H Ladjal
email: hamid.ladjal@ensi-bourges.fr

D Folio

A Ferreira
email: antoine.ferreira@ensi-bourges.fr

J Kim
email: jskim@kaist.ac.kr

Evaluation of Telerobotic Shared Control Strategy for Efficient Single-Cell Manipulation

Jungsik Kim, Hamid Ladjal, David Folio, Antoine Ferreira, Member, IEEE, and Jung Kim, Member, IEEE Abstract-Microinjection using a glass capillary is a highly efficient method for the delivery of exogenous materials into cells and is widely used in biomedical research areas such as transgenics and genomics. However, this direct injection is a time-consuming and laborious task, resulting in low throughput and poor reproducibility. Here, we describe a telerobotic shared control framework for microinjection with high manipulation efficiencies, in which a micromanipulator is controlled by the shared motion commands of both the human operator and the autonomous controller. To determine the optimal gains between the operator and the controller, we proposed a quantitative evaluation method using a model of speed/accuracy trade-offs in human movement. The results showed that a 40-60 % weighting on the human operator (or the controller) produced the best performance for both speed and accuracy of task completion suggesting that some level of both automation and human involvement is important for microinjection tasks.

Note to Practitioners-In single-cell microinjection, for the small size and delicate structure of a cell, to date, most human operators have manipulated biological cells manually; therefore, low manipulation efficiency and poor reproducibility has been reported for this task. Most manipulation systems have primarily focused on limited visual feedback in conjunction with a dialbased console system, requiring extensive operator training to perform injection tasks with reproducible results. To address these problems, a telerobotic shared control method for microinjection was developed by integrating the automatic and direct manipulation functions of a robotic system. While a controller retains cells and glass pipettes within a desired path or space, the operator can concentrate on the injection task, thus achieving high throughput and dexterity.

Index Terms-Fitts' and steering laws, microrobotic control, single-cell microinjection, telerobotic shared control.

I. INTRODUCTION

T HE highly efficient transfer of foreign materials into cells remains a challenge in biotechnology, both for fundamental cellular and molecular biology research and in biomedicine. Several methods have been developed for the successful delivery of exogenous materials into cells. Among them, single-cell microinjection is performed to directly introduce foreign materials, such as DNA, proteins, sperm and drugs, into individual cells [START_REF] Luo | Synthetic DNA delivery systems[END_REF]- [START_REF] Zhang | Microinjection as a tool of mechanical delivery[END_REF], and it is a relatively more efficient delivery method than electrical [START_REF] Bestman | In vivo single-cell electroporation for transfer of DNA and macromolecules[END_REF], viral [START_REF] Luo | Synthetic DNA delivery systems[END_REF], chemical [START_REF] Luo | Synthetic DNA delivery systems[END_REF], and other transfer methods [START_REF] Tirlapur | Cell biology -Targeted transfection by femtosecond laser[END_REF], [START_REF] Kim | Ultrasound-mediated transfection of mammalian cells[END_REF]. Microinjection is used in many different biotechnologies such as cell transfection with DNA in mammalian cells [START_REF] Luo | Synthetic DNA delivery systems[END_REF], induced pluripotent stem (iPS) cell transfer [START_REF] Zhao | Production of mice using iPS cells and tetraploid complementation[END_REF], and in vitro fertilization (IVF) by intracytoplasmic sperm injection (ICSI) [START_REF] Kimura | Intracytoplasmic Sperm Injection in the Mouse[END_REF], [START_REF] Yoshida | Piezo-actuated mouse intracytoplasmic sperm injection (ICSI)[END_REF]. However, although microinjection has relatively high delivery efficiencies, the injection task is time-consuming and labor-intensive work that limits the manipulation of large numbers of single cells [START_REF] Luo | Synthetic DNA delivery systems[END_REF], [START_REF] Stephens | The many ways to cross the plasma membrane[END_REF]. The elaborate manipulation of small, delicate cells demands certain technical requirements, such as the development of micro-motion stages and the fabrication of micro-pipettes with a fine tip. In addition, great manipulation skills are required of a human operator requiring extensive training to perform these injection tasks; thus, microinjection has low manipulation efficiencies, resulting in low throughput and poor reproducibility.

The manipulation of micrometer-sized cells is currently hindered by the limited capability of sensing technologies. These manipulations are often performed with insufficient visual information, within a small field of view and a narrow depth of field, creating a difficult situation for the detection and positioning of cells or glass pipettes. In addition, there is no force/tactile feedback for robot control or haptic interaction. Insensate glass pipettes can easily damage themselves or the target cells. To address these problems, several single-cell microinjection systems have been proposed to improve the manipulation efficiency. Automated microinjection systems have been developed to remove human involvement from the injection process [START_REF] Sun | Biological cell injection using an autonomous microrobotic system[END_REF]- [START_REF] Lu | A micromanipulation system with dynamic force-feedback for automatic batch microinjection[END_REF], where a visual servoing approach is usually used to control the position and force of a micromanipulator; however, it is challenging to create a fully automated manipulation system because microinjection is conducted under diverse and complex conditions such as varying cell size (from one micrometer to hundreds of micrometers), cell types (e.g., suspended or attached ones) and liquid environments. Fig. 1 compares the various cells typically manipulated in many research and clinical fields, showing that the differences in the geometry and the material properties are greater than one-thousand-fold and one-million-fold, respectively. Therefore, there are difficulties in the dexterous manipulation of cells with multiple degrees of freedom (DOF) and in target selection (e.g., cell nucleus or cytoplasm) in visual servoing [START_REF] Liu | Cell Contour Tracking and Data Synchronization for Real-Time High-Accuracy Micropipette Aspiration[END_REF], [START_REF] Jang | Shape recognition of the embryo cell using deformable template for micromanipulation[END_REF]- [START_REF] Wang | Machine Vision and Image Processing for Automated Cell Injection[END_REF]. Teleoperated microinjection systems have been developed to provide haptic feedback during manipulation A comparison of sizes and mechanical properties of various cells [START_REF] Zhang | Microinjection as a tool of mechanical delivery[END_REF], [START_REF] Tirlapur | Cell biology -Targeted transfection by femtosecond laser[END_REF], [START_REF] Zhao | Production of mice using iPS cells and tetraploid complementation[END_REF], [START_REF] Sun | Biological cell injection using an autonomous microrobotic system[END_REF], [START_REF] Mattos | Blastocyst Microinjection Automation[END_REF], [START_REF] Boukallel | Smart microrobots for mechanical cell characterization and cell convoying[END_REF]- [START_REF] Yanagida | The usefulness of a piezo-micromanipulator in intracytoplasmic sperm injection in humans[END_REF].

[21]- [START_REF] Kim | A Haptic Interaction Method Using Visual Information and Physically Based Modeling[END_REF]. However, most of them have provided force sensing and feedback for only a small number of DOF.

Here, we present a telerobotic shared control (TSC) framework developed for single-cell microinjection with high manipulation efficiency. The motivation of the TSC arose from the idea that the collaboration of a human and a robotic system can increase the quality and capability of manipulation by exploiting a human's ability to skillfully manipulate objects with dexterity and disturbance adaptation along with a robot's accuracy and repeatability [START_REF] Taylor | Medical Robotics and Computer-Integrated Surgery[END_REF]- [START_REF] Griffin | Feedback strategies for telemanipulation with shared control of object handling forces[END_REF]. To reduce the difficulties in biomicromanipulation mentioned above and simultaneously achieve high throughput and dexterity, both automatic and direct manipulation functions of the system are needed in microinjection. In the TSC approach, a human operator can control the manipulator as much as possible while a controller retains cells and glass pipettes within a desired manipulation path or space to provide adequate performance. In the remainder of this paper, the TSC strategy is first presented, and the sharing method for the teleoperation and automation modules of the microinjection system is described. In addition, we provide a quantitative analysis to determine what level of automation (or direct manipulation) is needed for optimal TSC. Most previous studies on shared control have not addressed how to determine the degree of autonomy (or human involvement) to implement in the telemanipulation for the best performance. Although shared control has been applied to various applications [START_REF] Griffin | Feedback strategies for telemanipulation with shared control of object handling forces[END_REF]- [START_REF] Bertocchi | Human-Robot Shared Control for Robot-Assisted Endoscopy of the Spinal Cord[END_REF], this work is, to the authors' knowledge, the first providing an evaluation method for determining the optimal shared control gains and the first application of shared control in a cellular micromanipulation. This work focused on the evaluation of a shared control for microinjection using the proposed model with Fitts' and steering laws.

II. METHOD

A. Telerobotic Microinjection System Design

A microinjection task consists of guiding a micropipette to a cell, puncturing the cellular membrane, and depositing foreign materials at a targeted position. The cells in the injection chamber with a liquid medium are attached to a substrate or suspended by a second (holding) glass pipette. The pipette tip is angled to approach a cell in a liquid medium.

The telerobotic biomicromanipulation system for microinjection consists of a master robot to input the operator's motion command, micromanipulators with glass micropipettes and a vision system (Fig. 2).

