Olivier David
email: odavid@ujf-grenoble.fr

Agata Woźniak

Lorella Minotti

Philippe Kahane

Preictal short-term plasticity induced by intracerebral 1 Hz stimulation

Short-term changes of intrinsic properties of neural networks play a critical role in brain dynamics.

In that context, epilepsy is a typical pathology where the fast transition between interictal and ictal states is probably associated to intrinsic modifications of underlying networks. In this study, we focused on the correlates of plastic neural mechanisms in the intracerebral electroencephalogram (iEEG). Data were obtained during 1 Hz electrical stimulation in twenty patients suffering from temporal lobe epilepsy and implanted with intracerebral electrodes for clinical evaluation before resective surgery. First, we developed a procedure of analysis for localisation of the seizure onset zone based on brain excitability and plasticity defined as the average, and as the first-order (linear) modulation respectively, of the standard deviation of iEEG responses over stimulations. Our results suggest that the candidate epileptic focus is particularly prone to exhibiting short-term plasticity.

Second, we used Dynamic Causal Modelling (DCM) to model explicitly short-term plasticity as a fast modulation of synaptic efficacies in either intrinsic or extrinsic connections to the focus. We found the two types of modulation both likely. Third, we used DCM to study the fast modulation of synaptic connectivity of long-range connections in neuronal networks restricted to the temporal lobe. Using DCM, we were able to estimate which structures expressed a strong modulatory input to the epileptic focus. Such early changes in interregional connectivity might be important for the initiation of electrically-induced seizures. They may also reflect some aspects of the pathogenesis of epilepsy in those patients.

I. Introduction

Short-term changes of intrinsic properties of neural networks play a critical role in brain dynamics (David et al., 2006b;[START_REF] Zucker | Short-term synaptic plasticity[END_REF]. In cognitive neuroscience, the different shape of brain responses corresponding to different experimental conditions may be associated to fast modifications of connectivity between different responses (David et al., 2006a;[START_REF] Kiebel | Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization[END_REF].

In clinical neuroscience, a typical disease related to brain plasticity is epilepsy where the fast transition between interictal (between epileptic seizures) and ictal (epileptic seizures, i.e. paroxysmal electro-clinical events) states is probably associated to intrinsic modifications of underlying networks. However, except when averaging evoked responses between experimental conditions, short-term plasticity is not usually modelled explicitly in human electrophysiology. It suggests that the importance of this brain mechanism may not be fully appreciated when analysing electroencephalographic (EEG) and magnetoencephalographic (MEG) signals.

In this study, we focus on the intracerebral EEG (iEEG) correlates of plastic neural mechanisms. Using the same data set obtained during 1 Hz electrical neurostimulation in patients suffering from temporal lobe epilepsy, our goal is to show how plasticity in iEEG responses can be modelled by adopting different levels of analysis. As a first approach, plasticity can be defined as the modification of the shape of successive induced responses to a particular stimulation. An implicit assumption is that plasticity is merely expressed at the synaptic level as changes in neural couplings. As a second approach, we show how synaptic plasticity can be explicitly modelled using generative models of iEEG [START_REF] David | Modelling event-related responses in the brain[END_REF][START_REF] Rennie | Unified neurophysical model of EEG spectra and evoked potentials[END_REF][START_REF] Wendling | Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG[END_REF]. We use Dynamic Causal Modelling (DCM) (David et al., 2006a;[START_REF] Kiebel | Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization[END_REF] which consists in (i) assuming several neural networks with some connections expressing synaptic plasticity via a modulation of their strength between several conditions, (ii) estimating their parameters (intrinsic neural parameters and coupling between regions) from measured induced responses, (iii) comparing the different networks using Bayesian model selection to identify the most plausible functional hypothesis to explain the measured data [START_REF] Penny | Comparing dynamic causal models[END_REF].

A cortical imbalance between excitation and inhibition is likely to trigger abnormal electrical activity measured in iEEG and, thus, to be the pathophysiological basis for human partial epilepsy.

In particular, repeated electrical stimulation of the cortex may induce transient modifications of neural network properties which can lead to the occurrence of epileptic events in patients [START_REF] Chauvel | Electrical stimulation with intracerebral electrodes to evoke seizures[END_REF][START_REF] Kalitzin | Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy[END_REF][START_REF] Schulz | Localization of epileptic auras induced on stimulation by subdural electrodes[END_REF][START_REF] Valentin | Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo[END_REF][START_REF] Wilson | Paired pulse suppression and facilitation in human epileptogenic hippocampal formation[END_REF].

Although useful, neurostimulation for epilepsy evaluation is not a usual clinical practice because it is invasive and complex. Only large centres fully equipped do perform this clinical exam as part of invasive recordings for presurgical evaluation following different protocols. These protocols include single pulse [START_REF] Valentin | Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo[END_REF]Valentin et al., 2005b;Valentin et al., 2005a), paired pulse [START_REF] Wilson | Paired pulse suppression and facilitation in human epileptogenic hippocampal formation[END_REF], and repetitive (i.e. trains of stimuli, usually at frequencies comprised between 1 and 50 Hz ) [START_REF] Buser | Unilateral connections between amygdala and hippocampus in man. A study of epileptic patients with depth electrodes[END_REF][START_REF] Kahane | Electroclinical manifestations elicited by intracerebral electric stimulation "shocks" in temporal lobe epilepsy[END_REF][START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF][START_REF] Kalitzin | Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy[END_REF] stimulation. They have been used particularly to localise the seizure onset zone (SOZ) in patients suffering from various forms of focal epilepsy. In clinical practice, when intracerebral stimulation is used, localisation of the SOZ is mostly based on the visual detection of pathological brain excitability during trains of stimuli [START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF].

In this study, we describe a methodological approach to short-term plasticity of 1 Hz stimulation-induced evoked responses measured using intracranial electrodes in implanted patients.

First, we develop an automatic procedure of analysis for standard localisation of the seizure onset zone based on brain excitability and plasticity. Second, we use DCM to model explicitly short-term plasticity as a modulation of synaptic strength. We first adopt a local DCM approach by focusing on the putative epileptic focus. In a subsequent DCM approach at a global level, we estimate the modulation of connectivity in networks distributed in the temporal lobe when a temporal structure is stimulated repetitively.

II. Materials and Methods

II.1 Experimental protocol

Patients included in this study have been selected for resective surgery and have undergone the standard presurgical evaluations clinical exams (high resolution MRI, video-EEG monitoring, neuropsychological testing). In addition, stereotaxic intracerebral EEG (SEEG) recordings were judged necessary before surgery to better delineate the brain areas to be resected. They were conducted according to a methodology described in [START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF] which included 1 Hz intracerebral stimulation. This stimulation protocol is part of SEEG investigation at the Grenoble University Hospital for more than fifteen years [START_REF] Kahane | Electroclinical manifestations elicited by intracerebral electric stimulation "shocks" in temporal lobe epilepsy[END_REF][START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF]. Fifty Hz stimulation is more classically used in other epilepsy centres, although there is no gold standard. Fifty Hz stimulation does not allow to easily observe brain responses online in a clinical environment, whereas this is the case for 1 Hz stimulation. This is the main reason why 1 Hz stimulation is also applied in our centre.

Patients were fully informed and gave their consent before being implanted and stimulated.

Eleven to fifteen semirigid intracerebral electrodes were implanted per patient, either unilaterally (n=16) or bilaterally (n=4), in various cortical areas depending on the suspected origin of seizures.

Each electrode was 0.8 mm in diameter and included 5, 10, 15 or 18 leads 2 mm in length, 1.5 mm apart (Dixi, Besançon, France), depending on the target region. A preoperative stereotaxic magnetic resonance imaging and a stereotaxic teleradiography matched with Talairach and Tournoux's stereotaxic atlas [START_REF] Talairach | Co-planar stereotaxic atlas of the human brain[END_REF] were used to assess anatomical targets. Implantation of the electrodes was performed in the same stereotaxic conditions, with the help of a computer-driven robot. The location of the electrode contacts was subsequently reported on a stereotaxic scheme for each patient and defined by their coordinates in relation to the anterior commissure/posterior commissure plane.

Intracerebral recordings were performed using an audio-video-EEG monitoring system (Micromed, Treviso, Italy) that allowed to record simultaneously up to 128 contacts, so that a large range of mesial and cortical areas were sampled. Stimulation at 1 Hz (pulse width 3 milliseconds) was applied between two contiguous contacts at different levels along the axis of each electrode.

The goals of the stimulation were the reproduction of the aura, the induction of an electroclinical seizure, and/or the localisation of an eloquent cortical area that has to be spared during surgery. Bipolar stimuli were delivered using a constant current rectangular pulse generator designed for a safe diagnostic stimulation of the human brain (Micromed, Treviso, Italy), according to parameters proved to produce no structural damage. The intensity used was 3 mA or less, depending on the stimulated site. Stimulation lasted 40 seconds or less (minimum: 8 seconds), depending on the type of the induced clinical response. When stimulation lasted less than 40 seconds, stimulation was stopped by the neurologist doing the experiment (L.M., P.K.) when fast synchronising discharges (low-voltage fast activity or activity recruiting fast discharges of spikes, [START_REF] Kahane | The Bancaud and Talairach view on the epileptogenic zone: a working hypothesis[END_REF]) were observed. Low current stimulation (1 mA) was first performed and current was gradually increased (up to 3 mA, with an increment of 0.2 mA) if no effect was perceived. Seizures included in this study were induced using 3 mA. All bipolar derivations located in the cortex (and not in the white matter) were tested successively to find regions capable to initiate seizures.

