PhD Youna Vandaele
email: youna.vandaele@chuv.ch

Magalie Lenoir

Caroline Vouillac-Mendoza

Karine Guillem

PhD Serge Ahmed
email: serge.ahmed@u-bordeaux.fr

Probing the decision-making mechanisms underlying choice between drug and nondrug rewards in rats Short title: Decision-making mechanisms driving preference in a drug choice setting

Keywords: Choice, decision-making, sequential choice model, behavioral ecology, deliberation, habit, goal-directed, cocaine, saccharin, rats

Investigating the decision-making mechanisms underlying choice between drug and nondrug rewards is essential to understand how their alterations can contribute to substance use disorders. However, despite some recent effort, this investigation remains a challenge in a drug choice setting, notably when it comes to delineate the role of goal-directed versus habitual control mechanisms. The goal of this study was to try probing these different mechanisms by comparing response latencies measured during sampling (i.e., only one option is available) and choice trials. A deliberative goal-directed control mechanism predicts a lengthening of latencies during choice whereas a habitual control mechanism predicts no change in latencies. Alternatively, a race-like response competition mechanism, such as that postulated by the behavioral ecology-inspired Sequential Choice Model (SCM), predicts instead a shortening of response latencies during choice compared to sampling. Here we tested the predictions of these different mechanisms by conducting a systematic retrospective analysis of all cocaine versus saccharin choice experiments conducted in rats in our laboratory over the past 12 years. Overall, we found that rats engage a deliberative goal-directed mechanism after limited training, but shift to a SCM-like response selection mechanism after more extended training. The latter finding suggests that habitual control is engaged in a choice setting via a race-like response competition mechanism, and thus, that the SCM is not a general model of choice, as formulated initially, but a specific model of habitual choice.

Introduction

Investigating the decision-making mechanisms underlying choice between drug and nondrug rewards is essential to understand how their alterations can contribute to substance use disorders. Over the past decade, a growing research effort has been made to model this choice situation in animals, particularly in rodents [START_REF] Luscher | The transition to compulsion in addiction[END_REF][START_REF] Ahmed | Individual decision-making in the causal pathway to addiction: contributions and limitations of rodent models[END_REF][START_REF] Ahmed | Trying to make sense of rodents' drug choice behavior[END_REF]. Overall, when given a choice, most rats prefer the nondrug alternative (e.g., sweet water; social interaction) over potent drugs of abuse, such as cocaine or heroin [START_REF] Cantin | Cocaine is low on the value ladder of rats: possible evidence for resilience to addiction[END_REF][START_REF] Lenoir | Extended heroin access increases heroin choices over a potent nondrug alternative[END_REF][START_REF] Lenoir | Intense sweetness surpasses cocaine reward[END_REF][START_REF] Caprioli | Role of Dorsomedial Striatum Neuronal Ensembles in Incubation of Methamphetamine Craving after Voluntary Abstinence[END_REF][START_REF] Caprioli | Persistent palatable food preference in rats with a history of limited and extended access to methamphetamine selfadministration[END_REF][START_REF] Venniro | Volitional social interaction prevents drug addiction in rat models[END_REF][START_REF] Vandaele | Choosing Under the Influence: A Drug-Specific Mechanism by Which the Setting Controls Drug Choices in Rats[END_REF]. Only few individual rats prefer the drug. This pattern of individual preferences is observed even after extended drug use and regardless of the drug dose available [START_REF] Cantin | Cocaine is low on the value ladder of rats: possible evidence for resilience to addiction[END_REF][START_REF] Lenoir | Intense sweetness surpasses cocaine reward[END_REF]. Recently, we found that preference for the nondrug option in the majority of rats is under habitual control, not goal-directed control, as it persists even after its devaluation [START_REF] Vandaele | Habitual Preference for the Nondrug Reward in a Drug Choice Setting[END_REF][START_REF] Vandaele | Inflexible habitual decisionmaking during choice between cocaine and a nondrug alternative[END_REF]. This was observed after extended training, but we do not know if habitual control of preference is due to extended training or other factors known to promote habitual control, such as, for instance, prior drug exposure [START_REF] Miles | Oral cocaine seeking by rats: action or habit?[END_REF][START_REF] Leblanc | Repeated cocaine exposure facilitates the expression of incentive motivation and induces habitual control in rats[END_REF][START_REF] Corbit | Effects of repeated cocaine exposure on habit learning and reversal by N-acetylcysteine[END_REF][START_REF] Schmitzer-Torbert | Post-training cocaine administration facilitates habit learning and requires the infralimbic cortex and dorsolateral striatum[END_REF][START_REF] Zapata | Shift from goal-directed to habitual cocaine seeking after prolonged experience in rats[END_REF]. In addition, we currently ignore if preference for the drug is also under habitual control, mainly because there is no effective method of drug reward devaluation in animals, particularly for drugs administered intravenously [START_REF] Ostlund | On habits and addiction: An associative analysis of compulsive drug seeking[END_REF][START_REF] Vandaele | Defining the place of habit in substance use disorders[END_REF]. An alternative approach, sufficiently versatile to probe the decision-making mechanisms underlying individual preferences in a drug choice setting, is thus needed.

Numerous studies suggest that there is a speed-accuracy tradeoff in the exercise of goal-directed and habitual decision-making processes [START_REF] Keramati | Speed/accuracy trade-off between the habitual and the goal-directed processes[END_REF][START_REF] Wickelgren | Speed-accuracy tradeoff and information processing dynamics[END_REF][START_REF] Gold | The neural basis of decision making[END_REF]. Indeed, it is generally assumed that goal-directed behaviors are flexible but require higher cognitive demand and are time-consuming whereas habitual behaviors are inflexible in adapting to new environmental contingencies but are faster to execute. Based on this assumption, the decision-making mechanisms underlying choice should be, in theory, inferable from a detailed analysis of response latencies. Specifically, if choice behavior is goal-directed and involves a deliberation over the values of the different reward options, one would expect an increase in response latency during choice trials in comparison to sampling trials where the different options are presented separately and successively. In contrast, if behavior is habitual and performed automatically without representation of the values of the options, response latencies during choice and sampling trials should be similar. Furthermore, since behavior typically transitions from goaldirected to habitual control after extended training [START_REF] Holland | Relations between Pavlovian-instrumental transfer and reinforcer devaluation[END_REF][START_REF] Dickinson | Actions and habits: the development of behavioural autonomy[END_REF][START_REF] Adams | Variations in the sensitivity of instrumental responding to reinforcer devaluation[END_REF], the predicted difference in response latency between choice trials and sampling trials should be observed after limited training but not after extended training. Thus, analysis of response latencies has the potential to help us distinguishing between goal-directed and habitual control of preference.

A similar approach was previously used to assess the validity of different models of animal choice, including behavioral ecology-inspired models [START_REF] Kacelnik | Darwin's "tug-of-war" vs. starlings' "horse-racing": how adaptations for sequential encounters drive simultaneous choice[END_REF][START_REF] Monteiro | Choosing fast and simply: Construction of preferences by starlings through parallel option valuation[END_REF][START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF][START_REF] Vasconcelos | Choice in multi-alternative environments: a trial-by-trial implementation of the sequential choice model[END_REF]. One such model, the Sequential Choice Model (SCM), is of particular interest here because it makes a prediction that is different from both goaldirected and habitual control models (see below). This model explains choice without postulating any explicit deliberation and/or valuation processes. Specifically, when facing different options, animals would consider each option sequentially, not simultaneously, and would decide whether to accept or reject it with no consideration of the other option available. It is hypothesized that such sequential decision process has evolved as an adaptation to the natural "reward ecology" of most animals. Unlike in laboratory choice settings, successive encounters with different rewards are the rule in natural environments while simultaneous encounters are the exception [START_REF] Charnov | Optimal foraging, the marginal value theorem[END_REF][START_REF] Lea | The mechanisms of optimality in foraging[END_REF]. Based on this assumption, the SCM proposes that there would be no genuine decision and that preference would be the result of a race-like competition between independent responses. Briefly, during choice, each option would automatically elicit a specific response with a certain latency: the shorter the latency, the more likely the corresponding response will win the race and thus, the more likely the animal will prefer that option. Importantly, due to this race-like selection process, the SCM uniquely predicts that the choice latencies should be shorter, not longer, than the sampling latencies and this, independently of the degree of prior training (see the Supplement for additional information).