The task space of the master robot is represented by the coordinate frame {M : (x, y, z)} ∈ ℜ 3 , the task space of the micro manipulator is represented by the coordinate frame {S : (X,Y, Z)} ∈ ℜ 3 , and the image space of the fixed camera is defined by the image coordinate frame {I :

(u, v)} ∈ ℜ 2 .
Visual information is a main sensing modality in the biomicromanipulation tasks. The processing of visual data determines the focal plane and the guidance path of the pipette tip within the image frame. In this study, because the input of the manipulator is given in the slave frame {S}, the mapping between {S} and the image frame {I} forms a critical component for servoing the pipette tip. To compute this mapping, a calibration methodology was developed and implemented. Several calibration methods exist in the literature and are mostly used in macroscale vision applications [START_REF] Zhang | A flexible new technique for camera calibration[END_REF]. Unfortunately, those methods cannot be directly applied to calibrate an optical microscope due to the characteristics of the optical system (e.g., high optical magnification and very narrow depth of field), and some dedicated methods have, therefore, been proposed [START_REF] Ammi | Automatic Camera-Based Microscope Calibration for a Telemicromanipulation System Using a Virtual Pattern[END_REF]. In this work, the camera calibration parameters are defined in the focal plane (image space); therefore, the non-sharp tip image, due to the depth directional motion of the robot, is ignored. For an orthographic camera projection model, the tip position S p = (X,Y, Z) T in the slave frame can be related to its position I p in an image space as follows:

I p = ( u v ) = ( X/S u + u 0 Y /S v + v 0 ) (1)
where S u and S v are the pixel dimensions (µm/px) and the initial tip position I p 0 = (u 0 v 0 ) T is measured by the templatematching method in the focal plane [START_REF] Kim | A Haptic Interaction Method Using Visual Information and Physically Based Modeling[END_REF].

When assuming an accurately position-controlled micromanipulator and an orthographic projection model, it is important to initially locate the tip in the focal plane. In this work, the precise placement of the tip along the Z depth is achieved by the comparison of the pixel gradient magnitudes at the tip because the tip is angled and has pure translational motion. The gradient magnitudes are computed along the center line in the u direction of the tip, and the maximum values for each Z depth are compared. The gradient magnitude is at a maximum when the tip is in the focal plane (Fig. 3).

B. Telerobotic Shared Control

The overall control architecture of the TSC system is described in Fig. 4. The TSC has two levels; the lower level consists of the direct manipulation and the autonomous controller based on an artificial potential field, and the higher level is the shared controller to integrate both modules.

The total input command S u for the micromanipulator is defined as the weighted sum of motion commands from the operator (u h ) and the autonomous controller (u c ):

S u = K

h u h + K c u c K h + K c = 1 0 ≤ (K h , K c ) ≤ 1 (2)
where K h and K c are the weighting factors for the operator and the controller, respectively.

The input from the human operator is the position command of the micromanipulator under position control: u h = α p M p, where M p is the Cartesian tip position of a master device and α p is the scaling factor. An artificial potential field-based control algorithm [START_REF] Khatib | Real-Time Obstacle Avoidance for Manipulators and Mobile Robots[END_REF] is applied for the autonomous manipulation module, in which the manipulator is considered to be a particle and is controlled under a force field by attractive and repulsive potential functions. An attractive potential field is constructed to attract the manipulator to the guidance path, and a repulsive potential field is generated around a cell membrane to prevent the micro pipette tip from passing the membrane in any direction other than the injection direction. The total potential field is defined by the sum of both potential fields, and each potential function is defined as follows:

U total (p) = U att (p) +U rep (p) U att (p) = 1 2 K a |p d -p| 2 U rep (p) = 1 2 K r |p -p o | 2 , for |p -p o | ≤ r (3)
where p d = nearest point from the tip to the guidance path p o = nearest point from the tip position to the membrane K a , K r = constant gains r = radius of influence. Fig. 5 shows a two-dimensional example of a potential map for a zebrafish embryo. Finally, for the net force f p is defined by the negative gradient of the potential field, and the input command from the artificial potential field algorithm is defined as follows:

u c = c f f p = -c f ∇U total (4)
where c f is the compliance constant for modifying the input command in µm/N.

C. Optimal Shared Control Gain Tuning

In the TSC, the input commands from the teleoperation module and the autonomous module to the shared controller Total potential field map in the image space. High potential is represented in red.

have gains (weighting factors) of K h K c , respectively. These gains play an important role in deciding which module will be weighted more in the TSC. High human operator weight can lead to the microinjection system acting as a conventional direct manipulation system. Conversely, when more weight is given to the automation module, the human operator can lose control of the micromanipulator.

In this work, to quantitatively evaluate the effect of the weighting level of each module on the TSC and so obtain the optimal gains, we performed 2D pointing and steering tasks based on Fitts' law and the steering law. Fitts' law and the steering law were first developed to quantify the speed and accuracy trade-off in target-directed movements [START_REF] Fitts | The information capacity of the human motor system in controlling the amplitude of movement[END_REF], [START_REF] Accot | Beyond Fitts's Law: Models for Trajectory-Based HCI Tasks[END_REF]. They have been applied in various applications, such as human-computer interaction and robotics [START_REF] Cannon | Experiments with a Target-Threshold Control-Theory Model for Deriving Fitts Law Parameters for Human-Machine Systems[END_REF]- [START_REF] Choi | Development and Quantitative Performance Evaluation of a Noninvasive EMG Computer Interface[END_REF].

In Fitts' law, the Movement Time (MT ) in seconds to select a target of width W and at distance D is given by

MT = a + b log 2 ( D W + c) (5)
where a and b are empirical parameters determined by linear regression, and c is 0, 0.5 or 1 (we selected 1, following Mackenzei and Buxton [START_REF] Mackenzie | Extending Fitts' Law to Two-Dimensional Tasks[END_REF]). The term log 2 (D/W + c) refers to the Index of Difficulty (ID) in bits, which represents the difficulties of the tasks.

In a typical Fitts' law formulation, the target-pointing movement is only considered between the initial and final positions, and it is thus not appropriate for trajectory-based tasks. Therefore, if the movement is constrained along a predefined path, the steering law accurately predicts the MT with path length D and width W [START_REF] Accot | Beyond Fitts's Law: Models for Trajectory-Based HCI Tasks[END_REF]:

MT = a + b D W . ( 6
)
Although shared control between humans and robots has been widely studied and various applications for Fitts' law have been presented, the quantitative analysis for choosing ideal weighting in shared control using Fitts' law has not been studied. Here, we present a method for determining the ideal weights for shared control using Fitts' and steering law tests.

In the proposed TSC for microinjection, a micropipetteguiding task, i.e., directing the tip to a cell along the guidance path with depth directional motion compensation, can be seen as a steering law task, and target selection from the guidance path can be a Fitts' law task of compensating for targetdetection error. We propose a model combining Fitts' law and the steering law, in which we hypothesized that the total index of difficulty ID t (bits) is obtained by:

MT = a + bID t ID t = ID f + d • ID s ( 7
)
where ID f = log 2 (D/W + c) in (5) and ID s = D/W in (6), and d is 1 • bits for unit conversion.

III. EXPERIMENT

A. System Setup

A biomicromanipulation workstation is shown in Fig. 6. Zebrafish embryos were used as the manipulated suspended cells. The operator commanded the master robot (SensAble Technologies, PHANToM Premium 1.0, USA) to input the motion command for the micromanipulator in the slave space. The slave consists of a micromanipulator (Physik Instrumente, F-131, Germany) with an injection needle, and the other micromanipulator (Sutter Instrument, MP225, USA) consists of a holding glass pipette. The micromanipulator has three translational motions in the X, Y , and Z direction for input command u in µm. The vision system includes a CCD camera (SVS-Vistek, SVS340MUCP, Germany with a 640×480 pixel resolution and maximum frame rate of 250 fps) with a microscopic lens (Moritex, MML2-ST65D with 2× magnification) to capture images for the cells and the motion of the slave tip. The position of the cell in the image space is extracted using the Hough transform as the center point of the detected circle, and the focusing of the nonangled holding pipette with a cell was implemented based on the template-matching method [START_REF] Sun | Biological cell injection using an autonomous microrobotic system[END_REF]. Finally, the guidance path is determined as the linear path from the cell position to the injection starting position near the cell membrane, as shown in Fig. 7. The gains for artificial potential field were set to K a = 1.0, K r = 1.0, and c f = 1.0. The microinjection process is shown in Fig. 8.

B. Fitts' and Steering Laws Test Design

Five participants (four males and one female aged 26.8 ± 2.2 years) participated in the experimental task. All participants were right-hand dominant and inexperienced in microinjection and teleoperation systems.

Fig. 9 shows the task window for the proposed model ( 7). The steering task was performed first for the tunnel (Box 1 in Fig. 9), in which the desired path was on the center line along the horizontal direction. The participants were asked to guide a white plus sign (the micropipette tip position in image space) from the right side to the left side of the tunnel without crossing the top or bottom sides of the tunnel. The starting position of a white plus sign was randomly chosen every trial. After passing the tunnel, a square box appeared (Box 2), and the participants were instructed to tap inside Box 2 by clicking the button on the stylus of the haptic device. If the participants failed to perform the task by leaving the tunnel or mistargeting the square box, the white plus sign changed to a red plus sign, and the trial was restarted by positioning the tip in the starting position, reinitializing the timer. The completion time was recorded in seconds from the time of tunnel entry to the time of tapping Box 2, and the number of errors was also recorded. During the test, the participants were asked to perform the task as quickly as possible and were not informed on the principles of shared control. Five gains (K h = 0.2, 0.4, 0.6, 0.8and1.0) were selected to compare the performance with different gains, and 15 total indices of difficulty (Table I) were selected. Each participant performed 10 sets of trials and each set consisted of 75 trials (15 IDt × 5 gains) in random order. A total of 3,750 trials were analyzed, excluding failed trials.

C. Results and Discussion

The experimental results along with the linear regression for the proposed model [START_REF] Zhao | Production of mice using iPS cells and tetraploid complementation[END_REF] are shown in Table II and Fig. 10, respectively. The experimental data fitted the model with an r 2 value over 0.8183, showing that the MT s were predicted well by the proposed model and that the model can thus be used for the performance measurement to evaluate the system and determine the optimal TSC gains.