II.2 Patients

We selected 20 patients (8 males, 12 females, aged from 18 to 51 years), who suffered from right (n=9) and left (n=11) temporal lobe epilepsy and in whom 1 Hz electrical stimulation had elicited at least one electroclinical seizure identical to those spontaneously recorded. Two seizures were induced in seven patients and four seizures in one patient. Detailed information about patients can be found in Table 1.

Table 1 about here

II.3 Visual identification of the seizure onset zone

Electrically-induced seizures were inspected visually by clinicians (L.M., P.K.) to determine the site(s) of seizure onset and the exact timing of the first relevant electrical changes that occurred prior to the clinical onset of the seizure. Were considered as relevant the following iEEG patterns [START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF]): (i) low-voltage fast activity over 20 Hz; (ii) recruiting fast discharge (around 10 Hz or more) of spikes or polyspikes; (iii) rhythmic activity (around 10Hz) of low amplitude. The resulting ensemble of electrodes will be referred to as "Seizure Onset Zone" (SOZ) in the remaining of the study. Note that the sites of stimulation eliciting a seizure did not necessarily belong to the SOZ (see Table 1).

II.4 Analysis of EEG responses for identification of the seizure onset zone

EEG data were processed using an in-house developed toolbox of Statistical Parametric Mapping 5 (SPM5) software (www.fil.ion.ucl.ac.uk/spm, Wellcome Department of Imaging Neuroscience, University College London, UK) for dynamical analysis of iEEG. Electrode contacts were identified on each individual stereotaxic scheme, anatomically localised, and then reported in stereotaxic coordinates using Talairach and Tournoux's proportional atlas [START_REF] Talairach | Co-planar stereotaxic atlas of the human brain[END_REF]. Bipolar derivations were taken between adjacent electrode contacts (from 55 up to 114, depending on patients). Data were sampled at or down-sampled to 256 Hz (data in some patients were acquired at 512 Hz) and band-pass filtered between 0.8 and 40 Hz. To consider preictal activity only, stimulations performed after seizure onset were removed from any data analysis. Note that a seizure, once it has started, is not or very little influenced in the focus by subsequent stimulations (see Figures 2-3 below or Figure 2 in online Supplementary Materials).

Brain excitability E, i.e. responsiveness to stimulations of a given brain region, was assumed to be reflected into the standard deviation of evoked responses averaged over stimulations: is baseline iEEG recorded during a 20 second period just prior to the first stimulation. The excitability E is a normalised measure, i.e. it is close to 1 when the stimulation has no particular effect, greater than 1 when evoked responses can be measured, and lower than 1 when ongoing EEG is suppressed. We used the standard deviation instead of the power to quantify the responsiveness of evoked responses because we found it was more robust to artefacts or to other confounds such as low-frequency drift.

( ) ( ) ( )         = baseline i N i
As a consequence, Eq. 1 defines brain excitability as the increase of EEG deviations from the mean induced by the stimulation, which is very similar to the common acceptation of the term "excitability" used in electrophysiology.

Short-term plasticity P was assumed to correspond to first-order changes over stimulations of the standard deviation of evoked responses. It was obtained by linear regression:

( ) ( )         = 2 1 max
( ) [ ] T n m i n i n win i win y std win y std S + + = ,..., 1 , ,       = 1 ... 1 1 1 ... / 2 / 1 m m X = β , ( ) ( ) ( )
. n varies from 0 to N-m. In the Results section, m was set to 10 or was equal to N if N<10. P roughly expresses the percentage change of evoked responses standard deviation in a period of m stimulations (for instance, in the case of an increase of evoked responses due to plasticity, P=1 corresponds to a doubling of the strength of evoked responses -increase of 100% -between the beginning and the end of the window of interest).

Electrodes localising the epileptic focus were obtained by applying simultaneously a threshold T E to the excitability E and a threshold T P to the short-term plasticity P. The localisation accuracy was quantified as the minimal distance between selected electrodes by these thresholds and the visually pre-defined SOZ. The minimal distance from the electrodes belonging to the SOZ was computed for each electrode selected by the thresholds and averaged over all threshold-selected electrodes.

II.5 Dynamic causal modelling

II.5.1 Theory DCM (David et al., 2006a;[START_REF] Friston | Dynamic causal modelling[END_REF][START_REF] Kiebel | Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization[END_REF][START_REF] Kiebel | Dynamic causal modelling of evoked responses: The role of intrinsic connections[END_REF] explains brain responses to stimuli using biophysical models. This allows to rephrase observed differences in the shape of responses in terms of context-dependent coupling. The original generative model of DCM for EEG [START_REF] David | Modelling event-related responses in the brain[END_REF] combines the Jansen model [START_REF] Jansen | Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns[END_REF], a neural mass model originally developed for explaining visual responses, with rules of cortical-cortical connectivity derived from the analysis of connections between the different cortical layers in the visual cortex of monkey [START_REF] Crick | Constraints on cortical and thalamic projections: the no-strong-loops hypothesis[END_REF]. The resultant model [START_REF] David | Modelling event-related responses in the brain[END_REF] is a set of differential equations describing interactions between different inhibitory and excitatory neuronal populations, which can be easily manipulated to embed any hierarchical cortical-cortical network (see Figure 1 in online Supplementary Materials).

Because DCMs are biologically grounded, parameter estimation is constrained within a Bayesian framework and inferences about particular connections are made using their posterior or conditional density. The full set of equations for DCM specification and Bayesian parameter estimation can be found in the original papers [START_REF] David | Modelling event-related responses in the brain[END_REF]David et al., 2006a;[START_REF] Friston | Dynamic causal modelling[END_REF][START_REF] Kiebel | Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization[END_REF][START_REF] Penny | Comparing dynamic causal models[END_REF]. We just summarise below the most important steps.

A DCM is specified in terms of a state equation and an output equation:

( ) ( ) θ θ , , , x g y u x f x = = & (3)
where y are measured data, x are the neuronal states, u are the extrinsic inputs and θ are the model parameters (inhibitory and excitatory synaptic time constants and efficacies, intrinsic and extrinsic connectivity, propagation delay). Functions f and g explain the measured dynamics. In standard DCM for EEG (David et al., 2006a;[START_REF] Kiebel | Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization[END_REF], y are a few principal modes of the scalp data and the function g is the head model used for source localisation. Here, because iEEG signals (y) are assumed to be a fair measure of the local depolarisation of principal cells (x), the function g is a simple one-to-one linear regression between x and y. Put simply, we assume there is no source localisation issue in iEEG bipolar recordings.

The main interest of DCM is to test for competing functional hypotheses. For each functional hypothesis, a model m is specified in terms of anatomical connections between regions and of the modulation of some connections by the experimental context. This is equivalent to constructing a specific function f (Eq. 3) for each model. After the estimation of parameters of each competing model, the models are compared to find the most likely model, or functional hypothesis.

This can be done using Bayesian model selection where the evidence of each model, computed from estimated parameter distributions, is used to quantify the model plausibility [START_REF] Penny | Comparing dynamic causal models[END_REF]. The most likely model is the one with the largest evidence. The evidence can be decomposed into two components: an accuracy term, which quantifies the data fit, and a complexity term, which penalises models with a large number of parameters. Therefore, the evidence embodies the two conflicting requirements of a good model, that it explains the data and is as simple as possible.

Assuming each data set is independent of the others, the evidence at the group level (multi-subject analysis) is obtained by multiplying the marginal likelihoods or equivalently, by adding the logevidences from each subject [START_REF] Garrido | Dynamic causal modelling of evoked potentials: A reproducibility study[END_REF]. Note that the evidence can only be approximated under some assumptions. A possibility is to use the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) [START_REF] Penny | Comparing dynamic causal models[END_REF] to get two estimates of the evidence and to decide for a significant plausibility of one model if the inference obtained with AIC and BIC assumptions is concordant. In the case AIC and BIC did not agree, we have used the BIC logevidence to make a decision, if needed, because it is the most penalising criterion for complex models.

II.5.2 Functional hypotheses

II.5.2.1 The role of intrinsic and extrinsic connections

Because the candidate focus was not usually the stimulated region, it is important to evaluate whether the observed plasticity in the epileptic focus is due to the modulation of neural connections restricted to this region (intrinsic connections) or whether it is due to a modulation of the strength of afferent connections from surrounding networks (extrinsic connections). Using Bayesian model comparison, we will test these competing hypotheses (Figure 1). In the extrinsic model, we allow a variation of extrinsic inputs, keeping constant intrinsic efficacies. In the intrinsic model, we allow changes in intrinsic excitatory efficacies, keeping constant extrinsic inputs. In our models, extrinsic inputs are gamma functions which are parameterised by three parameters (amplitude, width and delay). Therefore, the modulation of N responses are particularly explained by 3*N parameters for the extrinsic hypothesis, whereas only N parameters (the N excitatory efficacies) are needed by the intrinsic hypothesis. This implies that, for an equivalent data fit, the model comparison will favour intrinsic models.

Figure 1 about here

II.5.2.2 The network hypothesis

Assuming that extrinsic connections have an important role, we modelled them using distributed neural networks composed of four regions of the temporal lobe (five regions were modelled in a patient shown in online Supplementary Materials). We allowed the connectivity between regions to vary in order to model the changes of evoked responses observed between stimulations. This approach is complex and requires several assumptions to make it practically feasible which are described in the next section.

II.5.3 Data analysis

II.5.3.1 Data preprocessing

Typical evoked responses to 1 Hz neurostimulation can roughly be decomposed into a fast component in the first 150 ms after the stimulation and a late component (around 1-2 Hz). For the DCM analysis, we focused on early components only. To isolate early components, we bandpassfiltered the data between 5 and 40 Hz and select the first 150 ms following each stimulation (see online Supplementary Materials for further discussion on DCM data preprocessing).

