions is more time-consuming and more prone to subjective interpretations. New software nevertheless allow to reduce these subjective elements in the analyses . Data extracted allow a qualitative description of the interactions.To avoid these influences, a standardized test was developed for the characterization of social deficits in mouse models of ASD and is now broadly used . The thrsoftware ,24). TheWe designed the present study to test specifically the influence of the cage shape/size and the habituation time that vary in a controlled way. We aimed at testing whether same-sex social interactions occurring in a reasonably sized test cage with reduced habituation time are not forced in comparison with social interactions in larger cages with longer habituation time. We used the semi-automated module Mice Profiler from Icy software , that alWe hypothesized that social interactions in the round cage will be the most different from the rectangle and the square cages. Indeed, corners in test cages appear to be very often used by animals see , and thead libitum. One week after their arrival, the 48 males considered as occupants were socially isolated for 3 weeks before the experiments to increase their social motivation and reduce their aggressiveness , round: 50 cm diameter [1963.5 cm2], square: 50 cm side [2500 cm2]; height: 30 cm in all cases). The occupant mouse was left 20 min or 30 min for habituation in the experimental cage  was introduced. The two animals were allowed to freely interact for 8 min [occupant; the contribution of the new-comer (if any) was negligible [occupants were used again. Between each interaction test, the experimental cage was emptied from bedding, and new fresh bedding was used.All experiments were conducted between 9:30 AM and 6:00 PM. Two pairs of mice were tested simultaneously with an opaque separation between the two cages. Pairs tested together underwent the same habituation time, but cage shape/size was balanced between right and left for each trial. Parameters tested in the experiment were the habituation time to the test cage (20 min or 30 min) and the shape and surface of the experimental Plexiglas cage , we tested and analyzed only 8 pairs of mice per condition to limit the number of animals involved. Mice Profiler provides sufficiently detailed information to extract robust data with such a sample size for each condition (see supplementary information in ). The ex- time spent in contact (threshold: 1 cm),occupant’s point of view and oro-genital from the new-comer’s point of view,- types of contacts: nose-to-nose, oro-genital from the occupant follows the new-comer, both animals moving),- follow behavior ,- approach behavior ,- escape behavior ,- approach-escape sequences .- stop behavior . We measured the latency for the first ultrasonic vocalization as well as the total number of ultrasonic vocalization and their duration.We used non-parametric statistical tests given the non-normal distribution of the data and the small sample-sizes . We used a Kruskal-Wallis rank sum test to evaluate the effect of the cage shape for each habituation time separately. When significant differences emerged, we used Wilcoxon-Mann-Whitney U-tests to identify where the difference stemmed from in 2-by-2 comparisons. To evaluate the effect of the habituation time for each cage shape separately, we used Wilcoxon-Mann-Whitney U-tests. All statistical results are compiled in the We tested the effect of the cage shape/size and the habituation time on different events and sequences of events occurring in affiliative social interactions between adult male mice. Results are presented for the first 4 min of the recordings. Results for the complete 8-min recordings are overall similar to the ones for the first 4-min recordings unless otherwise specified . The number of occurrences of the occupant escaping from the new-comer had also been significantly lower after 30-min habituation than after 20-min habituation in the square cage  and in comparison with the square cage  after 30-min habituation. The occupant also escaped from the new-comer significantly more frequently in the rectangle cage in comparison with the square cage after 30-min habituation . Results for the duration of the same behavioral events were similar (data not shown).The effects of the cage shape/size were not significant in both the 20-min and the 30-min habituation conditions on the number of occurrences of the ew-comer , the numoccupant , and theoccupant . There hoccupant approaches new-comer & new-comer escapes” . No significant differences in the frequency of occurrences , which occurred significantly more frequently in the round cage in comparison with the square cage  and the time the new-comer kept the occupant in its vision field . Both measures were more elevated in the round cage in comparison with the rectangle cage .We also examined the effect of cage shape/size and habituation time on the time spent by the animals in the vision field of each other when the animals are not moving. The on field . The cagon field . In contFinally, we tested the effect of the cage shape/size