As a performance index for the comparison of system behavior with different gains, throughput (T P) was calculated [START_REF] Soukoreff | Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI[END_REF], here defined as T P = (1/m)Σ(1/n)Σ(ID ti j /MT i j ) in bitspersecond(bps), where n is the number of trials and m is the number of participants. T P is a useful measure for the speed and accuracy performance of movements by the integrated interpretation of the slope and intercept parameters of the regression model. The one-way within-subjects analysis of variance (ANOVA) test was used, and post-hoc analysis was performed using the Tukey's test.

Table III lists the ANOVA test results, showing a significant effect of the gains for T P (p < 0.001), which indicated that the different TSC gains affected the task performance. Fig. 11 shows the T P values of the TSC gains. When K h = 0.2, none of the participants performed better due to the strong constraints of the movements on the guidance path in performing the Fitts' targeting task. This meant that a greater weighting of the ability of the autonomous controller to control the manipulator hindered the operator's conscious actions to move the tip out of the guidance path to handle erroneous situations such as failed target detection, target movement during manipulation, and target occlusion by floating particles in the liquid medium. Conversely, a greater weighting on K h means that more manipulation capability is given to the operator; when K h = 1.0, the manipulation is the same as with the direct manipulation mode (no intervention of the controller). In the direct manipulation, the participants easily strayed from the guidance path in performing the steering task and had a lower T P. Fig. 12 shows the total number of errors for all trials (F 4,12 = 30.4476, p < 0.001). As defined above, the error was counted when the tip failed to track the guidance path (steering law) or in pointing to the target (Fitts' law). For higher K h , the accuracy was lower due to weak constraints on the guidance II). The dashed line represents the unit slope for an ideal model. path, and the direct manipulation showed a larger number of errors during the manipulation. In addition, as shown in Table II, higher K h values led to an increase in the slope of the regression model. This indicated that there was relatively smaller performance difference at higher K h between a low-ID t task and a high-ID t task. In other words, the intervention of the autonomous controller reduced the influence of operator skill in performing the manipulation task.

The participants showed the best performance at K h values of 0.4 and 0.6, with higher T P values and a lower number of errors (there was statistically no significant difference between K h = 0.4 and K h = 0.6 in T P and the number of errors). This range thus represents the optimal gain of the TSC for the microinjection task, and the TSC showed better results than direct manipulation. Some level of automation or human involvement was needed in the development of the robotic biomicromanipulation system from the standpoint of speed and accuracy performance.

In this study, the evaluation for K h = 0 could not be given because it was impossible to perform the Fitts' targeting task due to strong constraints; the operator could only control the manipulator in the guidance path. Although the fully automated system or the intervention of the autonomous function in telemanipulation could enhance microinjection performance by reducing human deficiencies, as the weight of the controller was increased, the operators lost their ability to control the manipulator's motion. Microinjection is a tedious task requiring much skill and practice to manipulate tiny cells with delicate glass tools, which requires cell separation, selection, targeting, tracking and multidimensional manipulation. Therefore, the loss of control ability is problematic for this complicated task. In addition, automatic dexterous manipulation including the rotation of cells has not yet been reported, and there are difficulties in target selection in visual processing. Jang et al. [START_REF] Jang | Shape recognition of the embryo cell using deformable template for micromanipulation[END_REF] reported that the failed recognition rate in the worst case was 47.8% for the detection of the nucleus and the polar body of mouse embryos. In [START_REF] Becattini | Diffusion Tensor Driven Contour Closing For Cell Microinjection Targeting[END_REF] and [START_REF] Wang | Machine Vision and Image Processing for Automated Cell Injection[END_REF], approximately 15% of the attached cells (CHO-K1 and endothelial cells, respectively) were missed in the visual targeting process.

IV. CONCLUSIONS

In the context of the single-cell microinjection system, we proposed a TSC to achieve simultaneously high throughput and dexterity. In particular, we provided a quantitative analysis method to determine what level of automation (or direct manipulation) is needed for optimal TSC. Most previous studies on shared control have not addressed how to decide how much autonomy will be integrated in the teleoperation (or vice versa) for the best performance. In this paper, for the evaluation of TSCs with different weightings, the microinjection task was modeled through the Fitts' and steering laws, which describe the speed/accuracy trade-offs in human movement. The results showed that a 40-60 % weighting on the human operator (or the controller) produced better performance for both speed and accuracy of task completion in microinjection. Although the fully automated function could not be measured by the proposed method, the test results suggested some level of automation or human involvement was needed in the development of the robotic biomicromanipulation system. In addition, the proposed evaluation method provides a theoretical basis for the selection of shared control gain in as system requiring the simultaneous achievement of high throughput and dexterity. The design of the optimal control weights on a human operator and a robot is the most challenging issue for the shared control area. The weighting can be different depending on the application of the shared control concept, for which the proposed method can be used to quantitatively evaluate performance via the modeling of other manipulation tasks of interest using Fitts' and steering laws.

In future work, microinjection experiments with biological samples will be performed with clinical evaluations. In addition, different shared control methodologies, such as a DOF partitioning and a virtual fixture, will be compared for effectiveness in microinjection. Finally, the proposed method will be applied to evaluate the effects of micromanipulation factors such as scaling factors and the field of view.

  Fig.1. A comparison of sizes and mechanical properties of various cells[START_REF] Zhang | Microinjection as a tool of mechanical delivery[END_REF],[START_REF] Tirlapur | Cell biology -Targeted transfection by femtosecond laser[END_REF],[START_REF] Zhao | Production of mice using iPS cells and tetraploid complementation[END_REF],[START_REF] Sun | Biological cell injection using an autonomous microrobotic system[END_REF],[START_REF] Mattos | Blastocyst Microinjection Automation[END_REF],[START_REF] Boukallel | Smart microrobots for mechanical cell characterization and cell convoying[END_REF]-[START_REF] Yanagida | The usefulness of a piezo-micromanipulator in intracytoplasmic sperm injection in humans[END_REF].

Fig. 2 .

 2 Fig. 2. Schematic view of micromanipulation system for microinjection.

Fig. 3 .

 3 Fig. 3. Maximum pixel gradient magnitudes along the center line of the tip image for different Z-depths.

Fig. 4 .

 4 Fig. 4. TSC block diagram.

  Fig.5.Total potential field map in the image space. High potential is represented in red.

Fig. 6 .

 6 Fig.6. (a) Experimental system setup for microinjection and (b) coordinate frames are for master space {M} with a master robot, slave space {S} with a micromanipulator, and image space {I}. p is the position vector and γ is the injection angle of a slave tip in the Y direction.

Fig. 7 .Fig. 8 .

 78 Fig. 7. (a) Captured image of a cell (zebrafish embryo). I p t is the cell position in the yolk of a zebrafish embryo, and the green circle represents the edge detection of the yolk by the Hough transform. (b) Path of the slave robot tip in the XZ plane. Subscripts 0 and s represent the initial point and the injection starting point, respectively. The guidance path is defined from S p s to S p t .

Fig. 9 .

 9 Fig. 9. The Fitts' law and steering law task window for the proposed model. Two boxes are displayed: box 1 is the tunnel for the steering law task and box 2 is the target for the Fitts' law task. Both tasks have the same width but different amplitudes.

Fig. 10 .

 10 Fig.10. Relationship between the actual and predicted values (from equations in TableII). The dashed line represents the unit slope for an ideal model.

Fig. 11 .

 11 Fig. 11. TP for TSC gains ( * p < 0.05).

Fig. 12 .

 12 Fig. 12. Number of errors during the task execution for different gains.

TABLE I TOTAL

 I INDEX OF DIFFICULTY (ID t )

	W (px)	D s (px)	D f (px)	ID t (bits)
	40	50	90	2.95
	30	50	95	3.73
	40	100	80	4.08
	20	50	80	4.82
	40	200	70	6.46
	30	150	80	6.87
	20	100	70	7.17
	10	50	40	7.32
	30	180	90	8.00
	30	200	90	8.67
	20	150	90	9.96
	20	180	80	11.32
	10	90	90	12.32
	10	100	70	13.00
	10	120	50	14.58

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2010-0022871). J.S. Kim is with the Department of Mechanical Engineering, Korea


David Folio

Christian Dahmen

Tim Wortmann

M Arif Zeeshan

Kaiyu Shou

Salvador Pane

Bradley J Nelson

Antoine Ferreira

Sergej Fatikow

Salvador Pané

S Shou

B Pané

Nelson

MRI magnetic signature imaging, tracking and navigation for targeted micro/nano-capsule therapeutics

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

Currently, Magnetic Resonance Imaging (MRI)-based medical micro-/nano-robotic platforms are investigated to reach locations deep in the human body while enhancing targeting efficacy. A recent breakthrough in interventional MRIguided in vivo procedures demonstrated that real-time MRI systems can offer a well-suited integrated environment for the imaging, tracking, and control of a ferromagnetic device, which was done in the carotid artery of a living swine [START_REF] Chanu | Adapting the clinical mri software environment for real-time navigation of an endovascular untethered ferromagnetic bead for future endovascular interventions[END_REF]. These successful experiments pointed out critical challenges in terms of real-time imaging, tracking and navigation for future therapeutic applications, that are controlled targeted drug delivery in brain [START_REF] Park | Magnetic iron oxide nanoworms for tumor targeting and imaging[END_REF], navigable magnetic carriers for implantable biosensors or controlled ultrasensitive imaging for early diagnosis and treatment of diseases [START_REF] Abbott | How should microrobots swim?[END_REF].

