Samuel Medina Villalon

Rodrigo Paz

Nicolas Roehri

Stanislas Lagarde

Francesca Pizzo

Bruno Colombet

Fabrice Bartolomei

Romain Carron

Christian-G Bénar
email: christian.benar@univ-amu.fr

S Medina

Roehri 1+

C-G Bénar

Epitools, a software suite for presurgical brain mapping in epilepsy : Intracerebral EEG

Keywords: Epilepsy, SEEG, automatic segmentation, contacts localization, 3D rendering, CT

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

Introduction

About one third of patients with epilepsy are refractory to medical treatment [START_REF] Kwan | Early identification of refractory epilepsy[END_REF]. Some of them suffer from focal epilepsy; for these patients, epilepsy surgery is an efficient option [START_REF] Ryvlin | Epilepsy surgery in children and adults[END_REF]. The goal of epilepsy surgery is to remove the epileptogenic zone (EZ), i.e., the structures involved in the primary organization of seizures, and to spare the functional cortices (e.g. involved in language or motor functions). For this purpose, a pre-surgical work-up is needed including a non-invasive phase with clinical history and examination, cerebral structural and functional imaging (MRI and PET), neuropsychological assessment and long-term surface EEG recordings. Nevertheless, for about one quarter of these patients, it is difficult to correctly identify the EZ and/or its relationship with functional areas. For these difficult cases, an invasive study with intracranial EEG is required [START_REF] Jayakar | Diagnostic utility of invasive EEG for epilepsy surgery: Indications, modalities, and techniques[END_REF]. Stereo-EEG (SEEG) is a powerful method to record local field potentials from multiple brain structures, including mesial and subcortical structure. It consists in a stereotaxic surgical procedure performed under general anesthesia and aiming at implanting 10 to 20 electrodes within the patient's brain. Each electrodes being made up of 5 to 18 contacts, [START_REF] Mcgonigal | Stereoelectroencephalography in presurgical assessment of MRI-negative epilepsy[END_REF], [START_REF] Cossu | Explorations préchirurgicales des épilepsies pharmacorésistantes par stéréo-électro-encéphalographie : principes, technique et complications[END_REF]. SEEG allows high resolution mapping of both the interictal, i.e. between seizures, and ictal activity, and helps delineate the epileptic network, determine which specific areas need to be surgically removed [START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: A quantified study from intracerebral EEG[END_REF], and identify the functional regions to be spared [START_REF] Fonseca | Hemispheric lateralization of voice onset time (VOT) comparison between depth and scalp EEG recordings[END_REF]. The SEEG interpretation crucially relies on combined use of three sources of information: clinical findings, anatomy and electrophysiology. Therefore, it is of utmost importance to precisely localize the origin of the SEEG signal within the patient's anatomy, as was done in the field of EcoG [START_REF] Dykstra | Individualized localization and cortical surface-based registration of intracranial electrodes[END_REF], [START_REF] Groppe | iELVis : An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data[END_REF].

The intracerebral position of SEEG electrodes is usually assessed visually by clinicians, based on the registration of MRI scan on CT image. However, this approach is time consuming (100 to 200 contacts by patients) and a potential source of error. The use of a software allowing automatic localization of electrodes contact within the patient's MRI would thus be very helpful. Such software should be able to perform: i) an automatic and optimal localization of contact positions with respect to each patient's cerebral anatomy, ii) an automatic labelling of the SEEG contacts within an individualized atlas.

Currently, there isn't any software available to perform automatically such processes. Indeed, previous studies have proposed semi-automatic registration of intracranial electrodes contacts, based on Computerized tomography (CT) and Magnetic resonance imaging (MRI) images and prior information such as planed electrodes trajectory [START_REF] Arnulfo | Automatic segmentation of deep intracerebral electrodes in computed tomography scans[END_REF], [START_REF] Narizzano | SEEG assistant: a 3DSlicer extension to support epilepsy surgery[END_REF] or manual interactions [START_REF] Princich | Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates[END_REF], [START_REF] Qin | Automatic and precise localization and cortical labeling of subdural and depth intracranial electrodes[END_REF].

On the other hand, advances in signal analysis (including connectivity) of epileptic activities in SEEG have been major in recent years and now constitute key factors in the understanding of epilepsy [START_REF] Bartolomei | Defining epileptogenic networks: Contribution of SEEG and signal analysis[END_REF]. The translation of such advanced analyses to clinical practice is challenging but should lead to improvement in SEEG interpretation. Therefore, there is a need to easily apply some signal analysis to raw SEEG signal and then to graphically display their results in the patient's anatomy.

Our objective was thus to design a suite of software tools, with user-friendly graphical user interfaces (GUIs), which enables to i) identify with minimal user input the location of SEEG contacts within individual anatomy ii) label contacts based on a surface approach as implemented in FreeSurfer (Fischl, 2012) or MarsAtlas [START_REF] Auzias | MarsAtlas: A cortical parcellation atlas for functional mapping[END_REF] iii) interact with our in-house AnyWave software [START_REF] Colombet | AnyWave: A cross-platform and modular software for visualizing and processing electrophysiological signals[END_REF] and associated plugins in order to display signal processing results in the patient's MRI or in a surface rendering of the patient's cortex. Hereafter, we will describe step by step the implementation of our suite "EpiTools". The suite as well as the full documentation are freely available on our web site http://meg.univ-amu.fr/wiki/GARDEL:presentation. It includes mainly the following software programs: GARDEL (for "GUI for Automatic Registration and Depth Electrodes Localization"), the 3Dviewer for 3D visualization of signal processing results within the AnyWave framework.

Material and Methods

The complete pipeline is illustrated in Fig. 1. Firstly, FreeSurfer pipeline (1) can be run optionally on MRI image to obtain pial surface and Atlases. Then, "GARDEL" (2) tool was developed to co-register MRI on CT scan, detects automatically SEEG contacts and label them if an atlas is available. It saves electrodes coordinates to be reused. Finally, the "3Dviewer" (4) tool is designed to display signal analysis results, obtained thanks to "AnyWave" (3) and attached plugins, inside patient individual brain mesh or MRI slices. Inputs are : i) MR pre-implantation and CT post-implantation images, ii) if needed, results of FreeSurfer pipeline (Pial surface and Atlases) (Fischl, 2012) or MarsAtlas [START_REF] Auzias | MarsAtlas: A cortical parcellation atlas for functional mapping[END_REF], iii) SEEG electrophysiological signal processing results. The output consists of a 3D visualization of these data in the patient's own anatomical rendering, a selection of SEEG contacts found inside grey matter for signal visualization in AnyWave software and brain matter or label associated with each contact. The different tools of our pipeline are written in Matlab (Mathworks, Natick, MA), and can be compiled to be used as standalone in all operating systems (requiring only the freely available Matlab runtime).

SEEG and MRI data

In our center, SEEG exploration is performed using intracerebral multiple-contact electrodes (Dixi Medical (France) or Alcis (France): 10-18 contacts with length 2 mm, diameter 0.8 mm, and 1.5 mm apart; for details see [START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: A quantified study from intracerebral EEG[END_REF]). Some electrodes include only one group of contacts regularly spaced (5,10, 15 and 18 contacts per electrode), and others are made up of 3 groups of 5 or 6 contacts spaced by 7-11 mm (3x5 or 3x6 contacts electrodes). There are two types of electrodes implantation: orthogonal and oblique. Electrodes implanted orthogonally are almost orthogonal to the sagittal plane and almost parallel to the axial and coronal planes. Electrodes implanted obliquely can be implanted with variable angle. The choice of the type and number of electrodes to be implanted depends on clinical needs. Intracerebral recordings were performed in the context of their pre-surgical evaluation. Patients signed informed consent, and the study was approved by the Institutional Review board (IRB00003888) of INSERM (IORG0003254, FWA00005831).

All MR examinations were performed on a 1.5 T system (Siemens, Erlangen, Germany) during the weeks before SEEG implantation. The MRI protocol included at least T1-weighted gradient-echo, T2weighted turbo spin-echo, FLAIR images in at least two anatomic planes, and a 3D-gradient echo T1 sequence after gadolinium based contrast agents (GBCA) injection. Cerebral CT (Optima CT 660, General Electric Healthcare, 120 kV, 230-270 FOV, 512x512 matrix, 0.6mm slice thickness), without injection of contrast agents, were performed the day after SEEG electrodes implantation. Each CT scan was reconstructed using the standard (H30) reconstruction kernel to limit the level of streaks or flaring.