II.5.3.2 Prior values of model parameters

As a first intracranial DCM study, we have chosen to make as few modifications as possible from the standard DCM for EEG (David et al., 2006a). To fit fast responses, we changed the DCM prior expectation of two sets of parameters: (i) excitatory and inhibitory fast time constants were set to 4 ms (instead of the standard 10 ms), (ii) propagation delays were set to 1 ms (instead of the standard 5 ms) because studied structures were close to each other. Otherwise, we took standard prior values (David et al., 2006a).

II.5.3.3 Patient selection, network architecture and searching procedure to test the network hypothesis

Testing the network hypothesis required to record activity in the stimulated region to allow the reconstruction of information transfer from the stimulated site to other parts of the network. Four contacts in the same region (two for stimulation, two for recording) were thus needed, which is not common to obtain due to the respective size of the electrode leads and of the structures explored.

We were able to select only four patients (Patients 2, 7, 13 and 19) who met this important requirement.

Acknowledging the restricted neurophysiological plausibility of the DCM neural mass model for the different temporal structures, the distinction between forward, backward and lateral connections which is based on the distinction of the connection pattern between cortical layers was not tenable. Therefore, we have chosen to assume that all connections used in tested models were lateral. Lateral connections are symmetrical, which implies in neurodynamical terms that, if two regions are strongly bidirectionally coupled, they will oscillate with the same phase. If the coupling is asymmetrical, then the driving region can be easily differentiated from the driven region based on temporal precedence analysis.

The neurostimulation was modelled with a single extrinsic input to the region which was stimulated. Assuming (i) that all regions were connected in some direct or indirect way to the stimulated region, and (ii) that all possible connections were lateral and could express a short-term plasticity (i.e. were modulated through time), we calculated that 2432 models, in models composed of four regions, were possible candidates. Those models differ by their topology (existing connections between regions) assumed a priori. Given that it took approximately 2 hours to estimate the parameters of each model, it was obviously not possible to test every model. Thus, an alternative searching procedure was designed to converge quickly towards what could be the most plausible model:

Step 1:

Initialisation: Create the model with connections from the stimulated region to all other regions. Estimate parameters and compute the model evidence. Initialise the best model with this model.

Step 2:

Step 2.1: Identify the connections possible to add (in one step).

Step 2.2: Loop on these connections and, for each added connections, compute the model evidence.

Step 2.3: If a better model was found in Step 2.2, update the best model and go to

Step 2. Otherwise go to Step 3.

Step 3:

Step 3.1: Identify the connections possible to remove (in one step).

Step 3.2: Loop on these connections and, for each removed connections, compute the model evidence.

Step 3.3: If a better model was found in Step 3.2, update the best model and go to

Step 2.

Following this procedure, about 40 models were tested for each train of stimulations. Also, to save computational time, the searching procedure was performed on a data set composed only of the three first stimulations averaged together and of the three last (before seizure onset) stimulations averaged together. Then the parameters of the best model which was identified by the searching procedure were re-estimated using the full data set with all preictal stimulations.

III. Results

III.1 Data description

A prerequisite to 1 Hz electrical stimulation in the clinical setup at the Grenoble University Hospital is to induce seizures that resemble to spontaneous ones (Figure 2). This is particularly important to define those structures that are of low excitability threshold and may participate to the epileptogenic network. Only induced seizures exhibiting such an electro-clinical similarity were selected for further analysis of preictal evoked responses to stimulation.

Preictal evoked responses are only seen in brain regions thought to be either directly or indirectly connected to the site of stimulation. They are considered as physiological responses rather than closely related to some epileptic property of induced neural networks [START_REF] Valentin | Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo[END_REF].

The number of components in evoked responses varied between recording sites and between patients. This is probably due to different patterns of connection where re-entrant loops do not have exactly the same effects on measured dynamics. The other observed phenomenon is the slow modulation of evoked responses over stimulation (Figure 3). We were able to distinguish a potentiation of evoked responses in most patients (18/20) and a depression of evoked responses in two patients only (Patients 4 and 6). We found no correlation between the type of response modulation and/or the stimulation sites, the recording sites, the underlying pathology, the postoperative outcome described in Table 1. (left) The sensitivity, which is the probability over patients that at least one electrode was selected;

(middle) The localisation accuracy, which is the average over patients and selected electrodes of the minimal distance between each selected electrodes and the SOZ; (right) The average over patients of the number of electrodes used to localise the seizure onset region.

Under the assumption that a sensitivity of 0.9 is the minimal acceptable sensitivity for an automatic procedure of epileptic focus detection (white border in Figure 4), the green asterisk indicates the optimal T E value (T E =6.8) when no information about plasticity is taken into account.

On average, the localisation accuracy is then equal to 7.8 mm and 4.1 electrode contacts are used for SOZ localisation. As a comparison, the SOZ was visually defined with 4.7 electrode contacts on average. The magenta asterisk indicates optimal threshold values when information about both excitability and plasticity is used (T E =5.75; T P =5.05). Then the localisation accuracy decreases to 4.4 mm and only 3.7 electrode contacts are used on average.

The best localisation accuracy on average was 2.3 mm (T E =8.475; T P =5.225, white asterisk in Figure 4) using only 2.4 electrode contacts. This corresponds to strong effects in both excitability and plasticity. However, the specificity with such parameters was equal to 0.73 only (because of multiple induced seizures in some patients, SOZ localisation was in fact successful in 17/20=85% of patients with these thresholds). The results of SOZ localisation with these optimal parameters are detailed in Table 2. It appears that there is no overlap between the SOZ defined visually and automatically in only one patient (Patient 5: amygdala versus anterior hippocampus). Otherwise the automatic procedure usually highlights a small subset of what can be defined visually.

Figure 4 & Table 2 about here These results highly suggest that at least a part of the seizure onset zone is particularly prone to exhibit short-term plasticity during preictal 1 Hz stimulation. We will now investigate this phenomenon further using DCM.

III.3 Dynamic Causal Modelling: The role of intrinsic and extrinsic connections

DCM showed a good ability to fit the data as illustrated in Figure 5 with examples of recorded (grey curves) and adjusted (black curves) time series in two patients (Patient 8/Seizure 1 and Patient 18).

According to the log-evidence obtained using AIC and BIC assumptions (Figure 6), six patients showed evidence for a modulation on intrinsic excitation (Patients 4,5,8,11,12,20) and seven showed evidence of a modulation of extrinsic connections (Patients 1, 2, 7, 9, 13, 15, 18).

Remaining seven patients showed no consistent or non-reproducible (in the case of several seizures) evidences. For the group analysis, the log-evidence averaged over all available seizures (30) was not consistent between AIC and BIC (Figure 6, right). As shown in Figure 5, the difference between extrinsic and intrinsic hypotheses was small, suggesting either that intrinsic excitatory connections alone, or extrinsic excitatory inputs to the focus alone, are modulated. However, we did often observe that several regions were modulated together by focal stimulation. This speaks for a more complex connectivity analysis, elaborated in the next section. This patient showed strong evoked responses in amygdala (Am, stimulated region), anterior hippocampus (AH, candidate focus), temporal pole (TP) and fusiform gyrus (FG). Those regions were included in the DCMs. Additional responses (weak and/or noisy) were found in the orbitofrontal cortex and insula. They were a priori excluded from the analysis. In the most plausible model (Figure 7), (i) Am sends projections to AH, TP and FG; (ii) AH is bidirectionnally coupled to Am and TP,; (iii) FG receives inputs from the Am and TP.

A progressive (between stimulations 1 and 20) increase of connectivity can be observed between Am and AH, in both directions. After stimulation 21, there is a large increase of connectivity from Am to AH and a decrease of connectivity from AH to Am. The connection between Am and AH appears thus critical in the preictal state. Am has also an impact on AH via TP since the connections from Am to TP and from TP to AH exhibit the same increase of connectivity as stimulations go along. The action of TP is enhanced by the recurrent loop formed by the feedback from AH onto TP. FG has a marginal role (receives inputs only and weak modulation of responses).

To sum up, the DCM analysis in this patient suggests a critical role of recurrent loops between AH/Am and between AH/TP. The repetitive stimulation of Am may have the property to initiate a short term potentiation of the excitatory outputs of Am towards AH and TP which extends to the entirety of the recurrent loops which connect AH to TP and Am. The regions included in the DCMs of this patient were Am (stimulation site), AH (candidate focus), TP and middle temporal gyrus (T2). Additional weak responses were found in the anterior cingulate cortex and the insula and were a priori excluded from the analysis. Figure 8 shows the architecture of the most likely model: (i). Am sends projections to AH, TP and T2; (ii) AH receives inputs from AM via a relay in TP; (iii) T2 and TP are interconnected.

According to the strength of output connections, TP is the preferential target of Am, although the strength of that connection is reduced by repeated stimulations. The TP projections to T2 and AH are potentiated over stimulations and probably cause the observed dynamic changes in AH and T2. A weak suppression of TP responses due to a small decrease of the connection from Am to TP and from T2 to TP, is observed concomitantly with the potentiation of responses in AH and T2.

Similarly to the previous patient, the estimated extrinsic inputs on Am are approximately constant over time, suggesting only weak intrinsic modulation of the amygdala excitability to neurostimulation.

To conclude, the DCM analysis suggests that, in this patient, repetitive stimulation of Am may induce a potentiation of projections of TP onto T2 and AH. The increase of inputs from TP to may subsequently propagate to other brain regions.