Thus, different models make different testable predictions about the differences between choice and sampling latencies (i.e., longer, similar or shorter) (Table 1). Interestingly, however, these different models also make one general common prediction regarding the relationship between sampling latencies and preference, though for different reasons. All models predict that individuals that respond faster for one option relative to its alternative during sampling trials should also choose the former more frequently than the later during choice trials. In other terms, there should be a positive correlation between individual sampling latencies and individual preferences during choice. According to the SCM, the nature of this relationship should also be predictive since response latencies play a causal role in the establishment of preference in this model [START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF][START_REF] Vasconcelos | Choice in multi-alternative environments: a trial-by-trial implementation of the sequential choice model[END_REF]. In the other two models, sampling latencies rather represent another measure of the options relative values.

The goal of the present study is to test these various predictions by conducting a systematic retrospective analysis of all cocaine versus saccharin choice experiments that have been conducted in rats in our laboratory over the past 12 years. This allowed us to probe the decision-making mechanisms underlying choice between drug and nondrug rewards as a function of prior training (limited or extended).

Results

Lengthening of choice latencies after limited training

We first assessed distributions of response latencies and preference in experiments without prior instrumental training before choice testing (Table 2, W/O training set), assuming that behavior would be goal-directed under these conditions. All three decision-making models predict that the relative latency of each response option when encountered sequentially (i.e., sampling phase) should predict which one will be selected when encountered simultaneously (i.e., choice phase). That is, the fastest the response for an option during sampling, the more likely this option will be selected during choice. We first tested this prediction by correlating the relative response latency during sampling trials with the preference. We found that both LR and WL were positively correlated with the percentage of cocaine choice (Fig 1A -B; LR: R=0.62, p<0.001; WL: R=0.42, p<0.05). These results suggest that the faster animals respond for cocaine relative to saccharin during sampling trial, the more they would prefer cocaine. To determine whether a lengthening of choice latencies could be observed when choice behavior is presumably goal-directed, we analyzed the distribution of sampling and choice latencies during saccharin trials in saccharin-preferring (SP) rats. Among the 31 rats included in the analysis, 29 expressed a preference for saccharin (Fig 1C). In this subgroup, the distribution of choice latencies was shifted to the right compared to the distribution of sampling latencies (Fig 1D). Accordingly, choice latencies were significantly longer than sampling latencies (Fig 1E ; N=29, Wilcoxon-test: p<0.0001; effect size: -0.99). These results are consistent with the involvement of a deliberative and goal-directed decision-making mechanism.

Shortening of choice latencies after extended training

We next assessed preference and response latencies during sampling and choice in experiments including prior instrumental training before choice testing, assuming that at least responding for saccharin would be under habitual control, based on prior studies [START_REF] Vandaele | Habitual Preference for the Nondrug Reward in a Drug Choice Setting[END_REF][START_REF] Vandaele | Inflexible habitual decisionmaking during choice between cocaine and a nondrug alternative[END_REF]. We first tested the general common prediction that sampling latencies should correlate with preference and found that, similarly to the W/O training set, both LR and WL were positively correlated with the percentage of cocaine choice (Fig 2A -B; LR: R=0.67, p<0.0001; WL: R=0.64, p<0.0001), indicating that fast responses for cocaine during sampling trials are associated with higher preference for this option during choice.

The SCM not only predicts that preference should be correlated with the relative sampling speed but also proposes that preference could be directly predicted from sampling latencies [START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF][START_REF] Vasconcelos | Choice in multi-alternative environments: a trial-by-trial implementation of the sequential choice model[END_REF]. To test this hypothesis, we trained a LDA model on the mean cocaine and saccharin sampling latencies to classify the preference of individual rats as saccharin-preferring (SP), indifferent (IND) or cocaine-preferring (CP). As expected from prior studies [START_REF] Cantin | Cocaine is low on the value ladder of rats: possible evidence for resilience to addiction[END_REF][START_REF] Lenoir | Intense sweetness surpasses cocaine reward[END_REF], the majority of rats preferred saccharin with a preference score below 33.3% (SP rats: N=109, 72.7%; Fig 2C E). This result further validates the first SCM prediction by showing that the preference profile can be predicted above chance, from the options' sampling latencies.

Goal-directed, habitual and sequential choice models make different predictions when comparing sampling and choice latencies (Table 1). To test these predictions, we compared the individual distributions of sampling and choice latencies for each reward, separately. To avoid any selection bias resulting from saccharin preference in the majority of rats, choice and sampling latencies distributions were compared within-subjects in different preference groups, separately.

In SP rats, the distribution of sampling and choice latencies on saccharin trials did not significantly differ, although there was a trend toward longer choice latencies (Fig 3A These results partially validate the second prediction of the SCM, since choice latencies were shorter than sampling latencies for the cocaine option but not for the saccharin option. Importantly, although rare, omissions were more frequent during sampling than choice trials, which could have biased the results reported here. However, similar results were found when omission trials were excluded (saccharin trials in SP rats: p=0.06; Cocaine trials in CP rats: p<0.01; saccharin trials in IND rats: p>0.9; cocaine trials in IND rats: p<0.0001).

Discussion

The aim of this study was to determine the decision-making processes underlying choice by comparing sampling and choice response latencies, after limited or extended training. An increase in latencies during choice is predicted from the deliberative goal-directed decision-making model whereas latencies should remain the same under habitual control after extended training. Alternatively, the SCM predicts a shortening of latencies during choice compared to sampling, independently of the training duration. Here we tested these different predictions in a systematic retrospective analysis of all the choice experiments conducted in the laboratory over the past 12 years. Our analysis shows a lengthening of choice latencies after limited training, when behavior is presumably goal-directed, suggesting the involvement of a deliberative process. However, when prior training is included before choice testing, we observed similar choice and sampling latencies on saccharin trials and a shortening of choice latencies on cocaine trials. These results are consistent with both the SCM and habitual learning.

In agreement with previous experiments [START_REF] Monteiro | Choosing fast and simply: Construction of preferences by starlings through parallel option valuation[END_REF][START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF][START_REF] Vasconcelos | Choice in multi-alternative environments: a trial-by-trial implementation of the sequential choice model[END_REF][START_REF] Freidin | Sequential and simultaneous choices: testing the diet selection and sequential choice models[END_REF][START_REF] Vasconcelos | Context-dependent preferences in starlings: linking ecology, foraging and choice[END_REF][START_REF] Vasconcelos | Irrational choice and the value of information[END_REF][START_REF] Ojeda | Paradoxical choice in rats: Subjective valuation and mechanism of choice[END_REF], results from our analysis validated in both experimental sets (with or without prior training) the general common prediction that the relative response latency during sampling trials should correlate with preference during choice trials. Indeed, the parameters of relative latency were both strongly correlated with the percentage of cocaine choices. Furthermore, in agreement with the SCM, which assumes that the relative sampling latency causally determines preference, sampling latencies could predict whether rats preferred cocaine, saccharin or were indifferent. However, these findings are not sufficient to favor the SCM over the habitual and goal-directed choice models, which also predict (or do not exclude) a relationship between sampling latencies and preference. For instance, in a deliberative goal-directed choice model, options are compared and selected based on their relative value [START_REF] Kacelnik | Darwin's "tug-of-war" vs. starlings' "horse-racing": how adaptations for sequential encounters drive simultaneous choice[END_REF][START_REF] Rangel | A framework for studying the neurobiology of value-based decision making[END_REF][START_REF] Rangel | Neural computations associated with goal-directed choice[END_REF] and numerous studies have reported that latencies are inversely related with options' relative value [START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF][START_REF] Bateson | Preferences for fixed and variable food sources: variability in amount and delay[END_REF][START_REF] Killeen | The principal components of response strength[END_REF][START_REF] Lagorio | Risky choice in pigeons: preference for amount variability using a token-reinforcement system[END_REF][START_REF] Shull | Delay and number of food reinforcers: Effects on choice and latencies[END_REF]. Thus, sampling latencies and preference should be correlated under goal-directed control. More generally, it was suggested that response latencies could be used as a sensitive metric for response strength and subjective value [START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF][START_REF] Bateson | Preferences for fixed and variable food sources: variability in amount and delay[END_REF][START_REF] Killeen | The principal components of response strength[END_REF][START_REF] Lagorio | Risky choice in pigeons: preference for amount variability using a token-reinforcement system[END_REF][START_REF] Shull | Delay and number of food reinforcers: Effects on choice and latencies[END_REF]. To our knowledge, the habitual choice model makes no specific prediction about the relationship between sampling latencies and preference, but the engagement of habit cannot be excluded based on these results. The analysis and comparison of sampling and choice latencies is therefore needed to arbitrate between the different models.