The AnyWave framework

Our tools GARDEL and the 3Dviewer are intended to interact with the AnyWave1 software, developed in our institution for the visualization of electrophysiological signals and for subsequent signal processing [START_REF] Colombet | AnyWave: A cross-platform and modular software for visualizing and processing electrophysiological signals[END_REF]. AnyWave is multi-platform and allows to add plugins created in Matlab or Python2 . Some specific plugins were created and added to implement measure for SEEG analysis as Epileptogenicity Index (EI) [START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: A quantified study from intracerebral EEG[END_REF], non-linear correlation h 2 [START_REF] Wendling | Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG[END_REF], Interictal spikes or high frequency oscillations (HFO) detections [START_REF] Roehri | What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations[END_REF][START_REF] Roehri | Time-Frequency Strategies for Increasing High-Frequency Oscillation Detectability in Intracerebral EEG[END_REF] as well as graph measures [START_REF] Courtens | Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy[END_REF].

Electrode contact segmentation and localization

GARDEL localizes the SEEG contacts in the patient's anatomy. Unlike most existing techniques [START_REF] Arnulfo | Automatic segmentation of deep intracerebral electrodes in computed tomography scans[END_REF][START_REF] Narizzano | SEEG assistant: a 3DSlicer extension to support epilepsy surgery[END_REF][START_REF] Princich | Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates[END_REF][START_REF] Qin | Automatic and precise localization and cortical labeling of subdural and depth intracranial electrodes[END_REF], it relies on an automatic segmentation of each contact. Minimal user intervention is required (only for attributing the electrode names). It also labels each individual contact with respect to a chosen atlas (Desikan-Kiliany (Desikan et al., 2006), Destrieux [START_REF] Destrieux | Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature[END_REF], or in particular MarsAtlas [START_REF] Auzias | MarsAtlas: A cortical parcellation atlas for functional mapping[END_REF]).

GARDEL takes as input a post-implantation CT-scan (where the SEEG contacts are visible as hyper density signals areas) and an anatomical MRI Volume. DICOM, NIFTI or Analyze formats are accepted.

Classically, clinical images are in the DICOM format, and are converted into a NIfTI format for easier manipulation, using a DICOM to NIfTI converter3 that can be launched within GARDEL. Image opening is made thanks to Jimmy Shen ("Tools for NIfTI and ANALYZE image") toolbox4 .

Since MRI and CT images have different resolution, size and origin, spatial alignment and registration are performed. In order to maintain the good quality of the CT image and keep an optimal visualization of electrodes, it was preferred to register the MRI to the CT space. The MRI is registered using maximization of Normalized Mutual Information and resliced into the CT space using a trilinear interpolation, thanks to the SPM85 toolbox [START_REF] Penny | Statistical Parametric Mapping: The Analysis of Functional Brain Images[END_REF].

With both images co-registered in the same space, the next step is to segment the electrodes on the CT scan. To do so, the resliced MRI is segmented into 3 regions: white matter, grey matter and cerebrospinal fluid using SPM (spm_preproc function). These three images are combined into a mask that enables us to remove extra-cerebral elements such as skull and wires (spm_imcalc function).

In order to segment the electrodes, a threshold is found from the histogram of grey values of the masked CT image. Electrode intensity values being significantly greater than brain structures [START_REF] Hebb | Imaging of deep brain stimulation leads using extended hounsfield unit CT[END_REF], they are defined as outliers, with a threshold based on median and quartiles : Thr= (Q3 + 1.5*IQR) with Q3 third quartile and IQR inter-quartile range (Q3-Q1) and could also be adjusted. The electrode segmentation is divided into 2 steps. The first step aims at segmenting each electrode individually and the second step aims at separating each contact of a given electrode. Thus, once the CT images have been thresholded, the resulting binary image is dilated using mathematical morphology (MATLAB Image Processing Toolbox, imdilate function) in order to bind the contacts of the same electrode together. We then find each connected component, which corresponds to each electrode, and label them separately (bwconncomp function) (Fig. 2a). This results in one mask per electrode. These masks are iteratively applied to the non-dilated thresholded CT to obtain a binary image of the contacts. We then apply a distance transform of the binary image preceding a watershed segmentation [START_REF] Meyer | Topographic distance and watershed lines[END_REF]

(Matlab watershed function).

A first issue is that the watershed technique may oversegment some contacts, i.e. identify several contacts instead of one, or may miss some contacts because the contacts were too small or removed after thresholding. To solve this issue, we calculated the distance between contacts within each electrode as well as their individual volume. We removed the outliers of each feature and reconstructed the missing contacts by building a model of the electrode using the median distance and the direction vector of the electrode. The vector is obtained by finding the principal component of the correctly segmented contacts. If contacts are missed between consecutive contacts, i.e. if the distance between two consecutive contacts is greater than the median, it is divided by the median to estimate the number of missing contacts, which are then placed on the line formed by the two contacts, equally spaced. If contacts are missed at the edge of the electrode, we place contacts at the median distance of the last contact on the line given by the direction vector until the electrode mask is filled. This method allows adding missing contacts within or at the tip of the electrode. It could add contacts inside the guide screw, if it belongs to the electrode mask, but can easily be deleted after a quick review. This method enables reconstructing electrodes even if they are slightly bent, and the error made during the reconstruction of missing contacts are minimized using piece-wise linear interpolation (in contrast with simple linear interpolation) (Fig. 2b).

A second issue is the segmentation of the 3x5 or 3x6 contacts electrodes, which are classically detected as 3 different electrodes. This is solved in two steps. Firstly, for each 5 or 6 contacts electrode, their direction vector is calculated. We then compute a dot product between them to check if they are collinear and group them. Secondly, only on these subsets of electrodes, dot products between a vector of a given electrode and vectors constructed with a point of this electrode and points of other electrodes are calculated to check if they need to be grouped.

A third issue is the difficulty to segments oblique electrodes because of the CT resolution that is not high enough in the vertical direction. To solve it, a second method for contact localization is applied after the fully automatic one, based on pre-defined electrodes characteristics (we have built in templates for Alcis and DIXI electrodes used in our center). The directions of clustered electrodes that failed the previous step are obtained by finding the principal component of each point composing these electrodes. Then, based on these electrodes characteristics and the estimated size of the clustered electrodes, a match is made and the electrodes can be reconstructed. Fig. 2c displays the end of the segmentation process. Electrodes are projected on the maximum intensity projection image of the CT scan.

After the automatic segmentation, the user has the possibility to delete and/or add manually electrodes and/or single contacts. In order to create an electrode, the user has to mark the first contact, as well as another one along the electrode, and to choose the electrode type. This electrode will be created with respect to pre-defined electrodes characteristics (number of contacts, contact size and spacing). In order to manually correct contact position, contacts can be deleted or added one by one. Contacts numbers will be reorganized automatically.

Another important feature of GARDEL is the localization of each contact within patients' anatomy. As manual localization is usually time consuming, our goal is to localize precisely and to label automatically each contact in the brain and in individualized atlases. Atlases from FreeSurfer (Desikan-Killiany [START_REF] Desikan | An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest[END_REF] or Destrieux [START_REF] Destrieux | Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature[END_REF]) or "MarsAtlas" [START_REF] Auzias | MarsAtlas: A cortical parcellation atlas for functional mapping[END_REF] can be imported after having performed a transformation from FreeSurfer space to the native MRI space. For each contact, its respective label and those of its closest voxels neighbors (that formed a 3x3x3 cube) are obtained. In case of multiple labels in this region of interest, the most frequent one is used to define contact label. GARDEL provides for each SEEG contact its situation within the grey and white matter and its anatomical localization based on this atlas.

At the end, contacts coordinates, their situation within cerebral grey or white matter and anatomical labels can be saved, for later use in the 3Dviewer (see below). GARDEL also saves automatically AnyWave montages (selection of contacts located inside grey matter and grouped them by area (frontal, occipital, parietal, temporal) for visualization of SEEG signals in AnyWave software). Fig. 3 displays output rendering of GARDEL tool. It is possible to display electrode in patient anatomy (Fig. 3a), in an atlas (Fig. 3b) or all electrodes in a surface rendering of the cortex (Fig. 3c).