Recent development of various ferromagnetic filled carbon nanotubes has stimulated the research on their applications in diverse fields. Especially, carbon nanotubes filled with ferromagnetic core are considered to be the most important building blocks for nanomedicine applications [START_REF] Leonhardt | Synthesis of ferromagnetic filled carbon nanotubes and their biomedical application[END_REF], [START_REF] Mönch | Ferromagnetic filled carbon nanotubes and nanoparticles: synthesis and lipid-mediated delivery into human tumor cells[END_REF]. For instance, they have the potential to carry drugs into the organism as they are much smaller than the blood cells and can be easily functionalized by means of a broad range of chemical methods [START_REF] Balasubramanian | Chemically functionalized carbon nanotubes[END_REF]- [START_REF] Klingeler | Carbon nanotube based biomedical agents for heating, temperature sensoring and drug delivery[END_REF]. For these reasons, in this work ferromagnetic nanowire (NW)-filled multiwalled carbon nanotubes (FMWNT) were designed. Dynamic tracking of ferromagnetic materials are mostly used due to significant magnetic susceptibility artifacts when present inside the MRI during imaging [START_REF] Graft | Rf artifacts caused by metallic implants or instruments which get more mrominent at 3t: an in vitro study[END_REF]. Moreover, image acquisition delays offer a great challenge for real-time interventional MRI. In [START_REF] Felfoul | In vivo mr tracking based on magnetic signature selective excitation[END_REF], the authors demonstrate a new concept of magnetic signature selective excitation tracking (MS-SET) providing high position update rate. However, the poor position accuracy of the MS-SET tracking technique induces navigation or trajectory control errors over pre-planned paths [START_REF] Sabra | Use of 3D potential field and an enhanced breath-first search algorithms for path-planning of microdevices propelled in the cardiovascular system[END_REF]. In contrast, few works address the robust navigation planning problem, that in our context is dealing with the MRI constraints. As improvement, this paper reports on the MRI-based planning and sensing of micro/nano-devices aiming to navigate within vessels. More precisely, the motivation of this paper is to propose a framework to plan a robust pathway, wrt. MRIbased tracking capabilities to enhance the navigation ability to reach deep locations in the human body. Through a specially developed software environment integrating pathplanning methods and real-time tracking, a clinical MRI system is adapted to provide new functionality for controlled interventional targeted therapeutic applications.

The paper is organized as follows. We first describe the experimental setup using a clinical MRI System. Section III introduces the navigation path planning approach. The MRI artifact imaging and the ferromagnetic nanowire designed as payload for the microcapsule are described in section IV-A. Then, the tracking procedure and the pathway robust to uncertainty are described. Finally, the last section shows the obtained experimental results.

II. EXPERIMENTAL MRI SYSTEM DESCRIPTION

A clinical 3.0T Siemens Verio MRI platform is used here for planning, imaging, tracking and propulsion of a micro/nanocarrier. A MRI-based navigation system requires observation of the scene in order to either plan the trajectory by off-line mapping, or to correct the device pose error online between the planned and the observed trajectory. To ensure a smooth conveyance of the capsule to its destination, while avoiding collisions and the risk to be trapped in wrong pathways, navigation performance will be affected by external perturbations and MRI technological constraints (such as nonnegligible pulsatile flow, limitations on the magnetic gradient amplitude, MRI overheating avoidance, etc.). Moreover, the overall concept of the MRI-tracking system is based on the fact that both tracking and propulsion are possible with the manufacturer-supplied gradient coils of the MRI system. At any instant only one of the functions could be applied (i.e. either tracking or propulsion), but both will be executed over the same MRI interface. The MRI interface has therefore to be shared and a time-divisionmultiple-access scheme for it has to be developed. In this work we will focus on the planning and tracking process.

To conduct the various experiments, we use a clinical 3.0T Siemens Verio MRI system providing real-time capabilities. Steel balls of different sizes were used and placed in the cube shape acrylic box depicted on Fig. 1(b). The acrylic box was filled with water and put into the bore of the clinical MRI platform, as shown on Fig. 1(a).

III. NAVIGATION PLANNING

To allow our microdevice to navigate properly within a tubular structure network we first need to find a valid pathway between a initial and final states. The path planning problem has been studied for ages by mathematicians, and has been solved numerically using graph theory or dynamic programming. Among proposed methods are the breadthfirst search, heuristic search and hybrid search that are very popular in path planning (such as Dijkstra or A algorithms). Sethian [START_REF] Sethian | Level set methods and fast marching methods: evolving interfaces in computational geometry[END_REF] has illustrate that Dijkstra's method (and by generalization A ) is inconsistent with the underlying continuous problem: the resultant minimal path is bounded to the discrete grid. In opposition, with a similar complexity to common graph-search techniques, Sethian has proposed the Fast Marching Methods (FMM) which converges to a smooth solution in the continuous domain even when it is implemented on a sampled environment [START_REF] Sethian | Level set methods and fast marching methods: evolving interfaces in computational geometry[END_REF]. Moreover the FMM needs a very simple initialization (see Fig. 8) and leads to global minimum of a snake-like energy, thus avoiding local minima. Therefore, our proposed planner is built upon the Fast Marching minimal path extraction framework set out in [START_REF] Sethian | Level set methods and fast marching methods: evolving interfaces in computational geometry[END_REF] [START_REF] Mueller | Robust semi-automated path extraction for visualising stenosis of the coronary arteries[END_REF]. However, choosing an appropriate and efficient cost function c which drives the front propagation efficiently is the most difficult part of the entire process. In our previous work [START_REF] Belharet | Endovascular navigation of a ferromagnetic microrobot using MRI-based predictive control[END_REF], [START_REF]3D MRI-based predictive control of a ferromagnetic microrobot navigating in blood vessels[END_REF] we have described a framework allowing to find a minimal centered navigation path, where a cost function c Tub could be defined as follow:

c Tub : x ∈ C → Enhance(x) ∈ R + ( 1
)
where C is the input MRI data, Enhance is a tubelike enhancement function. To design the Enhance function we use some a priori knowledge about tubiform shape and intensity in MRI data. In this context, a typical cost function is designed by using a Frangi filter [START_REF] Frangi | Multiscale vessel enhancement filtering[END_REF]. This filter computes for a voxel a pseudo-probability of belonging to a tubular structure using geometric properties of the image given by the Hessian matrix. Therefore, this measure highlights tubular structures in the image: with high values inside and nearly zero outside (see Fig. 2(b)). Finally, this approach allows to find a minimal centered navigation path between the start and target seed. We have applied this procedure to different sets of data, in 2D [START_REF] Belharet | Endovascular navigation of a ferromagnetic microrobot using MRI-based predictive control[END_REF] as well as in 3D [START_REF]3D MRI-based predictive control of a ferromagnetic microrobot navigating in blood vessels[END_REF]. In this work we have considered a 2D slice of a "phamtom" acrylic box with tubular structure representing a vessel (ie.

artery) network depicted on Fig. 1(c). The corresponding centered navigation path extraction procedure is illustrated on Fig. 2. Nevertheless, the cost function c Tub (1) is based only on spatial interpretation of the MRI data. In the following, we first describe the MRI artifact imaging, and the tracking procedure, and then we will propose to extend the navigation path extraction framework to incorporate a simulation of the microdevice's tracking process.

IV. MRI-BASED SENSING

A. MRI susceptibility artifact imaging

Since the beginning of MRI, the occurrence of magnetic susceptibility artifacts has been studied. In clinical practice, the focus is usually on avoiding artifacts and producing correct images of anatomical structure [START_REF] Port | Quantification and minimization of magnetic susceptibility artifacts on gre images[END_REF]. In the targeted system, susceptibility artifacts caused by the ferromagnetic microrobotic device will be exploited for position determination. For this purpose, the imaging parameters and artifact formation principles have been investigated.

The image acquisition procedure applied in MRI relies on the presence of a strong homogeneous main magnetic field B 0 and three well-defined gradient fields. Insertion of magnetic material into the field of view clearly violates the assumption of homogeneity. This has mainly two impacts:

• Intravoxel Dephasing: Spins inside a single voxel are dephased due to field inhomogeneity, this leads to a damping or loss of signal. • Spatial misregistration: The spatial coding scheme is corrupted and signals are registered incorrectly, causing bright fringes around metallic objects. An actual artifact shape depends on several factors related to the magnetic object and the imaging sequence. Saturation magnetization and object volume are the most important object specific parameters. For small aspect ratios, object shape is of minor importance. The most important sequence dependent parameters are echo type (gradient echo or spin echo) and echo time. Generally, gradient echo sequences produce much larger artifacts than spin echo sequences, in terms of affected volume. This is due to their inability to compensate temporally constant field inhomogeneities. The imaging parameters have been studied by experiment using a General Electric Signa 3T clinical MRI scanner (for details see [START_REF] Wortmann | Study of MRI Susceptibility Artifacts for Nanomedical Applications[END_REF]). A 2.5 mm steel sphere has been embedded into a 2000 ml container of agarose gel which produces a homogeneous background signal. A representative selection of imaging sequences has been tested: conventional gradient echo (GE), conventional spin echo (SE) and the real-time sequence single-shot fast spin echo (SSFSE). The artifacts have been segmented using the expectation maximization (EM) algorithm [START_REF] Forsyth | Computer Vision: A Modern Approach[END_REF]. Sample scans taken from the imaging experiments and the segmentation results for the GE sequence can be seen in Fig. 3. The artifact observed in the GE sequence shows the highest dimensions. Highest signal peaks are found using the conventional SE sequence. SSFSE produces the least severe artifacts. It has to be noted, that the artifact dimensions are orders of magnitude above the object dimensions. From the segmented susceptibility artifacts centroid, the object location can be concluded using a fixed offset vector. This position serves as the initialization position for the tracking procedure described in the section IV-C.

Let us recall that the aim of our MRI driven approach is the control of agglomerated nanoparticles. Subsequently, new nanoparticles have been designed wrt. magnetic steerability and artifact imaging. We describe in the sequel this new nanocarrier design.