Contrary to all prior studies testing predictions of the SCM [26-29, 34-37, 44], analysis of experiments without instrumental training prior to choice testing reveals a lengthening of choice latencies compared to sampling latencies. These results are consistent with deliberative models of decision making which assume that processing of information during simultaneous encounters would increase the response latency compared to sequential encounters, due to the time cost of the evaluation process [START_REF] Keramati | Speed/accuracy trade-off between the habitual and the goal-directed processes[END_REF][START_REF] Gold | The neural basis of decision making[END_REF][START_REF] Kacelnik | Darwin's "tug-of-war" vs. starlings' "horse-racing": how adaptations for sequential encounters drive simultaneous choice[END_REF][START_REF] Rangel | A framework for studying the neurobiology of value-based decision making[END_REF][START_REF] Rangel | Neural computations associated with goal-directed choice[END_REF] (Table 1). Interestingly, in these experiments, reward-seeking behavior is presumably goal-directed. Indeed, habit typically develops across extended training [START_REF] Holland | Relations between Pavlovian-instrumental transfer and reinforcer devaluation[END_REF][START_REF] Dickinson | Actions and habits: the development of behavioural autonomy[END_REF][START_REF] Adams | Variations in the sensitivity of instrumental responding to reinforcer devaluation[END_REF], particularly when rats are trained under ratio schedule as in our experiments [START_REF] Dickinson | The Effect of the Instrumental Training Contingency on Susceptibility to Reinforcer Devaluation[END_REF][START_REF] Corbit | Habitual alcohol seeking: time course and the contribution of subregions of the dorsal striatum[END_REF]. Furthermore, in these conditions, rats were less exposed to cocaine, also known to promote habit [START_REF] Miles | Oral cocaine seeking by rats: action or habit?[END_REF][START_REF] Leblanc | Repeated cocaine exposure facilitates the expression of incentive motivation and induces habitual control in rats[END_REF][START_REF] Corbit | Effects of repeated cocaine exposure on habit learning and reversal by N-acetylcysteine[END_REF][START_REF] Schmitzer-Torbert | Post-training cocaine administration facilitates habit learning and requires the infralimbic cortex and dorsolateral striatum[END_REF]. The discrepancy with prior findings could result from the relatively long training in instrumental tasks involving discrete trial designs, shown to promote rapid habitual learning [START_REF] Vandaele | Lever Insertion as a Salient Stimulus Promoting Insensitivity to Outcome Devaluation[END_REF][START_REF] Vandaele | Distinct recruitment of dorsomedial and dorsolateral striatum erodes with extended training[END_REF] or from species differences [26-29, 34-36, 44]. Indeed, in this analysis, the lengthening of latencies was observed in naïve rats trained for a very limited number of sessions while most studies investigating SCM predictions were conducted in European starlings or pigeons (but see [START_REF] Ojeda | Paradoxical choice in rats: Subjective valuation and mechanism of choice[END_REF]), with an extensive instrumental training.

In the dataset with prior instrumental training, the shortening of cocaine response latencies during choice and the absence of difference for saccharin trials is consistent with both the SCM and habitual learning. We have previously shown that preference for the nondrug reward is habitual in the discrete trial choice procedure [START_REF] Vandaele | Habitual Preference for the Nondrug Reward in a Drug Choice Setting[END_REF][START_REF] Vandaele | Inflexible habitual decisionmaking during choice between cocaine and a nondrug alternative[END_REF]. In contrast to goal-directed behaviors which rely on a representation of the outcome value, habits are mediated by a stimulus-response association and do not require processing of the outcome value to be executed. Thus, under habitual control, an option should be selected with the same latency during choice and sampling (Table 1), which is what we observed for saccharin trials, in SP and IND rats. However, if rats select options according to the SCM, a shortening of choice latencies should be observed. Indeed, the race-like competition process implies that only the shortest latencies should be expressed during choice trials, the selection of one option excluding the opportunity to select the alternative option (Supplemental Figure 1). This cross-censorship between distributions of latencies results in a shortening of choice latencies. This prediction was validated for cocaine but not saccharin trials. The negative result for saccharin trials is however consistent with prior studies testing SCM predictions [27-29, 35-37, 44, 49]. Indeed, none of these studies but one [START_REF] Ojeda | Paradoxical choice in rats: Subjective valuation and mechanism of choice[END_REF] has reported a shortening of choice latencies for the preferred option. Demonstrating this decrease in latency is difficult because when animals strongly prefer one option, the distribution of latencies for that option is minimally censored (Supplemental Figure 1). Furthermore, the high relative value of this option results in short latencies during sampling, limiting the room for further decrease. Analysis of prior studies conducted in our laboratory indicates that response latencies cannot go below 3-s, likely representing the reaction time.

Although cocaine was the preferred option in CP rats, we were able to observe a shortening of latencies at choice. In contrast to saccharin, cocaine is generally selected with longer latencies during sampling, preventing the floor effect. Longer latencies to select cocaine could result from the ambivalent and anxiogenic effects of the drug [START_REF] Yang | Anxiogenic effects of acute and chronic cocaine administration: neurochemical and behavioral studies[END_REF][START_REF] Paine | Cocaine-induced anxiety: alleviation by diazepam, but not buspirone, dimenhydrinate or diphenhydramine[END_REF][START_REF] Ettenberg | Opponent process properties of self-administered cocaine[END_REF][START_REF] Ettenberg | Animal model for investigating the anxiogenic effects of self-administered cocaine[END_REF] or from its delayed pharmacological effects on the brain [START_REF] Canchy | Pharmacokinetics trumps pharmacodynamics during cocaine choice: a reconciliation with the dopamine hypothesis of addiction[END_REF]. Interestingly the gap between cocaine choice and sampling latencies was more pronounced in IND rats compared to CP rats. This result further supports the SCM, which predicts that trimming of the leftward side of the distribution of latencies would be more pronounced when the overlap between response distributions is stronger [START_REF] Kacelnik | Darwin's "tug-of-war" vs. starlings' "horse-racing": how adaptations for sequential encounters drive simultaneous choice[END_REF][START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF] (Supplemental Figure 1). Since rats are indifferent, the drug and nondrug rewards are closer in value and selected with more comparable response latencies during sampling. The stronger cross-censorship between distributions of latencies during choice could have increased the shortening of cocaine choice latencies in this subgroup of rats. However, at indifference, the SCM predicts a shortening of choice latencies for both options [START_REF] Macías | The functional equivalence of two variants of the suboptimal choice task: choice proportion and response latency as measures of value[END_REF]. The similarity between saccharin sampling and choice latencies in IND rats is thus unexpected. Given the already low value of latencies during sampling (mean: 4.7±0.49s; median: 3.97s), a floor effect cannot be excluded.

Like habit, the automatic selection process of option based on the response latency implied by the SCM, is oblivious to the options' value. The SCM assumes that during sequential encounters, each option are processed independently and choice results from a race between the mechanisms generating response latencies for each available option [START_REF] Kacelnik | Darwin's "tug-of-war" vs. starlings' "horse-racing": how adaptations for sequential encounters drive simultaneous choice[END_REF]. Thus, although the SCM proposes a mechanism for the selection of responses based on their latency, this model makes no assumption on the valuation process generating distributions of latencies. It is tempting to speculate that the habitual system would process the action value (or "action policy") while the SCM offers a mechanism by which habit translates in preference in a choice setting between two simultaneous options. This assumption is in agreement with a recent theory suggesting that decision-making does not necessarily require computation of the economic value by the brain [START_REF] Hayden | The case against economic values in the brain[END_REF]. Overall, our analysis suggests that habitual responding for the drug and the nondrug reward is engaged after extended training and selected via a race-like competition mechanism, thus indicating that the SCM is not a general model of choice as initially proposed [START_REF] Kacelnik | Darwin's "tug-of-war" vs. starlings' "horse-racing": how adaptations for sequential encounters drive simultaneous choice[END_REF][START_REF] Shapiro | Simultaneous and sequential choice as a function of reward delay and magnitude: normative, descriptive and process-based models tested in the European starling (Sturnus vulgaris)[END_REF], but rather constitutes a specific model of habitual choice.

To conclude, this systematic analysis has begun to probe the decision-making processes underlying choice between sweet water and cocaine in rats. It shows that when simultaneously facing these two options, rats first engage a deliberative strategy with computation and comparison of the options value. However, after more extended training, rats adopt a SCM-like response selection process while their behavior becomes habitual. Clearly, more research is needed to determine the time-course and conditions of habitual learning for the drug and nondrug rewards in our discrete-trial choice procedure. However, these results suggest that the comparison of sampling and choice latencies could be used as a proxy to determine whether behavior is habitual or goal-directed. Given the difficulty to devalue drugs administered intravenously, such analysis of latencies could be particularly useful to infer the nature of behavioral control in drug settings. Furthermore, this study offers a new framework to understand how habit can translate in preference in a choice setting between a drug and a nondrug reward. This theoretical framework will ultimately allow progress in our understanding of the relation between habits and choice and their respective role in substance use disorder [START_REF] Vandaele | Habit, Choice and Addiction[END_REF].