3D representation (3Dviewer)

3Dviewer tool, closely linked to GARDEL, permits to display in a 3D way a series of relevant information inside individual mesh of the cortex:

-SEEG electrodes, -mono-variate values such as the Epileptogenicity Index, spike or high frequency oscillation rate, -bi-variate values such as non-linear correlation h 2 or co-occurrences graph.

Data required for this tool are the following: patient's MRI scan, electrodes coordinates as given by GARDEL, pial surface made by FreeSurfer or cortex mesh made by any other toolbox (e.g. SPM), and mono-variate or bi-variate values based on format created by AnyWave and associated plugins. The parameters to be displayed can be easily set by the user: mesh, electrodes, mono or bi-variate values.

Each SEEG contact is reconstructed as a cylinder with the dimension of electrodes used. Mono-variate values can either be displayed as a sphere or a cylinder with diameter and color proportional to the values. Bi-variate values (connectivity graphs) are displayed as cylinders with diameter and color proportional to the strength of the value and an arrow for the direction of the graph. Views can be switched from 3D to 2D. In the 2D view SEEG contacts are displayed on the MRI with mono-variate values in color scale (Fig. 4a). Values are listed as tables.

As explained above, the 3Dviewer allows the 3D visualization of several signal analysis measures obtained from AnyWave software within the patient's anatomy (Fig. 4b). One type of measure is the quantification of ictal discharges as can be done by the Epileptogenicity Index [START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: A quantified study from intracerebral EEG[END_REF].

Briefly, the EI is a tool quantifying epileptic fast discharge during a seizure, based on both the increase of the high frequency content of the signal and on the delay of involvement at seizure onset. It gives for each channel a measure between 0 and 1 that can be used to assess the epileptogenicity of the underlying cortex. Typically, a threshold between 0.2 and 0.3 is used for delineating the epileptogenic zone. The results could be automatically exported as a Microsoft Excel file with numerical data.

Another type of measure comes from the interictal activity, i.e. the activity between seizures. Both spikes and high frequency oscillations (HFO) are markers of epileptic cortices, and their detection and quantification important in clinical practice. Spikes and HFO are detected by the Delphos plugin.

This detector is able to automatically detect in all channels, oscillations and spikes based on the shape of peaks in the normalized ("ZH0") time-frequency image [START_REF] Roehri | Time-Frequency Strategies for Increasing High-Frequency Oscillation Detectability in Intracerebral EEG[END_REF][START_REF] Roehri | What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations[END_REF]. Results could be exported as histogram of spike, HFO rates and combination of these two markers. A step further, the co-occurrence of inter-ictal paroxysms could bring some information about the network organization of the spiking cortices. Co-occurrence graphs can be built using the time of detection of spikes or HFOs [START_REF] Malinowska | Interictal networks in Magnetoencephalography[END_REF].

Furthermore, epilepsy also leads to connectivity changes both during and between seizures. The study of such changes is important in the understanding of seizure organization, semiology, seizure onset localization, etc. The non-linear correlation h 2 is a connectivity analysis method, based on non-linear regression, usefully applied in the study of epilepsy [START_REF] Wendling | Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG[END_REF] [START_REF] Bartolomei | Defining epileptogenic networks: Contribution of SEEG and signal analysis[END_REF]. It is computed within the core part of AnyWave. The results could be visualized on a graph with weighted edges, directionality and delay and could also be exported on a Matlab file in order to proceed others connectivity analyses. The GraphCompare plugin quantifies the number and the strength of links between selected contacts based on h2 results to compare , with statistical testing, the connectivity of a period of interest with that of a reference period [START_REF] Courtens | Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy[END_REF]. The results are represented in form of boxplots and histograms representing: total degree and strengths of nodes, and the repartition of degree of the entire network. Statistical results are also automatically exported. Finally, graph representations of edge with significant changes could also be represented. SEEG contacts. We investigated 30 patients with 10 to 18 electrodes each, resulting in a total of 4590 contacts. The validation was made by an expert clinician (SL). The measure was the concordance of each reconstructed contact with the real contact as visualized on the native CT. Then, we estimated the sensitivity and precision of GARDEL. Sensitivity was defined as the number of good detections of contacts (true positive) divided by the total number of contacts of the patient and precision as the number of good detection of contacts over the total number of detections.

Our segmentation tool has a mean sensitivity of 97% (first quartile 98%, median and third quartile 100%) and a mean precision of 95% (first quartile 92%, median and third quartile 100%). Only a small subset of contacts was missed (129 out of 4590) and few false detections were obtained (243). The decrease in performance is mostly due to oblique electrodes that are not clearly distinguishable in some CT scans or when electrodes cross each other.

We also did a multi-rater comparison to confirm our results. We chose 3 more patients (more than 600 detections) that were validated by 3 raters. We obtained 83.1 % of inter-rater agreement using Krippendorf's alpha. Discrepancies were due to oblique electrodes. Separating electrode types, we obtained a full agreement among raters for orthogonal electrodes (477 contacts) and an alpha equal to 0.78 for obliques electrodes (130 contacts).

The comparison with another tool (iElectrodes [START_REF] Blenkmann | iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization[END_REF]) showed a mean difference of 0.59mm between round voxel coordinates obtained by the two tools. This difference could potentially be explained by rounding effect of centroid computation at the voxel level (our CT images had 0.42*0.42*0.63mm voxel size). Therefore, our method appears to be efficient and can be used in clinical practice. It is automatic (except for naming electrodes), contrary to previous studies that used planned electrodes trajectory [START_REF] Arnulfo | Automatic segmentation of deep intracerebral electrodes in computed tomography scans[END_REF][START_REF] Narizzano | SEEG assistant: a 3DSlicer extension to support epilepsy surgery[END_REF] or manual interaction [START_REF] Princich | Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates[END_REF][START_REF] Qin | Automatic and precise localization and cortical labeling of subdural and depth intracranial electrodes[END_REF]).

In the few cases where there are errors, it is possible to easily correct manually the segmentation (explained in method section). Moreover, automatic contacts labelling using individualized atlas opens accelerated and more robust interpretation of SEEG data in correlation with patient's anatomy. It is also possible to co-register post-resection MRI with electrode position to identify whether electrode sites were resected. However, such registration has to be performed with care for large resections, for which the brain tissues may have moved (Supp.Fig. 1).

Nevertheless, a limitation of our tool resides in the fact that, in the current version, it requires intercontact distance to be the same across electrodes. Indeed, to estimate the position of missing contacts, it takes the median distance between all contacts.

Validation of labelling from Atlases

The second step was to validate the concordance between the obtained labels of each contact and its actual anatomical location. For this purpose, a senior neurosurgeon (RC) reviewed the data of 3 patients to check if our tool properly assigned each contact to its correct label according to a given atlas (different atlas per patient). Concordance results are the following: 534 over 598 contacts were accurately labeled (89.3%), 28 were uncertain (4.7%), i.e., contacts difficult to label automatically because of their location at the junction between two areas or between grey and white matters, and 36 (6%) were wrong. These errors were mostly due to incorrect segmentation of individual MRIs because of abnormalities/lesions, or in rare cases to a mismatch between atlas label and clinician labeling. It is to be noted that segmentation can be corrected at the level of the FreeSurfer software6 .

Results across patient were concordant (90%, 88% and 89%).

3Dviewer: representation of physiological data

Signal processing is increasingly used for the analysis of SEEG [START_REF] Bartolomei | Defining epileptogenic networks: Contribution of SEEG and signal analysis[END_REF]. The major interest of our pipeline is the possibility to represent on the patient's MRI scan the data from advanced electrophysiological signal analyses. This is the goal of the 3Dviewer. Data could be represented on the 3D mesh of the patients or MRI slices in the 3 spatial planes (Fig. 4). This two modes of representation are complementary in SEEG interpretation making it possible not only to visualize the estimate of the epileptic abnormalities on 3D but also to precisely localize them within brain structures and provide potentially useful guidance for surgical planning on 2D MRI slices.

CONCLUSION

In this study, we presented a suite of tools, called EpiTools, to be used for SEEG interpretation and related clinical research applications. The SEEG section of EpiTools is mainly based on 2 distinct parts.

The first part, GARDEL , is designed for automatic electrode segmentation and labelling. It is, to the best of our knowledge, the first software to perform automatic segmentation, electrode grouping and contact labelling within individual atlas, needing only from the user to name electrodes and to correct results if necessary. We validated the contact detection and obtained good results both for sensitivity and precision. The second part consists of the 3Dviewer, which displays on the patient's MRI scan or on a 3D surface rendering, the results of signal processing at the contact location. It creates advanced link between individual anatomy and electrophysiological data analyses. In the future, we will present the application of EpiTools to non-invasive electrophysiological data such as EEG and MEG.

médecin" from Aix Marseille Université (PhD program ICN).