Figure 8 about here

IV. Discussion

Intracerebral stimulation is a powerful approach for epilepsy evaluation during presurgical monitoring of patients [START_REF] Buser | Unilateral connections between amygdala and hippocampus in man. A study of epileptic patients with depth electrodes[END_REF][START_REF] Chauvel | Electrical stimulation with intracerebral electrodes to evoke seizures[END_REF][START_REF] Kahane | Electroclinical manifestations elicited by intracerebral electric stimulation "shocks" in temporal lobe epilepsy[END_REF][START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF][START_REF] Kalitzin | Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy[END_REF][START_REF] Schulz | Localization of epileptic auras induced on stimulation by subdural electrodes[END_REF][START_REF] Valentin | Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo[END_REF]Valentin et al., 2005b;Valentin et al., 2005a;[START_REF] Wilson | Paired pulse suppression and facilitation in human epileptogenic hippocampal formation[END_REF]. A significantly greater paired pulse suppression of population post-synaptic potentials was found on the epileptogenic side compared to the controlateral side in MLTE patients [START_REF] Wilson | Paired pulse suppression and facilitation in human epileptogenic hippocampal formation[END_REF]. Using single pulse electrical stimulation, it has been suggested that epileptogenic regions, in either temporal or frontal lobe epilepsy, could exhibit more delayed response (up to 1 s) than other regions which show only early responses (<100 ms) [START_REF] Valentin | Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo[END_REF]Valentin et al., 2005b;Valentin et al., 2005a). Using intermittent pulse stimulation in the 10-20 Hz frequency range, the spatial distribution of the excitability state in patients with TLE was found to be correlated with the most probable seizure onset zone [START_REF] Kalitzin | Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy[END_REF]. In addition, and most importantly, the brain excitability was found to be the highest when seizure intervals were the shortest. This indicates that changes of excitability are a strong indicator of epileptogenic mechanisms.

Neurophysiological mechanisms underlying short-term plasticity in epileptic patients are best understood using in vitro experiments [START_REF] Beck | Synaptic plasticity in the human dentate gyrus[END_REF][START_REF] Behr | Kindling induces transient NMDA receptor-mediated facilitation of high-frequency input in the rat dentate gyrus[END_REF][START_REF] Feng | Short-term frequency-dependent plasticity at recurrent mossy fiber synapses of the epileptic brain[END_REF][START_REF] Koch | Intrinsic excitability, synaptic potentials, and short-term plasticity in human epileptic neocortex[END_REF][START_REF] Reichova | Somatosensory corticothalamic projections: distinguishing drivers from modulators[END_REF][START_REF] Schmitz | Presynaptic kainate receptor mediation of frequency facilitation at hippocampal mossy fiber synapses[END_REF]. In mesio-temporal lobe epilepsy (MTLE), paired-pulse induced changes of synaptic and intrinsic excitability are usually more easily observed in the hippocampus than in the neocortex [START_REF] Koch | Intrinsic excitability, synaptic potentials, and short-term plasticity in human epileptic neocortex[END_REF]. Systematically, modulation of NMDA or kainate receptors, depending on the structures involved, is found to be associated with electrically induced short-term plasticity in epilepsy. As shown by using trains of stimuli at different frequencies [START_REF] Feng | Short-term frequency-dependent plasticity at recurrent mossy fiber synapses of the epileptic brain[END_REF][START_REF] Schiller | Cellular mechanisms underlying antiepileptic effects of low-and high-frequency electrical stimulation in acute epilepsy in neocortical brain slices in vitro[END_REF], short-term plasticity is frequency-dependent and can be either inhibitory or excitatory. When repetitive stimulation has an anti-epileptic effect, this effect is mediated mainly by short-term synaptic depression of excitatory neurotransmission [START_REF] Schiller | Cellular mechanisms underlying antiepileptic effects of low-and high-frequency electrical stimulation in acute epilepsy in neocortical brain slices in vitro[END_REF]. Analysing the different patterns of paired-pulse facilitation and paired-pulse inhibition may also give some insights for distinguishing neuronal drivers from neuronal modulators [START_REF] Reichova | Somatosensory corticothalamic projections: distinguishing drivers from modulators[END_REF]. In summary, in vitro experiments strongly suggest that repeated stimulations induce changes in neuronal properties that may be modelled and, possibly, estimated from iEEG signals in implanted patients.

In this study, we have first operationally evaluated 1 Hz intracerebral stimulation for the identification of epileptogenic regions. We have correlated the visually defined seizure onset zone with regions selected by the means of the average standard deviations of stimulation-induced responses (excitability) and of the linear trend of the standard deviations of those responses (shortterm plasticity). As suggested by previous studies [START_REF] Kalitzin | Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy[END_REF][START_REF] Schulz | Localization of epileptic auras induced on stimulation by subdural electrodes[END_REF][START_REF] Valentin | Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo[END_REF]Valentin et al., 2005b;Valentin et al., 2005a) and by more than a decade of clinical practice with this experimental protocol [START_REF] Kahane | Invasive EEG in the definition of the seizure onset zone: depth electrodes[END_REF], a strong correlation was found between regions which showed strong responses to stimulation and clinically identified seizure onset zones. We then showed that regions exhibiting slowly varying responses were even more correlated to the SOZ, in part because they were more focal, adding new evidence for the involvement of fast synaptic plasticity in epileptogenic mechanisms. In agreement with [START_REF] Kalitzin | Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy[END_REF], we found that short-term potentiation was correlated to seizure onset in most patients (18/20). Our results do not mean that it is clinically relevant to delineate more focal epileptogenic regions, but they point towards the fact that, in the pool of putative epileptic regions, a limited number show fast changes of the shape of responses induced by stimulation. Besides the localisation issue, searching for progressive modifications of neural responses to stimulations allows one to try to understand the transition between the interictal state and the ictal state. In that respect, further work is in progress to evaluate whether regions showing strong plasticity are correlated to regions exhibiting high frequency oscillations at seizure onset [START_REF] Jirsch | High-frequency oscillations during human focal seizures[END_REF]. Of course, our results rely on the assumption that the clinically defined (visually inspected) SOZ is a good marker of epileptogenic regions, which can be obviously a debatable issue (although used in clinical practice).

In contrast to in vitro studies, it is usually not possible to interact pharmacologically with the human brain in vivo to investigate neural mechanisms at the origins of the observed modulation of responses. Instead, we used a simple neural mass model to generate the iEEG signals of one or of several brain areas composed of inhibitory and excitatory neural populations [START_REF] David | Modelling event-related responses in the brain[END_REF].

Our goal was to know whether the modulation of measured iEEG responses could be indeed thought of as a consequence of short-term plasticity of excitatory synaptic efficacies induced by repetitive stimulations. To do so, we have used DCM with two objectives: (i) to know whether it would be sufficient to assume that the modulation of synaptic efficacies is restricted to the putative focus or whether it would be expressed in other regions as well (extrinsic/intrinsic hypothesis); (ii)

to study the connectivity in the temporal lobe and its short-term modulation during the preictal state of induced epileptic seizures (network hypothesis). In comparison to standard data driven approaches, DCM is unique because it proceeds to a connectivity analysis of macroscopic electrical data using neuronal models. Therefore, the estimated connectivity can be expressed directly in neuronal terms, i.e. synaptic efficacies, in contradistinction with other approaches based on correlation/synchronisation [START_REF] David | Evaluation of different measures of functional connectivity using a neural mass model[END_REF][START_REF] Schindler | Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG[END_REF] or multivariate autoregressive models [START_REF] Baccala | Partial directed coherence: a new concept in neural structure determination[END_REF][START_REF] Kaminski | Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance[END_REF]. This represents an important step forward a better understanding of brain functions.

For simplicity, we used the standard architecture of the neural mass model of DCM for EEG (David et al., 2006a;[START_REF] Kiebel | Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization[END_REF]. This is a basic model of a cortical macro-column of the visual cortex [START_REF] Jansen | Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns[END_REF]. Given that the cytoarchitecture of most structures in the temporal lobe does not conform to what can be found in the striate visual cortex, this model is certainly not the optimal neuronal model. However our objective was not to deal with a highly plausible neuronal model. Using the Jansen model as modified in [START_REF] David | Modelling event-related responses in the brain[END_REF] was simply a way to summarise the complex neuronal interactions in a neural mass model which has been shown

to capture the basic dynamics of most types of EEG signals [START_REF] David | Evaluation of different measures of functional connectivity using a neural mass model[END_REF][START_REF] David | Modelling event-related responses in the brain[END_REF]David et al., 2006a;David et al., 2006b;[START_REF] David | A neural mass model for MEG/EEG: coupling and neuronal dynamics[END_REF][START_REF] Jansen | Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns[END_REF]Kiebel et al., 2006;[START_REF] Wendling | Relevance of nonlinear lumpedparameter models in the analysis of depth-EEG epileptic signals[END_REF][START_REF] Wendling | Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG[END_REF]. In fact, the departure from the proposed neuronal architecture of the true underlying neural networks was somewhat corrected for by the estimation of model parameters which adapts the dynamics generated by the model to the measured data. In addition, as we were most interested in the modulation of excitatory connections in a broad sense, the intrinsic architecture of brain regions was, to some extent, of no interest. More realistic models, i.e. with neural circuitry adapted to the different structures of the brain, would certainly be more convincing to study closely neural mechanisms that may be involved in specific regions or pathologies. However, development of specific models for temporal lobe epilepsy was out of the scope of this study. Future research is needed to evaluate the importance of refining the neural architecture of the models used in DCM.

After a group analysis including 30 seizures recorded in 20 patients, we did not find any evidence in favour of the two competing hypotheses about the role of extrinsic and intrinsic connections to the epileptic focus. It suggests first that observed short-term plasticity may be restricted to neural networks located in the epileptic focus. But it is equally plausible that the modulation of the shape of evoked responses may be due to the modulation of interregional connectivity. Given the fact that strong modulations were often observed simultaneously in multiple regions, we investigated further the hypothesis that the modulation is expressed at the level of connections between different regions belonging to the distributed network.