and the habituation time on the latency for the first ultrasonic vocalization, the number of ultrasonic vocalizations and the mean duration of the calls. No significant differences emerged between the habituation time conditions for each cage shape and between cage shapes for each habituation time condition for the latency of the first ultrasonic vocalization , the numoccupant was behind the new-comer, and much fewer in the reverse situation. This would explain why very few vocalizations were recorded during nose-to-nose contacts and when the new-comer was sniffing the ano-genital region of the occupant, while many vocalizations were recorded when the occupant was sniffing the ano-genital region of the new-comer. For vocalizations not emitted during contact, a high proportion of vocalizations recorded when the occupant was approaching the new-comer (with either the new-comer or the occupant escaping). In contrast, much fewer vocalizations were recorded when the new-comer was approaching the occupant .The behavioral events during which ultrasonic vocalizations were recorded did not vary significantly with habituation time or cage shape/size . In all Using different settings for cage shape/size and habituation time, we ascertained mouse social behaviors and sequences of social behaviors, as well as ultrasonic vocalizations. We found very little difference caused by the cage shape/size in these features . Only thoccupant.Previous studies only analyzed the influence of housing conditions on the behavior of the animals . In contrast, the habituation time had some effect on social interactions mostly in the round cage. This might suggest that the lack of corners renders it more difficult to the animals (used to cages with corners throughout their life) to habituate to such environments. The exploratory phase might last longer in round cages than in cages with corners, delaying the social investigation.We would have expected major differences in the round cage in comparison with the square and the rectangle ones given the absence of corners. Indeed corners are used very often  and mighoccupant and the new-comer) was similar between the two studies during the first 4 min of interaction. Overall, most discrepancies were found for the nose-to-nose contact, while data for other behaviors were similar. The fact that mice from the present study were isolated for 3 weeks and those from the de Chaumont study for 4 weeks might explain some of the differences between the durations of the follow behavior and of the types of contacts.The Mice Profiler module of Icy software from de Chaumont and colleagues  was origoccupant has been isolated for 3 weeks before the test. To what extent social communication is affected by this long period of social isolation remains to be quantified, given the high stress level probably generated by social isolation  of interactions if the  juvenile and in f juvenile mice). S juvenile. ContinuFor the first time, we manage to correlate the emission of ultrasonic vocalizations and the detailed social events extracted from Mice Profiler. We confirmed that ultrasonic vocalizations are not emitted randomly and therefore provide further support for their role in social communication. At the moment, it is still not possible to identify clearly the emitter. The fact that ultrasonic vocalizations are rarely emitted during nose-to-nose contacts could justify using triangulation to localize the emitter since the noses of the two animals are usually not close to one another during ultrasonic vocalization emission.occupant during social contacts  and during approach behaviors. The present study also suggested an effect of the emission of ultrasonic vocalizations on the outcome of an approach. The escape behavior of the new-comer or the occupant was often correlated with the emission of ultrasonic vocalizations. When the occupant approaches the new-comer, the later is often less likely to escape and end the contact if more vocalizations were emitted. The correlation between the ultrasonic vocalizations emitted and the type of social interactions described in this study is the first step towards a better understanding of the relationship between the types of vocalizations [Ultrasonic vocalizations are supposedly mostly emitted by the izations  and the occupant” adult male and a socially-housed “new-comer” adult male. A large amount of ultrasonic vocalizations can even be recorded in these conditions. Such a protocol will be useful in a better evaluation of social communication deficits in mouse models of neuropsychiatric disorders.Overall, the present study suggest that a rectangular cage (50 x 25 cm) with 20-min habituation to the test cage is sufficient to elicit non-aggressive interactions between an isolated “S1 TableInformation about the habituation time, the cage shape, the duration of the test, the background of the wild-type strain studied is compiled. Selected articles: ,22,30–49(PDF)Click here for additional data file.S2 TableComparisons were made to identify the effect of habituation time and cage shape/size.(PDF)Click here for additional data file.