Fig. 1

 1 Fig.1 Scheme of EpiTools pipeline for intracerebral EEG . Firstly, FreeSurfer(1) can be run to obtain

Fig. 2

 2 Fig.2 Steps of electrodes segmentation a) steps of the clustering of contacts within electrodes (left

Fig. 3

 3 Fig.3 Results of GARDEL tool a) MRI co-registered on the CT image with one electrode and its

Fig. 4

 4 Fig.4 Overview of 3Dviewer tool results a) coronal, sagittal and axial planes of the patient MRI with

Available at meg.univ-amu.fr

Available at https://www.python.org/

Available at http://fr.mathworks.com/matlabcentral/fileexchange/42997-dicom-to-nifti-converter--nifti-tooland-viewer

 4 Available at https://fr.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-

image 5 Available at http://www.fil.ion.uclac.uk/spm/

https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/TroubleshootingData

Acknowledgements

The authors wish to thank Olivier Coulon and Andre Brovelli for useful discussions on CT-MRI coregistration and MarsAtlas/Brainvisa use. We thank Dr Gilles Brun for helping with the writing of methods about CT imaging. The calcultation of Krippendorff alpha for this paper was generated using the Real Statistics Resource Pack software (Release 5.4). Copyright (2013 -2018) Charles Zaiontz. www.real-statistics.com. This work has been carried out within the FHU EPINEXT with the support of the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the "Investissements d'Avenir" French Governement program managed by the French National Research Agency (ANR). Part of this work was funded by a joint Agence Nationale de la Recherche (ANR) and Direction Génerale de l'Offre de Santé (DGOS) under grant "VIBRATIONS" ANR-13-PRTS-0011-01. Part of this work was funded by a TechSan grant from Agence Nationale de la recherche "FORCE" ANR-13-TECS-0013. F Pizzo was funded by "Bourse doctorale jeune

All these results could be imported in the 3Dviewer to be represented in the patient's anatomy.

Validation methodology

We validated the two aspects of our segmentation tool. Firstly, the validity of the segmentation of SEEG contact. Detected centroids were superimposed on the CT images and clinicians assessed whether centroids were entirely within the hyper-intensity region corresponding to the contact in the CT image. We performed a multi-rater analysis in order to evaluated the inter-rater agreement (using Krippendorf's alpha [START_REF] Krippendorff | Reliability in Content Analysis[END_REF]). Moreover, we compared our tool to another one (iElectrodes [START_REF] Blenkmann | iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization[END_REF]). On 3 patients we picked randomly 20 contacts detected by the two software applications and calculated the mean distance between their round voxels coordinates in the CT space.

Secondly, the validation of anatomical labels. The expert neurosurgeon verified if the label assigned to each contact was concordant with the true location of the contact in the anatomy. It was labelled as "good" if it was within the right anatomical region, as "uncertain" if it was at the boundary between 2 areas and as "wrong" if it was discordant with the region it assigned.

RESULTS and DISCUSSION

Validation of electrodes Segmentation

Manual localization is time consuming. It takes in average 49 minutes for 91 contacts as explained in [START_REF] Blenkmann | iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization[END_REF] or 75 minutes for 1 implant segmentation as explained in [START_REF] Narizzano | SEEG assistant: a 3DSlicer extension to support epilepsy surgery[END_REF]. Our method takes in average 19 minutes of automated processing (including MRI to CT coregistration, brain segmentation and contact localization) by implantation for a double 1.90Ghz processor with 6 cores each and 16 GB RAM machine (the automatic segmentation process is parallelized between the available cores) added to manual corrections if needed. Thus, our method saves significant time for users.

Concerning the validation of our method, the first important step was to validate the localization of

Conflict of Interest:

The authors declare that they have no conflict of interest.

Authors' contributions


Christopher Easthope Schmidt

Christophe Hausswirth

Julien Louis

Romuald Lepers

Fabrice Vercruyssen

Jeanick Brisswalter
email: brisswalter@unice.fr

C S Easthope

Effects of a trail

Keywords: Trail running, Ultra long distance, Master athlete, Eccentric contractions, Muscle damage, Efficiency

come

Introduction

While the popularity of running trail events has increased over the past 5 years [START_REF] Hoffman | The Western States 100-Mile Endurance Run: participation and performance trends[END_REF], limited information concerning the physiological responses of the tanner occurring during this type of contest is available. Trails can be defined as ultra-long-distance runs lasting over 5 h in duration which are performed in a mountain context, involving extensive vertical displacement (both uphill and downhill). One of the main performance determining components of trail runs is exercise duration. In general, ultra-endurance exercices such as marathon running, road cycling, or Ironman triathlons are well-known to impose a strenuous physical load on the organism, which leads to decreases in locomotion efficiency and concomitant substrate changes [START_REF] Brisswalter | Carbohydrate ingestion does not influence the charge in energy cost during a 2-h run in welltrained triathletes[END_REF][START_REF] Fernström | Reduced efficiency, but increased fat oxidation, in mitochondria from human skeletal muscle after 24-h ultraendurance exercise[END_REF], thermal stress coupled with dehydration [START_REF] Sharwood | Weight changes, medical complications, and performance during an Ironman triathlon[END_REF]), oxidative stress [START_REF] Nieman | Vitamin E and immunity after the Kona triathlon world championship[END_REF][START_REF] Suzuki | Changes in markers of muscle damage, inflammation and HSP70 after an Ironman triathlon race[END_REF], and specifically in running events, structural muscle damage [START_REF] Overgaard | Effects of running distance and training on Ca2+ content and damage in human muscle[END_REF][START_REF] Suzuki | Changes in markers of muscle damage, inflammation and HSP70 after an Ironman triathlon race[END_REF]. The second major characteristic of trail running events is the large proportion of eccentric work performed during the downhill segments of the race. Eccentric contractions involve force generation in a lengthening muscle, and are known to procure severe structural damage in muscles, affecting their contractile and recuperative properties [START_REF] Nicol | The stretch-shortening cycle: a model to study naturally occurring neuromuscular fatigue[END_REF]. Several studies in the last decade have investigated the effects of long-distance runs performed on level courses. Collective results show an increased release of muscular enzymes into the plasma, a structural disruption of the sarcomere, a substantial impairment in maximal force generation capacity (Lepers et al. 2000a;[START_REF] Millet | Alterations of neuromuscular function after an ultramarathon[END_REF][START_REF] Millet | Mechanisms contributing to knee extensor strength loss after prolonged running exercise[END_REF][START_REF] Overgaard | Effects of running distance and training on Ca2+ content and damage in human muscle[END_REF][START_REF] Place | Time course of neuromuscular alterations during a prolonged running exercise[END_REF], and a decrease in post-race locomotion efficiency [START_REF] Millet | Influence of ultra-long term fatigue on the oxygen cost of two types of locomotion[END_REF][START_REF] Millet | Running from Paris to Beijing: biomechanical and physiological consequences[END_REF], indicating that muscles are progressively damaged during the exercise. Specifically, maximal isometric knee extension force has been reported to decrease by 24% after a 30-km running race [START_REF] Millet | Mechanisms contributing to knee extensor strength loss after prolonged running exercise[END_REF], by 28% after 5 h of treadmill running [START_REF] Place | Time course of neuromuscular alterations during a prolonged running exercise[END_REF]) and by 30% after a 65-km ultra-marathon [START_REF] Millet | Alterations of neuromuscular function after an ultramarathon[END_REF]. Recently, [START_REF] Millet | Running from Paris to Beijing: biomechanical and physiological consequences[END_REF] reported a 6.2% decrease in running efficiency 3 weeks after a 8,500-km run between Paris and Beijing performed in 161 days. [START_REF] Gauché | Vitamin and mineral supplementation and neuromuscular recovery after a running race[END_REF] have reported that maximal voluntary force decreased by 37% at the end of a prolonged trail run. Repeated eccentric contractions may also affect locomotion efficiency, as demonstrated by [START_REF] Braun | The effects of a single bout of downhill running and ensuing delayed onset of muscle soreness on running economy performed 48 h later[END_REF], who observed a decrease of 3.2% in running efficiency 48 h after a 30-min downhill run. In a similar vein, [START_REF] Moysi | Effects of eccentric exercise on cycling efficiency[END_REF] found a 6% decrease in cycling efficiency after 10 series of 25 repetitions of squats, an eccentric exercise. Repeated eccentric contractions, independent of their context, seem to induce a decrease in locomotion efficiency, even if efficiency is evaluated in concentrically dominated cycling [START_REF] Moysi | Effects of eccentric exercise on cycling efficiency[END_REF]. Based upon the reviewed literature, it was assumed that trail running races would accentuate muscle damage when compared to level running, due to the large proportion of eccentric contractions occurring in the successive downhill segments of courses and therefore lead to both a decrease in muscular performance and locomotion efficiency. Few studies so far have analyzed the physiological aspects of trail running. The existing studies mainly focused on the origin of the decline in contraction capacity (e.g. [START_REF] Miles | Carbohydrate influences plasma interleukin-6 but not C-reactive protein or creatine kinase following a 32-km mountain trail race[END_REF][START_REF] Gauché | Vitamin and mineral supplementation and neuromuscular recovery after a running race[END_REF] or on pacing strategies during the race [START_REF] Stearns | Influence of hydration status on pacing during trail running in the heat[END_REF]). To our knowledge, only limited data is available on the impact of this type of events on locomotion efficiency [START_REF] Millet | Influence of ultra-long term fatigue on the oxygen cost of two types of locomotion[END_REF].