As an important selection criterion to test the network hypothesis, we imposed the stimulated structure to be also recorded, thereby restricting the analysis to four patients. Two patients were stimulated in amygdala (main text), one patient was stimulated twice in the fusiform gyrus (online Supplementary Materials), and the last patient was stimulated in the temporal pole and in the temporal neocortex T4 (online Supplementary Materials). By comparing the two patients stimulated in the amygdala (Patients 2 and 13), we will now discuss the reproducibility of DCM results between patients. Within-patient reproducibility is discussed in online Supplementary Materials using the patient stimulated twice in the fusiform gyrus (Patient 19). Finally, the reproducibility of the effects of neurostimulation in regard to a change of the site of stimulation is discussed using the fourth patient (Patient 7) in online Supplementary Materials.

The two patients stimulated in the amygdala showed a strong increase of the amplitude of responses in the anterior hippocampus a few stimulations (five in Patient 2, three in Patient 13) before the seizure onset. In both patients, this corresponded to an increase of effective connectivity between the temporal pole and the anterior hippocampus (Figures 7 &8). In Patient 2, the direct influence of the amygdala onto the anterior hippocampus was also increased (Figure 7). These connections are supported by anatomical studies [START_REF] Chabardes | Anatomy of the temporal pole region[END_REF]: the hippocampus receives afferents from all over the anterior temporal cortex and an amygdalo-temporal fascicle which originates at the rostrolateral surface of the amygdala and specifically connects to the temporal pole has been described in human [START_REF] Klingler | The connections of the amygdala and of the anterior temporal cortex in the human brain[END_REF]. Furthermore, it has been suggested from visual analysis of iEEG recordings that the temporal pole may be a determinant structure, concomitantly with the hippocampus, during the onset of seizures in temporal lobe epilepsy in many patients [START_REF] Chabardes | The temporopolar cortex plays a pivotal role in temporal lobe seizures[END_REF][START_REF] Kahane | The role of the temporal pole in the genesis of temporal lobe seizures[END_REF]. The results obtained from the DCM study in those two patients agree with this part of the literature. They also call for an interpretation of the genesis of a certain type of mesial temporal lobe epilepsy as a preictal increase of the hippocampal afferents coming from the temporal pole, which could be triggered by the amygdala.

This putative type of mesial temporal lobe epilepsy remains to be fully characterised. Note that an increase of synaptic connectivity induces an increase of synchrony between distant regions. Such pre-ictal synchronisation associated with a pre-ictal increase of synaptic connectivity has been described between mesial structures [START_REF] Bartolomei | Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy[END_REF].

V. Conclusion

We have shown that a limited number of structures in temporal lobe epilepsy, identifiable to the candidate epileptic focus, is particularly prone to express fast changes in their responses to repetitive 1 Hz neurostimulation. In our small cohort of patients, we did not find any correlation between expressed plasticity and patients' pathologies. However, studying short-term plasticity may constitute an added value more easily exploitable in prospective studies than in retrospective studies such as ours. The signal analysis to identify changes in the shape or amplitude of responses induced by neurostimulation is rather straightforward and can be used in clinical routine. The biophysical description of short-term plasticity in epilepsy is much more difficult to perform, though extremely interesting to understand epileptogenic mechanisms. In particular, it is time-consuming because it implies nonlinear identification of complex dynamical systems. However such approach is still in its infancy and is likely to be developed further in the near future. For instance, an important issue is to better understand the intrinsic dynamics of short term plasticity: Why is it progressive? What is the meaning of apparent random fluctuations? Can it be used to infer more global dynamical features on neural networks? A first direction to answer to these questions, among other possibilities, has been proposed where the theory of autonomous systems has been combined to the formalism of DCM [START_REF] David | Dynamic causal models and autopoietic systems[END_REF]. Being able to deal with this sort of interrogations is a step forward in neuroimaging and electrophysiology data analysis that will allow new interpretations of clinical and physiological data in integrated neuroscience.

Figure captions

FiguresFigure 4

 4 Figures 2 & 3 about here

Figures

  Figures 5 & 6 about here

Figure

  Figure 7 about here

Figure 1 :

 1 Figure 1: Alternative DCMs of the epileptic focus tested to explain observed short term plasticity

Figure 2 :

 2 Figure 2: Intracerebral EEG recordings of a spontaneous seizure and of an induced seizure in the

Figure 3 :

 3 Figure 3: Different observed types of stimulation-induced modulation: a gradual increase of evoked

Figure 4 :

 4 Figure 4: Seizure onset localisation as a function of excitability threshold T E and plasticity

Figure 5 :

 5 Figure 5: Neural modelling of short-term plasticity in the epileptic focus for two patients (left:

Figure 6 :

 6 Figure 6: Model comparison for the role of the modulation of extrinsic and intrinsic excitatory

Figure 7 :

 7 Figure 7: Most likely model estimated during the amygdala (Am) stimulation in Patient 2. Left:

C

  is the coupling strength of connection i at stimulation n. n varied from 0 to N-m. m was set to 10 or was equal to the total number of stimulations N if N<10. When computing the slopes, obvious outliers were removed to track only slow changes. Figure indicates the maximal value of n i M when n is varied. Broken arrows indicate connections inhibited by repetitive stimulation (slope<0). Thick arrows indicate connections strongly modulated (|slope|>1.5). Right: Measured (black) and adjusted (grey) time series, after normalisation of their variance.

Figure 8 :

 8 Figure 8: Most likely model estimated during the amygdala (Am) stimulation in Patient 13. Same

Acknowledgements

This study was supported by Inserm and the Erasmus program (A.W.).


Noémie Lalaoui

Kilian Gentil

Ilyass Ghandari

Serge Cosnier

Fabien Giroud

Nitrobenzoic acid-functionalized Gold Nanoparticles: DET promoter of Multicopper Oxidases and Electrocatalyst for NAD-Dependent Glucose Dehydrogenase

Introduction

Bioelectrocatalysis is used in bioanalysis, bioenergy and green synthesis. It involves the use of redox proteins to undergo the oxidation or reduction of specific targeted substrates for their quantification, clean electricity generation or their conversion to higher value-added chemicals [START_REF] Chen | The progress and outlook of bioelectrocatalysis for the production of chemicals, fuels and materials[END_REF]. For example, glucose is a widely studied substrate in this field because it is an affordable, non-toxic and stable analyte. In physiological conditions, glucose level can be associated to several health disorders such as diabetes, a higher risk to develop cancer [START_REF] Jee | Fasting Serum Glucose Level and Cancer Risk in Korean Men and Women[END_REF] and could be linked to worse prognosis for patients with COVID-19 [START_REF] Apicella | COVID-19 in people with diabetes: understanding the reasons for worse outcomes[END_REF]. Usually, its monitoring is performed from blood samples. It can also be found in other physiological fluids such as saliva [START_REF] Jurysta | Salivary Glucose Concentration and Excretion in Normal and Diabetic Subjects[END_REF], tears [START_REF] Yao | A contact lens with embedded sensor for monitoring tear glucose level[END_REF], sweat [START_REF] Xuan | A wearable electrochemical glucose sensor based on simple and low-cost fabrication supported micro-patterned reduced graphene oxide nanocomposite electrode on flexible substrate[END_REF], urine [START_REF] Scherstén | Subnormal Levels of Glucose in Urine: A Sign of Urinary Tract Infection[END_REF], or breath condensate [START_REF] Karyakin | Non-invasive monitoring of diabetes through analysis of the exhaled breath condensate (aerosol)[END_REF]. It has been shown that to some extent, glucose level in these fluids can be fluctuating in the same manner as blood glucose, and consequently its quantification in external body fluids can be correlated with health conditions. Hence, it is still of interest to propose new strategies to help in monitoring glucose level in physiological ranges (i.e. 50 µmol L -1 to 4.0 mmol L -1 ) for fast and non-invasive biosensing. Glucose can be oxidized enzymatically by several oxidoreductases. Among them, the NAD-dependent glucose dehydrogenase (GDH) utilizes nicotinamide adenine dinucleotide (NAD + ) as cofactor. This NAD + prosthetic group is shared by more than 300 other dehydrogenases involved in several metabolic pathways such as glycolysis and Krebs cycle.

Hence, it makes GDH an interesting model for point of care diagnostic but also for every other NAD-dependent enzymes (used in energy conversion or green synthesis).