Previous research has suggested that personality traits of the Five Factor Model play a role in worker's response to workload. The aim of this study was to investigate the association of personality traits of first responders with their perceived workload in real-life tasks. A flying column of 269 police officers completed a measure of subjective workload (NASA-Task Load Index) after intervention tasks in a major public event. Officers' scores on a measure of Five Factor Model personality traits were obtained from archival data. Linear Mixed Modeling was used to test the direct and interaction effects of personality traits on workload scores once controlling for background variables, task type and workload source . All personality traits except extraversion significantly interacted at least with one workload source. Perceived workload in flying column police officers appears to be the result of their personality characteristics interacting with the workload source. The implications of these results for the development of support measures aimed at reducing the impact of workload in this category of workers are discussed. Stress occurs when an environmental demand exceeds the natural regulatory capacity of an organism, in particular, situations that include unpredictability and uncontrollability  and physical  consequences ;Physical demand: the physical activity needed to accomplish the task ;Temporal demand: the time pressure experienced during the task ;Task-related characteristics:Performance): the subjective assessment of success in achieving the goals of the task, the satisfaction in accomplishing them;Behavior-related characteristics  and the possibility of substantial and unpredictable danger. This category includes first responders, peacekeepers, and emergency workers such as police officers, soldiers, fire-fighters, emergency medical staff, etc., whose main tasks are to protect human life, property and the environment in everyday emergencies, natural disasters, industrial and transport accidents, terrorist and criminal attacks, and massive public events. Although reliable statistics are not available, the increasing number of people affected by small scale emergencies and major disasters indicate that a significant proportion of the workforce is involved in emergency activities and disaster control all around the world  association between the factors  and perceived workload. This hypothesis would be supported by a significant interaction effect of each personality trait with either of the factors. Although this study had a mainly exploratory purpose, since there are no previous studies that addressed the same issues in flying column police officers (FCPOs) in actual duty, grounding on previous studies we could expect that:High levels of Extraversion should be associated with high levels of energy and dynamism, with more effective and active coping, with acceptance of responsibility  correlations with homologous dimensions of the NEO-PI-R , the other three tasks were performed during the event between July 8th and 10th, 2009.Redman is a planned activity consisting in a series of flying-column-specific training programs of varying difficulty. This task was designed to simulate realistic high-risk scenarios and prepare officers for critical decision-making when real-life confrontations occur during, e.g., public demonstrations and rallies. The training lasted approximately 6 h and consisted in performing individual  or group  tasks that were both psychologically and physically demanding. A debriefing was carried out at the end of the training to review and discuss errors and problems.The officer stayed at the police station and was on 24-h operational duty wearing full riot equipment during a high risk event , ready to be called to intervene.This was a planned routine activity during usual working hours with rest hours. Tasks typically involved patrolling potentially high-risk targets such as city or region council halls, embassies, monuments, etc.This was an emergency intervention that required active control and containment of protesters and could involve physical confrontation, with a high risk of being wounded or injured.RR = 0.60; OSw/o = 0.29; OSw = 0.24; RED = 0.03).Hart and Staveland 's NASA TOfficers were asked to complete a NASA-TLX soon after each task in the days of the event. The order of tasks could not be randomized, nor the time of measurement could have been kept constant for all officers, since operational needs obviously came first. Moreover, some officers completed the same task more than once, whereas some others never completed some of the tasks. In total we collected 7842 data points  from 1307 NASA-TLX score sets . Officers were then asked to provide their matriculation number for matching their NASA-TLX scores with their background information and BFQ scores from the unrestricted database. All of them accepted and gave written informed consent after being presented with a detailed description of the procedure, of the aims of the study and of how the confidentiality of their records and personal information would be managed. This study was carried out in accordance with the Ethical Principles of Psychologists and Code of Conduct , but in this case we had to take into account that some officers completed the same task more than once, whereas some others never completed some of the tasks. Hence, we allowed a multilevel structure to data, in which WS scores in each task (Level 1) were nested into officers (Level 2) and used Linear Mixed Modeling (LMM). LMM does not require participants to be measured on the same number of conditions and addresses the issue of non-independence of participants' scores at each measurement occasion through the variance/covariance structure of the model  that varies across participants. We also tested models with both random intercepts and random slopes . These models assume that the effects of the predictors vary between clustering units , but the fit of the random-intercepts-and-slopes model did not improve enough with respect to a random-intercept-only model to suggest substantial variations of the effects across