A further characteristic of trail running competitions is the increasing participation of master athletes [START_REF] Hoffman | The Western States 100-Mile Endurance Run: participation and performance trends[END_REF]. Tanaka and Seals defined master athletes in their 2008 article as individuals who regularly participate in endurance training and who try to maintain their physical performance level despite the aging process. In a competition context, competitors are traditionally classified as master athletes when over 40 years of age, the age at which a first decline in endurance peak performance is observed [START_REF] Lepers | Age related changes in triathlon performances[END_REF][START_REF] Sultana | Effects of age and gender on Olympic triathlon performances[END_REF][START_REF] Tanaka | Endurance exercise performance in masters athletes: age-associated changes and underlying physiological mechanisms[END_REF]. The aging process induces a great number of structural and functional transformations, which lead to an overall decline in physical capacity (Thompson 2009). The current rising of the average age in western countries has procured the need to design strategies which increase functional capacity in older people and so forth ameliorate the standard of living (e.g. [START_REF] Henwood | Short-term resistance training and the older adult: the effect of varied programmes for the enhancement of muscle strength and functional performance[END_REF]. Observing master athletes can give insight into age-induced changes in physiology and adaptability, thus enabling scientists to develop more concise and effective recuperation and mobilization programs. Recent studies have shown that master endurance athletes are able to maintain their performance despite exhibiting the structural changes in muscle performance and in maximal aerobic power which are classically associated with aging [START_REF] Lepers | Age related changes in triathlon performances[END_REF][START_REF] Tanaka | Endurance exercise performance in masters athletes: age-associated changes and underlying physiological mechanisms[END_REF][START_REF] Bieuzen | Age-related changes in neuromuscular function and performance following a high-intensity intermittent task in endurance-trained men[END_REF][START_REF] Louis | Muscle strength and metabolism in master athletes[END_REF].

In this context, the first purpose of our study was to evaluate muscle performance and efficiency of runners participating in a long-distance trail competition. Trail running was expected to procure important muscle damage and decrease in locomotion efficiency. The second purpose was to compare changes in these parameters between young and master participants. We hypothesized that neuromuscular alterations following the competition would be greater in master athletes compared to young athletes.

Materials and methods

Subjects

Eleven young and fifteen master athletes, all well-motivated, volunteered to participate in this study. The characteristics of the subjects are shown in Table 1. All subjects had to be free from present or past neuromuscular and metabolic conditions that could have affected the recorded parameters. The subjects had regular training experience in long-distance running prior to the study (8.4 ± 6.0 years for the young vs. 13.3 ± 7.8 years for the master runners), and had performed a training program of 72.1 ± 25.1 and 74.1 ± 23.6 km/week, respectively, for young and masters during the 3 months preceding the experiment. The local ethics committee (St Germain en Laye, France) reviewed and approved the study before its initiation and all subjects gave their informed written consent before participation.

Experimental procedure

The study was divided into four phases; preliminary testing and familiarization, pre-testing, trail race intervention and post-testing (see Fig. 1). During the first phase, subjects were familiarized with the test scheme and location, and preliminary tests were performed. During the third phase, subjects performed a 55-km trail running race in a medium altitude mountain context. During the second and the fourth phases, muscle performance and efficiency were analyzed and blond samples were collected. All physiological parameters were recorded 1 day before (pre) and 3 days after the trail running race (post 1, 24, 48, and 72 h).

Preliminary session

During a preliminary session that took place 1 month before the experiment, 26 subjects (11 young and 15 masters) underwent an incremental cycling test at a self-selected cadence on an electromagnetically braked ergocycle (SRM, Schoberer Rad Messtechnik, Jülich, Welldorf, Germany). In accordance with the recommendations of the ethic committee and the French Medical Society, a cycle ergometer protocol was chosen to evaluate ventilatory parameters and efficiency, even though a running protocol would have been preferred. Considerations were based on the assumption that extremely fatigued subjects would have difficulties running on a treadmill and that this might lead to injuries. [START_REF] Moysi | Effects of eccentric exercise on cycling efficiency[END_REF] have shown that eccentric muscle damage affects locomotion efficiency and ventilatory parameters in a cycling protocol in a similar way to a running protocol. The ergocycle allows subjects to maintain a constant power output which is independent of the selected cadence, by automatically adjusting torque to angular velocity. The test consisted of a warm-up lasting 6 min at 100 W, and an incremental period in which the power output was increased by 30 W each minute until volitional exhaustion. During this incremental cycling exercise, oxygen uptake (VO 2 ), minute ventilation (VE), and respiratory exchange ratio (RER) were continuously measured every 15 s using a telemetric system (Cosmed K4b2, Rome, Italy). The criteria used for the determination of VO2max were a plateau in VO 2 despite an increase in power output, a RER above 1.1, and a heart rate (HR) above 90% of the predicted maximal HR [START_REF] Howley | Criteria for maximal oxygen uptake: review and commentary[END_REF]. Maximal oxygen uptake (VO 2max ) was determined as the average of the last three highest VO 2 values recorded (58.8 ± 6.5 ml/min/kg for the young vs. 55.0 ± 5.8 ml/ min/kg for the master athletes). The ventilatory threshold (VT) was determined according to the method described by Wasserman et al. (1973). The maximal aerobic power output (MAP) was the highest power output completed in 1 min (352.5 ± 41.1 W for the young vs. 347.6 ± 62.9 W for the master athletes).

Race conditions

The running event was a 55-km trail race involving a 6,000 m vertical displacement (3,000-m up and 3,000-m down). The starting point and finishing line were at 694-m altitude, and the highest point of the race was at 3,050 m. Due to the competitive nature of the intervention, each subject was well motivated to perform maximally over the distance. From the initial group (11 young and 15 masters), only three subjects (one young and two master athletes) did not finish the course. Therefore, all data presented corresponds to the finalist group (10 young and 13 master athletes). Physical activity after the race was controlled (walking activities were limited and massages were prohibited). Mean race times performed by subjects are shown in Table 1.

Maximal isometric force and muscle properties

Ten minutes after the sub-maximal cycling exercise, the maximal voluntary isometric force of the right knee extensor (KE) muscles was determined using an isometric ergometer chair (type: J. Sctnell, Selephon, Germany) connected to a strain gauge (Type: Enertec, schlumberger, Villacoublay, France). Subjects were comfortably seated and the strain gauge was securely connected to the right ankle. The angle of the right knee was fixed at 100° (0° = knee fully extended). Extraneous movement of the upper body was limited by two harnesses enveloping the chest and the abdomen. For each testing session, the subjects were asked to perform three 2-3 s maximal isometric contractions (0 rad/s) of the KE muscles. The subjects were verbally encouraged and the three trials were executed with a 1-min rest period. The trial with the highest force value was selected as the maximal isometric voluntary contraction (MVC, in Newton). In addition to MVC, the M-wave of the vastus lateralis was recorded from a twitch evoked by an electrical stimulation. Changes in neuromuscular properties were evaluated throughout all the testing sessions (Lepers et al. 2000b;[START_REF] Place | Time course of neuromuscular alterations during a prolonged running exercise[END_REF]. Electrical stimulation was applied to the femoral nerve of the dominant leg according to the methodology previously described by [START_REF] Place | Time course of neuromuscular alterations during a prolonged running exercise[END_REF]. The following parameters of the muscular twitch were obtained: (a) peak twitch (Pt), i.e. the highest value of twitch tension production and (b) contraction time (Ct), i.e. the time from the origin of the mechanical response to Pt.