Unlike other cofactors, NADH/NAD + is loosely bound to the proteins and usually has to be added into the medium. Some literature reports its direct incorporation in the modified matrices to increase its local concentration [START_REF] Rincón | Enzymatic fuel cells: Integrating flow-through anode and air-breathing cathode into a membrane-less biofuel cell design[END_REF][START_REF] Reid | Contact lens biofuel cell tested in a synthetic tear solution[END_REF]. It has a low redox potential (E°' = -0.560 V vs. SCE at pH 7.0) but high overpotentials are required for its electrochemical conversion at electrode surfaces. As a result, the use of electrocatalysts like Meldola blue [START_REF] De S. Santos | Electrocatalytic NADH Oxidation Using an Electrode Based on Meldola Blue Immobilized on Silica Coated with Niobium Oxide[END_REF], Azure dyes [START_REF] Karyakin | New amperometric dehydrogenase electrodes based on electrocatalytic NADH-oxidation at poly (methylene blue)-modified electrodes[END_REF][START_REF] Cai | Electrocatalysis of NADH oxidation with electropolymerized films of azure I[END_REF] or quinones [START_REF] Reuillard | Non-covalent double functionalization of carbon nanotubes with a NADH oxidation Ru(II)-based molecular catalyst and a NAD-dependent glucose dehydrogenase[END_REF] is recommended to favor the reaction. Nitroaromatics are also known as good catalysts for NAD + regeneration. Literature reports on the number of activated nitro groups, the number of substitution and the mechanism of activation of the catalytically active form [START_REF] Casero | Electrochemically Triggered Reaction of a Surface-Confined Reagent: Mechanistic and EQCM Characterization of Redox-Active Self-Assembling Monolayers Derived from 5,5'-Dithiobis(2-nitrobenzoic acid) and Related Materials[END_REF][START_REF] Mano | Immobilized nitro-fluorenone derivatives as electrocatalysts for NADH oxidation[END_REF]. Due to their chemical structures, nitroaromatics have been immobilized on different electrode materials and with different methodologies such as adsorption on 2D surfaces [START_REF] Mano | Immobilized nitro-fluorenone derivatives as electrocatalysts for NADH oxidation[END_REF], 3D matrices [START_REF] Moscoso | Electrocatalysis of NADH on 3,5-Dinitrobenzoic Acid Encapsulated on Multiwalled Carbon Nanotube-Modified Electrode[END_REF], functionalization of polymers [START_REF] Santhiago | In situ activated 3,5dinitrobenzoic acid covalent attached to nanostructured platform for NADH electrooxidation[END_REF], non-covalent adsorption (with high π-π stacking affinity with CNTs) [START_REF] Giroud | 5,5-Dithiobis(2-nitrobenzoic acid) pyrene derivative-carbon nanotube electrodes for NADH electrooxidation and oriented immobilization of multicopper oxidases for the development of glucose/O2 biofuel cells[END_REF][START_REF] Moscoso | Electrocatalytic oxidation of NADH in a new nanostructured interface with an entrapped butylpyrene nitroaromatic derivative[END_REF] and covalent bonding through amide [START_REF] Jeong | Immobilization of a mediator onto carbon cloth electrode and employment of the modified electrode to an electroenzymatic bioreactor[END_REF] or Au-S bond formation [START_REF] Casero | Electrochemically Triggered Reaction of a Surface-Confined Reagent: Mechanistic and EQCM Characterization of Redox-Active Self-Assembling Monolayers Derived from 5,5'-Dithiobis(2-nitrobenzoic acid) and Related Materials[END_REF].

Additionally, we have previously shown that the increased aromaticity and electrodonating groups due to the specific structure of carboxylated nitroaromatics can improve the orientation of multicopper oxidases (MCOs) such as laccase (Lc) and bilirubin oxidase (BOx) onto CNTs films with no NADH cross-reaction possible when the nitro group is not activated in a first step [START_REF] Giroud | 5,5-Dithiobis(2-nitrobenzoic acid) pyrene derivative-carbon nanotube electrodes for NADH electrooxidation and oriented immobilization of multicopper oxidases for the development of glucose/O2 biofuel cells[END_REF]. This report focuses on the synthesis of gold nanoparticles (AuNPs) decorated with nitro groups and their direct contribution to enhance electrocatalysis of ORR and NADH oxidation. Gold substrates have been used previously in various studies as electronic bridges for enzyme "wiring" [START_REF] Gutiérrez-Sánchez | Gold Nanoparticles as Electronic Bridges for Laccase-Based Biocathodes[END_REF] and for the immobilization of electrocatalysts containing disulfide bonds [START_REF] Swiech | Gold Nanoparticles Tethered to Gold Surfaces Using Nitroxyl Radicals[END_REF].

Herein, we present their utilization along with the laccase, bilirubin oxidase for ORR and the NAD-dependent model GDH for glucose electrooxidation as a proof of concept. AuNPs characterization was carried with spectroscopy and microscopy techniques to determine their size and to validate the synthesis of the nanoparticles with a thiol capping ligand bearing a nitro group. Unactivated AuNPs-TNB were first assessed for MCO orientation to improve ORR on CNTs films. Next, the nitro group was activated electrochemically to form the active hydroxylamine/nitroso couple responsible for NADH oxidation. Associated with a CNT matrix and the GDH, the nanocomposite displayed good electrocatalytic properties for glucose oxidation reaching 400 µA cm -2 when the substrate was saturating the enzyme (>50 mmol L -1 glucose) at low potential. Finally, the complete assembly forming an enzymatic fuel cell, it was proposed as a device for glucose detection and quantification. It displayed linear relationships in the physiological glucose concentration regions that could potentially be useful for glucose sensing from external body fluids with minimum handling (i.e. no dilution). ), glucose, ethanol, methanol (MeOH) and 1-methyl-2pyrrolidone (NMP) were obtained from Merck (Sigma Aldrich) and used as received. Ascorbic acid, dopamine hydrochloride were purchased from Fisher Scientific. Uric acid was purchased from Alfa Aesar. Commercial grade thin Multi-Walled Carbon Nanotubes (9.5 nm diameter, 1.5 µm long, purity > 90 %) were obtained from Nanocyl and used as received.

Experimental details

Synthesis of TNB coated AuNPs (AuNPs-TNB)

Under argon, 0.15 mL of HAuCl 4 solution (215 µmol) was mixed with 60 mL of MeOH and 111 mg (280 µmol) of DTNB. Then, a freshly prepared solution of NaBH 4 (137 mg, 3.64 mmol) in 45 mL of MeOH was added slowly during half an hour and under vigorous stirring.

Upon addition, the initial yellow solution became brown. The solution was stirred under argon for 24 h at room temperature. After this time, the solution was concentrated in vacuum until precipitation of the particles, which were collected by centrifugation (Jouan B4i). The solid product was washed by sonication /centrifugation in ethanol until no traces of free ligands were found from thin layer chromatography experiments and then dried under vacuum at 40°C.

Characterization methods

UV-Vis, FT-IR. UV-vis spectra were obtained from methanol solution with an optic fiber probe purchased from Ocean Optics. Infrared (IR) spectra were recorded on a platinum ATR Vertex 70 BRUKER spectrometer.

TEM. For morphology and size analysis of AuNPs, one aliquot of 0.5 mg mL -1 AuNPs-TNB in MeOH was prepared. Droplets (4 µL) were deposited onto glow-discharged carbon-coated 200 mesh copper grids and allowed to air-dry. The preparations were observed using a JEOL JEM 2100-Plus transmission electron microscope (TEM) operating at 200 kV. Images were recorded with a Gatan Rio 16 digital camera.

X-ray Photoelectron Spectroscopy. XPS data was obtained using an ESCALAB 250 from Thermo Scientific with a monochromated Al Kα band (1486.6 eV) as the excitation source.

The diameter of the surface spot analyzed was 500 μm. Survey spectra were recorded from 0 to 1200 eV and referenced to the sp2 carbon energy contribution (C=C) C1s at 284.4 eV. The charge was compensated by an electron beam (-2 eV).

Electrochemistry. Glassy carbon electrodes (Ø = 3 mm) were obtained from ALS Co. (Japan) and HTDS (France). Prior to modification, the electrodes were polished with a 2 μm diamond paste purchased from Presi (France) and rinsed successively with water and ethanol and left to sonicate for 5 min in ethanol. The electrochemical characterizations for each electrode were During the irreversible reduction of NO 2 groups, the electrolyte was purged with argon (Ar) for 10 min. Distilled water was passed through a Milli-Q water purification system (Millipore, 18MΩ).

Electrodes Preparations

AuNPs-TNB/MWCNT modified electrodes. MWCNT films were prepared from a 3.0 mg mL -1 suspension in NMP kept for 30 min under sonication and finally shacked vigorously before pipetting. The UV-Vis spectra did not show an usual plasmon band (near 520 nm), indicating that most particles were below ca. 3 nm in size [START_REF] Hussain | Size-Controlled Synthesis of Near-Monodisperse Gold Nanoparticles in the 1-4 nm Range Using Polymeric Stabilizers[END_REF].

Results and discussion

Synthesis and physical characterization of AuNPs-TNB

FT-IR spectra (Figure S1B) for AuNPs-TNB showed similar peaks found in the DTNB precursor (υ = 500 to 1100 cm -1 for aromatic =C-H; υ = 1210 cm -1 for -C-O stretching; υ = 1320, 1560 cm -1 for N-O stretching; υ = 1610 cm -1 for C=O stretching; υ = 2985 cm -1 for =C-H stretching). The large band in the DTNB spectra (υ = 2200 to 3400 cm -1 ) disappeared while a new large band was found at higher wavenumber (υ = 2750 to 3700 cm -1 ) for the nanoparticles samples. This shift was still attributed to various O-H stretching mode and had been documented for other carboxylate capping ligand and hydrogen bonding with solvent molecules [START_REF] Chen | Synthesis and Characterization of Carboxylate-Modified Gold Nanoparticle Powders Dispersible in Water[END_REF]. Also, the merging and decrease in peak intensity could be attributed to the ordering of TNB molecule at the AuNP surface similarly to π-π stacking interactions [START_REF] Anson | FTIR study of π-stacking effects in (η6arene)tricarbonylchromium complexes: application as a molecular sensor for aromatic analytes[END_REF].

Next, TEM imaging was performed in order to determine size and size distribution of the AuNPs-TNB population. The data confirmed the small and narrow size of the AuNPs assumed from UV-Vis spectra. The diameter of the AuNPs were in the range of 1.6 ± 0.3 nm and were relatively monodisperse as shown by the distribution curve (Figure 1). 1:1:4:7 (expected) relative to N atom).