B. Application-specific Nanoparticles

The artifact imaging has been verified also using a ferrofluid containing nanoparticles of 10 nm size, and the artifact characteristics have been found equivalent (see Fig. 4(b)). Specifically, ferromagnetic nanowire (NW)-filled multiwalled carbon nanotubes (FMWNT) were designed by using template-assisted growth on silicon. In our process, anodized aluminum oxide (AAO) templates were selfassembled on silicon surface with a diameter of 65-100 nm. Pulsed electrodeposition (PED) was carried out to grow nickel NW inside AAO pores. After PED process, chemical vapor deposition (CVD) was utilized to grow carbon nanotubes inside AAO templates by encapsulating Ni nanowires. such as electrolyte concentration, voltage, temperature, the oxidation time and subsequent etching treatment [START_REF] Zeeshan | Nanorobotic drug delivery: If i only had a heart[END_REF], [START_REF] Nielsch | Uniform nickel deposition into ordered alumina pores by pulsed electrodeposition[END_REF]. AAO templates with uniform and parallel porous structure are obtained by anodic oxidation of aluminum in a solution of 0.3 M oxalic acid at a constant potential of 40 V.

2) Electrodeposition of Ni nanowires: Eletrodeposition is a common technique for the synthesis of 1D nanostructures and does not require expensive instrumentation, high temperatures or low-vacuum pressures. In PED electric pulses are passed through the electrolyte of metallic ions and nanowires are deposited inside the pores due to reduction of ions. An electrolyte containing nickel salts and other additives was used to deposit Ni nanowires [START_REF] Nielsch | Uniform nickel deposition into ordered alumina pores by pulsed electrodeposition[END_REF], [START_REF] Sklar | Pulsed electrodeposition into aao templates for cvd growth of carbon nanotube arrays[END_REF].

3) Growth of ferromagnetic filled multiwalled carbon nanotubes (FMWNT): CVD is a convenient method for the synthesis of different types of CNTs ranging from single-walled to multi-walled carbon nanotubes [START_REF] Kumar | Chemical vapor deposition of carbon nanotubes: a review on growth mechanism and mass production[END_REF]- [START_REF] Sato | Growth control of carbon nanotubes by plasma enhanced chemical vapor deposition[END_REF]. The method is suitable to produce large quantities with satisfactory quality and allows also a scaling-up with moderate cost for industrial mass production. In the CVD process, chemical reactions take place, which transforms hydrocarbon precursors into a solid CNT walls on the surface of a substrate. The process is a catalytic process where a catalyst (in this case a transition metal) is involved to control the kinetics of reactions such as the decomposition of the precursor.

The Ni-filled CNTs were grown with a methane (CH4) precursor gas in CVD system (Centrothermo ATV PEO 603) at 850°C. We found that the nickel filling was distributed into several parts with the total length 200 to 400 nm. The diameter of the filling varied from 40 to 60 nm. The carbon layer was about 30 nm thick.

C. Artifact Tracking

Our objective in this section is to track the position of a a microdevice either in 2D or 3D. Due to the characteristic shape of the artifact (see Fig. 3), the particle can be located in the image in most cases by executing template matching. For this, during the recognition phase a template is extracted which will subsequently be used for tracking. The template matching approach chosen is based on correlation. With a template T and the input image I in the 2D case we have:

C(x p , y p ) = xt x=0 yt y=0 I(x p + x, y p + y) • T (x, y) (2)
The artifact's position depicted in the template is then derived from the maximum position inside the correlation matrix: C(x o , y o ) = max(C). The position of the maximal value in C corresponds to the position of the tracked object, and the center of gravity of the object can be computed.

1) Three Dimensional Tracking Algorithm: In order to determine the three-dimensional (3D) position of the artifact corresponding to the device, a correlation is applied with a template stack to deliver the third dimension coordinate. The approach used involves multiple correlation templates, which will be subsequently used and the best match determined. The principle of the algorithm can be seen in Fig. 5. C

n (x o , y o ) = max(C n ) (3)
and the best fitting matrix is determined:

C fit = max(C n ) ( 4
)
The fit is then further refined by interpolation, and a segmentation with size analysis is used to improve the results.

D. Experiment Description and Sensing Results

To evaluate the efficiency of the tracking algorithm, experiments have been executed in a clinical MRI system, illustrated in Fig. 1(a). A steel ball was positioned in the center of the box with a special mechanism after the box has been driven into the system, to prevent earlier movements due to the strong static magnetic field gradients during insertion. The steel ball can be seen marked by an arrow in Fig. 1(b). Hence, the tracking process uncertainty has been evaluated. For this, steel balls of different sizes have been fixed and repeatedly scanned in static case. The imaging sequences used for this were a HASTE sequence, and a FLASH sequence. The results for the experiment are summarized in table I. As can be seen, the standard deviation (std) of the extracted positions is mostly better when using the FLASH sequence. No clear dependency on size can be observed. Generally, the std lies below 0.2 voxels, which in this case with the voxel spacings from the sequence parameters delivers a standard deviation well below 200 µm.

V. PLANNING WITH UNCERTAINTY

There are several important sources of uncertainty which should be considered in MRI-based navigation planning. These include uncertainty in MRI data acquisition and processing, carrier model and position tracking. Failure to account for these uncertainty in the planning method could result in trapping the device in a wrong tube segment. In our context we have to ensure efficient sensing capability with a low level of uncertainty. For that, we simulate in real-time the microdevice's tracking process in order to assess each candidate configuration wrt. a sensing objective.

Our goal is to define a navigation path that maximizes the likelihood of successful execution by ensuring that sensor information can be gathered at all crucial stages. Therefore, our aim is to define a new cost function c Bel that maps the system's belief in its position x wrt. a sensor model.

c Bel : x ∈ C → I(x) ∈ R (5)
One way to design such a map ( 5) is to use the Sensor Uncertainty Field (SUF) introduced in [START_REF] Takeda | Sensory uncertainty field for mobile robot navigation[END_REF]. The SUF to define a mapping from a state x to an expected information gain I(x). This expected information gain (or, equivalently, the expected entropy reduction) quantifies the microdevice's ability to localize itself at different positions x in C: locations with high I(x) correspond to locations that generate sensor measurements that we expect to maximize the tracking process accuracy. In the SUF framework, I(x) is computed, given an observed data y, from the difference in Shannon entropy of the prior and posterior distributions:

I(x) = H(p(x)) -H(p(x|y)) (6)
where the Shannon entropy of a probability distribution p(x) is defined as: H(p(x)) = -p(x) log p(x) dx. The prior entropy H(p(x)) provides a measure of the certainty on the carrier's belief in its position x, before the sensory input y is received. H(p(x|y)) denotes the expected entropy change after measurement data y. The posterior probability p(x|y) is classically given by Bayes' rule: p(x|y) = p(y|x)p(x) p(y)

, where p(y) is the likelihood of observing data y, and p(y|x) is the likelihood of sensing data y at state x. p(y|x) is computed from the sensor model and the observed scene. The path that minimizes the uncertainty is then the one that maximizes I(x). Tracking process modeled as a white multivariate-Gaussian distribution.

A. Application and Results

In order to calculate the prior entropy H(p(x)), we need a probabilistic model for the physical space. To this aim we use our a priori knowledge, that is: each (unknown) state x must be within the tubular structures. Therefore, as previously introduced, Enhance(x) could be seen as a pseudoprobability for the device of belonging to a tubular structure, and leads to: p(x) ∝ Enhance(x). To compute H(p(x|y)), we assume that the sensing process could be modeled by a white multivariate-Gaussian distribution: N (0, Σ 2 y ), with zero mean and Σ 2 y the covariance matrix. To evaluate the efficiency of our proposed approach, we have considered a FLASH sequence tracking process of a 0.7mm steel ball. Thus the covariance matrix Σ 2 y of the Gaussian sensor model is computed using the table I, and is shown on Fig. 7. The sensor model has to be propagated over all states x. As this propagation could be not computationally efficient, we reduce the computation space to the level set provided by the FMM front propagation using c Tub [START_REF] Chanu | Adapting the clinical mri software environment for real-time navigation of an endovascular untethered ferromagnetic bead for future endovascular interventions[END_REF]. Moreover, it provides an expected information gain bounded by the tubular shape. Finally the secondary FMM step is executed to get a pathway that maximizes I(x) within the tubular structure. This new navigation path extraction procedure is robust to uncertainties as illustrated in Fig. 8 Fig. 6.a shows the corresponding computed expected information gain I 1 (x) and the resulting navigation path. As one can see, I 1 (x) is uniformly distributed within the the tubular structure. However, this does not correspond to the real case: the tracking action could not be performed in each state due to the MRI time-multiplexing constraints (see [START_REF] Belharet | Endovascular navigation of a ferromagnetic microrobot using MRI-based predictive control[END_REF], [START_REF]3D MRI-based predictive control of a ferromagnetic microrobot navigating in blood vessels[END_REF] for further details). Thus, to consider the tracking attenuation effects, we use the distance map from the initial state x 0 provided by the FMM (see Fig 2(c)) to improve the sensor model p(y|x). This leads to the secondary information gain map I 2 (x) depicted on Fig. 6.b.

Finally, the pathway based only on uncertainty, overestimates the navigation path leading to inconsistencies such as e.g., tube wall countouring or tube crossing. In such a case, a solution is to compute a weighted sum between the cost functions based on spatial information c (ie. using the Frangi filtering) and based on expected information gain c Bel , that is:

c T = λ 1 c Tub + λ 2 c Bel (7)
where λ 1 and λ 2 are weighting factors. Hence, this new cost function formulation allows to tune the navigation path to be either a centerline path or maximizing the expected information gain. The templates used for these image sequences are shown in Fig. 10. It can be seen that the templates differ in characteristics in each sequence. This strengthens the need for recognition and subsequent template extraction from the recognition data. As can be seen in Fig. 11, the x-direction motion in the first recorded sequence follows the camera-observed path. It has to be noted though, that only 6 data points were extracted, because of the 6 MRI images taken during the movement. For the second sequence the effect is the same. Also the x-direction motion of the third sequence is in line with expectations. The diagonal path of the third sequence is clearly visible in Fig. 11(d). The magnetic gradient applied here had the same strength for x and y-direction. This is also visible in the tracked data. For comparison, the path of the steel ball during the third sequence tracked in the camera image is also shown. As it can be observed, the movement is close to the MRI tracked movement. Finally, the experiments clearly show that paths are extracted which clearly fit the microdevice displacements. It can be concluded that the tracking algorithm is working, though the accuracy and precision has to be improved still. Improvements are possible by better tailoring of the used imaging sequence and optimization of the necessary parameters like slice spacing.