EMG recordings

During the MVC, electrical activity of the vastus lateralis (VL) muscle was monitored using bipolar surface electrodes (Blue sensor Q-00-S, Medicotest SARL, France). The pairs of pregelled Ag/AgC1 electrodes (interelectrode distance = 20 mm; area of electrode = 50 mm 2 ) were applied along the fibers at the height of the muscle belly, as recommended by the SENIAM. A low skin impedance (<5 kΏ) was obtained by abrading and cleaning the area with an alcohol wipe. The impedance was subsequently measured with a multimeter (Isotech, IDM 93N). To minimize movement artifacts, the electrodes were secured with surgical tape and cloth wrap. A ground electrode was placed on a bony site over the right anterior superior spine of the iliac crest. To ensure that the electrodes were precisely at the same place for each testing session, the electrode location was marked on the skin with an indelible marker. EMG signals were pre-amplified (Mazet Electronique Model, Electronique du Mazet, Mazet Saint-Voy, France) close to the detection site (common-mode rejection ratio = 100 dB; Z input = 10 GΏ; gain = 600; bandwidth frequency = 6-1,600 Hz). EMG data were sampled at 1,000 Hz and quantified by using the root mean square (RMS). Maximal RMS EMG of VL muscle was set as the maximal 500-ms RMS value found over the 3-s MVC (i.e.,500 ms window width, 1-ms overlap) using the propriety software Origin 6.1. During evoked stimulation performed before the MVC, peak-to-peak amplitude (PPA) and peak-to-peak duration (PPD) of the M-wave were determined for the VL muscle. Amplitude was defined as the sum of absolute values for maximum and minimum points of the biphasic (one positive and one negative deflection) M-wave. Duration was defined as the time from maximum to minimum points of the biphasic M-wave.

Blood markers of muscle damages

For each evaluations series, 15 ml of blood was collected into vacutainer tubes via antecubetal venipuncture. The pre-exercise sample was preceded by a 10-min rest period. Once the blood sample was taken, tubes were shuffled by turning and placed on ice for 30 s before centrifugation (10 min, 3,000 T/min, 4°C). The obtained plasma sample was then stored in multiple aliquots (Ependorf type, 500 µl per samples) at -80°C until analyzed for the markers described below. All assays were performed in duplicate on first thaw. As a marker of sarcolemma disruption, the activity of the muscle enzymes creatine kinase (CK) and lactodeshydrogenase (LDH) were measured spectrophotometrically in the blood plasma using commercially available reagents (Roche/Hitachi, Meylan, France).

Locomotion efficiency

Subjects were asked to perform a cycling control exercise (CTRL) at a self-selected cadence on the same ergocycle as used in the preliminary session. This cycling exercise involved 6 min at 100 W followed by 10 min at a relative power output corresponding to the ventilatory threshold. For each subject and each cycling session, metabolic data was continuously recorded to assess the efficiency in cycling.

Efficiency can be expressed as a ratio between (external) power output and the ensuing energy expenditure (EE). Efficiency may, however, be calculated in a variety of ways [START_REF] Martin | Has Armstrong's cycle efficiency improved?[END_REF]. In this study, two types of efficiency calculation were employed, gross efficiency (GE), and delta efficiency (DE). GE is defined as work rate divided by energy expenditure and calculated using the following equation [START_REF] Gaesser | Muscular efficiency during steady-rate exercise: effects of speed and work rate[END_REF]:

Gross efficiency (%)

DE is considered by many to be the most valid estimate of muscular efficiency [START_REF] Gaesser | Muscular efficiency during steady-rate exercise: effects of speed and work rate[END_REF][START_REF] Coyle | Cycling efficiency is related to the percentage of type I muscle fibers[END_REF]). DE calculations are based upon a series of work rates which are then subjected to linear regression analysis. In this study, work rates were calculated from the two intensity tiers completed in the test described at the beginning of this section.

Delta efficiency (%)

In order to obtain precise values for work rate utilized in the efficiency calculations, power output was assessed from the set work rate and the true cadence as monitored by the SRM crank system. EE was obtained from the rate of oxygen uptake, using the equations developed by [START_REF] Brouwer | One simple formulate for calculating the heat expenditure and the quantities of carbohydrate and fat oxidized in metabolism of men and animals from gaseous exchange (oxygen intake and calorie acid output) and urine-N[END_REF]. These equations take the substrate utilization into account, by calculating the energetic value of oxygen based on the RER value. To minimize a potential influence of the VO 2 slow component, which might vary between subject groups, the mean EE during the 3rd to 6th minute was used in the calculations of GE and DE.

Statistical analysis

All data presented are mean ± SD (tables and figures). Each dependent variable was then compared between the different testing conditions using a two-way ANOVA with repeated measures (period vs. group). Newman-Keuls post-hoc tests were applied to determine the between-mean differences, if the analysis of variance revealed a significant main effect for period or interaction of group x period. For all statistical analysis, a P < 0.05 value was accepted as the level of significance.

Results

Muscular performance

In all evaluations, MVC values of master athletes were significantly lower than the young group' s values (-21.8 ± 4.6%, P < 0.01). One hour after the intervention (post), maximal isometric strength values of knee extensors decreased significantly when compared with pre-race values, in nonsignificant different proportions for young (-32%, P < 0.01) and master athletes (-40%, P < 0.01). MVC values for young subjects returned to baseline at post 24 h, at which time the MVC reduction in masters remained significant (-13.6%, P = 0.04). A significant decrease in EMG activity (RMS) during MVC of the vastus lateralis (VL) muscle was observed at 1 and 24 h post-exercise without any differences between groups or periods. Compared with pre-race values, post-exercise MVC RMS values decreased in young by -40.2 ± 19% and in masters by -42 ± 19.2% (P < 0.01) (Fig. 2).

Muscular twitch and M-wave properties

Before the race, no significant effect of age was observed on peak twitch torque (Pt) or contraction time (Ct). One hour after the race, no effect was recorded on Ct or Pt independent of the group. Post 24 h, a slower contraction time (Ct) and a lower peak twitch torque (Pt) were recorded in both groups. Compared to pre-race values, Pt decreased by 18.2% (P = 0.04) in young and by 23.5% (P = 0.02) in master runners at post 24 h. These alterations in twitch properties returned close to pre-test values in young subjects, but remained significant for 48 h (P = 0.03) and 72 h (P = 0.04) in masters subjects (Table 2).

Before the race, no significant effect of age was observed on PPA or PPD values of the M-wave for the VL muscle (Table 2). One hour after the race, a significant increase in PPD was observed in both groups. This increase remained significant in the master athlete group at post 24 h (P = 0.02), while the young group returned close to baseline values. Furthermore, in masters, PPA values decreased below pre-race values 48 h (P = 0.04) and 72 h (P = 0.02) after the race, while no effects were observed in young subjects.

Blood markers of muscle damages

Twenty-four hours (Post 24 h) after the race, the plasma activities of CK and LDH increased significantly in comparison to pre-race values, with a greater increase for master subjects (P = 0.04). CK and LDH values remained significantly elevated at post 48 h and post 72 h, without any difference between groups (Table 3).

Locomotion efficiency and cycling cadence

Gross efficiency (GE), delta efficiency (DE) and cadence values are presented in Table 4. No significant difference in GE, DE or cadence was observed between groups before the race. After the race, results indicated a non-group specific, significant decline in GE from post 24 to 72 h (GE mean decrease in young vs. masters in % of pre-race values: -4.7 vs. -6.3%, respectively). In both groups, VE increased post 24, 48, 72 h in comparison to pre-test values (VE mean increase in young vs. masters in % of pre-race values: +11.7 vs. +10.1%, respectively). No significant change in DE was observed in young subjects alter the race. On contrary, a significant decrease in DE was recorded in master subjects (DE decrease in master athletes of post 24 h (P = 0.02), post 48 h (P = 0.01) and post 78 h (P = 0.03) in % of pre-race values: -10.6, -10.4, -11.5%, respectively).