Materiel and methods

Subjects

The data analyzed in this study have been obtained from previous experiments conducted in our laboratory over the past 12 years. All experimental subjects were male adult Wistar rats (Charles River, L'Arbresle, France). Rats were housed in groups of two or three and maintained in a temperaturecontrolled vivarium (23° C) with a 12-h reverse light-dark cycle. Testing occurred during the dark phase of the cycle and water and food were available ad libitum in all experiments. All experiments were carried out in accordance with institutional and international standards of care and use of laboratory

Initial operant training

In the first set of experiments, a total of 31 rats were directly tested in the choice schedule without prior operant training (Table 2; W/O training set).

In the second set of experiments, a total of 150 rats were first trained several weeks (3-5) under a fixed-ratio (FR) schedule of saccharin and cocaine self-administration on alternate daily sessions, six days a week, before choice testing (Table 2; W/ training set). FR training allowed rats to learn the value of each reward and to associate its delivery with a different response option before choice testing. On saccharin sessions, lever pressing on the saccharin lever was rewarded by a 20-s access to water sweetened with 0.2% of sodium saccharin delivered in the adjacent drinking cup. During the first 3-s of each 20-s access to sweet water, the drinking cup was filled automatically with sweet water; during the next 17-s, additional volumes of sweet water were obtained on demand by voluntary licking. On cocaine sessions, lever pressing on the alternative lever was rewarded by one intravenous dose of cocaine (0.25 mg delivered over 4 seconds). For both cocaine and saccharin sessions, reward delivery initiated a concomitant 20-s time-out period signaled by the illumination of the cue-light above the available lever. During the time-out period, responding had no scheduled consequences. Sessions ended after rats had earned a maximum of 20-30 saccharin or cocaine rewards or 2-3 h had elapsed (Table 2).

Discrete-trials choice protocol

Each daily choice session consisted of 12 trials, spaced by 10min inter-trials intervals, and divided into two successive phases, sampling and choice. The sampling phase was composed of 4 sampling trials in which each lever and thus each response option was presented alternatively and sequentially. If rats responded twice within 5 min on the available lever, they were rewarded by the corresponding reward.

Reward delivery was signaled by the immediate retraction of the lever and illumination of a cue-light above it. If rats failed to complete the response requirement within 5 min, the lever was retracted until the next trial 10 min later. The choice phase consisted of eight choice trials during which the two response options were presented simultaneously. Specifically, each choice trial began with the simultaneous presentation of both levers S and C and rats could select one of the two levers by responding twice consecutively on it to obtain the corresponding reward. Reward delivery was signaled by the simultaneous retraction of both levers and illumination of the cue-light above the selected lever. If rats failed to respond on either lever within 5 min, both levers were retracted and no cue-light and reward was delivered. A schematic diagram of the forced choice trials procedure can be found in [START_REF] Ahmed | The science of making drug-addicted animals[END_REF][START_REF] Lenoir | A choice-based screening method for compulsive drug users in rats[END_REF].

In the W/O training set, rats were first trained in the discrete-trials choice schedule with a FR1 for 10 sessions before testing with the final FR2 schedule for 5 sessions.

Systematic analysis

Selection of experiments and data analysis

Only choice experiments that were conducted under similar conditions (e.g., initial FR training, conditions of reward delivery, inter-trial intervals, …etc.) and that resulted in a stable preference within 5-10 choice sessions (i.e., no increasing or decreasing trend and significant correlation between preference scores over the last 3 sessions) were included in the present analysis (Table 2).

Sampling and choice latencies for each response option were analyzed for each individual rat over the last three stable choice sessions. In total, there were 6 sampling latencies per option and per rat and 24 choice latencies, with a variable number of responses for cocaine and saccharin, depending on the rat's preference. For convenience, performance during choice was expressed in percent of cocaine choices. Response latencies corresponded to the time to complete the FR2 requirement from trial onset (signaled by the lever insertion). When a rat failed to respond within 5 min after trial onset (omission), it was assigned a maximal response latency of 300 seconds. However, omissions occur rarely.

Test of the general common prediction

To estimate the relative speed at which the cocaine and saccharin options are selected during sampling trials, we computed for each individual rat the latency ratio (LR) and the proportion of winning latencies (WL). The LR was computed by dividing the mean saccharin sampling latency by the sum of saccharin and cocaine mean latencies. Thus, LR values close to zero indicate faster saccharin sampling latencies while LR values close to one indicate faster cocaine sampling latencies. The WL was computed by estimating the probability that each of the six cocaine sampling latencies was shorter (i.e., a win) than each of the six saccharin sampling latencies when compared two by two. The WL values ranged from 0 (all cocaine latencies are longer than the longest saccharin latency or cocaine never wins the race) to 100% (all cocaine latencies are shorter than the shortest saccharin latency or cocaine always wins the race). Correlation analyses were conducted between these two measures of relative latencies and the percentage of cocaine choices.

To determine whether and to what extent, cocaine and saccharin sampling latencies could predict rat's preference profile, a linear discriminant analysis (LDA) model (LinearDiscriminantAnalysis from sklearn library in Python) was trained on the mean cocaine and saccharin sampling latencies to classify the preference of individual rats as saccharin-preferring (SP), indifferent (IND) or cocaine-preferring (CP). Rats were considered as SP, IND or CP if their preference was below 33.3%, between 33.3-66.6% or above 66.6%, respectively. LDA models were trained on 90% of the dataset and used to classify rats' preference in the remaining 10% (stratified 10-Fold cross-validation with 20 iterations; RepeatedStratifiedKFold from sklearn library in Python). To account for the unbalanced number of SP, IND and CP rats, we performed the analysis on the same number of rats in each preference group by randomly sampling subjects in the SP and IND groups based on the number of CP rats (N=19). This analysis was performed on 50 random selections of SP and IND rats and the performance across all 50 repetitions was averaged to determine the model accuracy. The same analysis was conducted with the preference group identities shuffled to determine accuracy expected by chance. To assess whether decoding accuracy significantly departed from chance, a permutation test was conducted.

Comparison of choice and sampling latencies

To test predictions of the different decision-making models, we compared the distributions of sampling and choice latencies for each response option. Note that the number of choice latencies (i.e. max. 24) was larger than the number of sampling latencies (6 per option) and that among choice trials, the number of saccharin responses was disproportionally higher than cocaine responses because of preference for saccharin in the majority of rats. To avoid any selection bias related to differences in preference, choice and sampling responses for saccharin were compared in SP (<33.3% cocaine choice) and IND rats (33.3-66.6% cocaine choice) whereas choice and sampling responses for cocaine were compared in CP (>66.6% cocaine choice) and IND rats. Note that the IND group comprises comparable numbers of cocaine and saccharin choice latencies allowing for analysis of both reward responses in this subgroup of rats.

Statistical analysis

Linear regressions were tested with the Spearman's rank correlation test. Mean sampling and choice latencies were compared within subject using the non-parametric Wilcoxon test. The effect sizes were estimated with the rank-biserial correlation. Following the linear discriminant analysis, a permutation test was conducted to determine whether the decoding accuracy significantly departed from chance. All statistical analyses were conducted on Python.

  animals [UK Animals (Scientific Procedures) Act, 1986; and associated guidelines; the European Communities Council Directive (2010/63/UE, 22 September 2010) and the French Directives concerning the use of laboratory animals (décret 2013-118, 1 February 2013)]. All experiments have been approved by the Committee of the Veterinary Services Gironde, agreement number B33-063-5.

Figures

  Figures

Figure 1 :

 1 Figure 1: Lengthening of saccharin choice latencies compared to saccharin sampling latencies in the W/O training set. (A) Correlation between the latency ratio and the percentage of cocaine choice. (B) Correlation between the winning latency and the percentage of cocaine choice. (C) Distribution of preference scores. Only SP rats (purple circles; N=29) were considered in the analysis. (D) Distributions of sampling and choice latencies during saccharin trials in SP rats. (E) Box plot of saccharin sampling and choice latencies. The box extends from the lower to the upper quartile values with a horizontal line at the median. The whiskers extend from the box at 1.5 times the interquartile range. ** p<0.0001.

Figure 2 :

 2 Figure 2: Sampling latencies correlate with preference and predict preference profiles. (A) Correlation between the latency ratio and the percentage of cocaine choice. (B) Correlation between the winning latency and the percentage of cocaine choice. (C) Distribution of preference scores and assignment of preference profiles in individual rats. SP: Saccharin-preferring rats, purple circles; IND: Indifferent rats, green circles; CP: Cocainepreferring rats, blue circles. (D) The mean decoding accuracy (± standard deviation) of the preference profile, SP, IND or CP, of individual rats based on their mean cocaine and saccharin sampling latencies (real -dark blue) is compared with the decoding accuracy expected from chance (Shuffled -red; Chance level 33.3% -horizontal dashed line). (E) Permutation test. The true difference between accuracy scores real-shuffled (vertical blue line) is compared to the distribution of differences in accuracy following permutations with 10000 iterations. P<0.0001.