VII. CONCLUSIONS AND FUTURE WORKS

In this paper, we described a framework to extract a robust pathway with respect to MRI-based sensing capabilities. The related dedicated artifact imaging and tracking procedure using a clinical MRI system is presented and evaluated. Sensor feedback for the navigation is extracted by tracking the artifact in the MRI images which are continuously acquired during sequence execution. From preoperative MRI scan data and the characterized sensing model, a reliable pathway is planned which will be used to control the ferromagnetic nanowire agglomeration through the cardiovascular system to properly reach deep locations in the human body. Each procedure has been experimentally verified using an adapted clinical MRI system.

In subsequent steps, the sensing methods have to be combined with a controller and the closed loop control has to be validated over the planned pathway. Also it has to be shown that this approach works in biologically inspired experimental setups.

Fig. 1 .Fig. 2 .

 12 Fig. 1. The experimental setup for the combined planning and tracking experiments

Fig. 3 .

 3 Fig. 3. MRI susceptibility artifact imaging experiment. Results for a 2.5 mm steel sphere, embedded into agarose gel: SSFSE sequence (top left), conventional SE (top right), conventional GE (lower left) and reconstructed artifact volume of GE (lower right).

1 )

 1 Fig.[START_REF] Leonhardt | Synthesis of ferromagnetic filled carbon nanotubes and their biomedical application[END_REF]. FMWNT with a shell thickness of around 30 nm and agglomeration artifact of an agglomeration of 10 nm iron oxide nanoparticles.

Fig. 5 .

 5 Fig. 5. The three dimensional tracking algorithm

Fig. 6 .

 6 Fig. 6. Cost map function and extracted path in presence of uncertainty: a) I 1 (x) using an uniform sensor model propagation; b) I 2 (x) using an attenuated sensor model propagation; c) isotropic cost function c T .

Fig. 7 .

 7 Fig. 7.Tracking process modeled as a white multivariate-Gaussian distribution.

Fig. 8 .

 8 Fig. 8. Navigation path extraction procedure robust to sensing uncertainty.

Fig. 6 .

 6 c presents a pondered pathway extracted using I 2 (x) and with λ 1 = 0.66 and λ 2 = 1 -λ .VI. EXPERIMENTAL RESULTSTo validate the above mentioned MRI-based navigation and tracking procedure, we implemented a simple propulsion procedure and applied them in the clinical MRI system. During the experiment, different motions have been executed, and the corresponding images taken as shown on figures 9.

Fig. 9 .

 9 Fig. 9. Sequences recorded with movement in X-direction (upper two) and diagonal displacements (lowest)

Fig. 10 .Fig. 11 .

 1011 Fig. 10. Templates used for the three sequences

VIII. ACKNOWLEDGMENTS This work was supported by European Union's 7th Framework Program and its research area ICT-2007.3.6 Micro/nanosystems under the project NANOMA (Nano-Actuactors and Nano-Sensors for Medical Applications).


F Cloppet

A Boucher

Segmentation of overlapping/aggregating nuclei cells in biological images

This

paper presents a method of overlapping/aggregating nuclei cells segmentation. This method is based on the watershed segmentation algorithm, but the specificity of this work is to introduce some prior information about the usual shape of normal/pathological nuclei cells. Such prior information will help to optimize the right set of markers, from which the flooding will be done. This approach has been implemented and tested, and the results evaluated by cell biology experts show the efficiency of the proposed approach.

Introduction

Over the past fifteen years, the field of optical imaging has been developing rapidly with the advent of a wide range of new probes (especially fluorescent ones), with the advances in microscopy (immunofluorescence light microscope, confocal microscope, video microscopy...) and image analysis. All these advances have revolutionized our knowledge and understanding of the dynamic structures and events related to living cells.

The biological background of this work is based on alterations in nuclear morphology of fibroblasts from Hutchinson-Gilford Progeria Syndrome (premature aging disease of children). The knowledge of the cellular structure that underlies nuclear morphometric changes with respect to a non pathological shape is still limited. But such changes are frequently observed in various nuclei, including several human diseases like cancer.

Cell nuclear morphology is a hot topic research in biology. Several biological functions seem to be related to major changes in the geometry of the nucleus. Nuclear size and shape are good visual descriptors of abnormal nuclear behavior, and could lead to a quick and efficient diagnosis of nucleus normality.

Highly reliable shape characterization and description [START_REF] Cloppet | Angular Bisector Network, a Simplified Generalized Voronoï Diagram : Application to Processing Complex Intersections in Biomedical Images[END_REF] involves computer vision problems for which efficient solutions have not yet been found, such as extracting in a high precise way the contour of each nucleus.

Section 2 gives some details about the typical problems encountered in the images that have to be processed and deals with the problem of segmentation, while Section 3 describes our contribution to segment aggregating/overlapping nuclei. The results are presented and discussed in section 4, and section 5 concludes the paper.

Segmentation

In this study, the images are taken with camera fixed on an immunofluorescence light microscope. In fact, the major problem with such images is that they are disturbed by out-of-focus light mostly due to the thickness of the specimen. This can lead to some overlapping areas between several nuclei since they are not on the same focal plane (see Figure 1).

Figure 1. Nuclei of cells in an immunofluorescence image

This problem of overlapping areas could be largely solved by using confocal microscope, but the acquisition of images is much slower. Indeed, at the moment, this is hardly compatible with large screening tests of drugs. Furthermore, both sources of images (confocal or light microscope) present similar disturbances due to the auto-fluorescence of the 978-1-4244-2175-6/08/$25.00 ©2008 IEEE medium, the acquisition noise induced by the camera detector, and various imperfections of the optical system. Therefore, the segmentation step developed to extract each nucleus has to be done with high precision.

Segmentation remains one of the most challenging process in image analysis. It refers to the task of detecting boundaries of objects of interest in an image. Several approaches have been proposed to deal with objects in contact, and they are mainly divided into two categories: energy-driven segmentation [2-3] and watershed-based segmentation [START_REF] Vincent | Watershed in digital spaces, an efficient algorithm based on immersion simulation[END_REF].

Recent methods, among the energy-driven segmentation methods, based on deformable contours [START_REF] Zimmer | Coupled Parametric Active Contours[END_REF] or level sets [START_REF] Zhang | Tracking fluorescent cells with coupled geometric active contours[END_REF], prevent the contours of the nuclei that are in contact from merging. They introduce in the energy functional an overlap penalty between each pair of contours. The multiphase level set method [START_REF] Vese | A Multiphase level Set Framework for Image Segmentation Using the Mumford and Shah Model[END_REF] can extract regions in contact, and overlapping regions. But all these methods are time consuming, with a lot of parameters to be tuned.

The watershed transform [START_REF] Vincent | Watershed in digital spaces, an efficient algorithm based on immersion simulation[END_REF] has proven to be a powerful and fast technique for both contour detection and region-based segmentation. Some interesting properties make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces an entire partition of the image. It has been applied to biological images [START_REF] Costa | Cell nuclei segmentation in noisy images using morphological watersheds[END_REF][START_REF] Chen | Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy[END_REF] and has given good results, especially on blurred grey level images. However, it has some drawbacks (oversegmentation, sub-segmentation, and sensitivity to noise…). This transform can be improved, in particular by the introduction of prior information [START_REF] Grau | Improved watershed transform for medical image segmentation using prior information[END_REF].

The initialization step is probably the most critical step of this kind of transform. A good set of markers will avoid severe over-segmentation. This paper will focus on this problem.

Segmenting aggregating/overlapping nuclei

The proposed system is illustrated on Figure 2. The main difficulty is to first detect whether a connected component extracted on the image corresponds to a single nucleus or to a group of several nuclei. This information will be helpful for selecting the right markers in order to avoid over or sub-segmentation given by the watershed transform.

Extraction of the right set of markers

Overlapping/aggregating nuclei have intersecting contours in points of high concavity. To select those involved in the segmentation process among the all set of concavity points, prior information about normal or abnormal nuclei has to be used. For example, normal nuclei have a slightly elongated but regular shape with a quite small area, while abnormal nuclei might be much bigger and/or lobulated. In fact, points of significant concavity that are extracted can correspond to aggregating or overlapping nuclei areas, but also to lobulated areas of abnormal nuclei.

In order to identify these three types of areas, segments linking significant concavity points (see Figure 3a) are explored for selecting the one(s) that correspond(s) to true separation segment(s) (see Figure 3b).

For this purpose, two templates of high biological plausibility (based on visual inspection of about 300 nuclei) have been established:

-overlapping template, -aggregating template. The overlapping template (see. Figure 4), is based on the hypothesis that if two nuclei overlap, the connected component has to be enough elongated to contain two nuclei.

[AB] is the potential segment of separation that connects two significant concavity points. All the points C, D, E, F, G, H of the template have to be inside the connected component. At the moment, the distances CD, EF=GH are evaluated by observing a high number of nuclei.

Figure 4. Overlapping template

The aggregating template (see. Figure 5) illustrates the fact that in aggregating areas, there is a narrow passage. The distances CD, EA=AF=GB=BH are also evaluated by observing a high number of nuclei. The angles are measured in function of tangent vectors left and right of concavity points A and B.