Electrochemical characterization of AuNPs-TNB

The electrochemical characterization of the AuNPs-TNB adsorbed on MWCNTs layer was performed in McIl buffer (pH 7.0) with cyclic voltammetry as shown in Figure 2. As expected, the AuNPs-TNB were activated electrochemically by reducing the nitro groups located on the outer sphere of the AuNPs as seen from the reduction tail on the CV scans in the cathodic region and starting at -0.300 V. This led to the formation of hydroxylamine groups (-NHOH) shown in equation [START_REF] Chen | The progress and outlook of bioelectrocatalysis for the production of chemicals, fuels and materials[END_REF]. This reduction was associated with the apparition of the new system describing the reversible interconversion of the hydroxylamine to the corresponding nitroso group (-NO) centered at E 1/2 = -0.031 ± 0.005 V according to the global equation ( 2).

(1)

(2) Unmodified MWCNTs electrodes polarized accordingly did not show any redox signal in the same electrochemical window. To guarantee a maximal electroactivation of hydroxylamines and stable electroactive layers the potential was continuously swept until the signal at the foot of the wave for the nitro reduction disappeared [START_REF] Casero | Electrochemically Triggered Reaction of a Surface-Confined Reagent: Mechanistic and EQCM Characterization of Redox-Active Self-Assembling Monolayers Derived from 5,5'-Dithiobis(2-nitrobenzoic acid) and Related Materials[END_REF]; this required 10 cycles from 0.35 to -0.55 V vs. SCE. A voltammetry survey at different pH values (from 3.0 to 8.0) exhibited a shift of the calculated E 1/2 by -65 ± 1.4 mV/pH unit (Figure S4). It confirmed the 1:1 ratio for proton/electron exchange and thus the assumption of n = 2 was validated.

Subsequently, cyclic voltammograms performed at different scan rates were used to determine the surface coverage of the activated hydroxylamine (Γ NHOH ) at the surface of the electrodes.

As the oxidation and the reduction peak currents were linearly dependent on the scan rate (Figure S5), the Laviron's equation was used for this purpose (equation (3)).

(

) 3
where I pf is the faradaic peak current, n is the number of electrons involved in the process, F is Faraday's constant, S is the geometric surface area of the CNTs layer, R is the gas constant, T is the temperature, and ν is the scan rate. The resulting average Γ NHOH = 8.80 ± 0.60 10 -9 mol cm -2 corresponds to an increase by a factor 50 of the apparent surface coverage obtained by adsorption of DTNB precursor on MWCNTs (Γ DTNB = 0.16 ± 0.03 10 -9 mol cm -2 ). Furthermore, the electroactivity of the activated AuNPs remained stable for a longer period since it retained 50% and 30% of its original activity after 7 and 21 days respectively. As a comparison, this is a drastic improvement compared to the poor stability of the activated DTNB adsorbed on MWCNTs layers where no electrochemical signal was detected after only 4 days (Figure S6).

The apparent electron transfer (ET) rate, k ET of the AuNPs adsorbed on the MWCNTs layer was calculated by recording cyclic voltammograms up to 15 V s -1 and plotting the change in the anodic and cathodic peak potentials as function of log(ν) according to the Laviron's model [START_REF] Laviron | General expression of the linear potential sweep voltammogram for a surface redox reaction with interactions between the adsorbed molecules: Applications to modified electrodes[END_REF] as shown in Figure 3. At relatively high scan rates, both anodic and cathodic branches tend to be linear with the variation of log(ν) forming two asymptotes with slopes equal to and respectively. As shown in equation ( 4), k ET can be obtained when ν a = ν c , or more explicitly at the intercept of the two asymptotes mentioned earlier. The apparent ET rate constant for AuNPs-TNB at MWCNTs electrodes was k ET = 12 ± 1 s -1 . This value is in the range of several literature reports based on nitroaromatics immobilized on carbon surfaces (from 1.7 s -1 to 14 s -1 ) [START_REF] Moscoso | Electrocatalysis of NADH on 3,5-Dinitrobenzoic Acid Encapsulated on Multiwalled Carbon Nanotube-Modified Electrode[END_REF][START_REF] Moscoso | Electrocatalytic oxidation of NADH in a new nanostructured interface with an entrapped butylpyrene nitroaromatic derivative[END_REF][START_REF] Corrêa | In situ activated nanostructured platform for oxidized glutathione biosensing[END_REF]. However, it appeared to be slower than another study reported by Kubota et al. utilizing a similar approach (MWCNTs, AuNPs and DTNB) [START_REF] Santhiago | An amperometric sensor for l-cysteine based on nanostructured platform modified with 5,5′-dithiobis-2-nitrobenzoic acid (DTNB)[END_REF]. In their work, k ET achieved 43 s -1 . The slower rate could be attributed to the lower purity of the MWCNTs used herein (purity >90%).

Bioelectroreduction of O 2 at MCO/AuNPs-TNB/MWCNT modified electrodes

The aforementioned unactivated AuNPs-TNB were assessed for the improvement of direct ET of two different MCOs commonly used for their electrocatalytic activity toward oxygen reduction reaction (ORR) (Figure 4). Before electrochemical survey for ORR, few electrodes were sacrificed to verify the presence of the AuNPs-TNB after MCO incubation through their electrochemical activation. In N 2 saturated electrolyte, the surface coverage was comparable with regard to the section 3. Stability measurements of such BOx-based architecture were assessed daily as they displayed the best performances. After 8 days, the bioelectrodes maintained 60% of their initial activity which is consistent with other AuNP-modified biocathodes [START_REF] Lalaoui | Direct Electron Transfer between a Site-Specific Pyrene-Modified Laccase and Carbon Nanotube/Gold Nanoparticle Supramolecular Assemblies for Bioelectrocatalytic Dioxygen Reduction[END_REF].

Bioelectrooxidation of glucose at GDH/AuNPs-TNB/MWCNT modified electrodes

Activated AuNPs-TNB were also evaluated as electrocatalysts for the oxidation of NADH. In the presence of 2 mmol L -1 NADH, the cyclic voltammogram showed an irreversible anodic peak at 0.003 V while the cathodic peak linked to the nitroso reduction disappeared (Figure S7). This confirmed the efficient electrocatalytic properties of the synthesized and activated AuNPs-TNB immobilized on MWCNTs surfaces towards the oxidation of NADH.

Furthermore, the use of these AuNPs notably enhanced the rate of NADH oxidation compared with its oxidation at unmodified MWCNTs electrodes. This was observed from the shift of the anodic peak potential (ΔE p,a = -150 mV) of the cofactor reaching diffusion limitation in transient condition and the 1.9-fold increase in I cat,max compared to unmodified MWCNTs electrodes. Interferent analysis was also carried out to get an assessment of the precision of the proposed architecture. We focused our attention on ascorbic acid (AA), dopamine (DA) and uric acid (UA), three most common interferents found in physiological fluids. It appears that in most cases, minimal glucose level is higher than the maximal concentration reported for each individual interfering compound (Table S1). Hence, experiments were performed with maximal interferent concentration found in body fluids (Figure S9). Only AA could lead to a less accurate glucose quantification as its oxidation takes place within the same potential window as the electrocatalysts. On the other hand, DA and UA are oxidized at sufficiently higher potential (> E pa, NHOH ) avoiding parasitic oxidation at our AuNPs-TNB based bioelectrodes.

AuNPs-TNB/MWCNT modified electrodes for self-powered glucose sensing

Based on previous results, in O 2 saturated solution, the BOx-based biocathode can be combined with the GDH-based bioanode to afford complete glucose/O 2 -EFC with no limitation from the cathode before saturation with glucose (j max, BOx = 920 ± 72 µA cm -2 and j max, GDH = 430 ± 24 µA cm -2 ). This EFC can be utilized to detect and sense glucose in solution based on the power generated by the device. From linear polarization curves of half-cell measurements, a power curve was constructed (Figure S10). It shows that the EFC possesses large open circuit voltage (OCV = 0.84 ± 0.02 V) and can sustain power generation in the presence of glucose. In these conditions, it can deliver maximal power density of ca. 160 µW cm -2 at 0.43 V. The operating potential of the EFC was chosen to ensure maximal polarization of the anode below the oxidation thresholds of DA and UA (hence, not exceeding 0.09 V vs. SCE). The power delivered by the device in the absence of glucose was solely due to the presence of the reduced NHOH on the bioanode surface during the first potential sweep and was not recovered afterwards for subsequent polarizations.

Hence, glucose sensing was performed potentiostatically at this specific voltage. This also ensured mitigation of capacitive current contribution as opposed to the power measurement from polarization curves. To ensure no diffusion limitation during acquisition process and minimal polarization from the biocathode, the solution was moderately stirred. Data were processed with the "adjacentaveraging" method from OriginPro Software to minimize noise contribution (Figure S11A).

Glucose detection was positive as low as 2.0 µmol L -1 . The lower detection limit (LOD) calculated taking in account a 3-fold signal/noise ratio was found to be 2.5 ± 0.9 µmol L -1 . The Figure S11B depicts the calibration curves obtained upon glucose addition to the electrolyte.

The power density response followed a Michaelis-Menten kinetics (K m = 6.1 ± 0.5 mmol L -1 ) with a limiting power density (P max ) upon enzyme saturation with substrate at P max = 156 ± 15 µW cm -2 . In low glucose level (i.e. 40 µmol L -1 to 4 mmol L -1 ), the calibration curves displayed a linear regions with average sensitivities of 1.76 (± 0.04) × 10 4 µW L mmol -1 cm -2 (R 2 = 0.992) (Figure 7). This range is of great interest for elucidating glucose concentration in physiological fluids (Table S1). From this linearization, the 95% confidence level projection for glucose quantification at a given catalytic current was associated with small errors ranging from ± 0.35 mmol L -1 to ± 16 µmol L -1 for 4 mmol L -1 and 50 µmol L -1 respectively. It is below the requirement set by the ISO 15197:2013 standard with accuracy of test strips within ± 0.83 mmol L -1 for glucose concentrations lower than 5.6 mmol L -1 [START_REF] Jendrike | ISO 15197: 2013 Evaluation of a Blood Glucose Monitoring System's Measurement Accuracy[END_REF].