Post-race cadence was significantly higher in all post-race evaluations for young subjects when compared with masters. Results indicate a significant increase in cycling cadence post 24 h (+4.4%, P = 0.04), post 48 h (+10.6%, P = 0.03), and post 72 h (+17%, P < 0.01) for young, and only in post 48 h (+3.9%, P = 0.04) and post 72 h (+10.8%, P = 0.03) for master athletes.

Discussion

The objective of the present study was to investigate changes in muscular performance and locomotion efficiency in well-trained endurance runners procured through a trail running competition and to compare these to literature data taken from level courses. The participation of two different age groups of runners (young vs. masters) allowed additional study of the effect of age on fatigue generation and recuperation. The main results of our study indicate that: (1) post-run muscular performance and locomotion efficiency decline, while the associated concentrations of muscle damage indicating blood markers rise, regardless of age, and (2) changes of proportion of the differences between groups are only visible in the recuperation phase (post 24-72 h).

The running event analyzed in this study was a 55-km trail race featuring a 6,000-m vertical displacement (3,000-m up and 3,000-m down). The average race time was 06:45 + 00:45. As stated, the main performance components of trail running are exercise duration and vertical displacement (uphill and downhill). From this perspective, trail running competitions induce an intensive physical work load on the organism. Considering the popularity of trail running and the abundance of competitions over the world, it appears important to precisely characterize the acute physiological reactions consecutive to such events.

One of the most significant consequences of the race was a reduction in muscular performance. The recorded data manifests a significant decline in maximal force-generating capacities in young (-32%) and master athletes (-40%) 1 h post-race. The intervention seems to have decreased MVC in a slightly greater magnitude compared to: (i) prolonged run on level such as 5-h treadmill running (-28%, [START_REF] Place | Time course of neuromuscular alterations during a prolonged running exercise[END_REF]) or shorter 30-km trait race (-24%, [START_REF] Millet | Mechanisms contributing to knee extensor strength loss after prolonged running exercise[END_REF] or race of longer duration but with lower altitude change (-30%, [START_REF] Millet | Alterations of neuromuscular function after an ultramarathon[END_REF]). An adaption for workload distribution, age, training status and a methodological standardization would have to be conducted before a precise comparison between races is possible. It is generally accepted though, that the structural muscle damage leading to MVC loss is generated by the eccentric muscle contractions occurring in running [START_REF] Millet | Alterations of neuromuscular function after an ultramarathon[END_REF][START_REF] Millet | Mechanisms contributing to knee extensor strength loss after prolonged running exercise[END_REF][START_REF] Overgaard | Effects of running distance and training on Ca2+ content and damage in human muscle[END_REF][START_REF] Place | Time course of neuromuscular alterations during a prolonged running exercise[END_REF]). As a logical consequence, interventions with a greater overall percentage of eccentric force production should induce a more pronounced MVC decline in comparison to level courses of same duration, in which the eccentric component is not as pronounced: the recorded MVC data of this study thus conforms to this idea. Additional studies will need to clarify the relative contribution of duration versus altitude changes to the decrease of muscular capacities following prolonged running exercices.

After the race (post 24-72 h), MVC values progressively returned to their pre-race level. In addition, results indicate a significant decrease in VL muscle activity (i.e. RMS values) recorded during MVC performed 1 h after the race which persisted until 72 h after the race. Further parameters used to characterize muscular fatigue included muscular twitch and M-wave properties. Pt decreased significantly 24 h after the race, accompanied by a concomitant increase in Ct from 24 to 72 h after the race, albeit only in masters. The main explanation for these perturbations of contractile parameters could be an alteration of the excitation-contraction coupling process that can be attributed to several mechanisms including, but not limited to, reduced Ca 2+ release from the sarcoplasmic reticulum (Westerblad et al. 1991), a decrease in blood pH and a reduced rate or force of crossbridge latching [START_REF] Metzger | Effects of tension and stiffness due to reduced pH in mammalian fast-and slow-twitch skinned skeletal muscle fibres[END_REF]). An increase in Ct after the race could also indicate an impairment in type II muscles fibers (i.e. fast contraction fibers) which may be compensated for by the more fatigue resistant type I muscle fibers (i.e. slow contraction fibers). Twitch muscle properties were unchanged at 1-h post-race, alterations appearing only 24 h after the race and later. This phenomenon might suggest that muscle fatigue was counterbalanced by potentiation mechanisms occurring immediately after the race [START_REF] Baudry | Postactivation potentiation influences differently the nonlinear summation of contractions in young and elderly adults[END_REF][START_REF] Shima | Mechanomyographic and electromyographic responses to stimulated and voluntary contractions in the dorsiflexors of young and old men[END_REF][START_REF] Louis | Muscle strength and metabolism in master athletes[END_REF]. By contrary, M-wave PPD was significantly reduced immediately after the race (post) and tended to return to basal values 24-72 h after the race. The master group exhibited increased M-wave PPA at 24-h post-race. As previously described in the literature, these increases in M-wave parameters suggest an alteration in muscle excitability; probably generated by impairments in neuromuscular propagation due to an increase in sarcolemma permeability to sodium, potassium and chloride [START_REF] Lepers | Advances in neuromuscular physiology of motor skills and muscle fatigue[END_REF]. These results support the assumption of muscle damage development through trail running. The data recorded for muscle damage indicating blood markers underscores this observation. A post-race increase in the plasma activity of muscle enzymes (CK and LDH), which persisted for several days after the race (Table 3), was recorded. Similarly, [START_REF] Suzuki | Changes in markers of muscle damage, inflammation and HSP70 after an Ironman triathlon race[END_REF] reported a significant increase in CK and LDH activities in the plasma soon after an Ironman triathlon, which remained elevated until 1 day after the race. Intracellular enzymes such CK and LDH indicate that the muscle injury arises from myofibrillar disruption [START_REF] Clarkson | Muscle function after exercise-induced muscle damage and rapid adaptation[END_REF][START_REF] Noakes | Effect of exercise on serum enzyme activities in humans[END_REF], and are classically used to assess the loss of sarcolemmal integrity after strenuous exercices [START_REF] Overgaard | Effects of running distance and training on Ca2+ content and damage in human muscle[END_REF]). As neither CK nor LDH is considered redundant indicators (Warren et al. 1999), the analysis was augmented by the acquisition of further physiological variables.

As an important determinant of performance in endurance events, locomotion efficiency is classically surveyed in athletes in order to evaluate the effects of particular training periods [START_REF] Santalla | Muscle efficiency improves over time in world-class cyclists[END_REF]). It has been reported that even small increments in cycling efficiency may lead to major improvements in endurance performance [START_REF] Moseley | The reliability of cycling efficiency[END_REF]. The efficiency of physical work is a measure of the body' s effectiveness in converting chemical energy into mechanical energy. Efficiency was here calculated as described in the methods section; the quotient of work rate and energy expenditure [START_REF] Gaesser | Muscular efficiency during steady-rate exercise: effects of speed and work rate[END_REF]. A decrease in locomotion efficiency can therefore be interpreted as either a relative increase in energy expenditure, or a relative decrease in work rate. Considering that work rate was fixed for all the tests, increased energy expenditure remains the only viable option. Recorded values show a decline in GE in both groups of athletes after the race, which persisted until 72 h post-race. Although commonly employed, GE has been criticized for its inclusion of energy-delivery processes that do not contribute to production of mechanical work in the denominator. Therefore, locomotion efficiency was also evaluated through DE calculation, which is by many considered to be the most valid estimate of muscular efficiency [START_REF] Gaesser | Muscular efficiency during steady-rate exercise: effects of speed and work rate[END_REF][START_REF] Coyle | Cycling efficiency is related to the percentage of type I muscle fibers[END_REF][START_REF] Mogensen | Cycling efficiency in humans is related to low UCP3 content and to type I fibres but not to mitochondrial efficiency[END_REF]. While GE values declined for both groups, DE values only declined in the masters group (P24, P48, and P72), thus confirming the increase in energy expenditure to ensure a continuous power output. This phenomenon is largely related to a decline in muscular performance. In order to produce the same locomotive work as in the pre-race condition, strategies such as an increase in spatio-temporal recruitment of muscle fibers or an increase in cycling cadence, involving a concomitant increase in VE (Table 4) could be engaged. The attained results provide evidence of an alteration of cycling efficiency in both groups tested.