As the template could be too rigid, some flexibility is introduced with the following rule: "Only one point among the points C, D, E, F, G, H could be outside the connected component".

Figure 5. Aggregating template

If a potential segment of separation [AB] fits the overlapping template, the aggregating template, which is more restrictive, is then tested. If the aggregating template is fitted, the segment [AB] is validated as a line of separation between nuclei in contact.

On the contrary, it could be validated as a segment of separation between overlapping nuclei. But, in some cases of very big and lobulated nucleus, a potential segment of separation might fit the overlapping template, though there is no overlapping area (see Figure 6b). Therefore, in order to distinguish these two configurations, a study of the grey levels intensity along [AB] is performed. In case of overlapping nuclei, the grey level intensity is far higher in the area of overlapping than the mean intensity of the connected component. In case of a big lobulated nucleus, no significant variation of grey levels can be observed, then the potential segment of separation is rejected.

Watershed transform

If the connected component includes several nuclei, they have to be separated. The watershed transform is then used. The critical step of this transform is the setting of the markers which are the starting points of flooding.

If the nuclei have been identified at the previous step as aggregating nuclei, a marker is set in the middle of the regions located on both sides of the validated segment of separation. If the nuclei overlap, there will be one more marker in the overlapping area along the segment [AB] (see Figure 7). Then the gradient image is considered as a topographical surface, which is flooded from the markers. Lastly, a post-processing is applied to connect each region to the corresponding nucleus (see Figure 8): regions 1 and 3 form the first nucleus, and regions 2 and 3 the second one.

Results

This method has been tested and evaluated on images that contain 345 nuclei. About 76.23% of these nuclei are isolated, while 16.81% are overlapping and 6,96 % aggregated. 1) show that 95.94% of nuclei are well segmented in terms of number of nuclei, according to biological experts. Furthermore, no minor deformation is missed. As these minor deformations play a fundamental role in drawing the right conclusions about shape normality, it is important not to miss them at the segmentation step.

In case of overlapping nuclei, most of the errors are due to the fact that only one point of high concavity is detected (see Figure 9), therefore no segment of separation can be extracted.

Conclusion

In this paper we have presented a method, based on the watershed transform that can deal with aggregating/overlapping nuclei. The critical step of this transform is the initializing step. Prior information about nuclei is used to improve the selection of the best markers from which the flooding will start. The first results are quite encouraging but need to be validated on a larger set of data.

Future works will be devoted to the introduction of a formal learning step in order to obtain optimal aggregating/ overlapping templates based on biological knowledge extracted as rules or image samples. This would increase the fidelity in global applicability of the method.

Figure 2 .

 2 Figure 2. Illustration of the segmentation step

a b Figure 6 .

 6 Potential segment separation fit overlapping template in case of (a)overlapping nuclei (b)an abnormal nucleus

Figure 7 .

 7 Figure 7. Initializing step of the watershed transform in case of overlapping nuclei

1 2 3 Figure 8 .

 38 Figure 8. Result of overlapping nuclei segmentation

Figure 9 .

 9 Figure 9. Example of overlapping nuclei where only 1 high concavity point is detected

Table 1 : Rate of well-segmented nuclei in terms of number of nuclei

 1

	Isolated	Aggregating	Overlapping	Total
	Nuclei	Nuclei	Nuclei
	263	23	45	331
	100%	95,83%%	77,59%	95.94%
	The results (see Table

Acknowledgements

The authors would like to thank N. Levy, P. Cau, C. Navarro and S. Perreira (both at Inserm U910 "Génétique Médicale et Développement", Faculté de Médecine de la Timone, 13385 Marseille Cedex 05) for given images, many useful suggestions and inspiring conversations and for their expertise.


E Van Der Loo

M Congedo

Van De Heyning

De Ridder

Insular Lateralization in Tinnitus Distress

Tinnitus affects 15% of the population. Of these 1-2% are severely disabled by it. The role of the autonomic system in tinnitus is hardly being investigated. The aim of this study is to investigate the relationship between tinnitus distress and lateralization of the anterior insula, known to be involved in interoceptive awareness and (para)sympathetic changes. For this, Tinnitus Questionnaire scores are correlated to Heart Rate Variability markers, and related to neural activity in left and right anterior insula. Our results show that tinnitus distress is related to sympathetic activation, in part mediated via the right anterior insula.

Introduction

Tinnitus is a symptom that affects 15% of the population [START_REF] Axelsson | Tinnitus--a study of its prevalence and characteristics[END_REF]. Most people who have tinnitus can effectively cope with it, however a small percentage of tinnitus sufferers demonstrate maladaptive coping [START_REF] Budd | Tinnitus coping style and its relationship to tinnitus severity and emotional distress[END_REF][START_REF] Scott | Predictors of tinnitus discomfort, adaptation and subjective loudness[END_REF][START_REF] Tyler | Tinnitus: standard of care, personality differences, genetic factors[END_REF]: 1-2% of tinnitus sufferers are severely disabled by their tinnitus [START_REF] Axelsson | Tinnitus--a study of its prevalence and characteristics[END_REF]. This maladaptive coping group suffers significantly more from associated somatic complaints such as headaches, neck and shoulder pain, low back pain, muscle tension, sleep and concentration problems [START_REF] Hiller | Association between tinnitus and somatoform disorders[END_REF][START_REF] Scott | Psychological profile and somatic complaints between help-seeking and non-help-seeking tinnitus subjects[END_REF] and demonstrates cognitive inefficiency [START_REF] Hallam | Tinnitus impairs cognitive efficiency[END_REF]), poor stress coping [START_REF] Scott | Psychological profile and somatic complaints between help-seeking and non-help-seeking tinnitus subjects[END_REF] and depression [START_REF] Dobie | Depression and tinnitus[END_REF][START_REF] Folmer | SSRI use by tinnitus patients: interactions between depression and tinnitus severity[END_REF][START_REF] Harrop-Griffiths | Chronic tinnitus: association with psychiatric diagnoses[END_REF][START_REF] Scott | Psychological profile and somatic complaints between help-seeking and non-help-seeking tinnitus subjects[END_REF][START_REF] Sullivan | Disabling tinnitus. Association with affective disorder[END_REF].

The amount of distress people experience related to tinnitus can be evaluated by the use of validated tinnitus questionnaires. Tinnitus distress is associated to a higher orthosympathetic (OS) tone [START_REF] Datzov | Tinnitus, heart rate variability, and some biochemical indicators[END_REF] and tinnitus suppression induces an increased parasympathetic (PS) tone [START_REF] Matsushima | Increased Parasympathetic Nerve Tone in Tinnitus Patients Following Electrical Promontory Stimulation[END_REF]. Previous functional imaging studies show that specific frontal cortical areas closely relate to emotion perception and interoception.

The right anterior insula seems to be specifically involved in the representation of subjective feelings [START_REF] Craig | Interoception: the sense of the physiological condition of the body[END_REF][START_REF] Critchley | Neural systems supporting interoceptive awareness[END_REF]. Based on human lesion and electrical stimulation studies it has also been suggested that the right insula controls cardiac OS activity whereas the left insula is predominantly associated to PS activity [START_REF] Oppenheimer | The anatomy and physiology of cortical mechanisms of cardiac control[END_REF][START_REF] Oppenheimer | Cerebrogenic cardiac arrhythmias: cortical lateralization and clinical significance[END_REF][START_REF] Oppenheimer | Cardiovascular effects of human insular cortex stimulation[END_REF][START_REF] Oppenheimer | Left-insular cortex lesions perturb cardiac autonomic tone in humans[END_REF]. Functional Magnetic Resonance Imaging (fMRI) studies of sympathetic skin conductance response seem to confirm this lateralization by revealing right insula activation [START_REF] Critchley | Neural activity relating to generation and representation of galvanic skin conductance responses: a functional magnetic resonance imaging study[END_REF]. Furthermore, when correlating dichotic visual stimuli with Heart Rate Variability (HRV) the same lateralization effect is found (Wittling et al., 1998a;[START_REF] Wittling | Hemisphere asymmetry in sympathetic control of the human myocardium[END_REF].

Heart Rate Variability (HRV) is a simple and non-invasive quantitative marker of autonomic function. As a result of continuous variations of the balance between OS and PS neural activity influencing heart rate, intervals between consecutive heartbeats (RR intervals) show spontaneously occurring oscillations. For HRV spectral analysis three main underlying frequencies have been used in literature: the very-low-frequency range (VLF ≤ 0.04 Hz), the low-frequency range (LF: 0.04 -0.15 Hz) and the high frequency range (HF: 0.15 -0.4 Hz).

The high frequency component of HRV is believed to be influenced by vagal activity and is also related to the frequency of respiration [START_REF] Yasuma | Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm?[END_REF]. Low-frequency (LF) power is modulated by baroreceptor activities and fluctuations in heart rate in the LF range reflect OS as well as PS influences. Low-frequency power, therefore, cannot be considered to reflect pure OS activity. However if normalized units of LF and HF are considered, the OS and PS influences respectively are emphasized [START_REF] Electrophysiology | Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology[END_REF]. In HRV frequency domain, normalized units (n.u.) of LF and HF components therefore reflect OS and PS influences respectively.

The aim of this study is to investigate the relation between tinnitus distress and lateralisation of the anterior insula, known to be involved in interoceptive awareness and OS as well as PS changes. For this, tinnitus questionnaire (TQ) scores [START_REF] Goebel | The tinnitus questionnaire. A standard instrument for grading the degree of tinnitus. Results of a multicenter study with the tinnitus questionnaire[END_REF] are correlated to HRV markers, and related to neural activity in left and right anterior insula.