Conclusions

We showed the synthesis of gold nanoparticles containing nitro groups (AuNPs-TNB) and their direct application in bioelectrocatalysis for orientation enhancement of MCOs and for the regeneration of the NAD + cofactor required for NAD-glucose dehydrogenase. Herein, we demonstrated their physical, chemical and electrochemical properties from a large variety of characterization techniques. The two different bioelectrodes were combined to afford a glucose/O 2 enzymatic biofuel cell with capabilities for self-powered glucose sensing. The AuNPs-TNB based EFC displayed interesting capabilities in term of current and power generation in high glucose concentration (>50 mmol L -1 ) reaching ca. 420 µA cm -2 and 156 µW cm -2 . In physiological glucose level, the device was also adapted for accurate glucose sensing with LOD as low as 2.5 µmol L -1 and a linear range from 40 µmol L -1 to 4 mmol L -1 .

Further studies will be devoted to rationalize the disulfide structure during AuNP synthesis to adapt the aromatic/electrostatic environments of the particles and to enhance the electrocatalytic activity of the AuNPs.

CRediT authorship contribution statement

Declaration of competing interest

There are no conflicts to declare.

  carried out in a three-electrode electrochemical cell using Biologic SP200, VMP3 Multi Potentiostats with EC-lab software or an Autolab potentiostat with Nova software. A platinum wire and a saturated calomel electrode (SCE) electrode served as the counter electrode and reference electrode respectively. All potentials are referred to the SCE reference electrode. McIlvaine (McIl) buffer solutions with pH range [3.0 ≤ pH ≤ 8.0] were prepared from 0.1 mol L -1 citric acid and 0.2 mol L -1 Na 2 HPO 4 stock solutions and used as supporting electrolytes for electrochemical characterizations of the AuNPs-TNB and GDH/AuNPs-TNB electrodes.

  20 µL of the black suspension was deposited onto glassy carbon electrode (GCE) and the solvent was removed under vacuum leading to the formation of a MWCNTs layer with a geometric surface area of 0.125 cm 2 . The MWCNTs electrodes were incubated in 100 µL AuNPs-TNB solution (0.5 mg mL -1 in MeOH) for 12 h, thoroughly rinsed with MeOH and deionized water to remove any loosely adsorbed nanoparticles and then dried in air. Before electrochemical testing, the electrodes were soaked into McIlvaine buffer to equilibrate. (step 1) Multicopper-based bioelectrodes. After step 1, AuNPs-TNB/MWCNTs modified electrodes were incubated in 50 µL of laccase or bilirubin oxidase solution (2.5 mg mL -1 in McIl pH 5.0 or 7.0, respectively) overnight at 4 °C. Before testing, electrodes were thoroughly rinsed with deionized water and soaked in fresh McIlvaine buffer solution to equilibrate. NAD-dependent glucose dehydrogenase-based bioelectrodes. After the previous step, AuNPs-TNB/MWCNTs modified electrodes were incubated in 50 µL of GDH solution (1 mg mL -1 in McIl pH 7.0) overnight at 4°C. Before testing, electrodes were thoroughly rinsed with deionized water and soaked in fresh McIlvaine buffer solution (pH 7.0) to equilibrate. BSA-modified electrodes were adapted from this method by substitution of GDH with the non-redox active BSA.

Scheme 1 :

 1 Scheme 1: Schematic representation of the AuNPs-TNB nanoparticles synthesis in MeOH

Figure 1 :

 1 Figure 1: (A) TEM image of AuNPs-TNB from a 0.5 mg mL -1 solution in EtOH. (B) Size distribution for AuNPs-TNB obtained from TEM images.

Figure 2 :

 2 Figure 2: Electrochemical activation of the AuNPs-TNB/MWCNTs electrodes through reduction of the nitro groups by cyclic voltammetry in McIl buffer (pH 7.0) purged with Ar and recorded at 50 mV s -1 during (red dash) 1 st activation scan, (thin black) 2 nd 4 th 6 th 8 th 10 th and (red solid) 12 th activation scans. (blue) CV scan for unmodified MWCNTs electrodes.

( 4 )Figure 3 :

 43 Figure 3: (A) Cyclic voltammograms of activated AuNPs-TNB/MWCNTs electrodes recorded at different scan rates from 0.01 V s -1 to 15 V s -1 in McIl buffer (pH 7.0). (B) Voltammetric peak potentials vs. scan rate for activated AuNPs-TNB/MWCNTs electrodes (■) E p,a and (□) E p,c . In red, the linearized portions for k ET determination.

2 .

 2 (I pa = 100 µA during 10 th activation scan). Functionalization of the MWCNTs surface with AuNPs did not affect drastically the open circuit potential (OCP) of the bioelectrodes compared to unmodified carbon film (OCP lac = 0.600 ± 30 mV and OCP BOx = 520 ± 30 mV. The small ΔOCP (∼ 80 mV) between the two MCOs was attributed to the pH change of the electrolytes. Characterization of the electrocatalytic properties of the bioelectrodes were performed by cyclic voltammetry. Onset potentials for ORR were in agreement with reported literature and again function with the electrolyte pH (E lac = 0.610 V E BOx = 0.530 V). Non-negligible electrocatalytic turnover was recorded based on 1-the MCO used in the study and 2-the degree of functionalization of the MWCNTs surface (unfunctionalized vs. AuNP-TNB-functionalized). In either case, the electrocatalytic currents recorded were superior for the BOx-based bioelectrodes. It appeared that unfunctionalizedMWCNTs were not suitable for the proper orientation of Lc as only j max = 68 ± 12 µAcm -2 was obtained at 0.1 V while the current reached j max = 544 ± 85 µAcm -2 for the BOx electrode. The functionalization step with AuNPs-TNB led to drastic enhancement of the ORR current densities at both electrodes; j max = 480 ± 50 µA cm -2 and j max = 920 ± 72 µA cm -2 which represents a 7.5-fold and a 2-fold increase for Lc and BOx respectively. The large increase obtained for laccase is attributed to the increase of aromatic species at the CNTs film (due to the benzene ring). The lower increase in current density obtained at BOx/AuNP-TNB could be explained by a "denser" packing of TNB molecule at the AuNP surface (partially hindering electrostatic interaction of the substrate pocket near the T1 Cu center involved in DET).

Figure 4 :

 4 Figure 4: Representative cyclic voltammograms (second scan) for (A) laccase and (B) BOx modified electrodes. MCO/AuNPs-TNB/MWCNT in Ar saturated solution (dashed line) and O 2 saturated solution (blue line) in McIl buffer [(A) pH 5.0; (B) pH 7.0] at 5 mV s -1 . Solid black lines represent CV at MCO/MWCNT in O 2 saturated solution. (For interpretation of the references to colors in this figure legend, the reader is referred to the web version of this article).

Figure

  S8 presents a control experiment for the electrocatalytic property of unactivated AuNPs-TNB/MWCNTs towards NADH oxidation (BSA was used as a protein model to mimic the adsorption of GDH over the AuNPs layer). It clearly demonstrated that the presence of the protein acts as an insulating layer on the CNTs surface making direct oxidation of NADH on CNTs more difficult. Hence, activated particles are envisioned as key components to afford oxidation of NADH at low potential on NAD-dependent enzymemodified electrode. Due to the diversity of NAD-dependent dehydrogenases and their known sensitivity to pH conditions, it was necessary to evaluate the ability of the newly synthesized AuNPs to oxidize NADH in different pH solutions. Chronoamperometric measurements were carried out at E pa, NHOH in electrolyte ranging from pH 5.0 to pH 8.0 to ensure identical driving force regardless of the E 1/2 shift due to pH change (Figure 5). It shows that best performing conditions are obtained in slightly acidic solutions. Nevertheless, the electrocatalyst can be used in slightly alkaline solutions (pH 8.0) since electrocatalytic current remained 70 % of its maximal performance recorded at pH 5.0.

Figure 5 :

 5 Figure 5: Calibration curves for AuNPs-TNB/MWCNT modified electrodes with increasing concentration of NADH in McIl buffer at various pH: 5.0 (black), 6.0 (red), 7.0 (blue) and 8.0 (green) obtained from chronoamperometric measurement performed at E pa, NHOH .

Figure 6 :

 6 Figure 6: (A) Representative cyclic voltammograms (second scan) of GDH/AuNPs-TNB/MWCNT in 2 mmol L -1 NAD + (dashed line) and in the presence of 100 mmol L -1 glucose (red solid line) in Ar saturated McIl buffer (pH 7.0) at 5 mV s -1 .

Figure 7 :

 7 Figure 7: Linear region for glucose sensing reported for AuNPs-TNB based EFCs polarized at 0.43 V. The colored zone depicts the 95% confidence level projection for glucose concentration at any power density (x-axis error).

  Lalaoui: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Validation, Writing -review, Supervision. Kilian Gentil: Investigation. Ilyass Ghandari: Investigation. Serge Cosnier: Funding acquisition, Writing -review. Fabien Giroud: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Validation, Writingoriginal draft & editing, Supervision.

Acknowledgements

FG and SC wish to acknowledge the ANR for partial funding of the project. FG and SC greatly thank the support from the platform Chimie NanoBio ICMG FR 2607 (PCN-ICMG) and granting access to the electron microscopy facility. FG and SC also thank the LabEx ARCANE (ANR-11-LABX-0003-01 and CBH-EUR-GS, ANR-17-EURE-0003).

Appendix A. Supplementary information

Supplementary information link to this article can be found online at xxx.