Figure 3 :

 3 Figure 3: Shortening of cocaine choice latencies compared to cocaine sampling latencies. (A-D) Distributions of sampling and choice latencies during saccharin trials in SP rats (A), cocaine trials in CP rats (B) and saccharin (C) or cocaine (D) trials in IND rats. Insets: Box plots of sampling and choice latencies. Boxes extend from the lower to the upper quartile values with a horizontal line at the median. The whiskers extend from the box at 1.5 times the interquartile range. * p<0.01, ** p<0.0001.

Table 1 : Unique predictions of the different decision-making models. Models: Predictions:

 1

	Goal-directed decision-making	Sampling latencies < Choice latencies
	Habitual decision-making	Sampling latencies = Choice latencies
	Sequential choice model	Sampling latencies > Choice latencies

Table 2 : Summary and conditions of experiments included in the analysis. N°Exp Exp set N Prior training Training session limit Nb training sessions Other conditions Nb choice sessions (FR2) Selected choice sessions

 2

	1	W/O training	9	N/A	N/A	0	***	5	s13-s15
	2	W/O training	11	N/A	N/A	0	***	5	s13-s15
	3	W/O training	11	N/A	N/A	0	***	5	s13-s15
		Total :	31
	4	W/ training	18	FR1	30 rewards/3h	26	***	8	s32-s34
	5	W/ training	19	FR1	30 rewards/3h	26	***	8	s32-s34
	6	W/ training	11	FR1	30 rewards/3h	18	1-month HC saccharin access	10	s26-s28
	7	W/ training	22	FR1/FR2 30 rewards/3h	26	cannula intra-OFC	10	s34-s36
	8	W/ training	21	FR1	30 rewards/3h	23	cannula intra-OFC	9	s30-s32
	9	W/ training	12	FR1/FR2 30 rewards/3h	21	***	9	s28-s30
	10	W/ training	23	FR1/FR2 30 rewards/3h	32	***	5	s35-s37
	11	W/ training	9	FR1	30 rewards/3h	19	***	5	s22-s24
	12	W/ training	15	FR1/FR2 20 rewards/2h	21	***	5	s24-s26
		Total :	150

  ). A subset of rats were considered as indifferent with preference scores ranging between 33.3 and 66.6% (IND rats: N=23, 15.2%) or preferred cocaine with more than 66.6% of cocaine choice (CP rats: N=19, 12.6%; Fig 2C). It was possible to predict the preference profile (SP, CP or IND) from the mean cocaine and saccharin sampling latencies with an accuracy of 53.1±4.4% (Fig 2D). A permutation test confirmed that the decoding accuracy significantly departed from chance, assessed by shuffling the preference profile (Fig 2D-

Authors contributions:

Conceptualization: YV, SHA; Formal analysis: YV; Investigation: ML, KG, CVM; Visualization: YV; Resource: SHA; Writing -Original Draft Preparation: YV; Writing -Final Version: YV, SHA; Writing -Review, Editing and Validation of the Final Version: YV, SHA, ML, KG, CVM

Supplementary figure

Supplemental figure 1: Illustration of assumptions and predictions of the sequential choice model (SCM). (A)

In a deliberative decision-making model, individuals assign values to the different options and compare these values at the time of choice to select to most profitable option (top panel). Alternatively, the SCM assumes that individuals make no comparison between options to make a choice (bottom panel). Instead, decision-making mechanisms are adapted to sequential encounters, in which individuals decide to accept or reject single opportunities. (B) The SCM proposes that individuals assign independently to each option a subjective value based on the option's profitability relative to background opportunities. This value is expressed as a probability to accept each option instead of pursuing the search (i.e. the response latency). During a choice between two options A and B, the tendency to respond for both options is compared to a threshold. The response reaching the threshold first wins the race and is selected, while the alternative response is aborted (top panel). This results in a cross-censorship between distributions of response latencies (bottom panel); only the fastest response is selected and produces a latency observation. (C) When options are presented sequentially during sampling trials, there is no cross-censorship and the distributions of latencies are entirely expressed. (D) However, when options are presented simultaneously during choice trials, the cross-censorship described in (B) leads to a shortening of latencies during choice compared to sampling trials; the preferred option A is more often selected because response latencies for this option are overall shorter (top panel) but because of the overlap in distributions of latencies, the least preferred option B can occasionally be selected (bottom panel). Choices of option A are less censored by longer responses for option B (blue gradient, top panel); thus, the expected shift toward shorter latencies is weaker for the preferred option. In contrast, choices of the least preferred option B are largely censored by responses for option A (pink gradient, bottom panel). Thus, the expected shortening of choice latencies is stronger for the least preferred option.


domain: chim spi sdv

text:
Valentin Saunier

Emmanuel Flahaut

Marie-Charline Blatché

Christian Bergaud
email: christian.bergaud@laas.fr

Ali Maziz
email: ali.maziz@laas.fr

Charline Blatché

Carbon nanofiber-PEDOT composite films as novel microelectrode for neural interfaces and biosensing

Keywords: Experiment, Investigation and Writing-reviewing-editing Supervision, Resources, Methodology, Investigation and Writing-reviewing- Supervision, Resources, Experiment design, Methodology, Investigation PEDOT:CNF, microelectrodes, neural interfaces, electrical stimulation, electrochemical sensing, carbon nanofibers

come

Carbon nanofiber-PEDOT composite films as novel microelectrode for neural interfaces and biosensing

INTRODUCTION

Technological approaches based on either electrical stimulation or neural recording involve the use of microelectrodes interfaced with a population of single neurons in close proximity (Ludwig et al. 2006;Nicolelis et al. 2003). Noble metals such as Platinum, or Iridium have been used for decades to make arrays of microscopic electrodes, which are now used in routine for neural recording and stimulation in several clinical contexts such as cochlear implants, deep brain stimulation for Parkinson's disease, or functional evaluation of epilepsy. [START_REF] Andrews | Progress in brain research[END_REF]Rebscher et al. 2008;Wagenaar et al. 2005) However, the drawback of metallic electrodes is their limited capability of safe neural stimulation (Heim et al. 2012), high intrinsic noise level for neural recording (Cogan 2008) and the mechanical mismatch between the electrode and surrounding tissue causing failure of the device (Polikov et al. 2005).

To overcome these limitations, research in nanostructured materials have received much attention for developing next-generation neural interfaces, which focused on improving charge storage capacity and reduction of electrode size at the level of single neuron. Among these, organic nanomaterials including conductive polymers (CPs) (Abidian et al. 2010;Green and Abidian 2015), carbon nanotubes (CNTs) (Ansaldo et al. 2011;Jan et al. 2009) or graphene (Hess et al. 2011) have been popular choices that allow direct delivery of electrical, electrochemical and electromechanical signals at the electrode-tissue interface. Poly (3,4ethylenedioxythiophene) (PEDOT), which has excellent electronic conductivity (Fabretto et al. 2012;[START_REF] Maziz | ACS Applied Materials & Interfaces[END_REF] providing low impedance and high capacitance (Abidian and Martin 2009;Yoon and Jang 2009) along with excellent biostability (Lecomte et al. 2017) and biocompatibility (Asplund et al. 2009) has been the most studied CP in the last decade (Fabretto et al. 2012). PEDOT-coated microelectrodes have been considered for biomedical (Smela 2003) and bioelectronic applications (Berggren and Richter-Dahlfors 2007), especially for the fabrication of devices for neuronal recording (Abidian et al. 2010;Abidian et al. 2009;Castagnola et al. 2015;Lecomte et al. 2017) and stimulation (Cogan 2008;Gerwig et al. 2012b;[START_REF] Venkatraman | 4th International IEEE/EMBS Conference on Neural Engineering[END_REF] as well as electrochemical sensing systems for neurochemicals monitoring (Larsen et al. 2012;Meng et al. 2019;Reddy et al. 2019;Su et al. 2017;Vreeland et al. 2015).