Methods

Ten patients with strictly right-sided unilateral tinnitus are analyzed. EEG and ECG signals are recorded simultaneously over 5 min in supine position using a 32 channel digital EEG (Neuroscan, Compumedics, Houston, TX) in a dimly illuminated and soundproof room (sampling rate = 500Hz, band passed 0.15-100Hz). Electrodes are referenced near the vertex and impedances checked to remain below 5 k . To minimize respiratory influences on HRV, respiration is controlled at 12 beats per minute using auditory cues. All patients complete a validated Dutch version of the TQ [START_REF] Meeus | Validation of the Dutch and the French version of the Tinnitus Questionnaire[END_REF], which reflects the amount of tinnitus related distress perceived by the patient [START_REF] Goebel | The tinnitus questionnaire. A standard instrument for grading the degree of tinnitus. Results of a multicenter study with the tinnitus questionnaire[END_REF]. 1). A spatial filter approach known as beamforming [START_REF] Congedo | Subspace Projection Filters for Real-Time Brain Electromagnetic Imaging[END_REF] targeting these two ROIs is applied in order to obtain current density estimations within these ROIs by the eLORETA method (Pascual-Marqui 2007). The logcurrent density is correlated with the TQ-scores, in all 1Hz spaced discrete Fourier frequencies in the range 1Hz-60Hz. Significant trends are formulated with a p < 0.05.

Results

TQ-scores (M = 40.2; SD = 13.7) correlate positively with the OS marker, the Low Frequency normalized units (r = 0.58), and negatively with the PS marker, the High Frequency normalized units (r = -0.58).

In addition, current density analyses show that increased cortical activity in the left anterior insula at 11Hz (r = 0.56; alpha band) and decreased activity at 4Hz (r = -0.63; theta band) and in the high gamma band frequencies (54Hz, r = -0.58; 59Hz, r = -0.74) relates to increased TQ-scores. In the right anterior insula increasing TQ-scores were found with increased activity in delta band frequencies (2Hz, r = 0.67) and gamma band frequencies (32Hz, r = 0.74; 39Hz, r = 0.56) no significant decreases are noted in this area.

Discussion

Our results show a positive relation between OS load and tinnitus distress as measured by the TQ [START_REF] Goebel | The tinnitus questionnaire. A standard instrument for grading the degree of tinnitus. Results of a multicenter study with the tinnitus questionnaire[END_REF]. In addition the right anterior insula, an area related to OS influence, shows increased delta and gamma activity related to increased tinnitus distress. On the other hand decreased theta and gamma activity are found in the left anterior insula, an area related to PS influence.

At a resting state the sensory cortices are characterized by alpha activity, which has been proposed to be an idling rhythm or a rhythm reflecting active inhibitory mechanisms (Klimesh et al., 2007). Gamma band activity is noted focally and waxes and wanes as it arises as a response to external stimuli, both in the visual (Crick & Koch, 1990), auditory [START_REF] Joliot | Human oscillatory brain activity near 40 Hz coexists with cognitive temporal binding[END_REF] and somatosensory [START_REF] Gross | Gamma oscillations in human primary somatosensory cortex reflect pain perception[END_REF] system and thus reflects the activation of a cortical area. We suggest this mechanism can be extended to the autonomic nervous system.

Gamma frequencies in this study increase or decrease together with low frequencies in the right or left anterior insula respectively, suggesting some type of nesting or coupling of high frequencies on low frequencies. Low frequencies (delta and theta) are widely distributed and activate larger networks [START_REF] Gollo | Dynamic control for synchronization of separated cortical areas through thalamic relay[END_REF] and the nesting of gamma on theta or delta allows synchronization of widely distributed focal gamma activations, providing a mechanism for effective communication between these distributed areas [START_REF] Canolty | High gamma power is phase-locked to theta oscillations in human neocortex[END_REF].

Increasing distress, as measured by the TQ, is associated with an increase of alpha in the left insula and a decrease in theta and gamma, suggesting the left insula is actively inhibited by increasing distress, by the same alpha oscillation based mechanism encountered in other (sensory) cortices [START_REF] Weisz | Alpha rhythms in audition: cognitive and clinical perspectives[END_REF]. The delta and gamma activity in the right insula suggests this area is activated and associated with increasing distress.

The right insula has been related to interoception [START_REF] Craig | Interoception: the sense of the physiological condition of the body[END_REF][START_REF] Critchley | Neural systems supporting interoceptive awareness[END_REF][START_REF] Taylor | Two systems of resting state connectivity between the insula and cingulate cortex[END_REF] and OS control [START_REF] Oppenheimer | The anatomy and physiology of cortical mechanisms of cardiac control[END_REF][START_REF] Oppenheimer | Cerebrogenic cardiac arrhythmias: cortical lateralization and clinical significance[END_REF][START_REF] Oppenheimer | Cardiovascular effects of human insular cortex stimulation[END_REF]. Damage to the left insula in humans can shift cardiovascular balance towards increased basal OS tone [START_REF] Oppenheimer | Left-insular cortex lesions perturb cardiac autonomic tone in humans[END_REF] and stimulation of the human right insula increases OS cardiovascular tone, whereas left insula stimulation increases parasympathetic (PS) tone [START_REF] Oppenheimer | The anatomy and physiology of cortical mechanisms of cardiac control[END_REF]. The right insula could therefore very well generate the subjective feelings of distress, i.e. the anxiety, associated with autonomic activity.

Many patients mention that tinnitus has developed in a stressful life episode and that it is worsened by stressful situations [START_REF] Budd | Tinnitus coping style and its relationship to tinnitus severity and emotional distress[END_REF][START_REF] Hebert | The sound of stress: Blunted cortisol reactivity to psychosocial stress in tinnitus sufferers[END_REF]. Tinnitus shares common pathophysiological, clinical and treatment characteristics with pain [START_REF] De Ridder | Electrical stimulation of auditory and somatosensory cortices for treatment of tinnitus and pain[END_REF][START_REF] Moller | Similarities between severe tinnitus and chronic pain[END_REF][START_REF] Tonndorf | The analogy between tinnitus and pain: a suggestion for a physiological basis of chronic tinnitus[END_REF] and the same observation is made in patients suffering from pain [START_REF] Price | Psychological and neural mechanisms of the affective dimension of pain[END_REF].

In patients suffering posttraumatic stress disorder (PTSD) the prevalence of tinnitus is 50% [START_REF] Hinton | Tinnitus among Cambodian refugees: relationship to PTSD severity[END_REF] and in soldiers presenting tinnitus 34% also suffer from PTSD [START_REF] Fagelson | The association between tinnitus and posttraumatic stress disorder[END_REF]. This prevalence is much higher than in the normal population, where it is 10-15% [START_REF] Axelsson | Tinnitus--a study of its prevalence and characteristics[END_REF] suggesting a relation between tinnitus and some types of stress related disorders.

Former studies have also shown that stellate ganglion blocks can sometimes improve tinnitus transiently [START_REF] Adlington | Stellate ganglion block in the management of tinnitus[END_REF][START_REF] Matoba | Stellate ganglion block for the relief of tinnitus in vibration disease[END_REF][START_REF] Warrick | Stellate ganglion block in the treatment of Meniere's disease and in the symptomatic relief of tinnitus[END_REF]. The stellate ganglion is a sympathetic ganglion, thus suggesting the OS system could be a possible target for tinnitus treatment. Exploring potential central mechanisms of sympathetically mediated modulation of tinnitus therefore seems mandated.

At a cortical level, a Magnetoencephalographic (MEG) study demonstrated that tinnitus related distress is associated with a right sided connectivity increase between the anterior cingulate and the frontal cortex and parietal cortex [START_REF] Schlee | Using auditory steady state responses to outline the functional connectivity in the tinnitus brain[END_REF]. However due to the technique used in this study it cannot be discerned which area of the frontal cortex is involved. On the other hand, a recent EEG study also showed that tinnitus distress involves a network which encompasses the amygdala, anterior cingulate, insula and parahippocampal area [START_REF] Vanneste | The neural correlates of tinnitus-related distress[END_REF] although no lateralization effect was investigated.

In conclusion, this study suggests that tinnitus distress is related to OS activation, in part mediated via the right anterior insula, via spontaneous gamma and delta band activity as well as PS influence. Left insular alpha activity, suggesting PS inactivation, is correlated with with associated decreased theta and gamma activity. These data extend the concept that tinnitus distress is related to autonomic changes in the sympathetico-vagal balance, mediated at least in part by right sided anterior insular activity. The coupled low-high frequency changes suggest that the left insular gamma decrease and right insular gamma increase might be part of a larger theta based central autonomic nervous system network. This is also consistent with previous MEG and EEG studies investigating the neural correlates of tinnitus distress.

  ECG analysesECG signals are processed by time and frequency domain methods as recommended by the Task force[START_REF] Electrophysiology | Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology[END_REF]: QRS complexes are recognized from the short-term artifact-free ECG recordings from which peaks (R-waves) are detected and from which intervals between two consecutive peaks (RR intervals) are calculated. Once HRV time series are extracted they are analyzed in the time and frequency domain using HRV Analysis Software 1.1 for windows developed by The Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland. Pearson correlations between OS (LF n.u.) and PS (HF n.u.) markers of HRV and TQ-scores are performed. EEG analyses EEG segments contaminated by artifacts are rejected offline by visual inspection. The remaining data are analyzed in the frequency domain by means of Fast Fourier Transform (FFT) analysis. Two regions of interest (ROI) corresponding to the right and left anterior insula are selected in the MNI atlas (Fig.

Fig. 1 :

 1 Fig. 1: about here.

  Fig. 1: Regions of interest: Right anterior insula (upper pannel) and left anterior insula (lower pannel). Displayed sections are the axial (left), sagittal (middle), and coronal (right) sections.

Acknowledgments

This study is performed with a grant from the American Tinnitus Association. The Tinnitus Research Initiative and St. Jude Medical Neurodivision are acknowledged for their support.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.