participants. Hence, these results are not reported.In order to test how much each independent variable added to the prediction of perceived workload scores we adopted a hierarchical strategy, i.e., a sequential process involving the entry of predictor variables into the model in steps. At each step the researcher can choose which predictors are to enter the model grounding on theory and/or past research  we entered all the background variables . In the second step (Model 2) we entered the main effect of task, as this was an exogenous variable . In the third step (Model 3) we entered the main effect of WS. In the fourth step (Model 4) we entered the task by WS interaction effect. This model allowed us to test whether the effect of task varied as a function of the WS considered. In the fifth step (Model 5) we entered the main effects of each personality trait. This model allowed us to test direct effects, i.e., whether each personality trait could independently and significantly predict perceived workload over and above the effects of task and WS. In the sixth step (Model 6) we entered the task by personality trait interaction effects. This model allowed us to test whether personality traits moderated the effect of task on perceived workload. In the seventh step (Model 7) we entered the WS by personality trait interaction effects. This model allowed us to test whether personality traits moderated the effect of WS on perceived workload. In the eighth and final step (Model 8) we entered the task by WS by personality trait interaction effects. This model allowed us to test whether the level of a personality trait could moderate the interaction effect of task and WS.At each step we evaluated the statistical significance of each predictor and its parameters and the difference in model fit with the previous step. As models were nested within each other, i.e., the parameters of the model at step k were a subset of the parameters of the model at step k+1, we could perform model comparison using the difference in deviance, i.e., a measure of the lack of fit between the data and the model which is obtained by multiplying the natural log of the likelihood  by minus two. The difference of the deviances from each model is distributed as a chi-square statistic with degrees of freedom equal to the difference in the number of parameters estimated in each model. Since deviance systematically decreases as the number of parameters in the model increases, in order to provide a more detailed assessment of the parsimony of the models we inspected also the Akaike Information Criterion (AIC) and the Schwarz's Bayesian Information Criterion (BIC), which are based on the deviance but incorporate a penalty for a greater number of parameters. Lower AIC and BIC values indicated a better model Luke, .Statistical analyses were carried out with the R packages lme4 , there was a significant increase in fit [Xp < 0.001], as the main effect of task was statistically significant. Post-hoc tests showed that perceived workload was significantly different between any pair of tasks and that RED and OSw were the tasks with the highest perceived workload  = 11.48, p = 0.718], and none of the parameters was statistically significant. ES was the only main effect of a personality trait that was still statistically significant.In Model 6 we entered in the model the interaction effects of personality traits with task to test whether personality moderated the effect of task. This hypothesis was not supported as the model fit did not significantly increase , supporting the hypothesis that personality moderates the effect of WSs. The inspection of parameters showed that there was no significant interaction effect of extraversion with WS, but also that: (a) higher levels of Agreeableness were associated with higher levels of perceived temporal demand and lower levels of frustration  hign Figure ; (c) hign Figure ; (d) hign Figure . No mainX2(75) = 68.53, p = 0.684].Finally, we did not find support for the hypothesis that the level of a personality trait could moderate the interaction effect of task and WS since the fit of Model 8 was not statistically higher than the fit of Model 7 [Seemingly for the first time, this study investigated whether personality traits moderated perceived workload, once controlling for background variables, task type and workload source, in real-life tasks of flying column police officers. Our results suggest that personality traits moderate the effect of the workload source. At a more general level, the results of this study are also consistent with the claim that workload is the result of the characteristics of the individual that interact with task characteristics Szalma, .Background variables did not show any significant effect (Model 1). The tasks performed by the officers of this study clearly differed in perceived workload, as suggested by the significant main effect of task (Model 2). Not surprisingly, the two most demanding tasks—regardless of the workload source—were the Redman, which is an intense training drill, and the Operational Service With Intervention, which is an emergency task that might lead to physical confrontation  effects of personality traits (Model 5) showed that higher levels of Extraversion were associated with higher levels of perceived workload, whereas higher levels of Conscientiousness and Emotional Stability were associated with lower levels of perceived workload. These results would have been consistent with expectations only if we assumed that the tasks could not have been very stimulating for the officers, thus making them perceive the tasks as aversive , disagreeable individuals are more likely to notice, attend to, and process antisocial or hostile cues. This increases the likelihood of hostile attributions and interactions, and thereby reinforces a range of aggressive schema and scripts behavior can have serious consequences on other individuals. To this aim, the NASA-TLX is a simple and efficient instrument for measuring the perceived workload and its application can be useful in monitoring the workers assigned to critical tasks.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.