Through electrochemical deposition, PEDOT can be precisely localized onto metallic microelectrode sites with coating thicknesses down to the nanoscale. The oxidative deposition of PEDOT results in a positively charged polymer backbone, which allows for the incorporation of negatively charged doping agents for charge balancing. To date, the most commonly used dopant material for PEDOT material is poly(styrene sulfonate) (PSS) because of its stability and reported biocompatibility (Schmidt et al. 1997). However, other negatively charged dopants have been also utilized for this purpose, such as tosylate, (Larsen et al. 2012) ClO 4 -, [START_REF] Maziz | Polymerized Ionic Liquids[END_REF][START_REF] Maziz | [END_REF]) Nafion (Vreeland et al. 2015) and more recently some carbonaceous nanomaterials such as carbon nanotubes CNTs (Gerwig et al. 2012b;Xu et al. 2013) or graphene oxide (GO) (Taylor et al. 2017) for the purpose of improving the electrochemical stability (Luo et al. 2011b), charge storage delivery (Gerwig et al. 2012b) and sensitivity of PEDOT electrodes for neurochemicals sensing (Wang et al. 2014;Weaver et al. 2014;Xu et al. 2013).

Carbon nanofibers (CNFs) are increasingly getting in the spotlight in bioanalytical area as through their properties of high surface area, non-toxicity and chemical and electrochemical stability. CNFs exhibit extraordinary strength and provide an extremely large surface area (compared to that of GO or PEDOT) for charge transfer and cell attachment, making them a very promising candidate material for recording and stimulation electrode sites (Nguyen-Vu et al. 2006). CNFs have also excellent electrical conductivity, which enables a better electron transfer at the interface of the electrode, and thus a better transduction of the electrochemical signal for redox-active molecules (Huang et al. 2008;Liu et al. 2008;Wu et al. 2007). Although direct substrate growth of the carbonaceous nanomaterials on metal-coated substrates has been used for the construction of biosensing devices (Koehne et al. 2011), very few studies have been performed regarding the micro-localized deposition of CNFs on any type of substrate (e.g. microelectrode array (MEA) or neural probes) due to the incompatibility of the CNF growing processes with most standard sensing devices (Koehne et al. 2011).

In this work, we report for the first time the electrochemical synthesis of PEDOT:CNF composite, and its deposition onto gold MEAs by a versatile and reproducible electrochemical route. PEDOT is electrochemically deposited to trap CNFs within its matrix, creating a nanocomposite in one step without the need of time-consuming grafting process (Fig . 1). We show that high-performance PEDOT:CNF composite exhibit remarkable electrochemical properties, yields microelectrodes combining low impedance, high surface area, high charge injection capability and can be effectively used for reliable neurotransmitters monitoring using amperometric techniques. Taken together, these results suggest the great potential of PEDOT:CNF composites for developing next-generation multifunctional bidirectional microelectrodes.

MATERIALS AND METHODS

The detailed experimental details are provided in Supplementary Information (SI).

RESULTS AND DISCUSSION

Electrochemical deposition

We initially fabricated PEDOT:CNF coatings and examined their morphology. As shown in Fig. 1B, we used microfabricated gold microelectrodes array, well-known tools allowing classical electrophysiological measurements. The microelectrodes have the same 20 µm diameter as commonly used for MEA arrays. We use this simple design to directly compare PEDOT:CNF to bare gold microelectrodes properties. First oxidized CNFs were synthesized for the following electrochemical synthesis of PEDOT:CNF composite (Fig. 1A). The oxidation of raw CNFs through oxidative acid treatment (mixture of H 2 SO 4 and HNO 3 ) was crucial for the electrodeposition procedure. Previous reports showed that the oxidation of carbon nanomaterials not only enhanced their hydrophilicity but also created on their surfaces a range of oxygenated functions such as carboxylic groups through the oxidation reactions (Bortolamiol et al. 2014;Rasheed et al. 2007;Santangelo et al. 2012). The oxidized CNFs were consequently negatively charged due to the presence of carboxylate functional groups grafted at their surface, which made them a very interesting candidate for charge-balancing anionic PEDOT dopant. In this study, the PEDOT:CNF films were galvanostatically deposited onto gold microelectrodes at a constant current density of 10 pA/µm² until a total charge 1200 nC (3.821 nC/µm²) was consumed (Fig. S1). The deposition took place through a simultaneous oxidative PEDOT polymer chain propagation and CNF trapping mechanisms, generating a fibrous and porous surface structure resulting from a three-dimensional growth mechanism of PEDOT around the oxidized CNFs (Fig. 1 C and D) (Temmer et al. 2013). It is worth mentioning that an increase in the electrodeposition charge will result in a significant expansion of the PEDOT content in PEDOT:CNF composite while keeping it still rather localized. Nonetheless, the electrodeposition charge should not exceed 6 µC as it induces a subsequent expansion of the overall deposit, which weakens the overall mechanical stability of the film, leading to the delamination of the film, and/or causes short-circuits on the neighbor microelectrodes (Fig. S2).

Morphology

To confirm the structural integrity of the PEDOT:CNF coating and its interface with the underlying bare gold contact, a critical factor for the properties of the modified microelectrodes, we investigated the morphological stability of the PEDOT:CNF coating. Images with optical microscopy showed no obvious morphological cracking or visible delamination after the direct current deposition method (Fig. 2A). A quite uniform and highly dense CNFs coating could be observed on the modified microelectrode surface. To study further at the surface stability, the morphology of the PEDOT:CNF coating was observed by SEM. As shown in Fig. 2 (B andC), SEM images confirmed that the PEDOT:CNF composite was uniformly distributed on the gold microelectrode with CNF randomly distributed in the deposit. The PEDOT:CNF appeared as a porous fibrous 3D network encapsulated on the microelectrode. We have previously demonstrated that the electrochemically synthesized PEDOT:PSS film has a smoother surface with a globular structure resulting from a three-dimensional nucleation growth mechanism (Castagnola et al. 2014). The morphology of the PEDOT:CNF was thus very different from PEDOT:PSS. The CNFs integration in the PEDOT matrix is believed to be driven by electrostatic diffusion toward the electrode and encapsulation in the PEDOT matrix, resulting in a mechanically coherent composite. CNFs act as dopant thanks to the negative charges on the nanofibers surface, behaving like counter anion to EDOT radical-cation during electrochemical polymerization. In addition, the PEDOT density around the CNFs of the PEDOT:CNF network increased when increasing the electrochemical deposition charge (Fig. S3), confirming that the negatively charged CNFs act as dopants with positively charged PEDOT during the electropolymerization, with a strong templating effect on this polymerization. The evidence on the growth of the polymer and its interaction with the CNFs is further deduced from the Energydispersive X-ray spectroscopy (EDS) analysis (Fig. S4). EDS mapping was used to distinguish the sulfur content in PEDOT (and not present in the carbon nanofibers) and shows that the conducting PEDOT and carbon nanofibers are connected seamlessly, thereby facilitating intraand interlayer ionic/electronic transport. The FT-IR spectra of the nanocomposite (Fig. S5) display the characteristic peaks of PEDOT at 920 and 985 cm -1 , which correspond to the C-S vibration modes of the thiophene ring in the PEDOT moiety. The absorption bands at 440, 850,

Electrochemical properties

High-performance electrodes materials play a crucial role at the interface of neural electrodes.

To achieve efficient bidirectional transduction between the neural tissue and neural microelectrodes, the electrode material must satisfy the function of both ionic/electronic charge injection, to stimulate nerve tissue, and record neuronal activity. Ideally, the electrode material should offer seamless neural interfaces with desirable characteristics including low impedance to lower signal-to-noise ratio and so increase recorded potentials, and high charge storage capabilities which would be helpful for stimulation of neuron cells (Aqrawe et al. 2018).

We first evaluated the impact of the PEDOT:CNF deposition on the electrochemical properties of the microelectrodes by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Non-coated gold microelectrodes (identical diameter) were also tested as controls. Fig. 3A and B show the Bode plots of the electrochemical impedance and phase angle across frequencies of interest for electrophysiological recordings (10 Hz-10 kHz). EIS analysis showed that the impedance of PEDOT:CNF microelectrode was more than two orders of magnitude lower than the bare microelectrode in the range of frequencies tested. The impedances of the microelectrodes at 1 kHz were used for comparison purposes as action potentials have a characteristic frequency band centered at that frequency. The mean impedance at 1 kHz for the unmodified gold microelectrode was around 600 kΩ, while after PEDOT:CNF electrochemical deposition, the mean impedance fell to 4.1 kΩ. This phenomenon is expected to be due to a significant increase in the effective surface area with the formation of PEDOT:CNF material, leading to the decrease in impedance of the microelectrode (Castagnola et al. 2014).

Furthermore, the specific impedance of the PEDOT:CNF modified microelectrodes (1.28 MΩ.µm 2 at 1 kHz) was ca. 150 times lower than gold microelectrodes (188 MΩ.µm 2 ) and lower than any of the best reported organic electrode materials (Wang et al. 2019) including conducting polymers e.g. PEDOT:PSS [START_REF] Venkatraman | [END_REF]) and PPy:PSS (Wang et al. 2019), CNTs (Wang et al. 2006) and neat graphene (Wang et al. 2019) nanomaterials, presented in Fig. 3D and Table S1. The combination of CNFs and PEDOT as a single composite material thus resulted in a strong synergetic effect leading to a superior microelectrode with lower impedance.