The second aim of this study was to analyze age-related effects on muscular performance and cycling efficiency after the trail race by comparing physiological variables recorded in young and master athletes. Race completion time did not significantly differ between groups (06:42 + 00:51 vs. 06:51 + 00:47, for young vs. masters, respectively). Despite the structural and functional alterations typically observed during the aging process, master athletes were able to produce the same level of performance as the young group. This observation confirms the realistic possibility of preventing the age-related decline of physical performance through physical activity.

The analysis of muscular performance in the two groups of athletes shows a classical decline in maximal force-generating capacity in masters (-21.8 ± 4.6%), when compared with young for all testing sessions performed before and after the race [START_REF] Louis | Muscle strength and metabolism in master athletes[END_REF][START_REF] Louis | Muscle strength and metabolism in master athletes[END_REF]. Results additionally indicate a similar decrease in MVC values at 1-h post-race in both age groups which, in the master subjects only, persisted until 24 h after the race, suggesting a slower recovery. Based on the results of [START_REF] Coggan | Histochemical and enzymatic characteristics of skeletal muscle in master athletes[END_REF], which were confirmed by Tarpenning et al. (2004), the age-induced decrease of MVC values in master athletes similar to our experimental population can be mainly explained by neural factors such as muscle recruitment and/or specific tension. The twitch analysis based assessment of muscular function seems to confirm this hypothesis. This study is to our knowledge the first to present twitch and M-wave data for master athletes after a trail running competition. As previously described in studies on long-distance exercise induced fatigue in young subjects (e.g. [START_REF] Millet | Alterations of neuromuscular function after an ultramarathon[END_REF], Pt and Ct parameters increased 24 h after the race. The proportions were similar in both groups tested. The alterations in muscular properties persisted several days after the race in masters only, further supporting the idea of a slower muscle recovery in this group. Master M-wave PPD values increased proportionally to the aforementioned development in the young group at 1-h post-race, and returned to pre-race values in all the following testing conditions. By contrary, master M-wave PPA values decreased significantly from 48 to 72 h after the race, while this decline was marginal in young athletes. Despite a similar training status in young and master athletes, the values of these parameters show a greater alteration in masters' muscular function (i.e. contractility and excitability) after the race, indicating a slower recovery of muscle strength. An assessment of VL muscle activity shows that this effect is not brought by an age-induced muscle activation impairment, as MVC RMS values declined in similar proportions between groups after the race.

As depicted in Table 3, CK and LDH activity in plasma increased in similar proportions after the race for both groups indicating a similar level of muscular deterioration between groups following the trail competition. This is in line with the above mentioned results, as the proportion of the competitioninduced reduction of MVC was similar between groups.

This might support the idea that regular endurance training reinforces active muscles, and therefore limits the structural and functional changes classically associated with aging [START_REF] Lexell | Human aging, muscle mass, and fiber type composition[END_REF].

Results of this study show an effect of aging on cycling efficiency before and after the running race. While GE declined in similar proportions in both groups after the race, DE declined only in masters 24, 48 and 72 h after the race (Table 4). The GE decline in both groups could be mainly related to increases in energy-delivery processes that do not contribute to mechanical work. Variations in these processes originate through modifications in cycling kinematics (e.g. cycling cadence) or muscular contraction patterns (e.g. recruitment of subsidiary muscles, increase in antagonistic co-activation) in fatigued muscles and must be considered when regarding the GE [START_REF] Braun | The effects of a single bout of downhill running and ensuing delayed onset of muscle soreness on running economy performed 48 h later[END_REF]. The decline of DE in masters could be strongly related to alterations in muscular performance, provoking an increase in muscle activity in cycling to produce the same external work. [START_REF] Gleeson | Effect of exercise-induced muscle damage on the blood lactate response to incremental exercise in humans[END_REF] suggested that an increase in type II fiber recruitment may occur when exercise is performed in a fatigued state. In addition, if force-generating capacity was compromised, more motor units would have to be activated to achieve the same submaximal force output, resulting in a concomitant increase in metabolic cost [START_REF] Braun | The effects of a single bout of downhill running and ensuing delayed onset of muscle soreness on running economy performed 48 h later[END_REF]. Such an effect could contribute to the significantly higher VE shown in the present study. The results demonstrate that master athletes reached a similar level of fatigue through the race, when compared to young athletes, but recovered significantly slower. The hypothesis that master athletes achieve a higher level of fatigue through similar exertion can therefore be no longer supported. This was surprising, as the input parameters of master athletes were considerably lower and therefore either a lower performance or a greater fatigue would be expected. Thus, it must be surmised that masters must in some form economize energy expenditure over the length of the course, for example through adaptations in strategy or locomotor patterns.

Conclusion

The aim of this study was to assess physiological responses to an exhaustive trail running competition and to analyze possible differences between young and master athletes.

A 55-km ultra-endurance event was used as a fatigue generating intervention. An especially large amount of muscular fatigue was generated through the large proportion of eccentric contractions occurring during the downhill sections of the race. Results indicate an acute fatigue in all subjects (young and masters), which is mainly represented by decreases in muscle performance. Despite similar race performances between groups, the generated fatigue was similar between groups. Post-race development of CK, and neuromuscular properties suggests a decrease in the recuperation kinetics of the master subjects. The results attained in this study give indication that regular endurance training cannot halt the age-related decline in muscle performance, but that performance level can non-the-less be maintained by global or local strategy adaptations or to-date unknown adaptations on a physiological level.

Tarpenning KM, Hamilton-Wessler M, Wiswell RA, Hawkins SA ( 2004

FIGURESFig. 1 AFig. 2

 12 FIGURES

Table 2 Twitch and M-wave parameters of the vastus lateralis muscle before (Pre), and 1 h (Post), 24 h (Post 24), 48 h (Post 48) and 72 h (Po; 72) alter the race

 2 SD) values of 10 young and 13 master athletes are shown Pt peak twitch, Ct contraction time, HRt half-relaxation time, PPA peak-to-peak amplitude, PPD peakto-peak duration * Significantly different from pre-exercise (P < 0.05)

	Variable		Pre	Post	Post 24	Post 48	Post 72
	Twitch
	Pt (N)	Young	36 (9)	35 (11)	29 (11)*	34 (9) †	35 (12) †
		Master	36 (11)	34 (12)	27 (12)*	28 (08)*	29 (12)*
	Ct (ms)	Young	63.3 (13.7)	63.4 (10.6)	68.8 (11.2) * 64.7 (9.5) †	66.9 (10.3)
		Master	61.3 (15.6)	64.9 (17.4)	71.1 (12.9)* 73.2 (10.8)* 76.2 (12.7)
	M-Wave
	PPA (mV)	Young	3.5 (1.4)	3.6 (1.6)	3.9 (1.5)	3.4 (1.7)	3.0 (1.4)
		Master	3.4 (1.5)	3.1 (1.3)	3.1 (1.5)	2.4 (1.4)*	2.3 (0.7)*
	PPD (ms)	Young	7.6 (1.5)	9.2 (1.2)*	7.0 (2.2) †	7.0 (2.5)	7.3 (2.8)
		Master	7.9 (1.5)	9.5 (2.5)*	9.3 (2.8)*	7.8 (2.7)	7.6 (3.3)
	Mean (

† Significantly different from master (P < 0.05)

Table 3 Changes in muscle damage indicating blond markers for young and master athletes before (Pre), 24 h (Post 24 h), 48 h (Post 48 h) and 72 h (Post 72 h) after the race

 3 CK creatine kinase, LDH lactate dehydrogenase * Significantly different from pre-exercise (P < 0.05) † Significantly different from masters (P < 0.05)

	Variable	Group	Normal	Pre	Post 24 h	Post 48 h	Post 72 h
			range
	CK (U/1)	Young	50-230	135 (26)	1,470 (565)* †	909 (303)* 430 (251)*
		Master	50-230	138 (107)	1,559 (593)* 920 (298)* 531 (271)*
	LDH (U/1)	Young	120-245	229 (52)	528 (164)*	453 (65)*	410 (65)*
		Master	120-245	194 (63)	482 (142)*	468 (105)* 473 (165)*

Table 4 Changes in efficiency, ventilation and cycling cadence for young and masters during cycling exercises performed before (Pre), 24 h (Post 24), 48 h (Post 48) and 52 h (Post 52) after the race

 4

	Variable	Pre	Post 24	Post 48	Post 72
	GE (bpm)