The phase plot of the impedance showed that PEDOT:CNF was capacitive in the low frequency range (10 Hz) (Fig. 3B), with a phase angle around 80-90°. The phase angle decreased for the coated microelectrodes in the frequency range of 10 Hz-2 kHz to ≈ 20°, while the phase angle of bare gold was about 90°. The shift in the phase of the impedance for modified PEDOT:CNF suggests an increase in effective surface area. Such changes were consistent with previous findings where carbon nanotubes were incorporated in the PEDOT matrix as a single composite material (Kozai et al. 2016;Xu et al. 2013;Zhou et al. 2013). These results show that PEDOT:CNF acts as a capacitive material for frequencies lower than 2 kHz and as a moreresistive material for frequencies higher than 2 kHz. The Nyquist plot recorded in H 2 SO 4 media is presented in Fig. 3D. In the higher frequency region, a semi-circle is observed which is related to the charge resistance between the electrode material and the surrounding electrolyte while at low frequencies the capacitive behavior becomes dominant. The incorporation of CNF in the PEDOT produced very small radius of the semi-circle on the Nyquist plot with a charge transfer resistance of about 1.9 kΩ as estimated from an equivalent circuit (Fig. S6). This result proves the low electron-transfer resistance associated with the PEDOT:CNF composite material, by which the electronic exchange in the systems is expected to be favored.

We next evaluated the charge transfer capabilities of the PEDOT:CNF microelectrodes. CVs have been carried out within the 0.0 to + 0.3 V potential range at a scan rate of 50 mV/s.

Compared to the bare gold microelectrode (Fig. 3C and Fig. S7), a significant increase in the electroactive area could be clearly observed. The capacity of charge transfer observably increased using PEDOT:CNF, as can be seen in Fig. 3C. The cathodal CSC (CSCc) of the composite film was calculated by the time integral of the cathodal currents within the cycled region. Obviously, the CSCc of the PEDOT:CNF coated microelectrode is significantly higher than that of bare gold microelectrode (≈ 80 times). At a density of 10 pA/µm² that was applied to electrodeposit the materials, the CSCc of the bare gold microelectrode increased from 0.1 mC/cm 2 to 7.89 mC/cm 2 for PEDOT:CNF within the 0.0-0.3 V potential range. This improvement is due to the higher surface area coating the PEDOT:CNF that allows effective diffusion of electrolyte ions at the electrode-solution interface, leading to a higher charge storage capability. It is worth mentioning that the symmetry of the cyclic voltammograms of the PEDOT:CNF coating is an indication of the high reversibility of the doping process, which makes the composite material a promising material as ionic-to-electronic transducer at the neural interface electrode. S1.

Electrical stimulation

The previously calculated charge capacity value can only be regarded as relative value, as it measures the charge capacity of the electrodes when subjected to a slow voltage ramp. In neural stimulation applications, cathodal-first, biphasic current pulses are frequently used for effective and safe stimulation of neural tissue (Gerwig et al. 2012a;Luo et al. 2011a;Mandal et al. 2015).

The electrodes conduct a high number of electrical stimuli when using only millisecond current pulses, therefore only a small fraction of the total charge capacity is available. Here, we used voltage transient responses to millisecond biphasic current pulses to have a more reliable information about the microelectrode stimulation performance. Fig. 4 reports the voltage transients measurements that were collected by the PEDOT:CNF and bare gold microelectrodes, using 1 ms-long biphasic current pulses with cathodic pulse first in a physiological media-mimicking buffer, Tris buffer 1X at pH=7.4. For a given pulse current, the preferred electrode should have a higher charge injection capability and generate lower electrode voltage, thanks to its higher charge transfer capability. Starting with a series of small pulses of ±1.256 µA corresponding to 4 pC/µm² (Fig. 4A), the highest cathodic voltage transient was observed for non-coated gold microelectrode, producing maximum negative voltage V max,neg of -1.92 V (Fig. 4 B-D). Under the same stimulation conditions, the amplitude of voltage transient is much less in PEDOT:CNF coated microelectrode producing V max,neg of -0.067 V. These values correspond to V mc (maximum negative polarization voltage) across electrode-electrolyte interface of -37 mV for PEDOT:CNF (Fig. 4 C andD). The negative potential excursion V mc was calculated by subtracting the access voltage (V a ), associated with the ohmic resistance of the electrolyte from the maximum negative voltage V max,neg (Carli et al. 2018). Indeed, voltage excursion displays first an initial ohmic voltage drop followed by a more gradual polarization of the interface as shown in Fig. 4D. Due to increased charge carriers at the interface, less polarization is seen at the PEDOT:CNF interface compared to gold microelectrodes. In this condition, the voltage drop V a was observed for PEDOT:CNF in the order of 29 mV, and 30 times higher for gold microelectrodes with 0.9 V, suggesting a substantially lower overpotential to be overcome in PEDOT:CNF microelectrodes. This result is in agreement with the electrochemical CV characterization that gives a higher electroactive surface area (Fig. 3C) for PEDOT:CNF with respect to bare gold control. The voltage transient experiments reflect the obvious improvement by PEDOT:CNF coating in delivering in a safer manner, for a stimulation current, a higher charge at the electrode-electrolyte interface. To fully assess the applicability of the PEDOT:CNF microelectrodes for tissue stimulation purposes, their charge injection limit (CIL) was measured using increasing pulse currents (Fig. 4F). The CIL is defined as the quantity of charge that can move from the electrode to the solution in a stimulation pulse without exceeding the water electrolysis limits. To ensure the safe polarization, the water window of the PEDOT:CNF microelectrodes in Tris buffer solution was determined using CV at 200 mV/s vs Ag/AgCl reference electrode (Fig. 4E). The water reduction and oxidation voltages were found to be at -1.4 and 0.6 V respectively. The polarization voltage V mc was used to determine the CIL by increasing the pulse currents before V mc is reaching the negative electrolysis frontier at -1.4 V. The measured corresponding maximum current before the water reduction potential was 31.5±3.4 µA (Fig. 4F). The calculated charge injection was calculated at V mc = -1.3 V, before the water reduction potential to be 10.032 ± 1.083 mC/cm² for the PEDOT:CNF microelectrodes. The calculated CIL of the composite material was higher than any of the best reported organic materials (Wang et al. 2019) including PEDOT:PSS (2.92 mC/cm²) [START_REF] Venkatraman | [END_REF], CNTs array (1-1.6 mC/cm²) (Wang et al. 2006), PEDOT:CNT (1.25 mC/cm²) (Gerwig et al. 2012b) or neat graphene fibers (8.9 mC/cm²) (Wang et al. 2019) presented in Fig. 3D and Table S1. These voltage transient experiments reflect the obvious improvement by PEDOT:CNF coating of delivering in a safer manner, for a stimulation current, a higher charge at the microelectrode-electrolyte interface. We believe that the combination of CNFs with high charge transfer capabilities and PEDOT results in as strong synergetic effect between the two components in the composite leading to remarkable charge injection capacity.

Electrochemical sensing

To demonstrate the extended functional aspects of the PEDOT:CNF composite, we explored the electrochemical sensing capabilities of the modified microelectrodes in a non-specific fashion of biomolecules, specifically neurotransmitters such as dopamine (DA) and serotonin (ST). DA and ST are ubiquitous neurotransmitters in mammalian brain tissues, and together with other neurotransmitters, play an important physiological role in the functioning of central nervous as extra cellular chemical messengers (Beaulieu and Gainetdinov 2011;Carhart-Harris and Nutt 2017). These biological analytes are commonly found together and their biodetection using macro-scaled unitary carbon fiber microelectrode has been the focus of a tremendous number of reports in the last decades (Vreeland et al. 2015;Wang et al. 2014;Weaver et al. 2014). However, it remains challenging to use similar electrode material deposited on localized electrodes on any type of substrate e.g. MEAs, neural probes or drug delivery devices, as very few reports have been produced using non-unitary carbon-based microelectrodes materials (Koehne et al. 2011). Initially, CV experiments in Tris buffer solution (pH= 7.4) with varying concentrations of DA and ST were used to identify the oxidation potentials of both DA and ST (Fig. S8 andS9).

PEDOT:CNF coated microelectrodes were used as working electrode, while SCE and platinum were respectively used as reference and counter electrodes. According to the CV results, 130 mV and 328 mV vs SCE were chosen for the following chronoamperometric detection of DA and ST, respectively. In separate experiments, specific amounts of DA and ST were added to Tris buffer (pH= 7.4) at various intervals after stabilization of background current. A sharp anodic increase in the current was obtained for each addition of analyte, which was attributed to the fast, direct electrochemical oxidation of the analyte at the electrode vicinity. A typical chronoamperometric responses obtained with a PEDOT-CNF modified microelectrode is shown in Fig. 5 (A andD).

The response of the PEDOT:CNF at the modified microelectrode was reached within 15 seconds, revealing the fast diffusion of the PEDOT:CNF on the microelectrode site. The current response increased linearly in the concentration range from 0.1 to 9 μM for DA and from 0.06 to 9 μM for ST (Fig. 5 B, C, E andF). The DA and ST direct current responses resulted in calibration plots i.e. amperometric current response vs. different concentration of DA or ST with correlation coefficients >0.999 for both DA and ST. The corresponding sensitivities were calculated as 14.3 pA/µM with the limit of detection (LOD) of 0.045 μM for dopamine and 22.32 pA/µM with a LOD of 0.056 μM for serotonin. In terms of concentration ranges, these LOD were well suited to the reported assay of these analytes in the medical field (Polidori et al. 2001). The stability of the proposed electrochemical biosensor was evaluated (Fig. S10). A 10% drop in sensitivity was observed after 5 h of continuous exposure of a polarized electrode to 1 µM of DA in Tris buffer.

Sensitivity can be restored by acidic rinsing of the microelectrode with H 2 SO 4 0.5 M. This sensitivity drop is believed to be due to dopamine adsorption, which correlate with the fact that it is reversible by electrode rinsing. The analytical performance of the fabricated PEDOT:CNF microelectrode (LOD, sensitivity and linear response range) of the sensor was compared with previously reported DA sensors using chronoamperometric technique (Table S2). It can be seen that the PEDOT:CNF is comparable or better with the previously reported DA sensors, hence it can be used for sensitive detection of neurotransmitters in biological analysis. The good analytical performance of the PEDOT:CNF towards neurochemical detection was attributed to the huge reactive surface area and excellent electron transfer rate, which has been shown to catalyze electron transfer at the electrode-electrolyte interface.

In vitro cytotoxicity assay

In addition to excellent electrical and electrochemical properties, the PEDOT:CNF composite material should being free of toxic elements, to be used as an in vivo neural interface. PEDOTbased materials on gold microelectrodes are commonly used for neural cell culturing and have been proven to be a biocompatible material with living systems usable to design neural interfaces (Asplund et al. 2009). Through cell viability assay, we set the preliminary objective to establish the non-cytotoxicity of the produced composite, after the electrochemical deposition. For that purpose, cytotoxicity was investigated with the MTT cell viability assay (ISO 10993-5 norm).

Two populations of cells were cultured for 24 h and then one population was exposed for 24 h to culture media containing PEDOT:CNF material extracts. After 48 h, MTT test was performed on both populations, in the test media and in a standard media as control. The results show that the population of living SH-SY5Y cells increases over time in a similar fashion for controls, demonstrating that no cytotoxicity could be observed as viability percentage highly exceeds 75 % (>99 %).

Cells growth & Morphology studies

To study to possibility of cells to grow on modified surfaces, human neuroblastoma SH-SY5Y cells were cultivated on PEDOT:CNF-modified MEAs to investigate the cell adhesion and neurite outgrowths on the modified microelectrodes. As shown in Fig. 6 (A andC), After 24 and 72 hours, SH-SY5Y cells grew uniformly and appeared to have spread homogeneously, demonstrating a good viability on the MEA substrate, even at 24 h, as no cell repulsion was found around coated electrodes. In order to investigate the state of SH-SY5Y cells cultured in detail, further work was conducted by fluorescent staining. As shown in Fig. 6 (B and D), SH-SY5Y cells grew evenly overall, and neurites were clearly observed on the composite materials.

These results indicate that PEDOT:CNF composite is well-suited for the adhesion and neurite outgrowth of SH-SY5Y cells. The morphology of the cells on the PEDOT:CNF microelectrodes was also assessed through SEM. SH-SY5Y cells were seeded in the MEA and cultured for 48 h, then the fixation procedure was performed before proceeding to the SEM observation. Individual cells can be clearly recognized on the top of the PEDOT:CNF microelectrodes in Fig. 6 (E andF). The SH-SY5Y cells display extensive processes and appear to be well attached on the surface of the PEDOT:CNF material. It is worth mentioning that the cells did not exhibit any particular preference for different materials i.e. gold or MEA passivation layer and, as can be observed, they spread homogeneously on the surface of the MEA. The attachment of cells on PEDOT:CNF coated microelectrode shown in SEM images means that it is possible to the get direct, reliable and functional contact with the target tissue required for interfacing purposes. For example, when accurate electrophysiology recordings are required, a single unit may be detected and recorded when the recording device is very close from the targeted single neuron.

CONCLUSIONS

We have successfully demonstrated the feasibility of PEDOT:CNF composite material on gold microelectrode arrays by a simple and reproductible electrodeposition method. PEDOT was used to electrochemically entrap oxidized carbon nanofibers as dopants in one step, without the need of time-consuming multi-step processes. The combination of CNFs and PEDOT resulted in as strong synergetic effect between the two components in the composite leading to remarkable electrochemical properties, combining low impedance, high charge injection capability, as well as a reliable neurotransmitter monitoring. The results from this study suggest the great potential of PEDOT:CNF composites for developing next-generation microelectrodes for applications in neural therapies. We are currently running in vivo studies to show that PEDOT:CNF microelectrodes implanted in the cerebral cortex can detect neuronal activity with remarkably high signal-to-noise ratio in an area as small as an individual neuron. We also envision the use the PEDOT:CNF microelectrodes for neurochemicals monitoring in vitro and in vivo using fast scan cyclic voltammetry techniques.

Fig. 1 .

 1 Fig. 1. PEDOT:CNF deposition pathway. (A) CNF functionalization scheme used for this study, from raw CNF to oxidized, negatively-charged CNFs obtained by chemical oxidation, which are used as

  1070 and 1120 cm -1 represent the stretching modes of the C-O-C and O-C-C group of PEDOT in the nanocomposite whereas the peak at 1260 cm -1 indicates the C-C stretching mode of the thiophene[START_REF] Anothumakkool | [END_REF]Shin et al. 2011;Tran-Van et al. 2001).

Fig. 2 .

 2 Fig. 2. Morphological characterization of PEDOT-CNF microelectrodes. Microscope picture of (A) PEDOT-CNF modified gold microelectrode (20 µm diameter). (B) SEM picture of PEDOT-CNF

Fig. 3 .

 3 Fig. 3. Electrochemical characterization of PEDOT:CNF modified microelectrodes. (A) EIS measurements on modified PEDOT:CNF and unmodified gold microelectrodes over a frequency range of

Fig. 4 .

 4 Fig. 4. In vitro biphasic stimulation assessment. (A) Biphasic excitation current waveform (±1.256 µA, 1 ms) tested in vitro in Tris buffer 1X, cathodic pulse first. (B) Voltage responses of PEDOT-CNF modified microelectrodes and bare gold microelectrode. (C) Voltage transient of PEDOT:CNF microelectrodes only. (D) Voltage transient of a PEDOT:CNF-modified microelectrode at 31.5 µA

Fig. 5 .

 5 Fig. 5. In vitro electrochemical sensing. Typical chronoamperometric response of a PEDOT-CNF

Fig. 6 .

 6 Fig. 6. In vitro cytotoxicity assay & cell morphology assessment. Optical images showing SH-SY5Y

Funding Sources

This work was supported by ANR NeuroMEDDLE Grant ANR-15-CE19-0006 and ANR 3DBrain Grant ANR-19-CE19-0002-01.

ACKNOWLEDGMENT

We thank Dr. Anne-Marie Galibert and Dr. Brigitte Soula (CIRIMAT, Université de Toulouse, CNRS, F-31062, France) for help in the work on raw carbon nanofibers. We thank M. Jean-Baptiste Doucet and M. Julien Roul for help and guidance during FT-IR measurements. The authors acknowledge fundings from the Agence Nationale de la Recherche (ANR-15-CE19-0006 and ANR-19-CE19-0002-01). This work was supported by French RENATECH network.

Supplementary Information.

The following files are available free of charge: Experimental details, Tables S1-S2 and Fig.

S1-S10.

Highlights

• We developed a simple approach for the electrochemical synthesis of PEDOT:CNF composite material on microelectrodes for neural interfaces and biosensing.

• PEDOT:CNF microelectrodes exhibit unrivalled electrochemical properties, combining low impedance, high surface area and high charge injection capabilities.

• PEDOT:CNF microelectrodes allowed the reliable detection of the neurotransmitters Dopamine and Serotonin with improved sensitivity and limit of detection.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Ali Maziz

Toulouse Toulouse, 10th April 2020